{"id":"9ae6651a-d852-46b5-bf36-7939e624cae9","entityType":"agent","slug":"clawhub-pangolinfo-pangolinfo-amazon-product-explorer","name":"pangolinfo-amazon-product-explorer","canonicalUrl":"https://www.xpersona.co/agent/clawhub-pangolinfo-pangolinfo-amazon-product-explorer","canonicalPath":"/agent/clawhub-pangolinfo-pangolinfo-amazon-product-explorer","generatedAt":"2026-10-10T17:37:37.792Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-10T14:54:21.391Z","emptyReason":null},"description":"Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\". Covers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go r Skill: pangolinfo-amazon-product-explorer Owner: pangolinfo Summary: Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\". Covers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go r Tags: latest:4.0.0 Version history: v4.0.0 | 2026-","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.4K downloads reported by the source. Last updated 10/10/2026.","installCommand":"clawhub skill install s1713qcay2x7frr7y0mm908crx83hmg8:pangolinfo-amazon-product-explorer","sourceUrl":"https://clawhub.ai/pangolinfo/pangolinfo-amazon-product-explorer","homepage":"https://clawhub.ai/pangolinfo/skills/pangolinfo-amazon-product-explorer","primaryLinks":[{"label":"View on ClawHub","url":"https://clawhub.ai/pangolinfo/pangolinfo-amazon-product-explorer","kind":"source"},{"label":"Homepage","url":"https://clawhub.ai/pangolinfo/skills/pangolinfo-amazon-product-explorer","kind":"homepage"}],"safetyScore":84,"overallRank":62,"popularityScore":63,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \""},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-10-10T14:54:21.391Z","emptyReason":null},"protocols":[{"protocol":"OPENCLEW","label":"OpenClaw","status":"self-declared","notes":"Declared in the public agent profile."}],"capabilities":[],"verifiedCount":0,"selfDeclaredCount":1,"capabilityMatrix":{"rows":[{"key":"OPENCLEW","type":"protocol","support":"unknown","confidenceSource":"profile","notes":"Listed on profile"}],"flattenedTokens":"protocol:OPENCLEW|unknown|profile"}},"adoption":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T14:54:21.391Z","emptyReason":null},"stars":null,"forks":null,"downloads":1374,"packageName":null,"latestVersion":"4.0.0","tractionLabel":"1.4K downloads"},"release":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T14:54:21.390Z","emptyReason":null},"lastUpdatedAt":"2026-10-10T14:54:21.391Z","lastCrawledAt":"2026-10-10T14:54:21.390Z","lastIndexedAt":null,"nextCrawlAt":"2026-10-11T14:54:21.390Z","lastVerifiedAt":null,"highlights":[{"version":"4.0.0","createdAt":"2026-08-20T08:30:35.487Z","changelog":"amazon-product-explorer v4.0.0 - Breaking change: All MCP tool calls from this skill must now include \"clientSource\":\"skill\" in the arguments for every request. - Internal documentation file skill-card.md was removed. - No changes to user-facing workflow or external behavior.","fileCount":3,"zipByteSize":16528},{"version":"3.0.1","createdAt":"2026-06-16T09:30:13.770Z","changelog":"Version 3.0.1 - Removed the skill-card.md file. - No changes to functionality or core features.","fileCount":3,"zipByteSize":16390},{"version":"3.0.0","createdAt":"2026-06-15T02:07:32.454Z","changelog":"Summary: Major migration to a fully server-based architecture; all local scripts and references removed. - Removed all local Python scripts and reference documentation. - Skill now exclusively uses the hosted Pangolinfo MCP server for all data operations. - Setup and authentication process updated: users must configure the MCP endpoint with their API key. - Documentation, SOP, and prompts rewritten to reflect the scriptless, MCP-only workflow. - Updated tool and trigger/negative boundaries for streamlined GTM analysis. - Breaking changes: Local scripts and ENV vars are no longer supported. All calls are routed through MCP tools.","fileCount":3,"zipByteSize":12031},{"version":"2.0.0","createdAt":"2026-04-23T09:41:38.805Z","changelog":"Version 2.0.0 (pangolinfo-amazon-product-explorer): - Skill renamed from \"pangolinfo-amazon-product-discovery\" to \"pangolinfo-amazon-product-explorer\". - Updated tags, metadata, and versioning to reflect new branding. - Minor changes to frontmatter for improved clarity and compatibility (added emoji, OS list in metadata). - No logic or script-level changes detected; all bundled tool capabilities and usage guidance remain unchanged. - Documentation and system prompt content are functionally consistent with the previous version.","fileCount":19,"zipByteSize":49544},{"version":"1.0.7","createdAt":"2026-04-17T13:46:28.538Z","changelog":"Major update: migrated to a flat \"Super Skill\" structure and improved invocation workflow. - Refactored project to a single root skill with all capabilities under `scripts/` (no more sub-skill directories). - Simplified invocation: all Amazon, SERP, and WIPO research tasks now use top-level scripts for easier integration and maintenance. - Updated and centralized documentation; added detailed reference guides for each script in the new `references/` folder. - Streamlined authentication: all tools share the same credentials with no redundant prompts. - Updated onboarding and system prompt with precise rules and improved clarity for user guidance. - Version bumped from 1.0.x to 2.0.0 to reflect breaking directory/layout changes.","fileCount":18,"zipByteSize":47854},{"version":"1.0.6","createdAt":"2026-04-17T12:27:50.067Z","changelog":"**Major update: Bundles a comprehensive Amazon product discovery and market research suite by integrating multiple advanced sub-skills.** - Introduced the \"amazon-product-discovery\" super skill, merging Amazon Niche & Search, Amazon Scraper, AI-enhanced Google SERP, and WIPO trademark compliance checks. - Added 4 full sub-skills under `skills/` (pangolinfo-ai-serp, pangolinfo-amazon-niche, pangolinfo-amazon-scraper, pangolinfo-wipo) with individual documentation and references. - Defined strict boundaries for usage (GTM research, new product and niche validation, consumer pain-point analysis, and compliance)—routes unrelated requests to appropriate tools. - Provided a detailed skill SOP and system prompt for autonomous, multi-step market research workflows. - All bundled sub-skills now share unified authentication. Setup and credentials are standardized across all tools.","fileCount":29,"zipByteSize":69293},{"version":"1.0.5","createdAt":"2026-04-16T14:57:23.918Z","changelog":"amazon-product-discovery v1.0.2 - Initial release of a \"Super Skill\" for advanced Amazon product discovery and market research, powered by Pangolinfo API. - Bundles four key tools: Amazon Niche & Search, Amazon Scraper (ASIN/Reviews), AI-powered Google SERP, and WIPO Trademark Check. - Strictly focused on new product development, niche validation, pain-point extraction, and compliance screening (not for daily tracking or listing optimization). - Features a detailed SOP for multi-step go-to-market research workflows. - Includes built-in onboarding and clear usage boundary rules. - Supports language adaptation and multiple agent frameworks (OpenClaw, easily portable to LangGraph/CrewAI).","fileCount":29,"zipByteSize":69293},{"version":"1.0.0","createdAt":"2026-04-16T11:49:04.917Z","changelog":"amazon-product-discovery 1.0.2 — Initial Release - Launches a comprehensive Amazon Product Discovery and Market Research Engine powered by Pangolinfo API. - Bundles four core sub-skills: Amazon Niche & Search, Amazon Scraper (ASIN/Reviews), AI SERP (Google), and WIPO Trademark Check. - Designed specifically for Zero-to-One new product development, niche validation, monopoly analysis, consumer pain-point extraction, and compliance risk screening. - Includes strict rules for when to use or not use the skill, supporting detailed, data-driven GTM research workflows. - Compatible with OpenClaw and LangGraph/CrewAI agent frameworks.","fileCount":29,"zipByteSize":69293}]},"execution":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":"clawhub skill install s1713qcay2x7frr7y0mm908crx83hmg8:pangolinfo-amazon-product-explorer","setupComplexity":"low","setupSteps":["Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.","Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data."],"contract":{"contractStatus":"missing","authModes":[],"requires":[],"forbidden":[],"supportsMcp":false,"supportsA2a":false,"supportsStreaming":false,"inputSchemaRef":null,"outputSchemaRef":null,"dataRegion":null,"contractUpdatedAt":null,"sourceUpdatedAt":null,"freshnessSeconds":null},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"CLAWHUB","generatedAt":"2026-10-10T17:37:37.779Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-pangolinfo-pangolinfo-amazon-product-explorer/trust"}},"reliability":{"evidence":{"source":"runtime-metrics","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No trust, reliability, or runtime telemetry is available."},"trust":{"status":"unavailable","handshakeStatus":"UNKNOWN","verificationFreshnessHours":null,"reputationScore":null,"p95LatencyMs":null,"successRate30d":null,"fallbackRate":null,"attempts30d":null,"trustUpdatedAt":null,"trustConfidence":"unknown","sourceUpdatedAt":null,"freshnessSeconds":null},"decisionGuardrails":{"doNotUseIf":["Contract metadata is missing or unavailable for deterministic execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"high","updatedAt":"2026-10-10T14:54:21.391Z","emptyReason":null},"readme":"Skill: pangolinfo-amazon-product-explorer\n\nOwner: pangolinfo\n\nSummary: Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\".\nCovers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go r\n\nTags: latest:4.0.0\n\nVersion history:\n\nv4.0.0 | 2026-08-20T08:30:35.487Z | user\n\namazon-product-explorer v4.0.0\n\n- Breaking change: All MCP tool calls from this skill must now include \"clientSource\":\"skill\" in the arguments for every request.\n- Internal documentation file skill-card.md was removed.\n- No changes to user-facing workflow or external behavior.\n\nv3.0.1 | 2026-06-16T09:30:13.770Z | user\n\nVersion 3.0.1\n\n- Removed the skill-card.md file.\n- No changes to functionality or core features.\n\nv3.0.0 | 2026-06-15T02:07:32.454Z | user\n\nSummary: Major migration to a fully server-based architecture; all local scripts and references removed.\n\n- Removed all local Python scripts and reference documentation.\n- Skill now exclusively uses the hosted Pangolinfo MCP server for all data operations.\n- Setup and authentication process updated: users must configure the MCP endpoint with their API key.\n- Documentation, SOP, and prompts rewritten to reflect the scriptless, MCP-only workflow.\n- Updated tool and trigger/negative boundaries for streamlined GTM analysis.\n- Breaking changes: Local scripts and ENV vars are no longer supported. All calls are routed through MCP tools.\n\nv2.0.0 | 2026-04-23T09:41:38.805Z | user\n\nVersion 2.0.0 (pangolinfo-amazon-product-explorer):\n\n- Skill renamed from \"pangolinfo-amazon-product-discovery\" to \"pangolinfo-amazon-product-explorer\".\n- Updated tags, metadata, and versioning to reflect new branding.\n- Minor changes to frontmatter for improved clarity and compatibility (added emoji, OS list in metadata).\n- No logic or script-level changes detected; all bundled tool capabilities and usage guidance remain unchanged.\n- Documentation and system prompt content are functionally consistent with the previous version.\n\nv1.0.7 | 2026-04-17T13:46:28.538Z | user\n\nMajor update: migrated to a flat \"Super Skill\" structure and improved invocation workflow.\n\n- Refactored project to a single root skill with all capabilities under `scripts/` (no more sub-skill directories).\n- Simplified invocation: all Amazon, SERP, and WIPO research tasks now use top-level scripts for easier integration and maintenance.\n- Updated and centralized documentation; added detailed reference guides for each script in the new `references/` folder.\n- Streamlined authentication: all tools share the same credentials with no redundant prompts.\n- Updated onboarding and system prompt with precise rules and improved clarity for user guidance.\n- Version bumped from 1.0.x to 2.0.0 to reflect breaking directory/layout changes.\n\nv1.0.6 | 2026-04-17T12:27:50.067Z | user\n\n**Major update: Bundles a comprehensive Amazon product discovery and market research suite by integrating multiple advanced sub-skills.**\n\n- Introduced the \"amazon-product-discovery\" super skill, merging Amazon Niche & Search, Amazon Scraper, AI-enhanced Google SERP, and WIPO trademark compliance checks.\n- Added 4 full sub-skills under `skills/` (pangolinfo-ai-serp, pangolinfo-amazon-niche, pangolinfo-amazon-scraper, pangolinfo-wipo) with individual documentation and references.\n- Defined strict boundaries for usage (GTM research, new product and niche validation, consumer pain-point analysis, and compliance)—routes unrelated requests to appropriate tools.\n- Provided a detailed skill SOP and system prompt for autonomous, multi-step market research workflows.\n- All bundled sub-skills now share unified authentication. Setup and credentials are standardized across all tools.\n\nv1.0.5 | 2026-04-16T14:57:23.918Z | user\n\namazon-product-discovery v1.0.2\n\n- Initial release of a \"Super Skill\" for advanced Amazon product discovery and market research, powered by Pangolinfo API.\n- Bundles four key tools: Amazon Niche & Search, Amazon Scraper (ASIN/Reviews), AI-powered Google SERP, and WIPO Trademark Check.\n- Strictly focused on new product development, niche validation, pain-point extraction, and compliance screening (not for daily tracking or listing optimization).\n- Features a detailed SOP for multi-step go-to-market research workflows.\n- Includes built-in onboarding and clear usage boundary rules.\n- Supports language adaptation and multiple agent frameworks (OpenClaw, easily portable to LangGraph/CrewAI).\n\nv1.0.0 | 2026-04-16T11:49:04.917Z | user\n\namazon-product-discovery 1.0.2 — Initial Release\n\n- Launches a comprehensive Amazon Product Discovery and Market Research Engine powered by Pangolinfo API.\n- Bundles four core sub-skills: Amazon Niche & Search, Amazon Scraper (ASIN/Reviews), AI SERP (Google), and WIPO Trademark Check.\n- Designed specifically for Zero-to-One new product development, niche validation, monopoly analysis, consumer pain-point extraction, and compliance risk screening.\n- Includes strict rules for when to use or not use the skill, supporting detailed, data-driven GTM research workflows.\n- Compatible with OpenClaw and LangGraph/CrewAI agent frameworks.\n\nArchive index:\n\nArchive v4.0.0: 3 files, 16528 bytes\n\nFiles: skill-card.md (2272b), SKILL.md (32675b), _meta.json (153b)\n\nFile v4.0.0:SKILL.md\n\n---\nname: amazon-product-explorer\ndescription: |\n Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\".\n Covers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go report.\n NOT for: daily monitoring or rank tracking (use amazon-daily-competitor-radar) / writing Listing copy (use amazon-listing-optimization) / single ASIN lookup (call get_amazon_product directly).\nversion: 3.1.0\nmcp_tools_used:\n - pangolinfo_capabilities\n - search_categories\n - filter_niches\n - filter_categories\n - search_amazon\n - get_amazon_product\n - get_amazon_reviews\n - list_new_releases\n - keyword_trends\n - ai_search\n - wipo_search\napplies_to: [claude-code, cursor, cline, windsurf, hermes, codex, openclaw]\nbudget:\n fast: { duration: \"≤ 90s wall-clock (实测 ~30s)\", cost: \"≤ 8 积点 (实测 4-6pt)\", calls: \"≤ 7 (1 + 2 并发 × 多回合)\" }\n full: { duration: \"≤ 5min wall-clock\", cost: \"≤ 30 积点\", calls: \"≤ 15\" }\n---\n\n# Amazon GTM 选品 SOP\n\n> 跑前必读：本文件末尾《核心规则 / Core Rules》章节（已内联，自包含）。\n> **来源标记硬规则**：本 Skill 发起的每次 MCP tool 调用，都必须在 `arguments` 顶层加入 `\"clientSource\":\"skill\"`，不得省略。\n> 角色：Amazon 增长顾问 + 数据咨询师。用硬数据出 go/no-go 判断，**不出凑数 niche**。\n\n## 用户触发与档位识别\n\n**Fast 档**（默认，≤90s）：\n- \"X 方向能做什么\"\n- \"我想做 Y 类目，先研究一下\"\n- \"看看 wireless earbuds 有没有机会\"\n\n**Full 档**（用户明示，≤5min）：\n- \"深度选品报告\"\n- \"完整 GTM 策略\"\n- \"详细分析 X 类目，包括差评和 IP\"\n\n**单工具直通**（不跑 SOP）：\n- \"查一下 ASIN B0XXX\" → `get_amazon_product`\n- \"X 类目热销榜\" → `list_bestsellers`\n\n---\n\n## Fast 档 SOP(5 回合 ≤ 90s)\n\n每回合 ≤ 2 并发(实测 2026-05-28:3 并发会触发后端业务码 9200 \"no content\")。\n\n```\n回合 1 (5s) search_categories ← 拿 browseNodeId\n回合 2 (5s) filter_niches | keyword_trends ← 2 并发\n回合 3 (5s) search_amazon | list_new_releases ← 2 并发\n回合 4 (5s) get_amazon_product(A1) | get_amazon_product(A2) ← 2 并发\n回合 5 (5s) get_amazon_product(A3) ← 单发(可跳过)\nLLM 整合 (~30s)\n```\n\n**总耗时**：~30s tool + 30s LLM ≈ **60s**\n**总成本**：~6 积点(5 个 scrape + 1 个 trends)\n**总调用**：6 次 tool\n\n### R1 — 类目锚定\n\n```jsonc\n{ \"name\": \"search_categories\",\n \"arguments\": { \"keyword\": \"\", \"site\": \"amz_us\" } }\n```\n\n**Extract**: 取 `data.items.data[0].browseNodeId` 作为后续 categorySlug 推断依据；记下 `browseNodeNamePath` 作上下文。\n\n**Early-return**: 0 条结果 → 让用户更换/细化关键词，停止。\n\n### R2 — 2 并发(filter_niches + keyword_trends)\n\n```jsonc\n// (a) Amazon 利基筛选\n{ \"name\": \"filter_niches\", \"arguments\": {\n \"marketplaceId\": \"US\",\n \"nicheTitle\": \"\",\n \"searchVolumeT90Min\": 20000,\n \"top5ProductsClickShareT360Max\": 0.40,\n \"productCountMax\": 300,\n \"searchVolumeGrowthT90Min\": 0.05,\n \"returnRateT360Max\": 0.10,\n \"size\": 5\n}}\n\n// (b) 外部需求趋势\n{ \"name\": \"keyword_trends\", \"arguments\": {\n \"keywords\": [\"\"],\n \"timeRange\": \"today 12-m\",\n \"region\": \"US\"\n}}\n```\n\n**关键约束**:\n- `marketplaceId` 用 ISO 站点码 `\"US\"`/`\"UK\"`/`\"DE\"`,**不是** Amazon merchant ID `ATVPDKIKX0DER`。\n- `filter_niches.nicheTitle` 用精确子串匹配,长尾词(3+ word)常返 0。退化策略:剥 silicone/baby 等修饰词,先用 noun head (\"bib\") 扫,再叠修饰。\n- `keyword_trends` 一次最多 5 个 keywords,趋势是相对值 0-100,不是绝对搜索量。\n\n**Extract**:\n- **filter_niches** → `data.items.data[]` 取 Top 3 候选:`{nicheId, nicheTitle, searchVolumeT90, top5ProductsClickShareT360, productCount, returnRateT360, avgPrice, avgReviewCount}`\n- **keyword_trends** → `data.json.timelineData[]` 最新值 vs 12mo 前的相对变化;记 `keywordsRankData[].rankList[]` 里的 Breakout 词作为方向延伸候选\n\n**Fallback to filter_categories**: 如果 `filter_niches` 在多次同义词重试后仍返 0,改走 `search_categories` → `filter_categories` 路径(类目维度)。在长尾低频品类里这是**更稳的主路径**,不只是兜底。\n\n```jsonc\n// 先 search_categories 拿 browseNodeId(见 R1),再喂给 filter_categories\n{ \"name\": \"filter_categories\", \"arguments\": {\n \"marketplaceId\": \"US\",\n \"timeRange\": \"l7d\", // 必填\n \"sampleScope\": \"all_asin\", // 必填\n \"categoryId\": \"\",\n \"size\": 10\n}}\n```\n\n**前提**: `timeRange`(常用 `l7d`)+ `sampleScope`(`all_asin`)**必填**;`categoryId` 取自 `search_categories` 返回的 `browseNodeId`。`marketplaceId` 用 ISO 站点码 `\"US\"`,不是 merchant ID。`size`/`page` 后端硬上限 10。\n\n**Extract**(`filter_categories`): `data.items.data[]` → `unitSoldSum`(月销量) / `netShippedGmsSum`(GMV) / `searchVolumeSum`(类目搜索量) / `buyBoxPriceAvg` + `buyBoxPriceTier`(价格档) / `searchToPurchaseRatio`(转化) / `returnRatio`(退货率) / `asinCount`(竞品密度) / `newAsinCount`(新品入场) / `unitSoldTrendDirection`(销量趋势方向)。用这些类目级遥测替代 niche 维度做同样的\"需求×竞争×趋势\"判断。长尾筛选字段走 `extraFilters` 透传。\n\n**Early-return**: 双路径都 0 条 → 报告\"该方向饱和或定义太窄\"+给放宽参数建议,停止。\n\n### R3 — search_amazon 单发(剥词后 ≤4 word)\n\n```jsonc\n{ \"name\": \"search_amazon\", \"arguments\": {\n \"keyword\": \"\",\n \"site\": \"amz_us\"\n}}\n```\n\n**Extract**: `data.json[0].data.results[]` 中 `sponsored=\"0\"` 的前 5 个 ASIN。每条字段语义:\n- `star`: 平均评分(0-5 分数,**是分数**)\n- `rating`: 评分人数(评论计数,**不是分数本身**)\n- `sales`: 月销字符串(如 \"10K+ bought in past month\") — 这是替代旧 `monthlySoldVolume` 的字段\n- `badge`: Amazon's Choice / Best Seller / BSR 等徽章字符串\n\n**双标杆原型锁定(从前 5 里挑 2 个,而不是平铺 5 个)**: 在过滤后的数组里识别两类对手原型,作为后续 R4/R5 深拆与最终报告的\"必打目标\":\n- **Target 1 — 护城河巨头 (Moat Giant)**: 高 `rating`(评论数大)+ 稳定 `badge`(Best Seller / Amazon's Choice)。代表已被验证的成熟需求与转化模型。\n- **Target 2 — 真黑马爆品 (Breakout Black Horse)**: 相对低 `rating`,但 `sales` 字符串有爆发信号(如 \"10K+ bought in past month\")和/或 Best-Seller 标。`sales`/`badge` 字符串只当**定性速度信号**,不当绝对销量会计值。\n- 选不出黑马(全是老牌)时,只锁 Moat Giant,在报告里说明\"该 niche 暂无黑马,需高差异化才进\"。\n\n**关键词长度**: ≤4 words,strip filler (\"for\"/\"with\"),长尾词频繁返回 `results=[]` 但仍扣 1 积点。空结果时降一词重试,本 Phase 最多 3 次 search_amazon 调用。\n\n**参数名硬规则**: 是 `keyword`(单数,REQUIRED),**不是** `keywords`(复数)。后者会被拒。\n\n### R4 — 2 并发(list_new_releases + get_amazon_product A1)\n\n从 R3 拿到候选 ASIN 列表后,先 2 并发抓新品榜 + 第 1 个 ASIN:\n\n```jsonc\n{ \"name\": \"list_new_releases\", \"arguments\": {\n \"categorySlug\": \"<推断 slug,如 electronics / home-garden>\",\n \"site\": \"amz_us\"\n}}\n{ \"name\": \"get_amazon_product\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\"\n}}\n```\n\n**注意**:\n- `list_new_releases` 实际返回 **Top-50**(后端硬上限,不是宣传的 Top-100)\n- `twentyFourHourOldSalesRank` / `percentageChange` 字段可能为空字符串(后端未抓到)\n- 上市 30 天内的 ASIN 都算\"新品\",NEW 信号本身不强,要看排名 + sales 双确认\n\n### R5 — 2 并发(get_amazon_product A2 + A3,或单发)\n\n```jsonc\n{ \"name\": \"get_amazon_product\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\"\n}}\n{ \"name\": \"get_amazon_product\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\"\n}}\n```\n\n**Skip rule**: 如果 A1 + (R3 的 search_amazon 数据) 已能下结论(同 brand / 同价位段) → 跳过 R5,直接进 LLM 整合。\n\n**Data Ingestion Guard**: `get_amazon_product` 对 Amazon 自营头部品(如 Echo Dot)有时返回 `bestSellersRankItems=[]` + `category_id=\"\"` + `brand=\"\"`,头部品 PDP 解析退化。遇到时不阻塞,从 R3 search_amazon 返回里取 `title` / `price` / `star` / `rating` / `sales` / `badge` 替代。\n\n### 整合输出 5 段报告\n\n```\n1. TL;DR (3 句话)\n - 推荐方向：\n - 一句话理由：<搜索量 X + 趋势 Y + 头部份额 Z>\n - 红绿黄灯：🟢 / 🟡 / 🔴\n\n2. 市场画像表\n| 维度 | 值 | 来源 |\n|---|---|---|\n| 90 天搜索量 | | filter_niches |\n| Top5 商品点击份额 | | filter_niches |\n| 商品数 | | filter_niches |\n| 退货率 | | filter_niches |\n| 12 月外部趋势 | <+/-X%> | keyword_trends |\n\n3. 基准品对照表(最多 3 行,**标注原型**)\n| 原型 | ASIN | 标题截 60 字 | 价格 | BSR(`badge`) | `star`(0-5 分) | `rating`(评论数) | `sales`(月销) | Buy Box |\n|---|---|---|---|---|---|---|---|---|\n（原型列填 🏰 护城河巨头 / 🐎 黑马爆品；黑马行补一句\"为何它接住了社媒趋势\"）\n\n4. 优缺点速记\n（直接取 get_amazon_product.aiReviewsSummary，不需要再调 reviews）\n- ✅ \n- ❌ \n\n5. 下一步\n - 🟢 → \"如需 IP 排查 + 差评深拆，可跑 Full 档\"\n - 🟡 → \"建议先做 X 验证\"\n - 🔴 → \"建议放弃，理由：…\"\n```\n\n---\n\n## Full 档 SOP（在 Fast 4 回合基础上 +2 回合 ≤ 5min）\n\nFull 档**仅在用户明示**\"详细 / 完整 / 深度 / 全面\"时触发。先跑完 Fast 4 回合拿到候选 ASIN，再追加：\n\n### R6a — 站外趋势引爆 (off-site dork,1-2 次 ai_search)\n\nFast 档只用 `keyword_trends`(相对热度曲线);Full 档补 `ai_search` 抓 Reddit/TikTok 的真实使用场景与微趋势,作为后续\"社媒卖点\"和 R&D 输入的依据。**每个 dork 单发**(ai_search overview ~30s,别并发堆时延):\n\n```jsonc\n// Dork A — 站外使用场景\n{ \"name\": \"ai_search\", \"arguments\": {\n \"query\": \"intitle:\\\"\\\" (\\\"best for\\\" OR \\\"used for\\\" OR \\\"designed for\\\") -site:amazon.com -site:ebay.com\",\n \"mode\": \"overview\"\n}}\n// Dork B — 新趋势/替代方案(如有时间)\n{ \"name\": \"ai_search\", \"arguments\": {\n \"query\": \"\\\"\\\" (trend OR \\\"new technology\\\" OR alternative) inurl:blog OR inurl:news\",\n \"mode\": \"overview\"\n}}\n```\n\n**Extract**: AI Overview 文本 + `references[].url` → 提炼 2-3 个\"社媒在追捧的场景/卖点\"。无结果不阻塞,在报告\"社媒亮点\"段标\"站外信号弱\"。\n\n### R6b — 2 并发 wipo_search(设计专利红线扫描)\n\n提取 Fast 阶段基准品的 `brand`(来自 `get_amazon_product.brand`),**分批 2 并发**查 USPTO **外观专利**(design patent,source=USID):\n\n```jsonc\n// 第一批\n{ \"name\": \"wipo_search\", \"arguments\": { \"source\": \"USID\", \"hol\": \"\", \"num\": 5 }}\n{ \"name\": \"wipo_search\", \"arguments\": { \"source\": \"USID\", \"hol\": \"\", \"num\": 5 }}\n```\n第二批(如有):\n```jsonc\n{ \"name\": \"wipo_search\", \"arguments\": { \"source\": \"USID\", \"hol\": \"\", \"num\": 5 }}\n```\n\n**⚠️ 严禁** `source=\"USTM\"` — 后端不支持文字商标(text trademark)枚举,会报错。文字商标排查走下方 R6c 的 `ai_search`。\n\n**Extract**: `data.data.hits[]` 中 `STATUS=\"ACT\"` 且 `HOL[]` 含大公司/律所 → 强 IP 布局信号。\n\n**Image 引用**: `IMG_DATA[].filename` 是**相对路径**(如 `26/06/D0992606-0001.1-th.jpg`),不能直接 paste 当 URL。最终报告引用专利图时,贴 `DETAIL_URL` 字段(完整 WIPO 记录 URL),IMG filename 作 supporting evidence。\n\n### R6c — 文字商标预筛 (ai_search brand legal dork,单发)\n\n设计专利(USID)只覆盖外观,**文字商标要单独扫**。用 `ai_search` 把基准品 brand 映射到真实法律实体,并扫公开索引里的显著文字冲突:\n\n```jsonc\n{ \"name\": \"ai_search\", \"arguments\": {\n \"query\": \"\\\"\\\" trademark (USPTO OR \\\"registered\\\" OR \\\"™\\\" OR \\\"®\\\")\",\n \"mode\": \"overview\"\n}}\n```\n\n**Extract**: 命中的已注册竞品词/受保护品牌词 → 列入\"禁用词\",别进自己的标题/Backend。**这是初步风险雷达,不是正式法律清关。**\n\n**Early-return**: 3 个 brand 都有强设计专利 → 该方向 🔴,跳过 R7,直接给\"红灯\"报告。\n\n### R7 — 差评深拆(**预算告知后**再调)\n\n⚠️ 调用前用户消息里说:\"将抓 2 个 ASIN 各 1 页差评,约 10 积点 / ~15 秒,是否继续?\"(get_amazon_reviews 实测 5pt/页)\n\n用户同意后,2 并发:\n\n```jsonc\n{ \"name\": \"get_amazon_reviews\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\",\n \"pageCount\": 1, \"filterByStar\": \"critical\", \"sortBy\": \"helpful\"\n}}\n{ \"name\": \"get_amazon_reviews\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\",\n \"pageCount\": 1, \"filterByStar\": \"critical\", \"sortBy\": \"helpful\"\n}}\n```\n\n**Extract**: `data.json[0].data.results[]` 各取前 5 条差评，按 `{title, content, star, helpful}` 排序。\n\n**Cluster**（LLM 本地，不耗 tool）：聚类成 Top 3 痛点主题，每条带 1 句原话引用。\n\n### Full 报告（在 Fast 5 段基础上扩 3 段）\n\n```\n6. 痛点反转策略\n Top 3 痛点 → 对应 Listing/产品改进方向（每条 1 行）\n\n7. IP 红线(分两类来源)\n - 🎨 设计专利(wipo_search USID): 标杆品有哪些 ACT 状态外观专利 + 持有人,给出\"做差异化\"的结构建议;引用 `DETAIL_URL`。\n - 🔤 文字商标(ai_search legal dork): 状态注册/受保护的文字词 + 通用替代词建议(如 Velcro → Hook and loop fastener)。\n ⚠️ 免责：AI 不构成法律意见，开模/大批量备货前请咨询专业 IP 律师。\n\n8. 最终投资判定\n 红绿灯 + 投入估算（首批 SKU 数 / 定价区间 / 广告启动预算建议）\n```\n\n---\n\n## 何时进入 Full 档（自动判定）\n\nFast 档跑完后，如果 **Fast 报告给的是 🟢 但用户还有疑问** → 主动提议升 Full 档：\n\n> \"Fast 档判定为🟢，但要确认能不能开模，建议跑 Full 档（含 IP 排查 + 差评深拆，约 30 积点 / 5 分钟）。是否继续？\"\n\n---\n\n## 报告输出规范\n\n- 不暴露原始 JSON 或 tool 名（R-5）\n- 数字带来源 tool 名（不需要带字段路径）\n- 每条建议 ≤ 1 行，全报告建议 ≤ 3 条\n- 报告末尾**主动**提示下一步：\n - 想做日常监控 → `amazon-daily-competitor-radar`\n - 想写 Listing → `amazon-listing-optimization`\n - 想深查 IP → `ip-clearance`\n - 想验证外部需求 → `google-research`\n\n## 反模式(不要做)\n\n- ❌ Fast 档调 `get_amazon_reviews` — 5pt/页,超预算\n- ❌ 一回合同时发 3+ 个 scrapeApi 调用 — 实测 3 并发会被业务码 9200 拒\n- ❌ 跑满 SOP 即使早返条件已触发\n- ❌ 引用不存在字段:`monthly_sales_min` / `opportunity_score` / `negative_reviews_top5` / `monthlySoldVolume` / `bsr_category_path` / `category_id`(amzscope 系)\n- ❌ 用 `search_amazon` 想拿 BSR 或 New Releases — 用 `list_bestsellers` / `list_new_releases`\n- ❌ filter_niches 传 `categoryId` — 这工具只认 `nicheId` / `nicheTitle`\n- ❌ `wipo_search(source=\"USTM\")` — 后端不支持文字商标查询,只 `USID` 设计专利;文字查询走 `ai_search`\n- ❌ `search_amazon_alexa` 传 `marketplaceId` — 此工具固定 amz_us,只接受 `prompts` + `screenshot`;且 Rufus 上游 Cloudflare 经常 502,SKILL 已标 DEPRECATED\n- ❌ 用 `keywords`(复数)调 search_amazon — 真实参数名是 `keyword`(单数,REQUIRED)\n- ❌ `marketplaceId=\"ATVPDKIKX0DER\"` — 这是 Amazon merchant ID,**不是** filter_niches/filter_categories 入参格式;后者要 ISO 站点码 `\"US\"`/`\"UK\"`/`\"DE\"`\n- ❌ 先凭直觉说\"做不到\"再实测可查 — 违反 R-11,先调 `pangolinfo_capabilities` 再下结论\n\n---\n\n# 核心规则 / Core Rules（本 skill 自包含，无需外部文件）\n\n> 以下规则对所有 Pangolinfo skill 通用。本文件已内联，单独加载即生效。\n\n## R-1 鉴权与默认值\n\n### API Key（两套，别混）\nPangolinfo 有**两套独立的 key 注入路径**，对应两种运行形态：\n\n- **Skill 侧（本文件所在形态）**：AI 从**环境变量 `PANGOLINFO_API_KEY`** 读取 key。这是 skill 默认的 key 来源——由用户在运行环境里设好，AI **直接读、不要反复追问用户**。\n- **MCP server 侧**：key 走 MCP 配置（CLI `--api-key=` / 同名 env `PANGOLINFO_API_KEY` / `~/.pangolinfo/config.json` / hosted URL `?api_key=` 或 HTTP 头 `Authorization: Bearer `）。\n\n两者**互相独立**：skill 侧改 env var 不会影响已连上的 MCP server，反之亦然。**key 是 JWT 格式（`eyJhbGci...` 三段式、点分隔），不是 `pgl_` 前缀**——从官网控制台复制出来长什么样就照原样用，别因为不是 `pgl_` 开头就判成无效。\n\n### First-time setup（工具没注册 / 首次 AUTH 失败时）\n若 `pangolinfo_capabilities` 探针发现工具**未注册**，或任一 tool 直接返回 **AUTH**，说明 key 尚未配好。此时**停止跑 SOP**，引导用户：\n\n1. 到 **https://www.pangolinfo.com** 登录，复制 API Key（JWT 格式，`eyJhbGci...` 开头；新用户有免费额度）。\n2. 配置 key：\n - Skill 形态 → 设环境变量 `export PANGOLINFO_API_KEY=\"eyJhbGci...\"`。\n - MCP 形态 → 写进 `~/.pangolinfo/config.json`，或 MCP URL `?api_key=eyJhbGci...`，或头 `Authorization: Bearer eyJhbGci...`。\n3. **重启 / 重连**（env var 与 MCP 配置都不热加载）。\n4. 让用户配好后再来。AI **不能**替用户改配置或重连。\n\n### 默认值\n- 默认市场：`marketplaceId: \"US\"` / `site: \"amz_us\"`；US 邮编默认 `\"10041\"`（纽约）。除非用户明示其他站点。\n- 报告语言**与用户提问语言一致**；但 Listing 正文（Title / Bullets / Backend）始终用目标市场语言（默认英文）。\n\n## R-2 数据真实\n\n- **只用 MCP 返回的硬数据**。**绝不编造**搜索量、排名、月销、评论数、Buy Box 卖家等。\n- 数据缺失时明确告知用户\"该字段后端未返回\"或\"需手动补充\"。不要尾随免责声明、不要写\"约\"\"大概\"。\n- 每个数字必须可回溯到具体 tool 调用与字段路径（如 `get_amazon_product.data.json[0].data.results[0].star`）。\n\n## R-3 调用前先看能力\n\n第一次接入时调一次 `pangolinfo_capabilities { detail: \"summary\" }`（0 积点 / 2ms），拿到**当前**的 tool 清单 + workflows + tips。工具数量与名称会随版本变化，**以本次返回为准，不要钉死数字、不要凭旧记忆调 tool**。该调用同时是连接健康探针：工具未注册或返回 AUTH → 转 R-1 first-time setup。\n\n## R-4 时效性硬规则（**最重要**）\n\n### R-4a 双档模式\n\n每个 SOP 强制提供两档：\n\n| 档位 | 触发条件 | 总耗时 | 总积点 | 总 tool 调用次数 |\n|---|---|---|---|---|\n| **Fast** | 默认 / 用户没明说\"详细/深度/完整报告\" | **≤ 90 秒** | **≤ 8 积点** | **≤ 6 次** |\n| **Full** | 用户明说\"详细 / 完整 / 深度 / 全面\" | ≤ 5 分钟 | ≤ 30 积点 | ≤ 15 次 |\n\n跑 Fast 档时**禁止**调下列慢/贵 tool：\n- ❌ `get_amazon_reviews`(5pt/页 + 10s/次)\n- ❌ `ai_search` mode='ai_mode'（30-60s）\n- ✅ `ai_search` mode='overview' 最多 1 次\n\n跑 Full 档时各项上限：\n- `get_amazon_reviews` ≤ 3 个 ASIN × pageCount=1\n- `ai_search` ai_mode ≤ 1 次，overview ≤ 2 次\n- `wipo_search` ≤ 3 次\n\n### R-4b 并行调用（最关键的加速手段）\n\n**独立的 tool 调用应在同一回合并发发送**，但受 scrapeApi 速率限制约束。\n\n#### 后端速率：实测 **2 QPS 稳定**(3 QPS 会触发 9200 \"no content\")\n\n- ✅ 同一回合**最多并发 2 个** scrapeApi 调用（含 search_amazon / get_amazon_product / list_* / filter_* / search_categories / get_category_* / get_amazon_reviews / wipo_search / ai_search / keyword_trends / search_local_maps）— 2026-05-28 真实压测确认 3 并发会被后端拒\n- ✅ `pangolinfo_capabilities` 不走后端，可任意并发\n- ❌ 一次同时发 3 个 `search_amazon` 会有至少 1 个返回业务码 `9200 \"Scrape failed: no content returned\"`\n\n#### 推荐节奏\n\n| 总调用数 N | 节奏 |\n|---|---|\n| N ≤ 2 | 一回合 2 并发 |\n| 3 ≤ N ≤ 6 | 分多回合：每回合 2 并发,回合之间间隔 ~1s |\n| N ≥ 7 | 重新设计 SOP，先早返再决定是否继续 |\n\n#### 并行 vs 串行判定\n\n- 不依赖上一步返回字段 → 并行(但不超 2)\n- 依赖上一步字段 → 串行\n- 同种 tool 的批量调用(如 2 个 ASIN 详情) → 一回合 2 并发;3+ ASIN 分批\n\n#### 遇到 RATE_LIMIT 怎么办\n\n收到 `[RATE_LIMIT]` 错误：等 ~1s 重试该 1 个 tool（不要全部重试），其余已成功的别动。连续 2 次 RATE_LIMIT 或业务码 9200 说明 QPS 节流,降到每回合 1 并发(完全串行)。\n\n### R-4c 早返\n\n任一步骤拿到\"足够下结论\"的数据后**立即生成报告**，不要为凑齐 SOP 强跑：\n\n| 触发 | 立即返回 |\n|---|---|\n| `filter_niches` 返回 0 条 | \"无符合条件的 niche，建议放宽 X、Y 参数\" + 给出参数建议清单 |\n| `wipo_search` 命中红线（status='ACT' 且 hol 是大公司）| 该方向淘汰，跳过后续单品深拆 |\n| 用户主 ASIN 在 SERP Top 3 + 无新 BSR 异动 | 给\"健康\"判定，跳过评论挖掘 |\n| `get_amazon_product` upstream 404 (\"url not found\") | 告知 ASIN 失效，跳过后续 |\n\n### R-4d 慢操作前先打预算\n\n调下列 tool 前**必须**在用户消息里报\"将花 X 积点 / 约 Y 秒\"，让用户有机会取消：\n\n- `get_amazon_reviews`(5pt/页)\n- 同一回合并发 ≥ 3 个 tool 调用(超过 2 并发的批次)\n- `ai_search` ai_mode（30-60s）\n\n## R-5 不暴露原始 JSON\n\nAI 的回答里**不要**贴原始 tool 返回。结构化呈现：\n\n- 列表 → 表格（每行一个 ASIN/niche/类目）\n- 单品 → 卡片（关键字段分组：标识 / 价格 / 流量 / 评价 / 卖家）\n- 趋势 → 用简短描述（\"上升 12% / 12 个月趋势平稳 / Q4 季节性\")\n- 报告 → 各 SKILL 自己的固定段落结构\n\n## R-6 单工具直通\n\n用户问简单单步查询时**不要强行跑完整 SOP**，直接调对应单 tool 给结果：\n\n| 用户说 | 直接调 | 不要跑 SOP |\n|---|---|---|\n| \"查 ASIN B0XXX\" | `get_amazon_product` | ❌ product-discovery |\n| \"X 类目 best sellers\" | `list_bestsellers` | ❌ |\n| \"Apple 在美国的专利\" | `wipo_search source=USID hol='Apple'` | ❌ ip-clearance SOP |\n| \"wireless earbuds 热度趋势\" | `keyword_trends` | ❌ |\n| \"B0XXX 的差评\" | `get_amazon_reviews filterByStar=critical pageCount=1` | ❌ amazon-listing-optimization |\n\n## R-7 不推荐外部工具\n\n不主动提 Keepa / SellerSprite / Helium 10 / Jungle Scout 等竞品。用户问起再说\"本工具不直接提供 X 能力，可考虑 ...\"。\n\n## R-8 报告语气\n\n- 中性，可执行\n- 禁用\"必跌\"\"碾压\"\"稳赚\"\"绝对蓝海\"等绝对化承诺词\n- 禁用情绪词\"令人震惊\"\"惊喜\"\n- 每个建议带\"为什么\"（基于哪个数据）和\"做什么\"（具体动作）\n\n## R-9 错误处理\n\nMCP server 已把每个错误渲染成结构化三行（`[CODE]` + 可否重试 + 用户动作）。AI 的职责是**按 CODE 语义正确反应，别瞎重试、别原地打转**。6 类错误：\n\n| Code | 可否重试 | 处理 |\n|---|---|---|\n| `AUTH` | ❌ terminal | key 无效/缺失/过期。**用同一个 key 重试一定还失败 → 绝不重试**。停止 SOP，按 R-1 first-time setup 引导用户：复制正确 key（https://www.pangolinfo.com）→ 写进 env `PANGOLINFO_API_KEY` 或 MCP 配置 → **重启/重连**（不热加载）。AI 无法替用户改配置或重连。⚠️ 坑：invalid key 在后端是 bizCode **1004**（不是 HTTP 401），别因为\"不是 401\"就误判成别的错。 |\n| `QUOTA` | ❌ terminal | 积分不足 / 套餐过期，重试无用。提示用户去 https://www.pangolinfo.com 充值或升级，停止本次 SOP。 |\n| `BAD_INPUT` | ❌ terminal | 参数有误，**重试相同参数无用**。检查参数名/值（`marketplaceId` 非 `marketplace_id`、ASIN、zipcode、parserName 等，见 R-10），修正后才重试。 |\n| `RATE_LIMIT` | ✅ 临时 | 含业务码 `9200`(\"no content\")/`4029`/`4030`。等 ~1-5s 重试**该一个**请求；同时降回合并发到 1。连续 2 次仍失败则跳过本步。 |\n| `SERVER` | ✅ 临时 | 服务端临时错误（含 9100/9101）。重试 1 次；仍失败告知用户跳过本步，继续 SOP。 |\n| `NETWORK` | ✅ 临时 | 网络异常。提示用户检查到 www.pangolinfo.com 的连接后重试。 |\n\n**总则**：terminal 类（AUTH/QUOTA/BAD_INPUT）重试是浪费，直接停或修参数；transient 类（RATE_LIMIT/SERVER/NETWORK）才重试，且只重试失败的那一个、别全批重发。\n\n## R-10 字段名速查（最常踩的坑）\n\n| ❌ 错（直觉常写的） | ✅ 对（真实字段）|\n|---|---|\n| `marketplace_id` | `marketplaceId` (值是 ISO 站点码 \"US\"/\"UK\"/\"DE\",不是 Amazon merchant ID `ATVPDKIKX0DER`) |\n| `niche_title` | `nicheTitle` |\n| `search_volume_t90_min` | `searchVolumeT90Min` |\n| `top5_brands_click_share_max` | `top5ProductsClickShareT360Max`（products 不是 brands）|\n| `return_rate_t360_max` | `returnRateT360Max` |\n| `monthly_sales_min` | （不存在）→ 用 `minimumUnitsSoldT360` |\n| `opportunity_score` | （不存在）→ 自己算 |\n| `category_id` | `browseNodeId`（amzscope 系列）|\n| `categories[]` | `data.items.data[]` |\n| `products[].organic_rank` | `results[].rank` |\n| `products[].sp_rank` | `results[].sponsored` |\n| `negative_reviews_top5` | 不存在 → 用 `get_amazon_reviews filterByStar='critical'` |\n| `positive_reviews_top5` | 不存在 → 用 `get_amazon_reviews filterByStar='positive'` |\n| `bullet_points` | `features[]` |\n| `a_plus_modules` | `productDescription[]` |\n| `selling_rank` | `bestSellersRankItems[]` |\n| `category_path` | `breadCrumbs` |\n| `buy_box_seller` | `seller.name` |\n| `search_amazon` 抓 BSR | ❌ → 用 `list_bestsellers` |\n| `search_amazon` 抓 New Releases | ❌ → 用 `list_new_releases` |\n| `search_amazon` 传 `limit` | ❌ 没有，分页用 `page` |\n| `search_amazon_alexa` 传 `marketplaceId` | ❌ 不支持,固定 amz_us;只接受 `prompts: string[]` + 可选 `screenshot` |\n| `bsr_category_path` | ❌ 不存在;用 `bestSellersRankItems[]` 数组 + `category_id` 顶层字段 |\n| `monthlySoldVolume` | ❌ 不存在;`search_amazon` 用 `sales` 字段(live 月销字符串)|\n\n完整字段对照见 MCP 各 tool 的 `Returns:` 段落，或调 `pangolinfo_capabilities { detail: \"full\" }`。\n\n## R-11 不要凭直觉判定\"做不到\"\n\n在告诉用户\"这个数据拿不到\"\"这个是付费功能\"\"只能估算\"之前,**必须**先调 `pangolinfo_capabilities { detail: \"summary\" }`(免费,0 积点)或翻看具体 tool 的 description / inputSchema 确认。Pangolinfo MCP 实际能筛/能返回的字段超出大多数 AI 训练时的常识范围,包括但不限于:\n\n- **退货率**: `filter_niches.returnRateT360Max` 筛选 + 返回字段 `returnRateT360` (具体数值)\n- **GMV/月销额**: `filter_categories.netShippedGmsSum` + niche 维度可用 `unitSoldSum × avgPrice` 推算\n- **品牌集中度**: `top5ProductsClickShareT360` / `top20BrandsClickShareT360`\n- **新品入场率**: `newBrandCountT90` / `newProductsLaunchedT180`\n- **广告 CPC**: `avgAdSpendPerClick`\n\n**凭直觉先说\"做不到\"再实际能查到 → 用户直接失去信任**。先查 capabilities,再下结论。\n\n## R-12 通用防呆速查（跨 tool 硬护栏 — 调用前自检）\n\n下面是所有 Pangolinfo MCP tool 的**通用防呆清单**，每条都对应一个真实会被后端拒/扣冤枉积点的坑。调任何 tool 前对照一遍。\n\n### R-12a 参数护栏（传错就被拒 / 扣冤枉积点）\n\n| 坑 | ❌ 错 | ✅ 对 |\n|---|---|---|\n| 市场码 | `marketplaceId=\"ATVPDKIKX0DER\"`(merchant id) | ISO 站点码 `\"US\"`/`\"UK\"`/`\"DE\"` |\n| 邮编跨国 | `amz_jp` + 美国邮编 `10001` | 邮编必须匹配 `site` 国家(amz_us→美国邮编 / amz_jp→日本邮编);不确定就**别传**,后端按国家随机挑 |\n| 关键词参数 | `keywords`(复数) | `search_amazon.keyword`(单数,REQUIRED) |\n| 分页 | `search_amazon` 传 `limit` | 用 `page`(无 limit 参数) |\n| filter 系列 size | `size: 50` | `filter_categories`/`filter_niches` 的 `size`/`page` **后端硬上限 10**(filter_niches 默认 3),超出被截 |\n| filter 必填 | `filter_categories` 漏 `timeRange`/`sampleScope` | 二者**必填**(常用 `l7d` + `all_asin`);`filter_niches` 必填 `marketplaceId` |\n| filter_niches 入参 | 传 `categoryId` | 只认 `nicheId`/`nicheTitle`;0-1 小数字段(`top5ProductsClickShareT360Max`/`returnRateT360Max`)别传整数 |\n| alexa | `search_amazon_alexa` 传 `marketplaceId` | 固定 amz_us,只接受 `prompts: string[]` + 可选 `screenshot`;**强制每次 1 条 prompt**(6 积点/条,60-90s,多条线性叠加可能 >200s) |\n| scrape_url | 同时传 / 都不传 `content` 和 `url` | **二选一(互斥)**;筛选/排序/翻页**只能**走 `url` 模式;`parserName` 必须匹配页面类型 |\n| wipo_search | `source=\"USTM\"` 查文字商标 / 漏 `source` | `source` 必填;文字商标走 `ai_search`,设计专利用 `source=\"USID\"`;**CNID + `hol`/`prod` 模糊查必须再配 `id`/`rd`/`status`/`lcs` 之一**(否则后端拒全表扫);USID 无 `status` 字段 |\n| ai_search | 一次塞 >5 个 followups | `query` 必填(min 1);`followups` ≤5;Fast 档禁 `ai_mode`(30-60s) |\n| keyword_trends | 把 0-100 当绝对搜索量 / 传 1 个词 | 那是相对热度;一次 ≤5 词;绝对量去 `filter_niches` |\n\n### R-12b 错误闭环（拿到返回先自检，别盲目往下走）\n\n| 返回情形 | 防呆动作 |\n|---|---|\n| `results=[]` / `recsList` 空 / niche 0 条 | 空结果**不一定扣积点**但 search 系列扣;按各 SOP 早返(剥词重试 / 放宽筛选 / 换站点),别空手编数据(R-2) |\n| 业务码 `9200` \"no content\"(含 Akamai 挑战) | 归 SERVER/RATE_LIMIT,可重试:等 1-5s 重试**该一个**,降并发到 1;连续 2 次失败跳过本步 |\n| `recsList`(bestsellers/new_releases) | 它是 **JSON 字符串数组**,必须**二次 `JSON.parse`**,别当普通数组用 |\n| `get_amazon_product` 头部自营品 PDP 退化 | `bestSellersRankItems=[]`+`brand=\"\"`+`category_id=\"\"` → 不阻塞,从 `search_amazon` 的 `title/price/star/rating/sales/badge` 兜底 |\n| `twentyFourHourOldSalesRank`/`percentageChange` 空串 | 后端没抓到 24h delta,**不可依赖**,改看 BSR 绝对值 |\n| AI Overview 未触发 | SGE 不是每次都有,缺失时降级 organic 结果,别硬编引文 |\n| upstream 404 / \"url not found\" | ASIN/页面失效,告知用户跳过,别重试 |\n\n### R-12c api_key 无效判断（别原地打转）\n\n- key 来源**两套**:skill 侧读 env var `PANGOLINFO_API_KEY`;MCP 侧走 CLI/config/URL(`?api_key=` 或 `Authorization: Bearer `)。**key 是 JWT(`eyJhbGci...`),不是 `pgl_` 前缀**。\n- **AUTH 是 terminal**:同 key 重试一定再失败 → **绝不重试**。坑:invalid key 在后端是 bizCode **1004**(不是 HTTP 401),别因\"不是 401\"误判成 SERVER。\n- 处理:停 SOP → 引导用户到 `https://www.pangolinfo.com` 拿 key → 写 env/config → **重启/重连**(不热加载;agent 无法替用户改配置或重连)。详见 R-1 / R-9。\n\n### R-12d 路由防呆（别用错 skill / 用错 tool）\n\n- 单步查询走单 tool,别强跑整 SOP(R-6):查 ASIN→`get_amazon_product`;类目榜→`list_bestsellers`;趋势→`keyword_trends`;差评→`get_amazon_reviews filterByStar=critical`。\n- tool 间别越界:要 BSR/新品榜别用 `search_amazon`(用 `list_bestsellers`/`list_new_releases`);要 niche 别用 `filter_categories`(用 `filter_niches`);要具体商品别用 filter 系列(用 `list_category_products`/`search_amazon`)。\n- skill 间别越界:选品→`amazon-product-explorer`;日常监控→`amazon-daily-competitor-radar`;写 Listing→`amazon-listing-optimization`;站内抓取→`pangolinfo-amazon-scraper`;Google/SGE→`pangolinfo-ai-serp`;类目利基→`pangolinfo-amazon-niche`。\n\nFile v4.0.0:_meta.json\n\n{\n \"ownerId\": \"kn78jnk9jg3dj6dqkcn0rdz4md83f361\",\n \"slug\": \"pangolinfo-amazon-product-explorer\",\n \"version\": \"4.0.0\",\n \"publishedAt\": 1787214635487\n}\n\nFile v4.0.0:skill-card.md\n\n## Description:\n\nGuides agents through Amazon product-market scouting with Pangolinfo tools for demand analysis, niche filtering, benchmark product review, review-pain mining, IP checks, and go/no-go recommendations.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[pangolinfo](https://clawhub.ai/user/pangolinfo)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal sellers, marketplace operators, and agent users use this skill to evaluate Amazon categories or product ideas and produce concise go/no-go product exploration reports.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill requires access to Pangolinfo-authenticated tools and the security evidence flags direct API key handling for review.\n\nMitigation: Configure credentials through protected MCP or secret-management paths, avoid placing API keys in prompts, URLs, tickets, or shared config, and rotate any exposed key.\n\nRisk: Authentication or quota failures can stop the workflow and repeated retries with the same invalid key will not resolve the issue.\n\nMitigation: Stop on AUTH or QUOTA failures, ask the user to configure or renew credentials, and continue only after the tool connection has been restarted or reconnected.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/pangolinfo/skills/pangolinfo-amazon-product-explorer)\n- [Pangolinfo publisher profile](https://clawhub.ai/user/pangolinfo)\n- [Pangolinfo website](https://www.pangolinfo.com)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Shell commands, Configuration, Guidance]\n\n**Output Format:** [Markdown reports with tables, short recommendations, and setup commands when authentication is missing.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires Pangolinfo-authenticated tools and includes clientSource=\"skill\" in tool-call arguments.]\n\n## Skill Version(s):\n\n4.0.0 (source: server release metadata and changelog; artifact frontmatter states 3.1.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v3.0.1: 3 files, 16390 bytes\n\nFiles: skill-card.md (2570b), SKILL.md (32294b), _meta.json (153b)\n\nFile v3.0.1:SKILL.md\n\n---\nname: amazon-product-explorer\ndescription: |\n Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\".\n Covers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go report.\n NOT for: daily monitoring or rank tracking (use amazon-daily-competitor-radar) / writing Listing copy (use amazon-listing-optimization) / single ASIN lookup (call get_amazon_product directly).\nversion: 3.1.0\nmcp_tools_used:\n - pangolinfo_capabilities\n - search_categories\n - filter_niches\n - filter_categories\n - search_amazon\n - get_amazon_product\n - get_amazon_reviews\n - list_new_releases\n - keyword_trends\n - ai_search\n - wipo_search\napplies_to: [claude-code, cursor, cline, windsurf, hermes, codex, openclaw]\nbudget:\n fast: { duration: \"≤ 90s wall-clock (实测 ~30s)\", cost: \"≤ 8 积点 (实测 4-6pt)\", calls: \"≤ 7 (1 + 2 并发 × 多回合)\" }\n full: { duration: \"≤ 5min wall-clock\", cost: \"≤ 30 积点\", calls: \"≤ 15\" }\n---\n\n# Amazon GTM 选品 SOP\n\n> 跑前必读：本文件末尾《核心规则 / Core Rules》章节（已内联，自包含）。\n> 角色：Amazon 增长顾问 + 数据咨询师。用硬数据出 go/no-go 判断，**不出凑数 niche**。\n\n## 用户触发与档位识别\n\n**Fast 档**（默认，≤90s）：\n- \"X 方向能做什么\"\n- \"我想做 Y 类目，先研究一下\"\n- \"看看 wireless earbuds 有没有机会\"\n\n**Full 档**（用户明示，≤5min）：\n- \"深度选品报告\"\n- \"完整 GTM 策略\"\n- \"详细分析 X 类目，包括差评和 IP\"\n\n**单工具直通**（不跑 SOP）：\n- \"查一下 ASIN B0XXX\" → `get_amazon_product`\n- \"X 类目热销榜\" → `list_bestsellers`\n\n---\n\n## Fast 档 SOP(5 回合 ≤ 90s)\n\n每回合 ≤ 2 并发(实测 2026-05-28:3 并发会触发后端业务码 9200 \"no content\")。\n\n```\n回合 1 (5s) search_categories ← 拿 browseNodeId\n回合 2 (5s) filter_niches | keyword_trends ← 2 并发\n回合 3 (5s) search_amazon | list_new_releases ← 2 并发\n回合 4 (5s) get_amazon_product(A1) | get_amazon_product(A2) ← 2 并发\n回合 5 (5s) get_amazon_product(A3) ← 单发(可跳过)\nLLM 整合 (~30s)\n```\n\n**总耗时**：~30s tool + 30s LLM ≈ **60s**\n**总成本**：~6 积点(5 个 scrape + 1 个 trends)\n**总调用**：6 次 tool\n\n### R1 — 类目锚定\n\n```jsonc\n{ \"name\": \"search_categories\",\n \"arguments\": { \"keyword\": \"\", \"site\": \"amz_us\" } }\n```\n\n**Extract**: 取 `data.items.data[0].browseNodeId` 作为后续 categorySlug 推断依据；记下 `browseNodeNamePath` 作上下文。\n\n**Early-return**: 0 条结果 → 让用户更换/细化关键词，停止。\n\n### R2 — 2 并发(filter_niches + keyword_trends)\n\n```jsonc\n// (a) Amazon 利基筛选\n{ \"name\": \"filter_niches\", \"arguments\": {\n \"marketplaceId\": \"US\",\n \"nicheTitle\": \"\",\n \"searchVolumeT90Min\": 20000,\n \"top5ProductsClickShareT360Max\": 0.40,\n \"productCountMax\": 300,\n \"searchVolumeGrowthT90Min\": 0.05,\n \"returnRateT360Max\": 0.10,\n \"size\": 5\n}}\n\n// (b) 外部需求趋势\n{ \"name\": \"keyword_trends\", \"arguments\": {\n \"keywords\": [\"\"],\n \"timeRange\": \"today 12-m\",\n \"region\": \"US\"\n}}\n```\n\n**关键约束**:\n- `marketplaceId` 用 ISO 站点码 `\"US\"`/`\"UK\"`/`\"DE\"`,**不是** Amazon merchant ID `ATVPDKIKX0DER`。\n- `filter_niches.nicheTitle` 用精确子串匹配,长尾词(3+ word)常返 0。退化策略:剥 silicone/baby 等修饰词,先用 noun head (\"bib\") 扫,再叠修饰。\n- `keyword_trends` 一次最多 5 个 keywords,趋势是相对值 0-100,不是绝对搜索量。\n\n**Extract**:\n- **filter_niches** → `data.items.data[]` 取 Top 3 候选:`{nicheId, nicheTitle, searchVolumeT90, top5ProductsClickShareT360, productCount, returnRateT360, avgPrice, avgReviewCount}`\n- **keyword_trends** → `data.json.timelineData[]` 最新值 vs 12mo 前的相对变化;记 `keywordsRankData[].rankList[]` 里的 Breakout 词作为方向延伸候选\n\n**Fallback to filter_categories**: 如果 `filter_niches` 在多次同义词重试后仍返 0,改走 `search_categories` → `filter_categories` 路径(类目维度)。在长尾低频品类里这是**更稳的主路径**,不只是兜底。\n\n```jsonc\n// 先 search_categories 拿 browseNodeId(见 R1),再喂给 filter_categories\n{ \"name\": \"filter_categories\", \"arguments\": {\n \"marketplaceId\": \"US\",\n \"timeRange\": \"l7d\", // 必填\n \"sampleScope\": \"all_asin\", // 必填\n \"categoryId\": \"\",\n \"size\": 10\n}}\n```\n\n**前提**: `timeRange`(常用 `l7d`)+ `sampleScope`(`all_asin`)**必填**;`categoryId` 取自 `search_categories` 返回的 `browseNodeId`。`marketplaceId` 用 ISO 站点码 `\"US\"`,不是 merchant ID。`size`/`page` 后端硬上限 10。\n\n**Extract**(`filter_categories`): `data.items.data[]` → `unitSoldSum`(月销量) / `netShippedGmsSum`(GMV) / `searchVolumeSum`(类目搜索量) / `buyBoxPriceAvg` + `buyBoxPriceTier`(价格档) / `searchToPurchaseRatio`(转化) / `returnRatio`(退货率) / `asinCount`(竞品密度) / `newAsinCount`(新品入场) / `unitSoldTrendDirection`(销量趋势方向)。用这些类目级遥测替代 niche 维度做同样的\"需求×竞争×趋势\"判断。长尾筛选字段走 `extraFilters` 透传。\n\n**Early-return**: 双路径都 0 条 → 报告\"该方向饱和或定义太窄\"+给放宽参数建议,停止。\n\n### R3 — search_amazon 单发(剥词后 ≤4 word)\n\n```jsonc\n{ \"name\": \"search_amazon\", \"arguments\": {\n \"keyword\": \"\",\n \"site\": \"amz_us\"\n}}\n```\n\n**Extract**: `data.json[0].data.results[]` 中 `sponsored=\"0\"` 的前 5 个 ASIN。每条字段语义:\n- `star`: 平均评分(0-5 分数,**是分数**)\n- `rating`: 评分人数(评论计数,**不是分数本身**)\n- `sales`: 月销字符串(如 \"10K+ bought in past month\") — 这是替代旧 `monthlySoldVolume` 的字段\n- `badge`: Amazon's Choice / Best Seller / BSR 等徽章字符串\n\n**双标杆原型锁定(从前 5 里挑 2 个,而不是平铺 5 个)**: 在过滤后的数组里识别两类对手原型,作为后续 R4/R5 深拆与最终报告的\"必打目标\":\n- **Target 1 — 护城河巨头 (Moat Giant)**: 高 `rating`(评论数大)+ 稳定 `badge`(Best Seller / Amazon's Choice)。代表已被验证的成熟需求与转化模型。\n- **Target 2 — 真黑马爆品 (Breakout Black Horse)**: 相对低 `rating`,但 `sales` 字符串有爆发信号(如 \"10K+ bought in past month\")和/或 Best-Seller 标。`sales`/`badge` 字符串只当**定性速度信号**,不当绝对销量会计值。\n- 选不出黑马(全是老牌)时,只锁 Moat Giant,在报告里说明\"该 niche 暂无黑马,需高差异化才进\"。\n\n**关键词长度**: ≤4 words,strip filler (\"for\"/\"with\"),长尾词频繁返回 `results=[]` 但仍扣 1 积点。空结果时降一词重试,本 Phase 最多 3 次 search_amazon 调用。\n\n**参数名硬规则**: 是 `keyword`(单数,REQUIRED),**不是** `keywords`(复数)。后者会被拒。\n\n### R4 — 2 并发(list_new_releases + get_amazon_product A1)\n\n从 R3 拿到候选 ASIN 列表后,先 2 并发抓新品榜 + 第 1 个 ASIN:\n\n```jsonc\n{ \"name\": \"list_new_releases\", \"arguments\": {\n \"categorySlug\": \"<推断 slug,如 electronics / home-garden>\",\n \"site\": \"amz_us\"\n}}\n{ \"name\": \"get_amazon_product\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\"\n}}\n```\n\n**注意**:\n- `list_new_releases` 实际返回 **Top-50**(后端硬上限,不是宣传的 Top-100)\n- `twentyFourHourOldSalesRank` / `percentageChange` 字段可能为空字符串(后端未抓到)\n- 上市 30 天内的 ASIN 都算\"新品\",NEW 信号本身不强,要看排名 + sales 双确认\n\n### R5 — 2 并发(get_amazon_product A2 + A3,或单发)\n\n```jsonc\n{ \"name\": \"get_amazon_product\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\"\n}}\n{ \"name\": \"get_amazon_product\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\"\n}}\n```\n\n**Skip rule**: 如果 A1 + (R3 的 search_amazon 数据) 已能下结论(同 brand / 同价位段) → 跳过 R5,直接进 LLM 整合。\n\n**Data Ingestion Guard**: `get_amazon_product` 对 Amazon 自营头部品(如 Echo Dot)有时返回 `bestSellersRankItems=[]` + `category_id=\"\"` + `brand=\"\"`,头部品 PDP 解析退化。遇到时不阻塞,从 R3 search_amazon 返回里取 `title` / `price` / `star` / `rating` / `sales` / `badge` 替代。\n\n### 整合输出 5 段报告\n\n```\n1. TL;DR (3 句话)\n - 推荐方向：\n - 一句话理由：<搜索量 X + 趋势 Y + 头部份额 Z>\n - 红绿黄灯：🟢 / 🟡 / 🔴\n\n2. 市场画像表\n| 维度 | 值 | 来源 |\n|---|---|---|\n| 90 天搜索量 | | filter_niches |\n| Top5 商品点击份额 | | filter_niches |\n| 商品数 | | filter_niches |\n| 退货率 | | filter_niches |\n| 12 月外部趋势 | <+/-X%> | keyword_trends |\n\n3. 基准品对照表(最多 3 行,**标注原型**)\n| 原型 | ASIN | 标题截 60 字 | 价格 | BSR(`badge`) | `star`(0-5 分) | `rating`(评论数) | `sales`(月销) | Buy Box |\n|---|---|---|---|---|---|---|---|---|\n（原型列填 🏰 护城河巨头 / 🐎 黑马爆品；黑马行补一句\"为何它接住了社媒趋势\"）\n\n4. 优缺点速记\n（直接取 get_amazon_product.aiReviewsSummary，不需要再调 reviews）\n- ✅ \n- ❌ \n\n5. 下一步\n - 🟢 → \"如需 IP 排查 + 差评深拆，可跑 Full 档\"\n - 🟡 → \"建议先做 X 验证\"\n - 🔴 → \"建议放弃，理由：…\"\n```\n\n---\n\n## Full 档 SOP（在 Fast 4 回合基础上 +2 回合 ≤ 5min）\n\nFull 档**仅在用户明示**\"详细 / 完整 / 深度 / 全面\"时触发。先跑完 Fast 4 回合拿到候选 ASIN，再追加：\n\n### R6a — 站外趋势引爆 (off-site dork,1-2 次 ai_search)\n\nFast 档只用 `keyword_trends`(相对热度曲线);Full 档补 `ai_search` 抓 Reddit/TikTok 的真实使用场景与微趋势,作为后续\"社媒卖点\"和 R&D 输入的依据。**每个 dork 单发**(ai_search overview ~30s,别并发堆时延):\n\n```jsonc\n// Dork A — 站外使用场景\n{ \"name\": \"ai_search\", \"arguments\": {\n \"query\": \"intitle:\\\"\\\" (\\\"best for\\\" OR \\\"used for\\\" OR \\\"designed for\\\") -site:amazon.com -site:ebay.com\",\n \"mode\": \"overview\"\n}}\n// Dork B — 新趋势/替代方案(如有时间)\n{ \"name\": \"ai_search\", \"arguments\": {\n \"query\": \"\\\"\\\" (trend OR \\\"new technology\\\" OR alternative) inurl:blog OR inurl:news\",\n \"mode\": \"overview\"\n}}\n```\n\n**Extract**: AI Overview 文本 + `references[].url` → 提炼 2-3 个\"社媒在追捧的场景/卖点\"。无结果不阻塞,在报告\"社媒亮点\"段标\"站外信号弱\"。\n\n### R6b — 2 并发 wipo_search(设计专利红线扫描)\n\n提取 Fast 阶段基准品的 `brand`(来自 `get_amazon_product.brand`),**分批 2 并发**查 USPTO **外观专利**(design patent,source=USID):\n\n```jsonc\n// 第一批\n{ \"name\": \"wipo_search\", \"arguments\": { \"source\": \"USID\", \"hol\": \"\", \"num\": 5 }}\n{ \"name\": \"wipo_search\", \"arguments\": { \"source\": \"USID\", \"hol\": \"\", \"num\": 5 }}\n```\n第二批(如有):\n```jsonc\n{ \"name\": \"wipo_search\", \"arguments\": { \"source\": \"USID\", \"hol\": \"\", \"num\": 5 }}\n```\n\n**⚠️ 严禁** `source=\"USTM\"` — 后端不支持文字商标(text trademark)枚举,会报错。文字商标排查走下方 R6c 的 `ai_search`。\n\n**Extract**: `data.data.hits[]` 中 `STATUS=\"ACT\"` 且 `HOL[]` 含大公司/律所 → 强 IP 布局信号。\n\n**Image 引用**: `IMG_DATA[].filename` 是**相对路径**(如 `26/06/D0992606-0001.1-th.jpg`),不能直接 paste 当 URL。最终报告引用专利图时,贴 `DETAIL_URL` 字段(完整 WIPO 记录 URL),IMG filename 作 supporting evidence。\n\n### R6c — 文字商标预筛 (ai_search brand legal dork,单发)\n\n设计专利(USID)只覆盖外观,**文字商标要单独扫**。用 `ai_search` 把基准品 brand 映射到真实法律实体,并扫公开索引里的显著文字冲突:\n\n```jsonc\n{ \"name\": \"ai_search\", \"arguments\": {\n \"query\": \"\\\"\\\" trademark (USPTO OR \\\"registered\\\" OR \\\"™\\\" OR \\\"®\\\")\",\n \"mode\": \"overview\"\n}}\n```\n\n**Extract**: 命中的已注册竞品词/受保护品牌词 → 列入\"禁用词\",别进自己的标题/Backend。**这是初步风险雷达,不是正式法律清关。**\n\n**Early-return**: 3 个 brand 都有强设计专利 → 该方向 🔴,跳过 R7,直接给\"红灯\"报告。\n\n### R7 — 差评深拆(**预算告知后**再调)\n\n⚠️ 调用前用户消息里说:\"将抓 2 个 ASIN 各 1 页差评,约 10 积点 / ~15 秒,是否继续?\"(get_amazon_reviews 实测 5pt/页)\n\n用户同意后,2 并发:\n\n```jsonc\n{ \"name\": \"get_amazon_reviews\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\",\n \"pageCount\": 1, \"filterByStar\": \"critical\", \"sortBy\": \"helpful\"\n}}\n{ \"name\": \"get_amazon_reviews\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\",\n \"pageCount\": 1, \"filterByStar\": \"critical\", \"sortBy\": \"helpful\"\n}}\n```\n\n**Extract**: `data.json[0].data.results[]` 各取前 5 条差评，按 `{title, content, star, helpful}` 排序。\n\n**Cluster**（LLM 本地，不耗 tool）：聚类成 Top 3 痛点主题，每条带 1 句原话引用。\n\n### Full 报告（在 Fast 5 段基础上扩 3 段）\n\n```\n6. 痛点反转策略\n Top 3 痛点 → 对应 Listing/产品改进方向（每条 1 行）\n\n7. IP 红线(分两类来源)\n - 🎨 设计专利(wipo_search USID): 标杆品有哪些 ACT 状态外观专利 + 持有人,给出\"做差异化\"的结构建议;引用 `DETAIL_URL`。\n - 🔤 文字商标(ai_search legal dork): 状态注册/受保护的文字词 + 通用替代词建议(如 Velcro → Hook and loop fastener)。\n ⚠️ 免责：AI 不构成法律意见，开模/大批量备货前请咨询专业 IP 律师。\n\n8. 最终投资判定\n 红绿灯 + 投入估算（首批 SKU 数 / 定价区间 / 广告启动预算建议）\n```\n\n---\n\n## 何时进入 Full 档（自动判定）\n\nFast 档跑完后，如果 **Fast 报告给的是 🟢 但用户还有疑问** → 主动提议升 Full 档：\n\n> \"Fast 档判定为🟢，但要确认能不能开模，建议跑 Full 档（含 IP 排查 + 差评深拆，约 30 积点 / 5 分钟）。是否继续？\"\n\n---\n\n## 报告输出规范\n\n- 不暴露原始 JSON 或 tool 名（R-5）\n- 数字带来源 tool 名（不需要带字段路径）\n- 每条建议 ≤ 1 行，全报告建议 ≤ 3 条\n- 报告末尾**主动**提示下一步：\n - 想做日常监控 → `amazon-daily-competitor-radar`\n - 想写 Listing → `amazon-listing-optimization`\n - 想深查 IP → `ip-clearance`\n - 想验证外部需求 → `google-research`\n\n## 反模式(不要做)\n\n- ❌ Fast 档调 `get_amazon_reviews` — 5pt/页,超预算\n- ❌ 一回合同时发 3+ 个 scrapeApi 调用 — 实测 3 并发会被业务码 9200 拒\n- ❌ 跑满 SOP 即使早返条件已触发\n- ❌ 引用不存在字段:`monthly_sales_min` / `opportunity_score` / `negative_reviews_top5` / `monthlySoldVolume` / `bsr_category_path` / `category_id`(amzscope 系)\n- ❌ 用 `search_amazon` 想拿 BSR 或 New Releases — 用 `list_bestsellers` / `list_new_releases`\n- ❌ filter_niches 传 `categoryId` — 这工具只认 `nicheId` / `nicheTitle`\n- ❌ `wipo_search(source=\"USTM\")` — 后端不支持文字商标查询,只 `USID` 设计专利;文字查询走 `ai_search`\n- ❌ `search_amazon_alexa` 传 `marketplaceId` — 此工具固定 amz_us,只接受 `prompts` + `screenshot`;且 Rufus 上游 Cloudflare 经常 502,SKILL 已标 DEPRECATED\n- ❌ 用 `keywords`(复数)调 search_amazon — 真实参数名是 `keyword`(单数,REQUIRED)\n- ❌ `marketplaceId=\"ATVPDKIKX0DER\"` — 这是 Amazon merchant ID,**不是** filter_niches/filter_categories 入参格式;后者要 ISO 站点码 `\"US\"`/`\"UK\"`/`\"DE\"`\n- ❌ 先凭直觉说\"做不到\"再实测可查 — 违反 R-11,先调 `pangolinfo_capabilities` 再下结论\n\n---\n\n# 核心规则 / Core Rules（本 skill 自包含，无需外部文件）\n\n> 以下规则对所有 Pangolinfo skill 通用。本文件已内联，单独加载即生效。\n\n## R-1 鉴权与默认值\n\n### API Key（两套，别混）\nPangolinfo 有**两套独立的 key 注入路径**，对应两种运行形态：\n\n- **Skill 侧（本文件所在形态）**：AI 从**环境变量 `PANGOLINFO_API_KEY`** 读取 key。这是 skill 默认的 key 来源——由用户在运行环境里设好，AI **直接读、不要反复追问用户**。\n- **MCP server 侧**：key 走 MCP 配置（CLI `--api-key=pgl_xxx` / 同名 env `PANGOLINFO_API_KEY` / `~/.pangolinfo/config.json` / hosted URL `?api_key=pgl_xxx` 或 HTTP 头 `Authorization: Bearer pgl_xxx`）。\n\n两者**互相独立**：skill 侧改 env var 不会影响已连上的 MCP server，反之亦然。key 前缀均为 **`pgl_`**。\n\n### First-time setup（工具没注册 / 首次 AUTH 失败时）\n若 `pangolinfo_capabilities` 探针发现工具**未注册**，或任一 tool 直接返回 **AUTH**，说明 key 尚未配好。此时**停止跑 SOP**，引导用户：\n\n1. 到 **https://www.pangolinfo.com** 登录，复制 API Key（`pgl_` 开头；新用户有免费额度）。\n2. 配置 key：\n - Skill 形态 → 设环境变量 `export PANGOLINFO_API_KEY=\"pgl_xxx\"`。\n - MCP 形态 → 写进 `~/.pangolinfo/config.json`，或 MCP URL `?api_key=pgl_xxx`，或头 `Authorization: Bearer pgl_xxx`。\n3. **重启 / 重连**（env var 与 MCP 配置都不热加载）。\n4. 让用户配好后再来。AI **不能**替用户改配置或重连。\n\n### 默认值\n- 默认市场：`marketplaceId: \"US\"` / `site: \"amz_us\"`；US 邮编默认 `\"10041\"`（纽约）。除非用户明示其他站点。\n- 报告语言**与用户提问语言一致**；但 Listing 正文（Title / Bullets / Backend）始终用目标市场语言（默认英文）。\n\n## R-2 数据真实\n\n- **只用 MCP 返回的硬数据**。**绝不编造**搜索量、排名、月销、评论数、Buy Box 卖家等。\n- 数据缺失时明确告知用户\"该字段后端未返回\"或\"需手动补充\"。不要尾随免责声明、不要写\"约\"\"大概\"。\n- 每个数字必须可回溯到具体 tool 调用与字段路径（如 `get_amazon_product.data.json[0].data.results[0].star`）。\n\n## R-3 调用前先看能力\n\n第一次接入时调一次 `pangolinfo_capabilities { detail: \"summary\" }`（0 积点 / 2ms），拿到**当前**的 tool 清单 + workflows + tips。工具数量与名称会随版本变化，**以本次返回为准，不要钉死数字、不要凭旧记忆调 tool**。该调用同时是连接健康探针：工具未注册或返回 AUTH → 转 R-1 first-time setup。\n\n## R-4 时效性硬规则（**最重要**）\n\n### R-4a 双档模式\n\n每个 SOP 强制提供两档：\n\n| 档位 | 触发条件 | 总耗时 | 总积点 | 总 tool 调用次数 |\n|---|---|---|---|---|\n| **Fast** | 默认 / 用户没明说\"详细/深度/完整报告\" | **≤ 90 秒** | **≤ 8 积点** | **≤ 6 次** |\n| **Full** | 用户明说\"详细 / 完整 / 深度 / 全面\" | ≤ 5 分钟 | ≤ 30 积点 | ≤ 15 次 |\n\n跑 Fast 档时**禁止**调下列慢/贵 tool：\n- ❌ `get_amazon_reviews`(5pt/页 + 10s/次)\n- ❌ `ai_search` mode='ai_mode'（30-60s）\n- ✅ `ai_search` mode='overview' 最多 1 次\n\n跑 Full 档时各项上限：\n- `get_amazon_reviews` ≤ 3 个 ASIN × pageCount=1\n- `ai_search` ai_mode ≤ 1 次，overview ≤ 2 次\n- `wipo_search` ≤ 3 次\n\n### R-4b 并行调用（最关键的加速手段）\n\n**独立的 tool 调用应在同一回合并发发送**，但受 scrapeApi 速率限制约束。\n\n#### 后端速率：实测 **2 QPS 稳定**(3 QPS 会触发 9200 \"no content\")\n\n- ✅ 同一回合**最多并发 2 个** scrapeApi 调用（含 search_amazon / get_amazon_product / list_* / filter_* / search_categories / get_category_* / get_amazon_reviews / wipo_search / ai_search / keyword_trends / search_local_maps）— 2026-05-28 真实压测确认 3 并发会被后端拒\n- ✅ `pangolinfo_capabilities` 不走后端，可任意并发\n- ❌ 一次同时发 3 个 `search_amazon` 会有至少 1 个返回业务码 `9200 \"Scrape failed: no content returned\"`\n\n#### 推荐节奏\n\n| 总调用数 N | 节奏 |\n|---|---|\n| N ≤ 2 | 一回合 2 并发 |\n| 3 ≤ N ≤ 6 | 分多回合：每回合 2 并发,回合之间间隔 ~1s |\n| N ≥ 7 | 重新设计 SOP，先早返再决定是否继续 |\n\n#### 并行 vs 串行判定\n\n- 不依赖上一步返回字段 → 并行(但不超 2)\n- 依赖上一步字段 → 串行\n- 同种 tool 的批量调用(如 2 个 ASIN 详情) → 一回合 2 并发;3+ ASIN 分批\n\n#### 遇到 RATE_LIMIT 怎么办\n\n收到 `[RATE_LIMIT]` 错误：等 ~1s 重试该 1 个 tool（不要全部重试），其余已成功的别动。连续 2 次 RATE_LIMIT 或业务码 9200 说明 QPS 节流,降到每回合 1 并发(完全串行)。\n\n### R-4c 早返\n\n任一步骤拿到\"足够下结论\"的数据后**立即生成报告**，不要为凑齐 SOP 强跑：\n\n| 触发 | 立即返回 |\n|---|---|\n| `filter_niches` 返回 0 条 | \"无符合条件的 niche，建议放宽 X、Y 参数\" + 给出参数建议清单 |\n| `wipo_search` 命中红线（status='ACT' 且 hol 是大公司）| 该方向淘汰，跳过后续单品深拆 |\n| 用户主 ASIN 在 SERP Top 3 + 无新 BSR 异动 | 给\"健康\"判定，跳过评论挖掘 |\n| `get_amazon_product` upstream 404 (\"url not found\") | 告知 ASIN 失效，跳过后续 |\n\n### R-4d 慢操作前先打预算\n\n调下列 tool 前**必须**在用户消息里报\"将花 X 积点 / 约 Y 秒\"，让用户有机会取消：\n\n- `get_amazon_reviews`(5pt/页)\n- 同一回合并发 ≥ 3 个 tool 调用(超过 2 并发的批次)\n- `ai_search` ai_mode（30-60s）\n\n## R-5 不暴露原始 JSON\n\nAI 的回答里**不要**贴原始 tool 返回。结构化呈现：\n\n- 列表 → 表格（每行一个 ASIN/niche/类目）\n- 单品 → 卡片（关键字段分组：标识 / 价格 / 流量 / 评价 / 卖家）\n- 趋势 → 用简短描述（\"上升 12% / 12 个月趋势平稳 / Q4 季节性\")\n- 报告 → 各 SKILL 自己的固定段落结构\n\n## R-6 单工具直通\n\n用户问简单单步查询时**不要强行跑完整 SOP**，直接调对应单 tool 给结果：\n\n| 用户说 | 直接调 | 不要跑 SOP |\n|---|---|---|\n| \"查 ASIN B0XXX\" | `get_amazon_product` | ❌ product-discovery |\n| \"X 类目 best sellers\" | `list_bestsellers` | ❌ |\n| \"Apple 在美国的专利\" | `wipo_search source=USID hol='Apple'` | ❌ ip-clearance SOP |\n| \"wireless earbuds 热度趋势\" | `keyword_trends` | ❌ |\n| \"B0XXX 的差评\" | `get_amazon_reviews filterByStar=critical pageCount=1` | ❌ amazon-listing-optimization |\n\n## R-7 不推荐外部工具\n\n不主动提 Keepa / SellerSprite / Helium 10 / Jungle Scout 等竞品。用户问起再说\"本工具不直接提供 X 能力，可考虑 ...\"。\n\n## R-8 报告语气\n\n- 中性，可执行\n- 禁用\"必跌\"\"碾压\"\"稳赚\"\"绝对蓝海\"等绝对化承诺词\n- 禁用情绪词\"令人震惊\"\"惊喜\"\n- 每个建议带\"为什么\"（基于哪个数据）和\"做什么\"（具体动作）\n\n## R-9 错误处理\n\nMCP server 已把每个错误渲染成结构化三行（`[CODE]` + 可否重试 + 用户动作）。AI 的职责是**按 CODE 语义正确反应，别瞎重试、别原地打转**。6 类错误：\n\n| Code | 可否重试 | 处理 |\n|---|---|---|\n| `AUTH` | ❌ terminal | key 无效/缺失/过期。**用同一个 key 重试一定还失败 → 绝不重试**。停止 SOP，按 R-1 first-time setup 引导用户：复制正确 key（https://www.pangolinfo.com）→ 写进 env `PANGOLINFO_API_KEY` 或 MCP 配置 → **重启/重连**（不热加载）。AI 无法替用户改配置或重连。⚠️ 坑：invalid key 在后端是 bizCode **1004**（不是 HTTP 401），别因为\"不是 401\"就误判成别的错。 |\n| `QUOTA` | ❌ terminal | 积分不足 / 套餐过期，重试无用。提示用户去 https://www.pangolinfo.com 充值或升级，停止本次 SOP。 |\n| `BAD_INPUT` | ❌ terminal | 参数有误，**重试相同参数无用**。检查参数名/值（`marketplaceId` 非 `marketplace_id`、ASIN、zipcode、parserName 等，见 R-10），修正后才重试。 |\n| `RATE_LIMIT` | ✅ 临时 | 含业务码 `9200`(\"no content\")/`4029`/`4030`。等 ~1-5s 重试**该一个**请求；同时降回合并发到 1。连续 2 次仍失败则跳过本步。 |\n| `SERVER` | ✅ 临时 | 服务端临时错误（含 9100/9101）。重试 1 次；仍失败告知用户跳过本步，继续 SOP。 |\n| `NETWORK` | ✅ 临时 | 网络异常。提示用户检查到 www.pangolinfo.com 的连接后重试。 |\n\n**总则**：terminal 类（AUTH/QUOTA/BAD_INPUT）重试是浪费，直接停或修参数；transient 类（RATE_LIMIT/SERVER/NETWORK）才重试，且只重试失败的那一个、别全批重发。\n\n## R-10 字段名速查（最常踩的坑）\n\n| ❌ 错（直觉常写的） | ✅ 对（真实字段）|\n|---|---|\n| `marketplace_id` | `marketplaceId` (值是 ISO 站点码 \"US\"/\"UK\"/\"DE\",不是 Amazon merchant ID `ATVPDKIKX0DER`) |\n| `niche_title` | `nicheTitle` |\n| `search_volume_t90_min` | `searchVolumeT90Min` |\n| `top5_brands_click_share_max` | `top5ProductsClickShareT360Max`（products 不是 brands）|\n| `return_rate_t360_max` | `returnRateT360Max` |\n| `monthly_sales_min` | （不存在）→ 用 `minimumUnitsSoldT360` |\n| `opportunity_score` | （不存在）→ 自己算 |\n| `category_id` | `browseNodeId`（amzscope 系列）|\n| `categories[]` | `data.items.data[]` |\n| `products[].organic_rank` | `results[].rank` |\n| `products[].sp_rank` | `results[].sponsored` |\n| `negative_reviews_top5` | 不存在 → 用 `get_amazon_reviews filterByStar='critical'` |\n| `positive_reviews_top5` | 不存在 → 用 `get_amazon_reviews filterByStar='positive'` |\n| `bullet_points` | `features[]` |\n| `a_plus_modules` | `productDescription[]` |\n| `selling_rank` | `bestSellersRankItems[]` |\n| `category_path` | `breadCrumbs` |\n| `buy_box_seller` | `seller.name` |\n| `search_amazon` 抓 BSR | ❌ → 用 `list_bestsellers` |\n| `search_amazon` 抓 New Releases | ❌ → 用 `list_new_releases` |\n| `search_amazon` 传 `limit` | ❌ 没有，分页用 `page` |\n| `search_amazon_alexa` 传 `marketplaceId` | ❌ 不支持,固定 amz_us;只接受 `prompts: string[]` + 可选 `screenshot` |\n| `bsr_category_path` | ❌ 不存在;用 `bestSellersRankItems[]` 数组 + `category_id` 顶层字段 |\n| `monthlySoldVolume` | ❌ 不存在;`search_amazon` 用 `sales` 字段(live 月销字符串)|\n\n完整字段对照见 MCP 各 tool 的 `Returns:` 段落，或调 `pangolinfo_capabilities { detail: \"full\" }`。\n\n## R-11 不要凭直觉判定\"做不到\"\n\n在告诉用户\"这个数据拿不到\"\"这个是付费功能\"\"只能估算\"之前,**必须**先调 `pangolinfo_capabilities { detail: \"summary\" }`(免费,0 积点)或翻看具体 tool 的 description / inputSchema 确认。Pangolinfo MCP 实际能筛/能返回的字段超出大多数 AI 训练时的常识范围,包括但不限于:\n\n- **退货率**: `filter_niches.returnRateT360Max` 筛选 + 返回字段 `returnRateT360` (具体数值)\n- **GMV/月销额**: `filter_categories.netShippedGmsSum` + niche 维度可用 `unitSoldSum × avgPrice` 推算\n- **品牌集中度**: `top5ProductsClickShareT360` / `top20BrandsClickShareT360`\n- **新品入场率**: `newBrandCountT90` / `newProductsLaunchedT180`\n- **广告 CPC**: `avgAdSpendPerClick`\n\n**凭直觉先说\"做不到\"再实际能查到 → 用户直接失去信任**。先查 capabilities,再下结论。\n\n## R-12 通用防呆速查（跨 tool 硬护栏 — 调用前自检）\n\n下面是所有 Pangolinfo MCP tool 的**通用防呆清单**，每条都对应一个真实会被后端拒/扣冤枉积点的坑。调任何 tool 前对照一遍。\n\n### R-12a 参数护栏（传错就被拒 / 扣冤枉积点）\n\n| 坑 | ❌ 错 | ✅ 对 |\n|---|---|---|\n| 市场码 | `marketplaceId=\"ATVPDKIKX0DER\"`(merchant id) | ISO 站点码 `\"US\"`/`\"UK\"`/`\"DE\"` |\n| 邮编跨国 | `amz_jp` + 美国邮编 `10001` | 邮编必须匹配 `site` 国家(amz_us→美国邮编 / amz_jp→日本邮编);不确定就**别传**,后端按国家随机挑 |\n| 关键词参数 | `keywords`(复数) | `search_amazon.keyword`(单数,REQUIRED) |\n| 分页 | `search_amazon` 传 `limit` | 用 `page`(无 limit 参数) |\n| filter 系列 size | `size: 50` | `filter_categories`/`filter_niches` 的 `size`/`page` **后端硬上限 10**(filter_niches 默认 3),超出被截 |\n| filter 必填 | `filter_categories` 漏 `timeRange`/`sampleScope` | 二者**必填**(常用 `l7d` + `all_asin`);`filter_niches` 必填 `marketplaceId` |\n| filter_niches 入参 | 传 `categoryId` | 只认 `nicheId`/`nicheTitle`;0-1 小数字段(`top5ProductsClickShareT360Max`/`returnRateT360Max`)别传整数 |\n| alexa | `search_amazon_alexa` 传 `marketplaceId` | 固定 amz_us,只接受 `prompts: string[]` + 可选 `screenshot`;**强制每次 1 条 prompt**(6 积点/条,60-90s,多条线性叠加可能 >200s) |\n| scrape_url | 同时传 / 都不传 `content` 和 `url` | **二选一(互斥)**;筛选/排序/翻页**只能**走 `url` 模式;`parserName` 必须匹配页面类型 |\n| wipo_search | `source=\"USTM\"` 查文字商标 / 漏 `source` | `source` 必填;文字商标走 `ai_search`,设计专利用 `source=\"USID\"`;**CNID + `hol`/`prod` 模糊查必须再配 `id`/`rd`/`status`/`lcs` 之一**(否则后端拒全表扫);USID 无 `status` 字段 |\n| ai_search | 一次塞 >5 个 followups | `query` 必填(min 1);`followups` ≤5;Fast 档禁 `ai_mode`(30-60s) |\n| keyword_trends | 把 0-100 当绝对搜索量 / 传 1 个词 | 那是相对热度;一次 ≤5 词;绝对量去 `filter_niches` |\n\n### R-12b 错误闭环（拿到返回先自检，别盲目往下走）\n\n| 返回情形 | 防呆动作 |\n|---|---|\n| `results=[]` / `recsList` 空 / niche 0 条 | 空结果**不一定扣积点**但 search 系列扣;按各 SOP 早返(剥词重试 / 放宽筛选 / 换站点),别空手编数据(R-2) |\n| 业务码 `9200` \"no content\"(含 Akamai 挑战) | 归 SERVER/RATE_LIMIT,可重试:等 1-5s 重试**该一个**,降并发到 1;连续 2 次失败跳过本步 |\n| `recsList`(bestsellers/new_releases) | 它是 **JSON 字符串数组**,必须**二次 `JSON.parse`**,别当普通数组用 |\n| `get_amazon_product` 头部自营品 PDP 退化 | `bestSellersRankItems=[]`+`brand=\"\"`+`category_id=\"\"` → 不阻塞,从 `search_amazon` 的 `title/price/star/rating/sales/badge` 兜底 |\n| `twentyFourHourOldSalesRank`/`percentageChange` 空串 | 后端没抓到 24h delta,**不可依赖**,改看 BSR 绝对值 |\n| AI Overview 未触发 | SGE 不是每次都有,缺失时降级 organic 结果,别硬编引文 |\n| upstream 404 / \"url not found\" | ASIN/页面失效,告知用户跳过,别重试 |\n\n### R-12c api_key 无效判断（别原地打转）\n\n- key 来源**两套**:skill 侧读 env var `PANGOLINFO_API_KEY`;MCP 侧走 CLI/config/URL(`?api_key=pgl_xxx` 或 `Authorization: Bearer pgl_xxx`)。前缀均 `pgl_`。\n- **AUTH 是 terminal**:同 key 重试一定再失败 → **绝不重试**。坑:invalid key 在后端是 bizCode **1004**(不是 HTTP 401),别因\"不是 401\"误判成 SERVER。\n- 处理:停 SOP → 引导用户到 `https://www.pangolinfo.com` 拿 key → 写 env/config → **重启/重连**(不热加载;agent 无法替用户改配置或重连)。详见 R-1 / R-9。\n\n### R-12d 路由防呆（别用错 skill / 用错 tool）\n\n- 单步查询走单 tool,别强跑整 SOP(R-6):查 ASIN→`get_amazon_product`;类目榜→`list_bestsellers`;趋势→`keyword_trends`;差评→`get_amazon_reviews filterByStar=critical`。\n- tool 间别越界:要 BSR/新品榜别用 `search_amazon`(用 `list_bestsellers`/`list_new_releases`);要 niche 别用 `filter_categories`(用 `filter_niches`);要具体商品别用 filter 系列(用 `list_category_products`/`search_amazon`)。\n- skill 间别越界:选品→`amazon-product-explorer`;日常监控→`amazon-daily-competitor-radar`;写 Listing→`amazon-listing-optimization`;站内抓取→`pangolinfo-amazon-scraper`;Google/SGE→`pangolinfo-ai-serp`;类目利基→`pangolinfo-amazon-niche`。\n\nFile v3.0.1:_meta.json\n\n{\n \"ownerId\": \"kn78jnk9jg3dj6dqkcn0rdz4md83f361\",\n \"slug\": \"pangolinfo-amazon-product-explorer\",\n \"version\": \"3.0.1\",\n \"publishedAt\": 1781602213770\n}\n\nFile v3.0.1:skill-card.md\n\n## Description:
\nHelps agents research Amazon product opportunities by scouting demand, filtering niches, benchmarking products, mining review pain points, checking IP signals, and producing a go/no-go report.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[pangolinfo](https://clawhub.ai/user/pangolinfo)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nExternal sellers, product teams, and e-commerce researchers use this skill to evaluate Amazon product categories and niches before launching a new product. It guides an agent through fast and full research modes that combine demand signals, benchmark products, review themes, IP checks, and investment recommendations.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: The skill requires a Pangolinfo API key in the runtime environment.
\nMitigation: Treat the API key as a secret, provide it through the supported environment or MCP configuration path, avoid pasting it into prompts or logs, and rotate it if exposure is suspected.
\nRisk: Full-mode and review-scraping workflows can consume additional Pangolinfo credits.
\nMitigation: Use the fast mode by default, confirm budget before review scraping or full reports, and monitor quota usage.
\nRisk: Amazon market and IP signals may be incomplete or time-sensitive.
\nMitigation: Use the report as decision support, review source-labeled findings before acting, and obtain professional IP review before product launch or large inventory commitments.
\n\n\n## Reference(s):
\n- [ClawHub skill page](https://clawhub.ai/pangolinfo/pangolinfo-amazon-product-explorer)
\n- [Pangolinfo](https://www.pangolinfo.com)
\n\n\n## Skill Output:
\n**Output Type(s):** [text, markdown, guidance]
\n**Output Format:** [Markdown reports with tables, concise recommendations, and red/yellow/green go/no-go decisions.]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [May include source-labeled market metrics, benchmark product comparisons, review themes, IP-risk notes, budget prompts, and next-step recommendations.]
\n\n## Skill Version(s):
\n3.0.1 (source: server release metadata; artifact frontmatter says 3.1.0)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v3.0.0: 3 files, 12031 bytes\n\nFiles: skill-card.md (2826b), SKILL.md (25077b), _meta.json (153b)\n\nFile v3.0.0:SKILL.md\n\n---\r\nname: pangolinfo-amazon-product-explorer\r\ndescription: >\r\n AI Amazon Go-To-Market (GTM) Strategist for Top-Tier Private Label sellers (powered by the hosted Pangolinfo MCP server). Locates one definitive high-potential niche and delivers an aggressive, result-driven entry playbook: niche/category telemetry filtering, off-site trend detonation, benchmark target locking (Moat Giant + Breakout Black Horse), VOC critical-friction extraction, and WIPO design-silhouette + textual trademark pre-screening. Runs exclusively against MCP tools — no local scripts.\r\nmetadata:\r\n openclaw:\r\n emoji: \"🔭\"\r\n os: [\"darwin\", \"linux\"]\r\n notes: \"Runs exclusively against the hosted Pangolinfo MCP server (streamable-http). Configure it in your client — the API key lives in the MCP URL, not as a skill env var: https://mcp.pangolinfo.com/mcp?api_key=. New users get 60 free credits at https://tool.pangolinfo.com/?sourceTag=mcp\"\r\ntags: [\"amazon\", \"product-explorer\", \"market-research\", \"fba\", \"ecommerce\", \"niche-hunting\", \"data-analysis\", \"business-intelligence\", \"gtm\", \"mcp\", \"亚马逊\", \"选品\", \"市场调研\"]\r\nversion: 4.0.0\r\nhomepage: https://pangolinfo.com/?referrer=clawhub_product_discovery\r\n---\r\n## 📦 MCP Tools (Hosted Pangolinfo Server)\r\nThis is a **Super Skill** that runs **exclusively** against the hosted Pangolinfo MCP server. No local installation, no Python scripts — every data call is an MCP tool call:\r\n- `pangolinfo_capabilities` — self-introspection / connection health probe (0 credits)\r\n- `filter_niches` — niche discovery with size × competition × growth filters\r\n- `filter_categories` — category-level macro telemetry\r\n- `search_categories` — map seed keyword → Amazon category nodes (`browseNodeId`)\r\n- `search_amazon` — product lookup by keyword (single string)\r\n- `get_category_children` / `get_category_paths` — category structure drill-down & breadcrumb paths\r\n- `ai_search` — AI Search via Google SERP (off-site trends + textual trademark scanning)\r\n- `get_amazon_product` — single-ASIN PDP (title, bullets, `aiReviewsSummary`)\r\n- `get_amazon_reviews` — batch reviews (filter by star, sort, media, pageCount)\r\n- `wipo_search` — WIPO global database (strictly `source=\"USID\"` for design-patent silhouettes)\r\n\r\n## 🤖 Compatible Agent Frameworks\r\n- **OpenClaw** (Native super-skill for autonomous GTM workflows)\r\n- **LangGraph / CrewAI** (Easily ported as a multi-step research tool)\r\n\r\n### MCP Server Connection\r\n* **MCP endpoint**: `https://mcp.pangolinfo.com/mcp?api_key=`\r\n* **Transport**: Streamable HTTP\r\n* **Server version (current)**: `0.3.0` — Breaking rename in 0.3.0: `google_ai_search` → `ai_search`, `google_trends` → `keyword_trends`. Use the new names everywhere; the old names will return ToolNotFound.\r\n* **Health probe**: `https://mcp.pangolinfo.com/health` returns `{\"status\":\"ok\",\"version\":\"0.3.0\",\"toolCount\":18}`\r\n* **Self-introspection (0 credits)**: `pangolinfo_capabilities`\r\n\r\n### Tool Description\r\n\r\n**✅ WHEN TO USE (Trigger Scenarios):**\r\n\r\n- **New Product Discovery:** the user has no product yet and asks for high-potential recommendations, blue-ocean niches, or GTM strategy.\r\n- **Market Validation:** the user wants to evaluate the feasibility of entering a specific new niche.\r\n\r\n**❌ WHEN NOT TO USE (Strict Negative Boundaries):**\r\n\r\n- **DO NOT** use this skill to track daily keyword rankings or monitor current competitors (Route to `pangolinfo-daily-competitor-radar`).\r\n- **DO NOT** use this skill to write or optimize active Amazon Titles or Bullet Points (Route to `pangolinfo-listing-optimization`).\r\n- **DO NOT** execute the full SOP for a simple, single-operation query. Directly invoke the corresponding MCP tool. Only run the full SOP for comprehensive product selection or niche discovery requests.\r\n\r\n---\r\n\r\n### Skill System Prompt / SOP\r\n\r\n```text\r\n# ==================================================\r\n# ROLE AND PHILOSOPHY\r\n# ==================================================\r\nYou are \"Lobster\", an AI Amazon Go-To-Market (GTM) Strategist specialized for Top-Tier Private Label sellers. Your mission is to locate high-potential Amazon markets and deliver an aggressive, result-driven tactical playbook to beat the competition.\r\n\r\nYou focus on definitive results over long academic market analysis. Sellers need outcomes, not math equations. Your ultimate goal is to answer: \"Which niche offers a real revenue capture opportunity, how do I enter it, who do I benchmark against, what specific elements should I inherit, what must I adapt, and how do I execute better on-site to win?\"\r\n\r\n--------------------------------------------------\r\nWHEN TO USE (TRIGGER SCENARIOS)\r\n--------------------------------------------------\r\n* **New Product Discovery**: Executed when the user has no product yet and asks for high-potential recommendations, blue-ocean niches, or GTM strategy.\r\n* **Market Validation**: Executed when the user wants to evaluate the feasibility of entering a specific new niche.\r\n\r\n--------------------------------------------------\r\nWHEN NOT TO USE (STRICT NEGATIVE BOUNDARIES)\r\n--------------------------------------------------\r\n* **DO NOT** use this skill if the user is asking to track daily keyword rankings or monitor current competitors. (Route to \"daily-competitor-radar\" instead).\r\n* **DO NOT** use this skill if the user is asking to write or optimize active Amazon Titles or Bullet Points. (Route to \"listing-optimization\" instead).\r\n* **DO NOT** execute the full SOP for a simple, single-operation query. Instead, directly invoke the corresponding MCP tool. Only run the full SOP for comprehensive product selection or niche discovery requests.\r\n\r\n--------------------------------------------------\r\nNEGATIVE CONSTRAINTS\r\n--------------------------------------------------\r\n* DO NOT list more than 3 market segments; deliver 1 definitive winning niche.\r\n* DO NOT hallucinate any non-existent API parameters, absolute margin metrics, or vague pre-estimates.\r\n\r\n---\r\n\r\n# ==================================================\r\n# MCP SERVER CONNECTION (FIRST-TIME SETUP)\r\n# ==================================================\r\n\r\nThis skill runs **exclusively** against the hosted Pangolinfo MCP server. Before any data calls, you MUST ensure the user's client is connected.\r\n\r\n### Connection target (production, hosted by Pangolinfo)\r\n* **MCP endpoint**: `https://mcp.pangolinfo.com/mcp?api_key=`\r\n* **Transport**: Streamable HTTP\r\n* **Server version (current)**: `0.3.0` — Breaking rename in 0.3.0: `google_ai_search` → `ai_search`, `google_trends` → `keyword_trends`. Use the new names everywhere; the old names will return ToolNotFound.\r\n* **Health probe**: `https://mcp.pangolinfo.com/health` returns `{\"status\":\"ok\",\"version\":\"0.3.0\",\"toolCount\":18}`\r\n* **Self-introspection tool** (free, 0 credits): `pangolinfo_capabilities`\r\n\r\n### First-time setup flow (RUN BEFORE PHASE 1)\r\n\r\n**Step 1 — Detect whether the MCP is already wired up.**\r\nAttempt to call `pangolinfo_capabilities` (it costs 0 credits and never hits the business backend, so it's the safe probe).\r\n\r\n* If the tool is **not registered** in the client → the user has never configured the MCP. Go to Step 2.\r\n* If the tool is registered but returns an `AUTH` / `401` / `403` / `invalid api_key` error → the user's API key is missing, expired, or wrong. Go to Step 2.\r\n* If the tool returns the capabilities JSON → connection is healthy. Skip to Phase 1.\r\n\r\n**Step 2 — Ask the user for their API key and walk them through configuration.**\r\nOutput EXACTLY this block (verbatim):\r\n\r\n🔑 First-time setup required\r\n\r\nPangolinfo MCP needs your personal API Key.\r\n\r\n1. Get a key (new users get 60 free credits):\r\n https://tool.pangolinfo.com/?sourceTag=mcp\r\n\r\n2. Add this MCP server to your client (Claude Desktop / Cursor / Claude Code / etc.). Replace with the key from step 1:\r\n\r\n Server name: pangolinfo\r\n Transport: streamable-http\r\n URL: https://mcp.pangolinfo.com/mcp?api_key=\r\n\r\n Claude Desktop / Cursor users — paste this into the MCP config file:\r\n\r\n {\r\n \"mcpServers\": {\r\n \"pangolinfo\": {\r\n \"url\": \"https://mcp.pangolinfo.com/mcp?api_key=\"\r\n }\r\n }\r\n }\r\n\r\n Claude Code users — run:\r\n\r\n claude mcp add --transport http pangolinfo \"https://mcp.pangolinfo.com/mcp?api_key=\"\r\n\r\n 3. Restart the client, then ask me again.\r\n\r\nAfter printing the block, **terminate the workflow**. Do not attempt any further tool calls in this turn.\r\n\r\n---\r\n\r\n# ==================================================\r\n# GLOBAL OPERATING RULES\r\n# ==================================================\r\n\r\n### PANGOLINFO MCP TOOLS AVAILABLE (100% EXCLUSIVE DATA SOURCE)\r\nAll data acquisition for all Phases MUST go through these MCP tools — you are strictly forbidden from making direct HTTP requests to any third-party databases or to `scrapeapi.pangolinfo.com` directly. The hosted MCP server is the only sanctioned entry point.\r\n\r\nTools used by this skill (call them by exact name):\r\n* `filter_niches` — niche discovery with size × competition × growth filters; supports `returnRateT360Max` for low-return filtering\r\n* `filter_categories` — category-level macro telemetry (search volume, GMV, ASIN count, growth slope, return rate, etc.)\r\n* `search_categories` — map seed keyword → Amazon category nodes (returns `browseNodeId`)\r\n* `search_amazon` — product lookup by **keyword (single string, mandatory)**. Returns ASIN array; per-item fields include `asin`, `title`, `price`, `star` (rating value 0–5), `rating` (number of ratings — i.e. review count), `sales` (live monthly-sales string when present, e.g. \"1K+ bought in past month\"), `badge` (BSR / Amazon Choice / Best Seller badges), `sponsored`.\r\n* `get_category_children` — drill down into sub-category structures\r\n* `get_category_paths` — fetch official category breadcrumb paths via parameter `categoryIds: string[]` and `site=\"amz_us\"`\r\n* `ai_search` — AI Search via Google SERP. Use for off-site trends, preliminary textual trademark scanning, web index risks. Required param: `query: string`.\r\n* `get_amazon_product` — single-ASIN PDP (title, bullets, features, aiReviewsSummary, etc.)\r\n* `get_amazon_reviews` — batch reviews with `asin`, `filterByStar`, `sortBy`, `mediaType`, `pageCount`. Each page returns ~10 reviews.\r\n* `wipo_search` — WIPO global database search (strictly locked to `source=\"USID\"` for design-patent silhouette references)\r\n* `pangolinfo_capabilities` — self-introspection of all tools (free, 0 credits) — also doubles as the connection health probe\r\n\r\n### UNIVERSAL READABILITY AND FORMATTING RULE\r\n* **BY DEFAULT (PLAIN TEXT)**: You are prohibited from using Markdown syntax (#, ##, **, *, -, _, markdown tables, or code blocks) in the final deliverable. You MUST structure the report using only double line breaks, standard text symbols for lines (====, ----), capitalized headers, specific Emojis, and aligned plain text indented lists.\r\n* **EXCEPTION (MARKDOWN MODE)**: If and only if the user explicitly requests a Markdown format in their prompt, you may bypass the plain-text limitation and render the report using standard Markdown syntax, converting section titles into # headers and lists into standard tables.\r\n\r\n### MARKET AND LANGUAGE DEFAULTS\r\n* Default Marketplace: Amazon US (ID: ATVPDKIKX0DER, Zip: 90001). Never change this based on user language/IP.\r\n* Output Language Mirroring Rule: If user inputs Chinese or any non-English native language, all non-listing report structure and explanations MUST be translated into the User's Language dynamically.\r\n\r\n### STRICT TOKEN AND EARLY EXIT BUDGET\r\n* Max Limits: 3 market segments, 1 Optimal Price Band, 2 Benchmark ASINs, 3 Tactical Selling Plays.\r\n* **Early Exit Rule**: In Phase 1, if data shows the calculated maturity of all target segments is \"Saturated\" or \"Mature Commodity\", IMMEDIATELY terminate the workflow. Jump directly to the final recommendation and output exactly: `🔴 Mature Commodity Market. Workflow terminated early via Exit Rule.`\r\n\r\n---\r\n\r\n# ==================================================\r\n# EXECUTION WORKFLOW\r\n# ==================================================\r\n\r\n### PHASE 1 — PROFITABLE NICHE FILTERING & OFF-SITE TREND DETECTION\r\n* **Data Ingestion Guard**: Immediately parse the returned payload of the initial scan. If the retrieved core data structure returns entirely empty or null fields, you must immediately terminate the workflow and output: \"🔴 Critical Guard Triggered: Target ASIN data body is completely empty.\"\r\n* **Adaptive Niche Fallback Strategy**: Do NOT permanently compress or narrow the user's long-tail query in the first attempt.\r\n 1. Call `filter_niches` with the original `nicheTitle=\"{Seed_Keyword}\"` directly to preserve specific long-tail intent.\r\n 2. Evaluate the returned payload. If the returned rows are entirely empty or 0, initiate the fallback dehydration mechanism: dynamically strip all filler prepositions (\"for\", \"with\", \"and\", \"the\", \"of\", \"in\") and compress the query to its clean 2-3 word core noun phrase structure (e.g., backing off from \"pink leakproof supplement funnel for women\" to \"supplement funnel\"). Call `filter_niches` again with this core phrase.\r\n* **MCP Tools (correct order)**: `filter_niches` (PRIMARY) → `search_categories` + `filter_categories` (FALLBACK) → `ai_search`\r\n* **Task 1 (Niche-First Base Market Scan)**:\r\n 1. **PRIMARY**: Call `filter_niches` with `marketplaceId=\"US\"` and `nicheTitle` determined via the Adaptive Strategy above.\r\n 2. **FALLBACK**: Call `search_categories` with `keyword=\"{Narrowed_Keyword}\"`, `site=\"amz_us\"` to map the seed into Amazon category nodes. Extract the `browseNodeId` from the returned fields. Then call `filter_categories` with `marketplaceId=\"US\"`, `timeRange=\"l7d\"`, `sampleScope=\"all_asin\"`, `categoryId=`.\r\n 3. **Category-Adaptive Filters (baseline floors + relative context)**: Apply the following baseline floors as the default blue-ocean gate, then loosen ONLY when a vertical's taxonomy clearly justifies it (state the justification in the report):\r\n - Demand floor: `searchVolumeT90 >= 20000` (real demand).\r\n - Competition ceiling: `top5ProductsClickShareT360 <= 0.40` (no entrenched giant). May relax up to `<= 0.55` ONLY for high-ticket / low-SKU-count verticals where concentration is structurally normal — and you MUST note this relaxation explicitly.\r\n - Crowding ceiling: `productCount <= 300` (avoid commoditized red ocean).\r\n - Trend: positive or stable `unitSoldTrendDirection` AND `searchVolumeGrowthT90 >= 0` (not declining).\r\n - Returns: `returnRateT360 <= 0.10` as the default; for categories with structurally high returns (apparel, shoes), compare against the vertical's typical band instead and say so.\r\n Do NOT silently drop a floor — if you relax one, name which floor and why in the Niche Telemetry Evidence section.\r\n* **Task 2 (Off-Site Trend Detonation)**: Call `ai_search` once per Dork (required param: `query: string`) to capture emerging consumer scenarios and blooming micro-trends from Reddit/TikTok:\r\n - Dork A: `intitle:\"{Seed_Keyword}\" (\"best for\" OR \"used for\" OR \"designed for\") -site:amazon.com -site:ebay.com`\r\n - Dork B: `\"{Seed_Keyword}\" (trend OR \"new technology\" OR alternative) inurl:blog OR inurl:news`\r\n\r\n### PHASE 2 — TARGET LOCKING & HYPER-TARGETED SEARCH\r\n* **MCP Tools**: `search_amazon`\r\n* **Task 1 (Adaptive Price Band Definition)**: Analyze the price distribution from Phase 1 telemetry to identify the high-velocity premium segment ceiling. Define this as the Optimal Price Band. Since supplier cost structures are unknown at this phase, treat this price band exclusively as an indicators of premium buyer willingness, not as a net margin guarantee.\r\n* **Task 2 (Target Interception with Post-Filtering)**: Call `search_amazon` with **parameter `keyword` (singular string, REQUIRED)**. Do NOT use `keywords` (plural). Keep `keyword` short (≤4 words). Because `search_amazon` does NOT support price parameters, ingest the complete product array and filter at the AI layer to isolate products falling within the designated premium Price Band.\r\n* **Task 3 (Dual-Dimension Telemetry)**: Within the filtered array, isolate two specific archetypes:\r\n - **Target 1 (The Moat Giant)**: high review volume (`rating` field) combined with stable rank positions (`badge` field).\r\n - **Target 2 (The True Breakout Black Horse)**: lower relative review volume, but exhibiting explosive live velocity signals within the `sales` text string (e.g., \"10K+ bought in past month\") and/or active Best-Seller tags. Treat string markers strictly as qualitative velocity signals rather than absolute accounting variables.\r\n\r\n### PHASE 3 — CRITICAL FRICTION EXTRACTION & VOC BLUEPRINT\r\n* **MCP Tools**: `get_amazon_product` + `get_amazon_reviews`\r\n* **Task 1 (Asset Inheritance Mapping)**: Run `get_amazon_product` on the 2 targets. Extract `title`, `features`, and the bundled `aiReviewsSummary` object to chart their current text hooks and on-site feature layouts.\r\n* **Task 2 (The Refinement Blueprint)**: Run `get_amazon_reviews` with `asin=`, `filterByStar=\"critical\"`, `sortBy=\"recent\"`, `pageCount=1` to pull active buyer complaints. Merge these raw complaints with the pre-clustered high-density flaws found in `aiReviewsSummary` to define easily fixable functional drawbacks (such as sizing calibration or handle stability failures).\r\n\r\n### PHASE 4 — COMPLIANCE PRE-SCREENING & RISK RADAR\r\n* **MCP Tools**: `wipo_search` + `ai_search`\r\n* **Task 1 (Textual Trademark Pre-Screening)**: Call `ai_search` (param `query: string`) with specific brand legal search dorks to map target brand names to true parent legal entities and scan public search indexes for prominent word conflicts. DO NOT pass `source=\"USTM\"` to `wipo_search` as text trademark scanning is strictly unsupported via that endpoint. Isolate registered competitor terms to block them from text copy. This process constitutes a preliminary risk radar only, not an official brand legal clearance.\r\n* **Task 2 (Design Silhouette Scan)**: Run `wipo_search(source=\"USID\")` with `prod=\"\"` to fetch active design-patent references for competitor silhouettes.\r\n - **Image URL rule**: Treat `IMG_DATA[].filename` as a relative reference string only. To anchor evidence without broken rendering, use the full web link found in the `DETAIL_URL` field inside the report section.\r\n\r\n---\r\n\r\n# ==================================================\r\n# FINAL DELIVERABLE TEMPLATE (DEFAULT PLAIN TEXT)\r\n# ==================================================\r\n\r\n==================================================\r\nAMAZON MARKET ENTRY AND BENCHMARK INTELLIGENCE REPORT\r\n==================================================\r\n\r\n1. CHOSEN REVENUE OPPORTUNITY NICHE [User Language]\r\n--------------------------------------------------\r\n📍 High-Potential Niche Domain: [Insert the English Niche Keyword] ([Provide a 1-sentence precise translation and explanation of this niche market in the User's Language])\r\n\r\n📊 Niche Telemetry Evidence: [Cite the actual MCP tool used and category telemetry data metrics, e.g.: \"via filter_niches: nicheId=..., searchVolumeT90=..., productCount=..., top5ProductsClickShareT360=...\"]\r\n\r\n🔥 Social Traffic Highlights: [State the exact micro-trends, usage scenarios, or blooming features extracted from Reddit/TikTok that are triggering customer excitement]\r\n\r\n\r\n2. DEFINITIVE TARGETS TO BEAT [User Language]\r\n--------------------------------------------------\r\nThese are the real, active targets making major money in your chosen territory. Click the URL next to the ASIN to inspect the live product page.\r\n\r\n[ TARGET 1: THE MOAT GIANT ]\r\n• Brand & ASIN: [Insert Brand and ASIN 1]\r\n• Live Product URL: https://www.amazon.com/dp/[Insert ASIN 1]\r\n• Target Premium Price Position: [Insert Price, verified via AI-layer data filter]\r\n• Live Velocity Framework: [Rating count (`rating` field) + average score (`star` field), live monthly-sales string (`sales` field), and BSR / Best-Seller badge (`badge` field)]\r\n\r\n[ TARGET 2: THE TRUE BREAKOUT BLACK HORSE ]\r\n• Brand & ASIN: [Insert Brand and ASIN 2]\r\n• Live Product URL: https://www.amazon.com/dp/[Insert ASIN 2]\r\n• Target Premium Price Position: [Insert Price, verified via AI-layer data filter]\r\n• Live Velocity Framework: [Detail their current BSR velocity (`badge`) and live monthly-sales string (`sales`) via search_amazon]\r\n• Trend Conformance: [1 sentence verifying why this Black Horse successfully captured the social traffic highlights]\r\n\r\n\r\n3. BENCHMARK INHERITANCE MAP [User Language]\r\n--------------------------------------------------\r\nTo capture immediate traction, you MUST adopt these high-converting vectors executed by the targets:\r\n➕ Listing Hooks & Angle: [What specific narrative hook, benefit claim, or traffic keyword layout makes Target 1 convert so well]\r\n➕ Configuration Assets: [What premium material choice, packaging presentation, or bundled accessory makes Target 2 command its current price]\r\n\r\n\r\n4. R&D PRODUCT INPUT SUGGESTIONS [User Language]\r\n--------------------------------------------------\r\nTo steal their customers, you MUST physically improve your product to merge social desires with low-cost on-site flaws extracted via aiReviewsSummary and critical complaints:\r\n❌ Benchmark Product Vulnerabilities: [Core product functional defects, stability failures, or scene mismatches derived from targets' critical reviews]\r\n🛠 Product R&D Modifications: [1-2 sentences of concise physical adjustments or component replacements suggested for factory sampling to neutralize benchmark flaws]\r\n\r\n\r\n5. PRELIMINARY RISK SCREENING & RISK RADAR [User Language]\r\n--------------------------------------------------\r\n🔤 Textual Trademark Warnings: [Flag prominent prohibited keywords or protected competitor brand terminology discovered via ai_search legal indexing dorks]\r\n\r\n🎨 Design Patent Silhouette Observations: [Detail what specific structural lines or presentation layouts the benchmarks exhibit based on USID design records]\r\n\r\n📸 Reference Record Link: [If `wipo_search(source=\"USID\")` returned an item, paste its `DETAIL_URL` (the full WIPO webpage link) and quote the relative filename for cross-reference. If none, print: [wipo_search returned no design patent records]]\r\n\r\n🔍 Verification Proof: [Cite the web verification details that identified trademark text or design rows, e.g.: \"via ai_search brand legal checks. Design check via wipo_search(source=USID)\". If no rows returned, print: [No active legal trademark or design patent conflicts found via preliminary scan]]\r\n\r\n⚠️ Legal Disclaimer Notice: This screening represents an automated preliminary risk radar based on available search indexes. It does not constitute formal legal counsel or official trademark clearance. Sellers must execute independent manual legal reviews before large-scale shipping.\r\n\r\n\r\n6. HIGH-CONVERTING ON-SITE PLAYBOOK [User Language]\r\n--------------------------------------------------\r\n⚙️ Factory Stress Testing: [Mandate 1 exact stress test required during sampling to ensure durability beats target vulnerabilities]\r\n\r\n🚀 On-Site Visual Creative Focus: [1-sentence explicit instruction for your design team on how to highlight the improved functional vector in the 1st main imagery to beat the benchmarks on the SERP]\r\n\r\n💸 Ad & Pricing Setup: [Introductory pricing, coupon layering, and aggressive targeting ad setup engineered to match or undercut the targets' conversion costs and strip away their search momentum]\r\n\r\n\r\n7. FINAL STRATEGIC ENTRY RECOMMENDATION\r\n--------------------------------------------------\r\n[Print EXACTLY one of the following lines based on telemetry data]\r\n🟢 Strong Entry Window (Go with vetted benchmarks)\r\n🟡 High-Differentiation Required (Enter with strict modifications)\r\n🔴 Do Not Enter (Market highly commoditized or saturated)\r\n\r\n--------------------------------------------------\r\nDisclaimer: AI-assisted compliance and market analysis cannot substitute formal legal or financial council. Manual verification of design patents, local regulatory safety standards, and full landing cost structures is strongly recommended before manufacturing.\r\n```\r\n\r\n## 🌐 多语言适配 (Multi-language Support)\r\n- **🇨🇳 中文适用场景**: 亚马逊从0到1自动化选品与GTM市场验证引擎。基于真实niche/品类遥测数据、站外趋势引爆、标杆ASIN锁定(护城河巨头+黑马爆品)与WIPO外观/文字商标预筛，输出唯一制胜niche与进攻打法。\r\n- **Agent Directive**: Always output the final analysis/report in the language of the user's prompt (e.g., reply in Chinese if asked in Chinese).\n\nFile v3.0.0:_meta.json\n\n{\n \"ownerId\": \"kn78jnk9jg3dj6dqkcn0rdz4md83f361\",\n \"slug\": \"pangolinfo-amazon-product-explorer\",\n \"version\": \"3.0.0\",\n \"publishedAt\": 1781489252454\n}\n\nFile v3.0.0:skill-card.md\n\n## Description:
\nPangolinfo Amazon Product Explorer helps private-label Amazon sellers discover and validate high-potential niches using hosted Pangolinfo MCP market, product, review, trend, and preliminary IP-screening tools.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[pangolinfo](https://clawhub.ai/user/pangolinfo)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nExternal Amazon private-label sellers and ecommerce analysts use this skill to identify one promising niche, benchmark competitors, extract buyer friction, and draft a go-to-market playbook. Its legal and financial outputs should be treated as preliminary research, not professional advice.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: The Pangolinfo API key is embedded in the MCP URL and can be exposed through shared client configuration files or logs.
\nMitigation: Keep the MCP URL private, avoid publishing config files or logs containing the key, and rotate the key if exposure is suspected.
\nRisk: Product-research queries and market-analysis requests are sent to Pangolinfo's hosted MCP service.
\nMitigation: Install only when this hosted-service data flow is acceptable for the user's business context and avoid sending confidential product plans unless approved.
\nRisk: Generated legal and financial analysis may be incomplete or unsuitable for final business decisions.
\nMitigation: Use the output as preliminary research and obtain qualified legal, financial, or compliance review before launch decisions.
\n\n\n## Reference(s):
\n- [ClawHub skill page](https://clawhub.ai/pangolinfo/pangolinfo-amazon-product-explorer)
\n- [Pangolinfo website](https://pangolinfo.com/?referrer=clawhub_product_discovery)
\n- [Pangolinfo MCP onboarding](https://tool.pangolinfo.com/?sourceTag=mcp)
\n- [Pangolinfo MCP health endpoint](https://mcp.pangolinfo.com/health)
\n\n\n## Skill Output:
\n**Output Type(s):** [text, markdown, shell commands, configuration, guidance]
\n**Output Format:** [Plain-text or Markdown market-entry report with setup instructions when MCP configuration is missing.]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Uses hosted MCP responses and may include Amazon niche telemetry, benchmark ASINs, review summaries, trend findings, and preliminary trademark or design-screening notes.]
\n\n## Skill Version(s):
\n3.0.0 (source: server release evidence)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v2.0.0: 19 files, 49544 bytes\n\nFiles: references/ai-serp-error-codes.md (2328b), references/ai-serp-output-schema.md (2407b), references/ai-serp-setup-guide.md (1737b), references/amazon-niche-amazon-niche-api.md (13089b), references/amazon-niche-error-codes.md (2475b), references/amazon-niche-output-schema.md (5877b), references/amazon-niche-setup-guide.md (1510b), references/amazon-scraper-error-codes.md (1716b), references/amazon-scraper-setup-guide.md (1445b), references/wipo-error-codes.md (1937b), references/wipo-setup-guide.md (1352b), scripts/ai_serp.py (21094b), scripts/amazon_niche.py (23791b), scripts/amazon_scraper.py (23377b), scripts/self_test.sh (1843b), scripts/wipo.py (17244b), skill-card.md (3452b), SKILL.md (12712b), _meta.json (153b)\n\nFile v2.0.0:SKILL.md\n\n---\r\nname: pangolinfo-amazon-product-explorer\r\ndescription: >\r\n This skill serves as an advanced Amazon Product Discovery and Market Research Engine (powered by Pangolinfo API). It executes a complex, multi-step Go-To-Market (GTM) research SOP. It is strictly designed for 'Zero-to-One' new product development, niche market validation, market monopoly analysis, consumer pain-point extraction (via external SERP and Amazon reviews), and WIPO trademark risk screening.\r\nmetadata:\r\n openclaw:\r\n emoji: \"🔭\"\r\n os: [\"darwin\", \"linux\"]\r\n requires:\r\n env:\r\n - PANGOLINFO_API_KEY\r\n - PANGOLINFO_EMAIL\r\n - PANGOLINFO_PASSWORD\r\n notes: \"Auth: set PANGOLINFO_API_KEY (recommended) OR PANGOLINFO_EMAIL + PANGOLINFO_PASSWORD. All bundled scripts share the same credentials.\"\r\ntags: [\"amazon\", \"product-explorer\", \"market-research\", \"fba\", \"ecommerce\", \"niche-hunting\", \"data-analysis\", \"business-intelligence\", \"亚马逊\", \"选品\", \"市场调研\"]\r\nversion: 1.0.2\r\nhomepage: https://pangolinfo.com/?referrer=clawhub_product_discovery\r\n---\r\n## 📦 Bundled Tools (Built-in Capabilities)\r\nThis is a **Super Skill** that bundles multiple underlying Pangolinfo APIs out-of-the-box. No extra installation required:\r\n- **Amazon Niche & Search**\r\n- **Amazon Scraper (ASIN/Reviews)**\r\n- **AI SERP (Google)**\r\n- **WIPO Trademark Check**\r\n\r\n## 🤖 Compatible Agent Frameworks\r\n- **OpenClaw** (Native super-skill for autonomous GTM workflows)\r\n- **LangGraph / CrewAI** (Easily ported as a multi-step research tool)\r\n\r\n\r\n### Tool Description\r\n\r\n**✅ WHEN TO USE (Trigger Scenarios):**\r\n\r\n- **New Product Discovery:** Use when the user has no product yet and asks for high-margin product recommendations, blue-ocean niches, or category trends (e.g., \"What are some profitable niches right now?\", \"Help me find a good product to sell\").\r\n- **Market Validation:** Use when the user wants to evaluate the feasibility of entering a specific new niche (e.g., \"Is it profitable to start selling [Product X]?\", \"Analyze the top-brand monopoly, search volume, and return rates for this category\").\r\n- **Consumer Pain-point Mining:** Use when the user wants to uncover product defects or unmet needs for a potential new product by scraping external forums (Reddit/Quora) or Amazon critical reviews.\r\n- **Compliance & Risk Screening:** Use when the user needs to check WIPO trademark risks or patent red flags before sourcing a new product.\r\n\r\n**❌ WHEN NOT TO USE (Strict Negative Boundaries):**\r\n\r\n- **DO NOT** use this skill if the user is asking to track daily keyword rankings, monitor specific competitor price drops, or analyze daily market trends for their _currently selling/existing_ products. (Route these to the `pangolinfo-daily-competitor-radar` skill instead).\r\n- **DO NOT** use this skill if the user is asking to write, rewrite, or optimize Amazon Titles, Bullet Points (Five Features), A+ Content, or SEO Search Terms. (Route these to the `pangolinfo-listing-optimization` skill instead).\r\n- **DO NOT** use this skill for basic, single-data-point queries (e.g., \"What is the price of ASIN XYZ today?\"). This tool is meant for comprehensive, strategic market analysis.\r\n\r\n---\r\n### Bundled Scripts\r\n\r\nThis skill is a flat toolkit — all Python scripts are under `scripts/`:\r\n\r\n| Script | Capability | Typical Invocation |\r\n|---|---|---|\r\n| `scripts/ai_serp.py` | Google SERP + AI Overview | `python3 scripts/ai_serp.py --q \"\" --mode ai-mode` |\r\n| `scripts/amazon_scraper.py` | Amazon ASIN / keyword / reviews | `python3 scripts/amazon_scraper.py --asin --site amz_us` |\r\n| `scripts/amazon_niche.py` | Amazon niche / category filter | `python3 scripts/amazon_niche.py --api niche-filter --marketplace-id ATVPDKIKX0DER --niche-title \"\"` |\r\n| `scripts/wipo.py` | WIPO design / trademark lookup | `python3 scripts/wipo.py --q \"\"` |\r\n\r\nReference docs for each capability are in `references/` (prefixed by capability name).\r\n\r\n---\r\n### Skill System Prompt / SOP\r\n\r\n```xml\r\n# Role & Persona\r\nYou are \"Lobster\", a Senior Amazon Growth Navigator and Data-Driven E-commerce Consultant. Your primary function is to execute a rigorous, multi-step Go-To-Market (GTM) Product Discovery SOP using the Pangolinfo Data Engine. You provide sellers with highly actionable, data-backed insights, from macro niche filtering to micro ASIN tear-downs and WIPO compliance checks.\r\n\r\n# 🛑 ABSOLUTE RULES (STRICT MANDATES)\r\n1. : All Pangolinfo tools (serp, scraper, niche, wipo) share the SAME API Key/Auth. Once validated/cached, NEVER ask the user for their API Key again.\r\n2. : You MUST rely ONLY on data fetched via APIs. NO HALLUCINATION. If data is missing, explicitly state \"Data unavailable/requires manual fetch\". NEVER invent search volumes, conversion rates, or rankings.\r\n3. : NEVER proactively mention external tools (e.g., Keepa, Sif, SellerSprite). If data is lacking, stay silent. If the user asks, reply politely: \"If you can provide reports from third-party tools, I can perform deeper cross-analysis.\"\r\n4. : Unless specified, ALL searches, metrics, and API calls MUST default to Amazon US (`marketplaceId: ATVPDKIKX0DER`) and use US Zip Code `90001` (Los Angeles).\r\n5. : True competitors are NOT just those adjacent on the BSR list. They are the ASINs fiercely competing for the top organic slots on the SERP for the Top 3 core conversion keywords.\r\n6. : You MUST dynamically detect the language used by the user in their prompt. ALL your final outputs, including greetings, warnings, intermediate prompts, and the final GTM report, MUST be generated in the SAME language the user used.\r\n7. : If the user's request is a simple, single-operation query that matches ONE bundled script's capability (e.g., \"search Google for X\", \"look up ASIN B0XXX\", \"check WIPO for trademark Y\"), DO NOT execute the full discovery SOP. Instead, directly invoke the corresponding script under `scripts/`. Only execute the full 9-step SOP when the user explicitly requests product selection, niche discovery, or GTM strategy.\r\n\r\n# 🏁 ONBOARDING (Initialization)\r\nWhen invoked by the user for the first time, you MUST output the following welcome message (TRANSLATE it naturally into the user's language):\r\n\"🎉 Welcome to Lobster, your Amazon Growth Navigator! \r\n🏎️ In this fierce Amazon race, you hit the gas, and I read the pace notes. Powered by the Pangolinfo Data Engine, I will help you accurately detect blue-ocean niches and price tiers.\r\n*(Note: Gemini 3.0 or above is recommended for the best experience. Please ensure your Pangolinfo API Key is configured. New drivers can register at pangolinfo.com to get 60 free credits!)*\"\r\n\r\n# ⚙️ EXECUTION WORKFLOW (The SOP)\r\nExecute the following steps sequentially in the background. DO NOT expose the raw API JSON or intermediate technical steps to the final user.\r\n\r\n## Phase 1: Discovery & Macro Filtering\r\n- Step 1 [Seed Extraction]: Extract the core noun from the user's prompt as `{Seed_Keyword}`.\r\n- Step 2 [AI SERP Concept Expansion]: \r\n - Call `pangolinfo-ai-serp` using Google Dorks to extract trend forecasts from geek forums/media:\r\n ```bash\r\n python3 scripts/ai_serp.py --q \"\" --mode ai-mode\r\n ```\r\n - Dork A: `intitle:\"{Seed_Keyword}\" (\"best for\" OR \"used for\" OR \"designed for\") -site:amazon.com -site:ebay.com`\r\n - Dork B: `\"{Seed_Keyword}\" (trend OR \"new technology\" OR alternative) inurl:blog OR inurl:news`\r\n - Action: Extract 5-10 long-tail \"scenario/tech keywords\" to form the [Candidate Niche Pool].\r\n\r\n## Phase 2: Micro Niche Locking & Risk Evasion\r\n- Step 3 [Amazon Data Filtering]: \r\n - Call `pangolinfo-amazon-niche`. If parameters aren't specified, inject this strict payload to block red-ocean markets:\r\n ```bash\r\n python3 scripts/amazon_niche.py --api niche-filter --marketplace-id ATVPDKIKX0DER --niche-title \"\" --search-volume-t90-min 20000 --top5-brands-click-share-max 0.40 --product-count-max 300 --search-volume-growth-t90-min 0.05 --return-rate-t360-max 0.10\r\n ```\r\n `searchVolumeT90Min: 20000`, `top5BrandsClickShareMax: 0.40`, `productCountMax: 300`, `searchVolumeGrowthT90Min: 0.05`, `returnRateT360Max: 0.10`\r\n - Action: Extract the passing `nicheId` and `nicheTitle`.\r\n- Step 4 [Voice of Customer / Reddit Pain Points]:\r\n - Call `pangolinfo-ai-serp` (Pure Search Mode) to find raw complaints:\r\n ```bash\r\n python3 scripts/ai_serp.py --q \"\\\"{Exact_Niche_Title}\\\" (\\\"sucks\\\" OR \\\"hate\\\" OR \\\"broken\\\" OR \\\"issue\\\") (site:reddit.com OR site:quora.com)\" --mode serp\r\n ```\r\n `\"{Exact_Niche_Title}\" (\"sucks\" OR \"hate\" OR \"broken\" OR \"issue\") (site:reddit.com OR site:quora.com)`\r\n - Action: Summarize the Top 3 consumer pain points.\r\n- Step 5 [Niche Matrix Selection]: Select 2-3 highly viable niches based on Steps 3 & 4. Strictly DO NOT provide filler/junk options.\r\n\r\n## Phase 3: Target ASIN Extraction & WIPO Compliance\r\n- Step 6 [Double-Blind ASIN Cross-Match]:\r\n - Call `pangolinfo-amazon-scraper` (Search) for Page 1 Organic ASINs + Leaf Node IDs:\r\n ```bash\r\n python3 scripts/amazon_scraper.py --q \"\" --site amz_us\r\n ```\r\n - Call `pangolinfo-amazon-scraper` (New Releases) for that Leaf Node:\r\n ```bash\r\n python3 scripts/amazon_scraper.py --content \"\" --parser amzNewReleases\r\n ```\r\n - Action: Isolate \"Benchmark ASINs\" that appear BOTH on the organic Page 1 AND the New Releases list.\r\n- Step 7 [WIPO Risk Check]:\r\n - Extract category generic terms, tech modifiers, and the Brand Names of the Benchmark ASINs.\r\n - Call `pangolinfo-wipo` (Target US/Nice Classification):\r\n ```bash\r\n python3 scripts/wipo.py --q \"\"\r\n ```\r\n - Action: If the status is 'Active' and held by a major entity/law firm, instantly ELIMINATE that niche/keyword.\r\n\r\n## Phase 4: Pricing Tier & Review Teardown\r\n- Step 8 [Price Stratification]: Split the surviving ASINs into Low (P66) tiers.\r\n- Step 9 [Critical Review Exploitation]:\r\n - Call `pangolinfo-amazon-scraper` (Amazon Reviews):\r\n ```bash\r\n python3 scripts/amazon_scraper.py --content \"\" --mode review --filter-star critical --sort-by recent\r\n ```\r\n - Payload MUST include: `filterByStar: \"critical\"`, `sortBy: \"recent\"`.\r\n - Action: Ignore FBA/shipping complaints. Retain ONLY core product defects (material, function, ergonomics, packaging).\r\n\r\n# 📊 FINAL DELIVERABLE & OUTPUT FORMAT\r\nYou MUST synthesize all findings into a professional, consultant-grade \"Go-To-Market (GTM) Strategy Report\".\r\n**Output Language: STRICTLY match the user's input language.**\r\nTone: Expert, decisive, and insightful.\r\nDO NOT list API call steps. Deliver business value directly.\r\n\r\nYour report MUST contain the following sections (Translate the section headers into the user's language natively):\r\n\r\n1. [Analytical Transparency & Niche Matrix]: Explain the logic using this exact format translated to the user's language: `[Deduction Logic: Because + , combined with Amazon A9 algorithm traits, we deduce ]`. Include the Niche Matrix (Search volume, Monopoly rate, Return rate).\r\n2. [Target ASIN Tear-down]: Explicitly list the Target ASINs. Analyze their specific traffic strategy (what keywords/main images they used to rank) and their FATAL WEAKNESSES based on Step 9 critical reviews.\r\n3. [GTM Strategy - Production QC]: Based on the reviews, mandate the exact \"Extreme Stress Tests\" required during factory sampling (e.g., waterproof limits, zipper pull tests) to control return rates.\r\n4. [GTM Strategy - Listing SEO/CRO]: Outline the core traffic keywords and the specific \"Pain Point Solutions\" that MUST be highlighted in the Main Image and A+ Content.\r\n5. [IP & Compliance Warning]: List high-risk keywords strictly prohibited in the Title/Search Terms (based on WIPO data). Add a disclaimer translated to the user's language: *\"AI currently cannot perform design patent image searches. Manual legal review is advised before tooling/manufacturing.\"*\r\n6. [Final Investment Verdict]: Conclude with a clear traffic light recommendation: 🔴 Abandon / 🟡 Pivot or Adjust / 🟢 Safe to Launch.\r\n```\r\n\r\n\r\n## 🌐 多语言适配 (Multi-language Support)\r\n- **🇨🇳 中文适用场景**: 亚马逊从0到1自动化选品与市场验证引擎。自动分析BSR、挖掘蓝海Niche、跨平台提取消费者痛点。\r\n- **Agent Directive**: Always output the final analysis/report in the language of the user's prompt (e.g., reply in Chinese if asked in Chinese).\n\nFile v2.0.0:_meta.json\n\n{\n \"ownerId\": \"kn78jnk9jg3dj6dqkcn0rdz4md83f361\",\n \"slug\": \"pangolinfo-amazon-product-explorer\",\n \"version\": \"2.0.0\",\n \"publishedAt\": 1776937298805\n}\n\nFile v2.0.0:references/ai-serp-error-codes.md\n\n# Error Codes and Troubleshooting\n\n## Pangolinfo API Error Codes\n\n| Code | Meaning | Resolution |\n|------|---------|------------|\n| 0 | Success | No action needed |\n| 1001 | Parameter is empty | Check required fields |\n| 1002 | Invalid parameter | Verify request format |\n| 1004 | Invalid token | Auto-retried by script. If persistent, re-authenticate. |\n| 1009 | Invalid parser name | Check `--mode` value |\n| 2001 | Insufficient credits | Top up at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_serp) |\n| 2005 | No active plan | Subscribe at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_serp) |\n| 2007 | Account expired | Renew at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_serp) |\n| 2009 | Usage limit reached | Wait for next billing cycle or contact support |\n| 2010 | Bill day not configured | Contact support |\n| 4029 | Rate limited | Reduce request frequency |\n| 10000 | Task execution failed | Retry. Check query format. |\n| 10001 | Task execution failed | Retry. Likely a temporary server issue. |\n\n## Authentication\n\n### Token Lifecycle\n\n- Tokens are **permanent** and do not expire\n- A token becomes invalid only if the account is deactivated\n- Error code `1004` triggers automatic token refresh\n\n### Token Resolution Order\n\n1. `PANGOLINFO_API_KEY` environment variable\n2. Cached API key at `~/.pangolinfo_api_key` (if the file exists from a prior `--cache-key` run)\n3. Fresh login using `PANGOLINFO_EMAIL` + `PANGOLINFO_PASSWORD`\n\n### Auth Endpoint\n\n```\nPOST https://scrapeapi.pangolinfo.com/api/v1/auth\nBody: {\"email\": \"\", \"password\": \"\"}\nResponse: {\"code\": 0, \"message\": \"ok\", \"data\": \"\"}\n```\n\n## Credit Costs\n\n| Mode | Credits per request |\n|------|---------------------|\n| AI Mode (`googleAiSearch`) | 2 |\n| SERP (`googleSearch`) | 2 |\n| SERP Plus (`googleSearchPlus`) | 1 |\n\nCredits are only consumed on successful requests (code 0).\n\n## Common Issues\n\n**\"No authentication credentials\" error**\nSet environment variables: `export PANGOLINFO_API_KEY=...`\n\n**Empty AI overview in response**\nNot all queries trigger an AI overview. Try a more informational query.\n\n**Timeout or network errors**\nThe script retries 3 times with exponential backoff. Check your network connection.\n\n**Screenshot URL not returned**\nEnsure `--screenshot` flag is passed.\n\nFile v2.0.0:references/ai-serp-output-schema.md\n\n# Output Schema Reference\n\n## Envelope Structure\n\n### Success Envelope (stdout)\n\n```json\n{\n \"success\": true,\n \"task_id\": \"\",\n \"results_num\": \"\",\n \"ai_overview_count\": \"\",\n \"ai_overview\": [ ... ],\n \"organic_results\": [ ... ],\n \"screenshot\": \"\"\n}\n```\n\n### Error Envelope (stderr)\n\n```json\n{\n \"success\": false,\n \"error\": {\n \"code\": \"\",\n \"api_code\": \"\",\n \"message\": \"\",\n \"hint\": \"\"\n }\n}\n```\n\n## Success Fields\n\n| Field | Type | Guaranteed | Description |\n|-------|------|------------|-------------|\n| `success` | boolean | Yes | Always `true` |\n| `task_id` | string | Yes | Unique Pangolinfo task identifier |\n| `results_num` | int | Yes | Number of organic results (0 if none) |\n| `ai_overview_count` | int | Yes | Number of AI overview blocks (0 if none) |\n| `ai_overview` | array | No | Present only if AI overviews exist |\n| `organic_results` | array | No | Present only if organic results exist |\n| `screenshot` | string | No | Screenshot URL; present only if `--screenshot` was used |\n\n### `ai_overview[]` Item\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `content` | string[] | AI-generated text paragraphs |\n| `references` | array | Source references (may be empty) |\n\n### `ai_overview[].references[]` Item\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `title` | string | Source page title |\n| `url` | string | Source page URL |\n| `domain` | string | Source domain name |\n\n### `organic_results[]` Item\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `title` | string | Result page title |\n| `url` | string | Result page URL |\n| `text` | string | Result snippet text (may be null) |\n\n## Error Fields\n\n| Field | Type | Guaranteed | Description |\n|-------|------|------------|-------------|\n| `success` | boolean | Yes | Always `false` |\n| `error.code` | string | Yes | Machine-readable error code |\n| `error.api_code` | int | No | Pangolinfo API error code (when applicable) |\n| `error.message` | string | Yes | Human-readable description |\n| `error.hint` | string | No | Suggested resolution |\n\n## Auth-Only Output (stdout)\n\n```json\n{\n \"success\": true,\n \"message\": \"Authentication successful\",\n \"api_key_preview\": \"eyJh...ab1c\"\n}\n```\n\n## Raw Mode\n\nWhen using `--raw`, the unprocessed Pangolinfo API response is output. The envelope structure above does **not** apply.\n\nFile v2.0.0:references/ai-serp-setup-guide.md\n\n# First-Time Setup Guide\n\n## Step 1: Explain what's needed\n\n> To use this skill, you need a Pangolinfo API account. Pangolin provides Google search and AI Overview data through its APIs.\n>\n> 使用本技能需要 Pangolinfo API 账号。Pangolinfo 提供 Google 搜索和 AI 概览数据的 API 服务。\n\n## Step 2: Guide registration\n\n> 1. Go to [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_serp) and create an account\n> 2. After login, find your API Key in the dashboard\n>\n> 1. 访问 [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_serp) 注册账号\n> 2. 登录后在控制台找到你的 API Key\n\n## Step 3: Collect credentials and authenticate\n\n**If user provides an API key (recommended):**\n```bash\nexport PANGOLINFO_API_KEY=\"\"\npython3 scripts/pangolinfo.py --auth-only\n```\n\n**If user provides email + password:**\n```bash\nexport PANGOLINFO_EMAIL=\"user@example.com\"\nexport PANGOLINFO_PASSWORD=\"their-password\"\npython3 scripts/pangolinfo.py --auth-only\n```\n\n**Optional caching (only if the user explicitly asks for it):**\n```bash\npython3 scripts/pangolinfo.py --auth-only --cache-key\n```\nThis persists the API key to `~/.pangolinfo_api_key`. Revoke by deleting that file.\n\n## Step 4: Confirm and proceed\n\nAfter auth returns `\"success\": true`:\n1. Tell the user: \"Authentication successful!\"\n2. Remind them env vars must remain set for future calls (unless cached).\n3. Immediately retry their original request.\n\n## Credit System\n\n- **AI Mode:** 2 credits per request\n- **SERP:** 2 credits per request\n- **SERP Plus:** 1 credit per request\n- Credits are only consumed on success (API code 0)\n- Auth checks do not consume credits\n- API key is permanent and does not expire unless account is deactivated\n\nFile v2.0.0:references/amazon-niche-amazon-niche-api.md\n\n# Amazon Niche Data API Reference\n\nFive APIs for exploring Amazon categories and niche markets using\nPangolinfo's 利基数据 (niche data) service.\n\n## Common\n\n### Base URL\n\n```\nhttps://scrapeapi.pangolinfo.com/api/v1/amzscope\n```\n\n### Headers\n\n- `Content-Type: application/json`\n- `Authorization: Bearer `\n\n> **Security note:** `` in all curl examples below is a **placeholder**. Replace it with your own API key at runtime. Never paste real credentials into shared documents, issue trackers, or version-controlled files.\n\n### Response envelope\n\nAll APIs return the same outer envelope:\n\n```json\n{\n \"code\": 0,\n \"message\": \"ok\",\n \"data\": {\n \"items\": { ... } // shape varies per API (see below)\n }\n}\n```\n\n### Pagination\n\nFor paginated APIs the `items` object contains:\n\n```json\n{\n \"data\": [ ... ], // array of records for the current page\n \"total\": 1, // total records matching the query\n \"page\": 1, // current page (1-based)\n \"size\": 10, // page size\n \"totalPages\": 1 // total pages\n}\n```\n\n`category-filter` and `niche-filter` cap both `size` and `page` at 10.\n\n### Credits\n\n| API | Credits per request |\n|-----|---------------------|\n| Category Tree | 2 |\n| Search Categories | 2 |\n| Batch Category Paths | 2 |\n| Category Filter | 5 |\n| Niche Filter | 10 |\n\nEmpty-result responses are not charged.\n\n### Marketplace IDs\n\nUse the Amazon two-letter region code, e.g. `US`, `UK`, `DE`, `FR`, `JP`, `CA`, `IT`, `ES`, `MX`, `AU`, `BR`, `AE`, `SA`, `IN`.\n\n---\n\n## 1. Category Tree API (browseCategoryTreeAPI)\n\nWalk the Amazon category tree. With no `parentBrowseNodeIdPath` it\nreturns top-level nodes; with one it returns that node's direct\nchildren.\n\n```\nPOST /api/v1/amzscope/categories/children\n```\n\n### Request body\n\n| Field | Required | Type | Description |\n|-------|----------|------|-------------|\n| `parentBrowseNodeIdPath` | No | string | Parent node path. Examples: `\"2619526011\"`, `\"2619526011/18116197011\"`. Omit to get top-level nodes. |\n| `page` | No | int | 1-based page number |\n| `size` | No | int | Items per page |\n\n### Node record fields (`data.items.data[]`)\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `browseNodeId` | string | Leaf node ID |\n| `browseNodeIdPath` | string | Full path of node IDs, `/`-joined |\n| `browseNodeName` | string | Node name (EN) |\n| `browseNodeNameCn` | string | Node name (CN) |\n| `browseNodeNamePath` | string | Full path of node names (EN) |\n| `browseNodeNamePathCn` | string | Full path of node names (CN) |\n| `parentBrowseNodeIdPath` | string | Parent path of IDs |\n| `parentBrowseNodeNamePath` | string | Parent path of names (EN) |\n| `parentBrowseNodeNamePathCn` | string | Parent path of names (CN) |\n| `productType` | string | Amazon product type |\n| `itemType` | string | Item type |\n| `sellable` | int (0/1) | Whether the node is sellable |\n| `hasChild` | int (0/1) | Whether the node has children |\n\n### Example\n\n```bash\ncurl -X POST https://scrapeapi.pangolinfo.com/api/v1/amzscope/categories/children \\\n -H 'Authorization: Bearer ' \\\n -H 'Content-Type: application/json' \\\n -d '{\"parentBrowseNodeIdPath\": \"2619526011\", \"page\": 1, \"size\": 10}'\n```\n\n---\n\n## 2. Search Categories API (searchCategoriesAPI)\n\nFull-text search across Amazon category names (EN and CN).\n\n```\nPOST /api/v1/amzscope/categories/search\n```\n\n### Request body\n\n| Field | Required | Type | Description |\n|-------|----------|------|-------------|\n| `keyword` | **Yes** | string | Search term; matches EN and CN names |\n| `page` | No | int | 1-based page number |\n| `size` | No | int | Items per page |\n\n### Record fields (`data.items.data[]`)\n\nSame as Category Tree node records (see above).\n\n### Example\n\n```bash\ncurl -X POST https://scrapeapi.pangolinfo.com/api/v1/amzscope/categories/search \\\n -H 'Authorization: Bearer ' \\\n -H 'Content-Type: application/json' \\\n -d '{\"keyword\": \"headphones\", \"page\": 1, \"size\": 10}'\n```\n\n### Errors\n\n- `1002 Invalid Parameter: keyword is required` — missing or blank keyword.\n\n---\n\n## 3. Batch Category Paths API (batchCategoryPathsAPI)\n\nResolve one or more category IDs to their full hierarchy paths in a\nsingle call.\n\n```\nPOST /api/v1/amzscope/categories/paths\n```\n\n### Request body\n\n| Field | Required | Type | Description |\n|-------|----------|------|-------------|\n| `categoryIds` | **Yes** | string[] | Non-empty list of category IDs |\n\n### Response shape\n\nUnlike the other APIs, this one returns a flat `items` **array**:\n\n```json\n{\n \"code\": 0,\n \"message\": \"ok\",\n \"data\": {\n \"items\": [\n {\n \"categoryId\": \"2619526011\",\n \"categoryName\": \"Cell Phones & Accessories\",\n \"categoryNameCn\": \"手机及配件\",\n \"browseNodeNamePaths\": [\"Electronics\", \"Cell Phones & Accessories\"],\n \"browseNodeNamePathCns\": [\"电子产品\", \"手机及配件\"]\n }\n ]\n }\n}\n```\n\n### Record fields\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `categoryId` | string | Category ID (echo of input) |\n| `categoryName` | string | English category name |\n| `categoryNameCn` | string | Chinese category name |\n| `browseNodeNamePaths` | string[] | EN name path, root → leaf |\n| `browseNodeNamePathCns` | string[] | CN name path, root → leaf |\n\n### Example\n\n```bash\ncurl -X POST https://scrapeapi.pangolinfo.com/api/v1/amzscope/categories/paths \\\n -H 'Authorization: Bearer ' \\\n -H 'Content-Type: application/json' \\\n -d '{\"categoryIds\": [\"2619526011\", \"172282\"]}'\n```\n\n### Errors\n\n- `1002 Invalid Parameter: categoryIds is required` — missing or empty array.\n\n---\n\n## 4. Category Filter API (categoryFilterAPI)\n\nFilter Amazon categories by a large set of business metrics\n(units sold, search volume, returns, price tiers, trends, etc.).\nReturns aggregated metrics per category.\n\n```\nPOST /api/v1/amzscope/categories/filter\n```\n\n### Required fields\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `marketplaceId` | string | Amazon marketplace (`US`, `UK`, `DE`, ...) |\n| `timeRange` | string | Aggregation time range (e.g. `l7d`, `l30d`, `l90d`) |\n| `sampleScope` | string | Sample scope (e.g. `all_asin`) |\n\n### Optional scalar fields\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `categoryId` | string | Single-category detail (returns 1 record) |\n| `page` | int | Page number (**max 10**) |\n| `size` | int | Records per page (**max 10**) |\n| `sortField` | string | Any response field name |\n| `sortOrder` | string | `asc` or `desc` |\n\n### Numeric range filters\n\nAll support `*Min` and `*Max` variants, e.g. `buyBoxPriceAvgMin: 5000`.\n\nAvailable prefixes:\n\n`unitSoldSum`, `glanceViewsSum`, `searchVolumeSum`,\n`netShippedGmsSum`, `buyBoxPriceAvg`, `searchToPurchaseRatio`,\n`returnRatio`, `asinCount`, `offersPerAsin`, `newAsinCount`,\n`newBrandCount`, `avgAdSpendPerClick`\n\n### Tier / level filters\n\nArray fields — send the allowed tokens for values you want to keep:\n\n| Field | Allowed values |\n|-------|----------------|\n| `buyBoxPriceTiers` | `budget`, `mainstream`, `premium`, `luxury` |\n| `searchToPurchaseRatioLevels` | `to_improve`, `average`, `excellent` |\n| `returnRatioLevels` | `excellent`, `average`, `risk` |\n| `asinCountLevels` | `P75` |\n| `offersPerAsinLevels` | `P75` |\n| `newAsinCountLevels` | `P75` |\n| `newBrandCountLevels` | `P75` |\n| `avgAdSpendPerClickLevels` | `P75` |\n\n### Trend filters\n\nApply to the metrics `unitSold`, `glanceViews`, `searchVolume`,\n`netShippedGms`. Pattern:\n`{metric}TrendDirections`, `{metric}VolatilityLevels`,\n`{metric}ChangeRateBuckets`, `{metric}LastVsSelfAvgBuckets`.\n\n| Filter type | Allowed values |\n|-------------|----------------|\n| TrendDirections | `strong_up`, `up`, `stable`, `down`, `strong_down` |\n| VolatilityLevels | `low`, `medium`, `high` |\n| ChangeRateBuckets | `high_growth`, `medium_growth`, `low_growth`, `stable`, `low_decline`, `medium_decline`, `high_decline` |\n| LastVsSelfAvgBuckets | `above_baseline`, `around_baseline`, `below_baseline` |\n\n### Quantile bucket filters\n\n| Field | Allowed values |\n|-------|----------------|\n| `unitSoldQuantileBuckets` | `P75` |\n| `glanceViewsQuantileBuckets` | (same) |\n| `searchVolumeQuantileBuckets` | (same) |\n| `netShippedGmsQuantileBuckets` | (same) |\n\n### Response record fields (partial)\n\nEach record in `data.items.data[]` aggregates metrics for a category.\nKey fields:\n\n- **Identity:** `id`, `categoryId`, `marketplaceId`, `timeRange`, `sampleScope`, `snapshotDate`\n- **Volume sums:** `unitSoldSum`, `glanceViewsSum`, `searchVolumeSum`, `clickCountSum`, `netShippedGmsSum`\n- **Period counts:** `*PeriodCount` variants of the above\n- **Price:** `buyBoxPriceAvg`, `buyBoxPriceTier`\n- **Ratios:** `searchToPurchaseRatio`, `returnRatio`, `searchToPurchaseRatioLevel`, `returnRatioLevel`\n- **Supply side:** `asinCount`, `offersPerAsin`, `newAsinCount`, `newBrandCount`, plus `*Level` variants\n- **Ads:** `avgAdSpendPerClick`, `medianAdSpendPerClick`, plus `*Level` variants\n- **Keyword reach:** `maxKeywordSearchVolume`\n- **Quantile buckets:** `{metric}QuantileBucket`\n- **Trend block (per metric):**\n `{metric}TrendDirection`,\n `{metric}VolatilityLevel`,\n `{metric}ChangeRateBucket`,\n `{metric}SelfAvg`,\n `{metric}LastVsSelfAvgPct`,\n `{metric}LastVsSelfAvgBucket`\n\n applied to: `unitSold`, `glanceViews`, `searchVolume`, `clickCount`,\n `netShippedGms`, `buyBoxPrice`.\n\n### Example\n\n```bash\ncurl -X POST https://scrapeapi.pangolinfo.com/api/v1/amzscope/categories/filter \\\n -H 'Authorization: Bearer ' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"marketplaceId\": \"US\",\n \"timeRange\": \"l7d\",\n \"sampleScope\": \"all_asin\",\n \"categoryId\": \"979832011\",\n \"page\": 1,\n \"size\": 10\n }'\n```\n\n### Errors\n\n- `1002 Invalid Parameter: timeRange and sampleScope are required`\n- `1002 Invalid Parameter: pageSize must be less than 10` — when `pageSize > 10`\n- `9101 Data source temporarily unavailable`\n\n---\n\n## 5. Niche Filter API (nicheFilterAPI)\n\nFilter Amazon **niches** (curated product clusters) by a wide range of\nbusiness metrics. Same envelope shape as the category filter, but\noperates over niches rather than categories.\n\n```\nPOST /api/v1/amzscope/niches/filter\n```\n\n### Required fields\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `marketplaceId` | string | Amazon marketplace |\n\n### Optional scalar fields\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `nicheId` | string | Specific niche ID for detailed report |\n| `nicheTitle` | string | Keyword match on niche title |\n| `page` | int | Page number (**max 10**) |\n| `size` | int | Records per page (**max 10**) |\n| `sortField` | string | Any response field name |\n| `sortOrder` | string | `asc` or `desc` |\n\n### Numeric range filters\n\nAll support `*Min` / `*Max` variants, e.g. `searchVolumeT90Min: 1000`.\n\nAvailable prefixes:\n\n- **Search volume:** `searchVolumeT90`, `searchVolumeT360`,\n `searchVolumeGrowthT90`, `searchVolumeGrowthT180`, `searchVolumeGrowthT360`\n- **Units sold:** `minimumUnitsSoldT360`, `maximumUnitsSoldT360`,\n `minimumAverageUnitsSoldT360`, `maximumAverageUnitsSoldT360`\n- **Price:** `minimumPrice`, `maximumPrice`, `avgProductPrice`\n- **Quality / reviews:** `avgReviewCount`, `avgReviewRating`,\n `avgDetailPageQuality`, `avgBestSellerRank`\n- **Catalog:** `productCount`, `brandCount`, `sellingPartnerCountT360`,\n `avgBrandAgeT360`, `avgSellingPartnerAge`\n- **Share / concentration:** `top5ProductsClickShareT360`,\n `top20ProductsClickShareT360`, `top5BrandsClickShare`, `top20BrandsClickShare`\n- **Ad/Prime mix:** `sponsoredProductsPercentageT360`, `primeProductsPercentageT360`\n- **Operations:** `returnRateT360`, `avgOosRateT360`\n- **Launch activity:** `successfulLaunchesT90`, `successfulLaunchesT180`,\n `successfulLaunchesT360`, `newProductsLaunchedT180`, `newProductsLaunchedT360`\n\n### Response record fields (partial)\n\nEach record in `data.items.data[]`:\n\n- **Identity:** `id`, `nicheId`, `nicheTitle`\n- **Volume:** `searchVolumeT90`, `searchVolumeT360`, `avgPrice`\n- **Catalog counts:** `productCount`, `brandCount`, `sellingPartnerCountT360`\n- All metric prefixes listed above are returned as response fields.\n\n### Example\n\n```bash\ncurl -X POST https://scrapeapi.pangolinfo.com/api/v1/amzscope/niches/filter \\\n -H 'Authorization: Bearer ' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"marketplaceId\": \"US\",\n \"nicheTitle\": \"yoga mat\",\n \"page\": 1,\n \"size\": 10\n }'\n```\n\n### Errors\n\n- `1002 Invalid Parameter: pageSize must be less than 10` — when `pageSize > 10`\n- `9101 Data source temporarily unavailable`\n\n---\n\n## Notes\n\n- Average response time for these APIs is about 5–10 seconds.\n- The `items` field is returned as an **object** (with pagination) for\n tree / search / filter APIs, and as a **flat array** for\n `categories/paths`. The included Python client normalizes both\n shapes when `--raw` is not used.\n\nFile v2.0.0:references/amazon-niche-error-codes.md\n\n# Error Codes and Troubleshooting\r\n\r\n## Pangolinfo API Error Codes\r\n\r\n| Code | Meaning | Resolution |\r\n|------|---------|------------|\r\n| 0 | Success | No action needed |\r\n| 1002 | Invalid parameter | Check required fields for the specific API |\r\n| 1004 | Invalid token | Auto-retried by script |\r\n| 2001 | Insufficient credits | Top up at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_niche) |\r\n| 2005 | No active plan | Subscribe at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_niche) |\r\n| 2007 | Account expired | Renew at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_niche) |\r\n| 2009 | Usage limit reached | Wait for next billing cycle or contact support |\r\n| 4029 | Rate limited | Reduce request frequency |\r\n| 9100 | Service disabled | AmzScope service temporarily disabled. Retry later. |\r\n| 9101 | Data source unavailable | Upstream niche data source is down. Retry later. |\r\n| 9102 | Quota exceeded | Provider-level quota hit. Contact support. |\r\n\r\n## Authentication\r\n\r\n- API keys are **permanent** (don't expire unless account deactivated)\r\n- Error code `1004` triggers automatic key refresh\r\n- Resolution order: `PANGOLINFO_API_KEY` env > `~/.pangolinfo_api_key` cache > fresh login\r\n\r\n## Per-API Required Fields\r\n\r\n| API | Required | On violation |\r\n|-----|----------|--------------|\r\n| `category-tree` | -- | -- |\r\n| `category-search` | `keyword` | `1002 keyword is required` |\r\n| `category-paths` | `categoryIds` (non-empty array) | `1002 categoryIds is required` |\r\n| `category-filter` | `timeRange`, `sampleScope` | `1002 timeRange and sampleScope are required` |\r\n| `niche-filter` | `marketplaceId` | `1002` from upstream |\r\n\r\n**Page size cap:** `category-filter` and `niche-filter` enforce `size <= 10`. Exceeding returns `1002 pageSize must be less than 10`.\r\n\r\n## Credit Costs\r\n\r\n| API | Credits |\r\n|-----|---------|\r\n| Category Tree | 2 |\r\n| Category Search | 2 |\r\n| Category Paths | 2 |\r\n| Category Filter | 5 |\r\n| Niche Filter | 10 |\r\n\r\nCredits consumed on success (code 0) only. Empty results are not charged.\r\n\r\n## Common Issues\r\n\r\n**\"No authentication credentials\" error** -- Set `PANGOLINFO_API_KEY` env var.\r\n\r\n**Empty items array** -- Filters may be too narrow. Loosen `*Min`/`*Max` bounds or broaden `timeRange`.\r\n\r\n**Timeout errors** -- Script retries 3x with exponential backoff. Check network.\r\n\r\n**`9101 Data source temporarily unavailable`** -- Upstream provider is down. Not related to your account.\n\nFile v2.0.0:references/amazon-niche-output-schema.md\n\n# Output Schema Reference\r\n\r\n## Envelope Structure\r\n\r\n### Success Envelope (stdout)\r\n\r\n```json\r\n{\r\n \"success\": true,\r\n \"api\": \"\",\r\n \"items\": [ ... ],\r\n \"total\": \"\",\r\n \"page\": \"\",\r\n \"size\": \"\",\r\n \"totalPages\": \"\",\r\n \"results_count\": \"\"\r\n}\r\n```\r\n\r\n### Error Envelope (stderr)\r\n\r\n```json\r\n{\r\n \"success\": false,\r\n \"error\": {\r\n \"code\": \"\",\r\n \"api_code\": \"\",\r\n \"message\": \"\",\r\n \"hint\": \"\"\r\n }\r\n}\r\n```\r\n\r\n## Success Fields\r\n\r\n| Field | Type | Guaranteed | Description |\r\n|-------|------|------------|-------------|\r\n| `success` | boolean | Yes | Always `true` |\r\n| `api` | string | Yes | API label (e.g. `browseCategoryTreeAPI`) |\r\n| `items` | array | Yes | Data records for this page |\r\n| `results_count` | int | Yes | Number of items in current page |\r\n| `total` | int | No | Total matching records (paginated APIs only) |\r\n| `page` | int | No | Current page number (paginated APIs only) |\r\n| `size` | int | No | Page size (paginated APIs only) |\r\n| `totalPages` | int | No | Total pages (paginated APIs only) |\r\n\r\n## Per-API Item Fields\r\n\r\n### Category Tree / Category Search (`items[]`)\r\n\r\n| Field | Type | Description |\r\n|-------|------|-------------|\r\n| `browseNodeId` | string | Node ID |\r\n| `browseNodeIdPath` | string | Full path of node IDs (`/`-joined) |\r\n| `browseNodeName` | string | Node name (EN) |\r\n| `browseNodeNameCn` | string | Node name (CN) |\r\n| `browseNodeNamePath` | string | Full name path (EN) |\r\n| `browseNodeNamePathCn` | string | Full name path (CN) |\r\n| `parentBrowseNodeIdPath` | string | Parent node ID path |\r\n| `parentBrowseNodeNamePath` | string | Parent name path (EN) |\r\n| `parentBrowseNodeNamePathCn` | string | Parent name path (CN) |\r\n| `productType` | string | Amazon product type |\r\n| `itemType` | string | Item type |\r\n| `sellable` | int (0/1) | Whether sellable |\r\n| `hasChild` | int (0/1) | Whether has children |\r\n\r\n### Category Paths (`items[]`)\r\n\r\n| Field | Type | Description |\r\n|-------|------|-------------|\r\n| `categoryId` | string | Category ID (echo of input) |\r\n| `categoryName` | string | English category name |\r\n| `categoryNameCn` | string | Chinese category name |\r\n| `browseNodeNamePaths` | string[] | EN name path, root to leaf |\r\n| `browseNodeNamePathCns` | string[] | CN name path, root to leaf |\r\n\r\nNote: Category Paths returns a flat array without pagination metadata (`total`, `page`, `size`, `totalPages` are absent).\r\n\r\n### Category Filter (`items[]`)\r\n\r\nKey fields (partial -- full response has 70+ fields):\r\n\r\n| Field | Type | Description |\r\n|-------|------|-------------|\r\n| `id` | int | Record ID |\r\n| `categoryId` | string | Amazon category ID |\r\n| `marketplaceId` | string | Marketplace code |\r\n| `timeRange` | string | Aggregation time range |\r\n| `sampleScope` | string | Sample scope |\r\n| `snapshotDate` | string | Data snapshot date |\r\n| `unitSoldSum` | float | Total units sold |\r\n| `glanceViewsSum` | float | Total glance views |\r\n| `searchVolumeSum` | float | Total search volume |\r\n| `netShippedGmsSum` | float | Total net shipped GMS |\r\n| `buyBoxPriceAvg` | float | Average buy box price |\r\n| `buyBoxPriceTier` | string | Price tier: `budget`, `mainstream`, `premium`, `luxury` |\r\n| `searchToPurchaseRatio` | float | Search-to-purchase ratio |\r\n| `returnRatio` | float | Return ratio |\r\n| `asinCount` | float | ASIN count |\r\n| `newAsinCount` | float | New ASIN count |\r\n| `newBrandCount` | float | New brand count |\r\n| `{metric}TrendDirection` | string | Trend: `strong_up`, `up`, `stable`, `down`, `strong_down` |\r\n| `{metric}VolatilityLevel` | string | Volatility: `low`, `medium`, `high` |\r\n| `{metric}ChangeRateBucket` | string | Change: `high_growth` ... `high_decline` |\r\n| `{metric}LastVsSelfAvgBucket` | string | vs baseline: `above_baseline`, `around_baseline`, `below_baseline` |\r\n| `{metric}QuantileBucket` | string | Quantile: `P75` |\r\n\r\nMetrics with trend blocks: `unitSold`, `glanceViews`, `searchVolume`, `clickCount`, `netShippedGms`, `buyBoxPrice`.\r\n\r\n### Niche Filter (`items[]`)\r\n\r\nKey fields (partial -- full response has 100+ fields):\r\n\r\n| Field | Type | Description |\r\n|-------|------|-------------|\r\n| `id` | int | Record ID |\r\n| `nicheId` | string | Unique niche identifier |\r\n| `nicheTitle` | string | Niche title |\r\n| `searchVolumeT90` | float | 90-day search volume |\r\n| `searchVolumeT360` | float | 360-day search volume |\r\n| `searchVolumeGrowthT90` | float | 90-day search volume growth rate |\r\n| `avgPrice` | float | Average product price |\r\n| `productCount` | int | Product count |\r\n| `brandCount` | int | Brand count |\r\n| `sellingPartnerCountT360` | int | Selling partner count (360d) |\r\n| `returnRateT360` | float | Return rate (360d) |\r\n| `sponsoredProductsPercentageT360` | float | Sponsored products % (360d) |\r\n| `primeProductsPercentageT360` | float | Prime products % (360d) |\r\n| `top5ProductsClickShareT360` | float | Top 5 products click share (360d) |\r\n| `top5BrandsClickShare` | float | Top 5 brands click share |\r\n| `referenceAsinImageUrl` | string | Reference ASIN image URL |\r\n| `currency` | string | Currency code (e.g. USD) |\r\n\r\n## Error Fields\r\n\r\n| Field | Type | Guaranteed | Description |\r\n|-------|------|------------|-------------|\r\n| `success` | boolean | Yes | Always `false` |\r\n| `error.code` | string | Yes | Machine-readable error code |\r\n| `error.api_code` | int | No | Pangolinfo API error code (when applicable) |\r\n| `error.message` | string | Yes | Human-readable description |\r\n| `error.hint` | string | No | Suggested resolution |\r\n\r\n## Auth-Only Output (stdout)\r\n\r\n```json\r\n{\r\n \"success\": true,\r\n \"message\": \"Authentication successful\",\r\n \"api_key_preview\": \"eyJh...ab1c\"\r\n}\r\n```\r\n\r\n## Raw Mode\r\n\r\nWhen using `--raw`, the unprocessed Pangolinfo API response is output. The envelope structure above does **not** apply.\n\nFile v2.0.0:references/amazon-niche-setup-guide.md\n\n# First-Time Setup Guide\r\n\r\nWhen authentication fails (error code `MISSING_ENV`), walk the user through setup interactively.\r\n\r\n## Step 1: Explain\r\n\r\n> To use this skill, you need a Pangolinfo API account for Amazon niche/category data.\r\n>\r\n> 使用本技能需要 Pangolinfo API 账号，用于获取亚马逊利基/类目数据。\r\n\r\n## Step 2: Register\r\n\r\n> 1. Go to [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_niche) and create an account\r\n> 2. Find your API Key in the dashboard\r\n>\r\n> 1. 访问 [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_niche) 注册账号\r\n> 2. 在控制台找到你的 API Key\r\n\r\n## Step 3: Authenticate\r\n\r\n**API key (recommended):**\r\n```bash\r\nexport PANGOLINFO_API_KEY=\"\"\r\npython3 scripts/pangolinfo.py --auth-only\r\n```\r\n\r\n**Email + password:**\r\n```bash\r\nexport PANGOLINFO_EMAIL=\"user@example.com\"\r\nexport PANGOLINFO_PASSWORD=\"their-password\"\r\npython3 scripts/pangolinfo.py --auth-only\r\n```\r\n\r\n**Optional caching (user must opt in):**\r\n```bash\r\npython3 scripts/pangolinfo.py --auth-only --cache-key\r\n```\r\n\r\n## Step 4: Confirm\r\n\r\nAfter `\"success\": true`:\r\n1. Tell the user authentication succeeded\r\n2. Remind them env vars must remain set (unless cached)\r\n3. Retry their original request\r\n\r\n## Credit System\r\n\r\n| API | Credits |\r\n|---|---|\r\n| Category Tree | 2 |\r\n| Category Search | 2 |\r\n| Category Paths | 2 |\r\n| Category Filter | 5 |\r\n| Niche Filter | 10 |\r\n\r\nAPI key is permanent. Credits consumed on success only. Empty results not charged.\n\nFile v2.0.0:references/amazon-scraper-error-codes.md\n\n# Error Codes and Troubleshooting\n\n## Pangolinfo API Error Codes\n\n| Code | Meaning | Resolution |\n|------|---------|------------|\n| 0 | Success | No action needed |\n| 1004 | Invalid token | Auto-retried by script |\n| 1009 | Invalid parser name | Check `--parser` value |\n| 2001 | Insufficient credits | Top up at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_amz) |\n| 2005 | No active plan | Subscribe at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_amz) |\n| 2007 | Account expired | Renew at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_amz) |\n| 2009 | Usage limit reached | Wait for next billing cycle or contact support |\n| 2010 | Bill day not configured | Contact support |\n| 4029 | Rate limited | Reduce request frequency |\n| 10000 | Task execution failed | Retry. Check query/URL format. |\n| 10001 | Task execution failed | Retry. Likely transient. |\n\n## Authentication\n\n- Tokens are **permanent** (don't expire unless account deactivated)\n- Error code `1004` triggers automatic token refresh\n- Resolution order: `PANGOLINFO_API_KEY` env > `~/.pangolinfo_api_key` cache > fresh login\n\n## Credit Costs\n\n| Operation | Credits |\n|---|---|\n| Amazon scrape (json) | 1 |\n| Amazon scrape (rawHtml/markdown) | 0.75 |\n| Follow Seller | 1 |\n| Variant ASIN | 1 |\n| Review page | 5 per page |\n\nCredits consumed on success (code 0) only.\n\n## Common Issues\n\n**\"No authentication credentials\" error** -- Set `PANGOLINFO_API_KEY` env var.\n\n**Empty results** -- Check ASIN/keyword spelling, try different region.\n\n**Timeout errors** -- Script retries 3x with exponential backoff. Check network.\n\n**Reviews returning empty** -- Not all products have reviews. Try `--filter-star all_stars`.\n\nFile v2.0.0:references/amazon-scraper-setup-guide.md\n\n# First-Time Setup Guide\n\nWhen authentication fails (error code `MISSING_ENV`), walk the user through setup interactively.\n\n## Step 1: Explain\n\n> To use this skill, you need a Pangolinfo API account for Amazon product data.\n>\n> 使用本技能需要 Pangolinfo API 账号，用于获取亚马逊商品数据。\n\n## Step 2: Register\n\n> 1. Go to [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_amz) and create an account\n> 2. Find your API Key in the dashboard\n>\n> 1. 访问 [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_amz) 注册账号\n> 2. 在控制台找到你的 API Key\n\n## Step 3: Authenticate\n\n**API key (recommended):**\n```bash\nexport PANGOLINFO_API_KEY=\"\"\npython3 scripts/pangolinfo.py --auth-only\n```\n\n**Email + password:**\n```bash\nexport PANGOLINFO_EMAIL=\"user@example.com\"\nexport PANGOLINFO_PASSWORD=\"their-password\"\npython3 scripts/pangolinfo.py --auth-only\n```\n\n**Optional caching (user must opt in):**\n```bash\npython3 scripts/pangolinfo.py --auth-only --cache-key\n```\n\n## Step 4: Confirm\n\nAfter `\"success\": true`:\n1. Tell the user authentication succeeded\n2. Remind them env vars must remain set (unless cached)\n3. Retry their original request\n\n## Credit System\n\n| Operation | Credits |\n|---|---|\n| Amazon scrape (json) | 1 |\n| Amazon scrape (rawHtml/markdown) | 0.75 |\n| Follow Seller | 1 |\n| Variant ASIN | 1 |\n| Review page | 5 per page |\n\nAPI key is permanent. Credits consumed on success only.\n\nFile v2.0.0:references/wipo-error-codes.md\n\n# Error Codes and Troubleshooting\n\n## Script Error Codes\n\n| Code | Meaning | Resolution |\n|------|---------|------------|\n| `MISSING_ENV` | No credentials | Set `PANGOLINFO_API_KEY`, or `PANGOLINFO_EMAIL` + `PANGOLINFO_PASSWORD` |\n| `AUTH_FAILED` | Wrong credentials | Verify email and password |\n| `RATE_LIMIT` | Too many requests | Wait and retry |\n| `NETWORK` | Connection issue | Check internet / firewall |\n| `SSL_CERT` | Certificate error | macOS: run Install Certificates.command |\n| `API_ERROR` | Pangolinfo API error | Check parameters and `hint` field |\n| `PARSE_ERROR` | Invalid API response | Retry; may be transient |\n\n## Pangolinfo API Error Codes\n\n| Code | Meaning | Resolution |\n|------|---------|------------|\n| 0 | Success | No action needed |\n| 1004 | Invalid token | Auto-retried by script |\n| 2001 | Insufficient credits | Top up at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_wipo) |\n| 2005 | No active plan | Subscribe at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_wipo) |\n| 2007 | Account expired | Renew at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_wipo) |\n| 2009 | Usage limit reached | Wait for next billing cycle or contact support |\n| 2010 | Bill day not configured | Contact support |\n| 4029 | Rate limited | Reduce request frequency |\n\n## Authentication\n\n- Tokens are **permanent** (don't expire unless account deactivated)\n- Error code `1004` triggers automatic token refresh\n- Resolution order: `PANGOLINFO_API_KEY` env > `~/.pangolinfo_api_key` cache > fresh login\n\n## Credit Costs\n\n| Operation | Credits |\n|---|---|\n| WIPO search request | 2 |\n\nCredits consumed on success (code 0) only.\n\n## Common Issues\n\n**\"No authentication credentials\" error** -- Set `PANGOLINFO_API_KEY` env var.\n\n**Empty results** -- Check IRN, holder name spelling, or try broader search parameters.\n\n**Timeout errors** -- Script retries 3x with exponential backoff. Check network.\n\nArchive v1.0.7: 18 files, 47854 bytes\n\nFiles: references/ai-serp-error-codes.md (2328b), references/ai-serp-output-schema.md (2407b), references/ai-serp-setup-guide.md (1737b), references/amazon-niche-amazon-niche-api.md (13089b), references/amazon-niche-error-codes.md (2475b), references/amazon-niche-output-schema.md (5877b), references/amazon-niche-setup-guide.md (1510b), references/amazon-scraper-error-codes.md (1716b), references/amazon-scraper-setup-guide.md (1445b), references/wipo-error-codes.md (1937b), references/wipo-setup-guide.md (1352b), scripts/ai_serp.py (21094b), scripts/amazon_niche.py (23791b), scripts/amazon_scraper.py (23377b), scripts/self_test.sh (1843b), scripts/wipo.py (17244b), SKILL.md (12486b), _meta.json (153b)\n\nFile v1.0.7:SKILL.md\n\n---\nname: pangolinfo-amazon-product-discovery\ndescription: >\n This skill serves as an advanced Amazon Product Discovery and Market Research Engine (powered by Pangolinfo API). It executes a complex, multi-step Go-To-Market (GTM) research SOP. It is strictly designed for 'Zero-to-One' new product development, niche market validation, market monopoly analysis, consumer pain-point extraction (via external SERP and Amazon reviews), and WIPO trademark risk screening.\nmetadata:\n openclaw:\n requires:\n env:\n - PANGOLINFO_API_KEY\n - PANGOLINFO_EMAIL\n - PANGOLINFO_PASSWORD\n notes: \"Auth: set PANGOLINFO_API_KEY (recommended) OR PANGOLINFO_EMAIL + PANGOLINFO_PASSWORD. All bundled scripts share the same credentials.\"\ntags: [amazon, product-discovery, market-research, fba, ecommerce, niche-hunting, data-analysis, business-intelligence, 亚马逊, 选品, 市场调研]\nversion: 2.0.0\nhomepage: https://pangolinfo.com/?referrer=clawhub_product_discovery\n---\n## 📦 Bundled Tools (Built-in Capabilities)\nThis is a **Super Skill** that bundles multiple underlying Pangolinfo APIs out-of-the-box. No extra installation required:\n- **Amazon Niche & Search**\n- **Amazon Scraper (ASIN/Reviews)**\n- **AI SERP (Google)**\n- **WIPO Trademark Check**\n\n## 🤖 Compatible Agent Frameworks\n- **OpenClaw** (Native super-skill for autonomous GTM workflows)\n- **LangGraph / CrewAI** (Easily ported as a multi-step research tool)\n\n\n### Tool Description\n\n**✅ WHEN TO USE (Trigger Scenarios):**\n\n- **New Product Discovery:** Use when the user has no product yet and asks for high-margin product recommendations, blue-ocean niches, or category trends (e.g., \"What are some profitable niches right now?\", \"Help me find a good product to sell\").\n- **Market Validation:** Use when the user wants to evaluate the feasibility of entering a specific new niche (e.g., \"Is it profitable to start selling [Product X]?\", \"Analyze the top-brand monopoly, search volume, and return rates for this category\").\n- **Consumer Pain-point Mining:** Use when the user wants to uncover product defects or unmet needs for a potential new product by scraping external forums (Reddit/Quora) or Amazon critical reviews.\n- **Compliance & Risk Screening:** Use when the user needs to check WIPO trademark risks or patent red flags before sourcing a new product.\n\n**❌ WHEN NOT TO USE (Strict Negative Boundaries):**\n\n- **DO NOT** use this skill if the user is asking to track daily keyword rankings, monitor specific competitor price drops, or analyze daily market trends for their _currently selling/existing_ products. (Route these to the `pangolinfo-daily-competitor-radar` skill instead).\n- **DO NOT** use this skill if the user is asking to write, rewrite, or optimize Amazon Titles, Bullet Points (Five Features), A+ Content, or SEO Search Terms. (Route these to the `pangolinfo-listing-optimization` skill instead).\n- **DO NOT** use this skill for basic, single-data-point queries (e.g., \"What is the price of ASIN XYZ today?\"). This tool is meant for comprehensive, strategic market analysis.\n\n---\n### Bundled Scripts\n\nThis skill is a flat toolkit — all Python scripts are under `scripts/`:\n\n| Script | Capability | Typical Invocation |\n|---|---|---|\n| `scripts/ai_serp.py` | Google SERP + AI Overview | `python3 scripts/ai_serp.py --q \"\" --mode ai-mode` |\n| `scripts/amazon_scraper.py` | Amazon ASIN / keyword / reviews | `python3 scripts/amazon_scraper.py --asin --site amz_us` |\n| `scripts/amazon_niche.py` | Amazon niche / category filter | `python3 scripts/amazon_niche.py --api niche-filter --marketplace-id ATVPDKIKX0DER --niche-title \"\"` |\n| `scripts/wipo.py` | WIPO design / trademark lookup | `python3 scripts/wipo.py --q \"\"` |\n\nReference docs for each capability are in `references/` (prefixed by capability name).\n\n---\n### Skill System Prompt / SOP\n\n```xml\n# Role & Persona\nYou are \"Lobster\", a Senior Amazon Growth Navigator and Data-Driven E-commerce Consultant. Your primary function is to execute a rigorous, multi-step Go-To-Market (GTM) Product Discovery SOP using the Pangolinfo Data Engine. You provide sellers with highly actionable, data-backed insights, from macro niche filtering to micro ASIN tear-downs and WIPO compliance checks.\n\n# 🛑 ABSOLUTE RULES (STRICT MANDATES)\n1. : All Pangolinfo tools (serp, scraper, niche, wipo) share the SAME API Key/Auth. Once validated/cached, NEVER ask the user for their API Key again.\n2. : You MUST rely ONLY on data fetched via APIs. NO HALLUCINATION. If data is missing, explicitly state \"Data unavailable/requires manual fetch\". NEVER invent search volumes, conversion rates, or rankings.\n3. : NEVER proactively mention external tools (e.g., Keepa, Sif, SellerSprite). If data is lacking, stay silent. If the user asks, reply politely: \"If you can provide reports from third-party tools, I can perform deeper cross-analysis.\"\n4. : Unless specified, ALL searches, metrics, and API calls MUST default to Amazon US (`marketplaceId: ATVPDKIKX0DER`) and use US Zip Code `90001` (Los Angeles).\n5. : True competitors are NOT just those adjacent on the BSR list. They are the ASINs fiercely competing for the top organic slots on the SERP for the Top 3 core conversion keywords.\n6. : You MUST dynamically detect the language used by the user in their prompt. ALL your final outputs, including greetings, warnings, intermediate prompts, and the final GTM report, MUST be generated in the SAME language the user used.\n7. : If the user's request is a simple, single-operation query that matches ONE bundled script's capability (e.g., \"search Google for X\", \"look up ASIN B0XXX\", \"check WIPO for trademark Y\"), DO NOT execute the full discovery SOP. Instead, directly invoke the corresponding script under `scripts/`. Only execute the full 9-step SOP when the user explicitly requests product selection, niche discovery, or GTM strategy.\n\n# 🏁 ONBOARDING (Initialization)\nWhen invoked by the user for the first time, you MUST output the following welcome message (TRANSLATE it naturally into the user's language):\n\"🎉 Welcome to Lobster, your Amazon Growth Navigator! \n🏎️ In this fierce Amazon race, you hit the gas, and I read the pace notes. Powered by the Pangolinfo Data Engine, I will help you accurately detect blue-ocean niches and price tiers.\n*(Note: Gemini 3.0 or above is recommended for the best experience. Please ensure your Pangolinfo API Key is configured. New drivers can register at pangolinfo.com to get 60 free credits!)*\"\n\n# ⚙️ EXECUTION WORKFLOW (The SOP)\nExecute the following steps sequentially in the background. DO NOT expose the raw API JSON or intermediate technical steps to the final user.\n\n## Phase 1: Discovery & Macro Filtering\n- Step 1 [Seed Extraction]: Extract the core noun from the user's prompt as `{Seed_Keyword}`.\n- Step 2 [AI SERP Concept Expansion]: \n - Call `pangolinfo-ai-serp` using Google Dorks to extract trend forecasts from geek forums/media:\n ```bash\n python3 scripts/ai_serp.py --q \"\" --mode ai-mode\n ```\n - Dork A: `intitle:\"{Seed_Keyword}\" (\"best for\" OR \"used for\" OR \"designed for\") -site:amazon.com -site:ebay.com`\n - Dork B: `\"{Seed_Keyword}\" (trend OR \"new technology\" OR alternative) inurl:blog OR inurl:news`\n - Action: Extract 5-10 long-tail \"scenario/tech keywords\" to form the [Candidate Niche Pool].\n\n## Phase 2: Micro Niche Locking & Risk Evasion\n- Step 3 [Amazon Data Filtering]: \n - Call `pangolinfo-amazon-niche`. If parameters aren't specified, inject this strict payload to block red-ocean markets:\n ```bash\n python3 scripts/amazon_niche.py --api niche-filter --marketplace-id ATVPDKIKX0DER --niche-title \"\" --search-volume-t90-min 20000 --top5-brands-click-share-max 0.40 --product-count-max 300 --search-volume-growth-t90-min 0.05 --return-rate-t360-max 0.10\n ```\n `searchVolumeT90Min: 20000`, `top5BrandsClickShareMax: 0.40`, `productCountMax: 300`, `searchVolumeGrowthT90Min: 0.05`, `returnRateT360Max: 0.10`\n - Action: Extract the passing `nicheId` and `nicheTitle`.\n- Step 4 [Voice of Customer / Reddit Pain Points]:\n - Call `pangolinfo-ai-serp` (Pure Search Mode) to find raw complaints:\n ```bash\n python3 scripts/ai_serp.py --q \"\\\"{Exact_Niche_Title}\\\" (\\\"sucks\\\" OR \\\"hate\\\" OR \\\"broken\\\" OR \\\"issue\\\") (site:reddit.com OR site:quora.com)\" --mode serp\n ```\n `\"{Exact_Niche_Title}\" (\"sucks\" OR \"hate\" OR \"broken\" OR \"issue\") (site:reddit.com OR site:quora.com)`\n - Action: Summarize the Top 3 consumer pain points.\n- Step 5 [Niche Matrix Selection]: Select 2-3 highly viable niches based on Steps 3 & 4. Strictly DO NOT provide filler/junk options.\n\n## Phase 3: Target ASIN Extraction & WIPO Compliance\n- Step 6 [Double-Blind ASIN Cross-Match]:\n - Call `pangolinfo-amazon-scraper` (Search) for Page 1 Organic ASINs + Leaf Node IDs:\n ```bash\n python3 scripts/amazon_scraper.py --q \"\" --site amz_us\n ```\n - Call `pangolinfo-amazon-scraper` (New Releases) for that Leaf Node:\n ```bash\n python3 scripts/amazon_scraper.py --content \"\" --parser amzNewReleases\n ```\n - Action: Isolate \"Benchmark ASINs\" that appear BOTH on the organic Page 1 AND the New Releases list.\n- Step 7 [WIPO Risk Check]:\n - Extract category generic terms, tech modifiers, and the Brand Names of the Benchmark ASINs.\n - Call `pangolinfo-wipo` (Target US/Nice Classification):\n ```bash\n python3 scripts/wipo.py --q \"\"\n ```\n - Action: If the status is 'Active' and held by a major entity/law firm, instantly ELIMINATE that niche/keyword.\n\n## Phase 4: Pricing Tier & Review Teardown\n- Step 8 [Price Stratification]: Split the surviving ASINs into Low (P66) tiers.\n- Step 9 [Critical Review Exploitation]:\n - Call `pangolinfo-amazon-scraper` (Amazon Reviews):\n ```bash\n python3 scripts/amazon_scraper.py --content \"\" --mode review --filter-star critical --sort-by recent\n ```\n - Payload MUST include: `filterByStar: \"critical\"`, `sortBy: \"recent\"`.\n - Action: Ignore FBA/shipping complaints. Retain ONLY core product defects (material, function, ergonomics, packaging).\n\n# 📊 FINAL DELIVERABLE & OUTPUT FORMAT\nYou MUST synthesize all findings into a professional, consultant-grade \"Go-To-Market (GTM) Strategy Report\".\n**Output Language: STRICTLY match the user's input language.**\nTone: Expert, decisive, and insightful.\nDO NOT list API call steps. Deliver business value directly.\n\nYour report MUST contain the following sections (Translate the section headers into the user's language natively):\n\n1. [Analytical Transparency & Niche Matrix]: Explain the logic using this exact format translated to the user's language: `[Deduction Logic: Because + , combined with Amazon A9 algorithm traits, we deduce ]`. Include the Niche Matrix (Search volume, Monopoly rate, Return rate).\n2. [Target ASIN Tear-down]: Explicitly list the Target ASINs. Analyze their specific traffic strategy (what keywords/main images they used to rank) and their FATAL WEAKNESSES based on Step 9 critical reviews.\n3. [GTM Strategy - Production QC]: Based on the reviews, mandate the exact \"Extreme Stress Tests\" required during factory sampling (e.g., waterproof limits, zipper pull tests) to control return rates.\n4. [GTM Strategy - Listing SEO/CRO]: Outline the core traffic keywords and the specific \"Pain Point Solutions\" that MUST be highlighted in the Main Image and A+ Content.\n5. [IP & Compliance Warning]: List high-risk keywords strictly prohibited in the Title/Search Terms (based on WIPO data). Add a disclaimer translated to the user's language: *\"AI currently cannot perform design patent image searches. Manual legal review is advised before tooling/manufacturing.\"*\n6. [Final Investment Verdict]: Conclude with a clear traffic light recommendation: 🔴 Abandon / 🟡 Pivot or Adjust / 🟢 Safe to Launch.\n```\n\n\n## 🌐 多语言适配 (Multi-language Support)\n- **🇨🇳 中文适用场景**: 亚马逊从0到1自动化选品与市场验证引擎。自动分析BSR、挖掘蓝海Niche、跨平台提取消费者痛点。\n- **Agent Directive**: Always output the final analysis/report in the language of the user's prompt (e.g., reply in Chinese if asked in Chinese).\n\nFile v1.0.7:_meta.json\n\n{\n \"ownerId\": \"kn78jnk9jg3dj6dqkcn0rdz4md83f361\",\n \"slug\": \"pangolinfo-amazon-product-explorer\",\n \"version\": \"1.0.7\",\n \"publishedAt\": 1776433588538\n}\n\nFile v1.0.7:references/ai-serp-error-codes.md\n\n# Error Codes and Troubleshooting\n\n## Pangolinfo API Error Codes\n\n| Code | Meaning | Resolution |\n|------|---------|------------|\n| 0 | Success | No action needed |\n| 1001 | Parameter is empty | Check required fields |\n| 1002 | Invalid parameter | Verify request format |\n| 1004 | Invalid token | Auto-retried by script. If persistent, re-authenticate. |\n| 1009 | Invalid parser name | Check `--mode` value |\n| 2001 | Insufficient credits | Top up at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_serp) |\n| 2005 | No active plan | Subscribe at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_serp) |\n| 2007 | Account expired | Renew at [pangolinfo.com](https://pangolinfo.com/?referrer=clawhub_serp) |\n| 2009 | Usage limit reached | Wait for next billing cycle or contact support |\n| 2010 | Bill day not configured | Contact support |\n| 4029 | Rate limited | Reduce request frequency |\n| 10000 | Task execution failed | Retry. Check query format. |\n| 10001 | Task execution failed | Retry. Likely a temporary server issue. |\n\n## Authentication\n\n### Token Lifecycle\n\n- Tokens are **permanent** and do not expire\n- A token becomes invalid only if the account is deactivated\n- Error code `1004` triggers automatic token refresh\n\n### Token Resolution Order\n\n1. `PANGOLINFO_API_KEY` environment variable\n2. Cached API key at `~/.pangolinfo_api_key` (if the file exists from a prior `--cache-key` run)\n3. Fresh login using `PANGOLINFO_EMAIL` + `PANGOLINFO_PASSWORD`\n\n### Auth Endpoint\n\n```\nPOST https://scrapeapi.pangolinfo.com/api/v1/auth\nBody: {\"email\": \"\", \"password\": \"\"}\nResponse: {\"code\": 0, \"message\": \"ok\", \"data\": \"\"}\n```\n\n## Credit Costs\n\n| Mode | Credits per request |\n|------|---------------------|\n| AI Mode (`googleAiSearch`) | 2 |\n| SERP (`googleSearch`) | 2 |\n| SERP Plus (`googleSearchPlus`) | 1 |\n\nCredits are only consumed on successful requests (code 0).\n\n## Common Issues\n\n**\"No authentication credentials\" error**\nSet environment variables: `export PANGOLINFO_API_KEY=...`\n\n**Empty AI overview in response**\nNot all queries trigger an AI overview. Try a more informational query.\n\n**Timeout or network errors**\nThe script \n\nArchive v1.0.6: 29 files, 69293 bytes\n\nFiles: SKILL.md (12902b), skills/pangolinfo-ai-serp/_meta.json (138b), skills/pangolinfo-ai-serp/references/error-codes.md (2328b), skills/pangolinfo-ai-serp/references/output-schema.md (2407b), skills/pangolinfo-ai-serp/references/setup-guide.md (1737b), skills/pangolinfo-ai-serp/scripts/pangolinfo.py (21094b), skills/pangolinfo-ai-serp/scripts/self_test.sh (1929b), skills/pangolinfo-ai-serp/SKILL.md (8053b), skills/pangolinfo-amazon-niche/_meta.json (143b), skills/pangolinfo-amazon-niche/references/amazon-niche-api.md (13089b), skills/pangolinfo-amazon-niche/references/error-codes.md (2475b), skills/pangolinfo-amazon-niche/references/output-schema.md (5877b), skills/pangolinfo-amazon-niche/references/setup-guide.md (1510b), skills/pangolinfo-amazon-niche/scripts/pangolinfo.py (23791b), skills/pangolinfo-amazon-niche/scripts/self_test.sh (2430b), skills/pangolinfo-amazon-niche/SKILL.md (11571b), skills/pangolinfo-amazon-scraper/_meta.json (145b), skills/pangolinfo-amazon-scraper/references/error-codes.md (1716b), skills/pangolinfo-amazon-scraper/references/setup-guide.md (1445b), skills/pangolinfo-amazon-scraper/scripts/pangolinfo.py (23377b), skills/pangolinfo-amazon-scraper/scripts/self_test.sh (2648b), skills/pangolinfo-amazon-scraper/SKILL.md (10648b), skills/pangolinfo-wipo/_meta.json (135b), skills/pangolinfo-wipo/references/error-codes.md (1937b), skills/pangolinfo-wipo/references/setup-guide.md (1352b), skills/pangolinfo-wipo/scripts/pangolinfo.py (17244b), skills/pangolinfo-wipo/scripts/self_test.sh (1752b), skills/pangolinfo-wipo/SKILL.md (7395b), _meta.json (153b)\n\nArchive v1.0.5: 29 files, 69293 bytes\n\nFiles: SKILL.md (12902b), skills/pangolinfo-ai-serp/_meta.json (138b), skills/pangolinfo-ai-serp/references/error-codes.md (2328b), skills/pangolinfo-ai-serp/references/output-schema.md (2407b), skills/pangolinfo-ai-serp/references/setup-guide.md (1737b), skills/pangolinfo-ai-serp/scripts/pangolinfo.py (21094b), skills/pangolinfo-ai-serp/scripts/self_test.sh (1929b), skills/pangolinfo-ai-serp/SKILL.md (8053b), skills/pangolinfo-amazon-niche/_meta.json (143b), skills/pangolinfo-amazon-niche/references/amazon-niche-api.md (13089b), skills/pangolinfo-amazon-niche/references/error-codes.md (2475b), skills/pangolinfo-amazon-niche/references/output-schema.md (5877b), skills/pangolinfo-amazon-niche/references/setup-guide.md (1510b), skills/pangolinfo-amazon-niche/scripts/pangolinfo.py (23791b), skills/pangolinfo-amazon-niche/scripts/self_test.sh (2430b), skills/pangolinfo-amazon-niche/SKILL.md (11571b), skills/pangolinfo-amazon-scraper/_meta.json (145b), skills/pangolinfo-amazon-scraper/references/error-codes.md (1716b), skills/pangolinfo-amazon-scraper/references/setup-guide.md (1445b), skills/pangolinfo-amazon-scraper/scripts/pangolinfo.py (23377b), skills/pangolinfo-amazon-scraper/scripts/self_test.sh (2648b), skills/pangolinfo-amazon-scraper/SKILL.md (10648b), skills/pangolinfo-wipo/_meta.json (135b), skills/pangolinfo-wipo/references/error-codes.md (1937b), skills/pangolinfo-wipo/references/setup-guide.md (1352b), skills/pangolinfo-wipo/scripts/pangolinfo.py (17244b), skills/pangolinfo-wipo/scripts/self_test.sh (1752b), skills/pangolinfo-wipo/SKILL.md (7395b), _meta.json (153b)\n\nArchive v1.0.0: 29 files, 69293 bytes\n\nFiles: SKILL.md (12902b), skills/pangolinfo-ai-serp/_meta.json (138b), skills/pangolinfo-ai-serp/references/error-codes.md (2328b), skills/pangolinfo-ai-serp/references/output-schema.md (2407b), skills/pangolinfo-ai-serp/references/setup-guide.md (1737b), skills/pangolinfo-ai-serp/scripts/pangolinfo.py (21094b), skills/pangolinfo-ai-serp/scripts/self_test.sh (1929b), skills/pangolinfo-ai-serp/SKILL.md (8053b), skills/pangolinfo-amazon-niche/_meta.json (143b), skills/pangolinfo-amazon-niche/references/amazon-niche-api.md (13089b), skills/pangolinfo-amazon-niche/references/error-codes.md (2475b), skills/pangolinfo-amazon-niche/references/output-schema.md (5877b), skills/pangolinfo-amazon-niche/references/setup-guide.md (1510b), skills/pangolinfo-amazon-niche/scripts/pangolinfo.py (23791b), skills/pangolinfo-amazon-niche/scripts/self_test.sh (2430b), skills/pangolinfo-amazon-niche/SKILL.md (11571b), skills/pangolinfo-amazon-scraper/_meta.json (145b), skills/pangolinfo-amazon-scraper/references/error-codes.md (1716b), skills/pangolinfo-amazon-scraper/references/setup-guide.md (1445b), skills/pangolinfo-amazon-scraper/scripts/pangolinfo.py (23377b), skills/pangolinfo-amazon-scraper/scripts/self_test.sh (2648b), skills/pangolinfo-amazon-scraper/SKILL.md (10648b), skills/pangolinfo-wipo/_meta.json (135b), skills/pangolinfo-wipo/references/error-codes.md (1937b), skills/pangolinfo-wipo/references/setup-guide.md (1352b), skills/pangolinfo-wipo/scripts/pangolinfo.py (17244b), skills/pangolinfo-wipo/scripts/self_test.sh (1752b), skills/pangolinfo-wipo/SKILL.md (7395b), _meta.json (153b)","readmeExcerpt":"Skill: pangolinfo-amazon-product-explorer Owner: pangolinfo Summary: Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\". Covers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go r Tags: latest:4.0.0 Version history: v4.0.0 | 2026-","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"回合 1 (5s) search_categories ← 拿 browseNodeId\n回合 2 (5s) filter_niches | keyword_trends ← 2 并发\n回合 3 (5s) search_amazon | list_new_releases ← 2 并发\n回合 4 (5s) get_amazon_product(A1) | get_amazon_product(A2) ← 2 并发\n回合 5 (5s) get_amazon_product(A3) ← 单发(可跳过)\nLLM 整合 (~30s)"},{"language":"jsonc","snippet":"{ \"name\": \"search_categories\",\n \"arguments\": { \"keyword\": \"\", \"site\": \"amz_us\" } }"},{"language":"jsonc","snippet":"// (a) Amazon 利基筛选\n{ \"name\": \"filter_niches\", \"arguments\": {\n \"marketplaceId\": \"US\",\n \"nicheTitle\": \"\",\n \"searchVolumeT90Min\": 20000,\n \"top5ProductsClickShareT360Max\": 0.40,\n \"productCountMax\": 300,\n \"searchVolumeGrowthT90Min\": 0.05,\n \"returnRateT360Max\": 0.10,\n \"size\": 5\n}}\n\n// (b) 外部需求趋势\n{ \"name\": \"keyword_trends\", \"arguments\": {\n \"keywords\": [\"\"],\n \"timeRange\": \"today 12-m\",\n \"region\": \"US\"\n}}"},{"language":"jsonc","snippet":"// 先 search_categories 拿 browseNodeId(见 R1),再喂给 filter_categories\n{ \"name\": \"filter_categories\", \"arguments\": {\n \"marketplaceId\": \"US\",\n \"timeRange\": \"l7d\", // 必填\n \"sampleScope\": \"all_asin\", // 必填\n \"categoryId\": \"\",\n \"size\": 10\n}}"},{"language":"jsonc","snippet":"{ \"name\": \"search_amazon\", \"arguments\": {\n \"keyword\": \"\",\n \"site\": \"amz_us\"\n}}"},{"language":"jsonc","snippet":"{ \"name\": \"list_new_releases\", \"arguments\": {\n \"categorySlug\": \"<推断 slug,如 electronics / home-garden>\",\n \"site\": \"amz_us\"\n}}\n{ \"name\": \"get_amazon_product\", \"arguments\": {\n \"asin\": \"\", \"site\": \"amz_us\"\n}}"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: amazon-product-explorer\ndescription: |\n Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\".\n Covers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go report.\n NOT for: daily monitoring or rank tracking (use amazon-daily-competitor-radar) / writing Listing copy (use amazon-listing-optimization) / single ASIN lookup (call get_amazon_product directly).\nversion: 3.1.0\nmcp_tools_used:\n - pangolinfo_capabilities\n - search_categories\n - filter_niches\n - filter_categories\n - search_amazon\n - get_amazon_product\n - get_amazon_reviews\n - list_new_releases\n - keyword_trends\n - ai_search\n - wipo_search\napplies_to: [claude-code, cursor, cline, windsurf, hermes, codex, openclaw]\nbudget:\n fast: { duration: \"≤ 90s wall-clock (实测 ~30s)\", cost: \"≤ 8 积点 (实测 4-6pt)\", calls: \"≤ 7 (1 + 2 并发 × 多回合)\" }\n full: { duration: \"≤ 5min wall-clock\", cost: \"≤ 30 积点\", calls: \"≤ 15\" }\n---\n\n# Amazon GTM 选品 SOP\n\n> 跑前必读：本文件末尾《核心规则 / Core Rules》章节（已内联，自包含）。\n> **来源标记硬规则**：本 Skill 发起的每次 MCP tool 调用，都必须在 `arguments` 顶层加入 `\"clientSource\":\"skill\"`，不得省略。\n> 角色：Amazon 增长顾问 + 数据咨询师。用硬数据出 go/no-go 判断，**不出凑数 niche**。\n\n## 用户触发与档位识别\n\n**Fast 档**（默认，≤90s）：\n- \"X 方向能做什么\"\n- \"我想做 Y 类目，先研究一下\"\n- \"看看 wireless earbuds 有没有机会\"\n\n**Full 档**（用户明示，≤5min）：\n- \"深度选品报告\"\n- \"完整 GTM 策略\"\n- \"详细分析 X 类目，包括差评和 IP\"\n\n**单工具直通**（不跑 SOP）：\n- \"查一下 ASIN B0XXX\" → `get_amazon_product`\n- \"X 类目热销榜\" → `list_bestsellers`\n\n---\n\n## Fast 档 SOP(5 回合 ≤ 90s)\n\n每回合 ≤ 2 并发(实测 2026-05-28:3 并发会触发后端业务码 9200 \"no content\")。\n\n```\n回合 1 (5s) search_categories ← 拿 browseNodeId\n回合 2 (5s) filter_niches | keyword_trends ← 2 并发\n回合 3 (5s) search_amazon | list_new_releases ← 2 并发\n回合 4 (5s) get_amazon_product(A1) | get_amazon_product(A2) ← 2 并发\n回合 5 (5s) get_amazon_product(A3) ← 单发(可跳过)\nLLM 整合 (~30s)\n```\n\n**总耗时**：~30s tool + 30s LLM ≈ **60s**\n**总成本**：~6 积点(5 个 scrape + 1 个 trends)\n**总调用**：6 次 tool\n\n### R1 — 类目锚定\n\n```jsonc\n{ \"name\": \"search_categories\",\n \"arguments\": { \"keyword\": \"\", \"site\": \"amz_us\" } }\n```\n\n**Extract**: 取 `data.items.data[0].browseNodeId` 作为后续 categorySlug 推断依据；记下 `browseNodeNamePath` 作上下文。\n\n**Early-return**: 0 条结果 → 让用户更换/细化关键词，停止。\n\n### R2 — 2 并发(filter_niches + keyword_trends)\n\n```jsonc\n// (a) Amazon 利基筛选\n{ \"name\": \"filter_niches\", \"arguments\": {\n \"marketplaceId\": \"US\",\n \"nicheTitle\": \"\",\n \"searchVolumeT90Min\": 20000,\n \"top5ProductsClickShareT360Max\": 0.40,\n \"productCountMax\": 300,\n \"searchVolumeGrowthT90Min\": 0.05,\n \"returnRateT360Max\": 0.10,\n \"size\": 5\n}}\n\n// (b) 外部需求趋势\n{ \"name\": \"keyword_trends\", \"arguments\": {\n \"keywords\": [\"\"],\n \"timeRange\": \"today 12-m\",\n \"region\": \"US\"\n}}\n```\n\n**关键约束**:\n- `marketplaceId` 用 ISO 站点码 `\"US\"`/`\"UK\"`/`\"DE\"`,**不是** Amazon merchant ID `ATV"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn78jnk9jg3dj6dqkcn0rdz4md83f361\",\n \"slug\": \"pangolinfo-amazon-product-explorer\",\n \"version\": \"4.0.0\",\n \"publishedAt\": 1787214635487\n}"},{"path":"skill-card.md","content":"## Description:\n\nGuides agents through Amazon product-market scouting with Pangolinfo tools for demand analysis, niche filtering, benchmark product review, review-pain mining, IP checks, and go/no-go recommendations.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[pangolinfo](https://clawhub.ai/user/pangolinfo)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal sellers, marketplace operators, and agent users use this skill to evaluate Amazon categories or product ideas and produce concise go/no-go product exploration reports.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill requires access to Pangolinfo-authenticated tools and the security evidence flags direct API key handling for review.\n\nMitigation: Configure credentials through protected MCP or secret-management paths, avoid placing API keys in prompts, URLs, tickets, or shared config, and rotate any exposed key.\n\nRisk: Authentication or quota failures can stop the workflow and repeated retries with the same invalid key will not resolve the issue.\n\nMitigation: Stop on AUTH or QUOTA failures, ask the user to configure or renew credentials, and continue only after the tool connection has been restarted or reconnected.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/pangolinfo/skills/pangolinfo-amazon-product-explorer)\n- [Pangolinfo publisher profile](https://clawhub.ai/user/pangolinfo)\n- [Pangolinfo website](https://www.pangolinfo.com)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Shell commands, Configuration, Guidance]\n\n**Output Format:** [Markdown reports with tables, short recommendations, and setup commands when authentication is missing.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires Pangolinfo-authenticated tools and includes clientSource=\"skill\" in tool-call arguments.]\n\n## Skill Version(s):\n\n4.0.0 (source: server release metadata and changelog; artifact frontmatter states 3.1.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\". Covers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go r Skill: pangolinfo-amazon-product-explorer Owner: pangolinfo Summary: Use when: user asks \"what should I sell\" / \"find a blue-ocean niche\" / \"is X category worth entering\" / \"I want to launch a new product in Y\" / \"从 0 到 1 选品\" / \"新品立项\". Covers: GTM scouting SOP — external demand → niche filter → benchmark products → review pain mining → IP clearance → final go/no-go r Tags: latest:4.0.0 Version history: v4.0.0 | 2026-","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":1525,"uniquenessScore":46,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-10T14:54:21.391Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-10T14:54:21.391Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-10T17:37:37.792Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs","url":"https://github.com/CherryHQ/cherry-studio","homepage":"https://cherry-ai.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-11T14:38:40.986Z","createdAt":"2026-02-25T03:38:19.379Z","downloads":null},{"id":"6f6582d0-5d76-4f0f-b81d-86520247950b","entityType":"agent","canonicalPath":"/agent/copilotkit-copilotkit","slug":"copilotkit-copilotkit","name":"CopilotKit","description":"The Frontend for Agents & Generative UI. React + Angular","url":"https://github.com/CopilotKit/CopilotKit","homepage":"https://docs.copilotkit.ai","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-03-25T09:50:57.846Z","createdAt":"2026-02-25T03:39:14.617Z","downloads":null}],"links":{"hub":"/agent","source":"/agent/source/clawhub","protocols":[{"label":"OpenClaw","href":"/agent/protocol/openclew"}]}}}