{"id":"1ce832f1-3c47-443d-a3ca-46f91e483b7f","entityType":"agent","slug":"abdullah4ai-personal-shopper-skill","name":"personal-shopper","canonicalUrl":"https://www.xpersona.co/agent/abdullah4ai-personal-shopper-skill","canonicalPath":"/agent/abdullah4ai-personal-shopper-skill","generatedAt":"2026-10-09T09:56:17.059Z","source":"GITHUB_OPENCLEW","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-04-15T05:21:22.124Z","emptyReason":null},"description":"Smart product research assistant using the Diamond Search methodology (7 specialized agents across 3 layers). USE WHEN: user wants a product recommendation, shopping advice, \"what's the best X\", \"compare X vs Y\", \"find me a good\", product comparison, buying decision help, \"is this a good deal\", \"should I buy X or Y\", \"أبغى أشتري\", \"وش أفضل منتج\", \"قارن لي\", \"ابحث لي عن\", \"مقارنة منتجات\". DON'T USE WHEN: user wants price tracking over time, order placement, returns/refunds help, market analysis for business entry (use mckinsey-research), general web search not about purchasing, reviewing or troubleshooting a product they already own, comparing companies as businesses. EDGE CASES: \"what's the best laptop\" → this skill. \"what's the laptop market size\" → mckinsey-research. \"compare two products by specs\" → this skill. \"compare two companies competitively\" → mckinsey-research. \"is this a good deal on Amazon\" → this skill. \"analyze the deals market\" → mckinsey-research. \"أبغى أشتري لابتوب\" → this skill. \"أبغى أفتح متجر لابتوبات\" → mckinsey-research. Output: Clear product recommendation with best price, availability, alternatives, and expert validation. Success: User gets an actionable buying decision backed by multi-source research, expert analysis, and real pricing. Inputs: Product type, budget (optional), use case, preferences. Tools involved: sessions_spawn (sub-agents), web_search/camofox/exa (search), web_fetch (page extraction). --- name: personal-shopper description: | Smart product research assistant using the Diamond Search methodology (7 specialized agents across 3 layers). USE WHEN: user wants a product recommendation, shopping advice, \"what's the best X\", \"compare X vs Y\", \"find me a good\", product comparison, buying decision help, \"is this a good deal\", \"should I buy X or Y\", \"أبغى أشتري\", \"وش أفضل منتج\", \"قارن لي\", \"ابحث لي عن\", \"مقا","descriptionLabel":"Technical summary","evidenceSummary":"Published capability contract available. No trust telemetry is available yet. Last updated 4/15/2026.","installCommand":"git clone https://github.com/Abdullah4AI/personal-shopper-skill.git","sourceUrl":"https://github.com/Abdullah4AI/personal-shopper-skill","homepage":null,"primaryLinks":[{"label":"View Source","url":"https://github.com/Abdullah4AI/personal-shopper-skill","kind":"source"}],"safetyScore":89,"overallRank":38.5,"popularityScore":0,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"Smart product research assistant using the Diamond Search methodology (7 specialized agents across 3 layers). USE WHEN: user wants a product recommendation, sho"},"coverage":{"evidence":{"source":"capability-contract + public-profile","verified":true,"confidence":"high","updatedAt":"2026-02-24T19:41:56.973Z","emptyReason":null},"protocols":[{"protocol":"MCP","label":"MCP","status":"verified","notes":"Confirmed by published contract metadata."}],"capabilities":[{"label":"use","status":"self-declared"},{"label":"saudi","status":"self-declared"}],"verifiedCount":1,"selfDeclaredCount":2,"capabilityMatrix":{"rows":[{"key":"MCP","type":"protocol","support":"supported","confidenceSource":"contract","notes":"Confirmed by capability contract"},{"key":"use","type":"capability","support":"supported","confidenceSource":"profile","notes":"Declared in agent profile metadata"},{"key":"saudi","type":"capability","support":"supported","confidenceSource":"profile","notes":"Declared in agent profile metadata"}],"flattenedTokens":"protocol:MCP|supported|contract capability:use|supported|profile capability:saudi|supported|profile"}},"adoption":{"evidence":{"source":"no-adoption-signals","verified":false,"confidence":"low","updatedAt":"2026-04-15T05:21:22.124Z","emptyReason":"No source adoption metrics were available."},"stars":0,"forks":0,"downloads":null,"packageName":null,"latestVersion":null,"tractionLabel":null},"release":{"evidence":{"source":"agent-index","verified":false,"confidence":"medium","updatedAt":"2026-03-01T06:03:24.207Z","emptyReason":null},"lastUpdatedAt":"2026-04-15T05:21:22.124Z","lastCrawledAt":"2026-03-01T06:03:24.207Z","lastIndexedAt":null,"nextCrawlAt":"2026-03-02T06:03:24.207Z","lastVerifiedAt":null,"highlights":[]},"execution":{"evidence":{"source":"capability-contract","verified":true,"confidence":"high","updatedAt":"2026-02-24T19:41:56.973Z","emptyReason":null},"installCommand":"git clone https://github.com/Abdullah4AI/personal-shopper-skill.git","setupComplexity":"low","setupSteps":["Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.","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":"ready","authModes":["mcp","api_key"],"requires":["mcp","lang:typescript","streaming"],"forbidden":[],"supportsMcp":true,"supportsA2a":false,"supportsStreaming":true,"inputSchemaRef":"https://github.com/Abdullah4AI/personal-shopper-skill#input","outputSchemaRef":"https://github.com/Abdullah4AI/personal-shopper-skill#output","dataRegion":"global","contractUpdatedAt":"2026-02-24T19:41:56.973Z","sourceUpdatedAt":"2026-02-24T19:41:56.973Z","freshnessSeconds":19577660},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["MCP"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"GITHUB_OPENCLEW","generatedAt":"2026-10-09T09:56:17.058Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/abdullah4ai-personal-shopper-skill/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":[],"safeUseWhen":["Contract is available with explicit auth and schema references.","Trust confidence is not low and verification freshness is acceptable.","Protocol support is explicitly confirmed in contract metadata."],"riskFlags":["trust_data_unavailable"],"operationalConfidence":"medium"},"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":"GITHUB OPENCLEW","verified":false,"confidence":"high","updatedAt":"2026-04-15T05:21:22.124Z","emptyReason":null},"readme":"---\nname: personal-shopper\ndescription: |\n  Smart product research assistant using the Diamond Search methodology (7 specialized agents across 3 layers).\n  USE WHEN: user wants a product recommendation, shopping advice, \"what's the best X\", \"compare X vs Y\",\n  \"find me a good\", product comparison, buying decision help, \"is this a good deal\", \"should I buy X or Y\",\n  \"أبغى أشتري\", \"وش أفضل منتج\", \"قارن لي\", \"ابحث لي عن\", \"مقارنة منتجات\".\n  DON'T USE WHEN: user wants price tracking over time, order placement, returns/refunds help,\n  market analysis for business entry (use mckinsey-research), general web search not about purchasing,\n  reviewing or troubleshooting a product they already own, comparing companies as businesses.\n  EDGE CASES: \"what's the best laptop\" → this skill. \"what's the laptop market size\" → mckinsey-research.\n  \"compare two products by specs\" → this skill. \"compare two companies competitively\" → mckinsey-research.\n  \"is this a good deal on Amazon\" → this skill. \"analyze the deals market\" → mckinsey-research.\n  \"أبغى أشتري لابتوب\" → this skill. \"أبغى أفتح متجر لابتوبات\" → mckinsey-research.\n  Output: Clear product recommendation with best price, availability, alternatives, and expert validation.\n  Success: User gets an actionable buying decision backed by multi-source research, expert analysis, and real pricing.\n  Inputs: Product type, budget (optional), use case, preferences.\n  Tools involved: sessions_spawn (sub-agents), web_search/camofox/exa (search), web_fetch (page extraction).\n---\n\n# Personal Shopper — Diamond Search\n\n## Overview\n\nDiamond Search is a multi-agent product research methodology. It starts narrow (user's request), expands wide (7 agents searching from different angles), then converges to one clear recommendation.\n\n```\n         [BRIEF]\n            |\n    --------+--------\n    |   |    |   |\n   [1] [2]  [3] [4]     ← Search Layer (parallel)\n    |   |    |   |\n    --------+--------\n            |\n        [5]   [6]        ← Expertise Layer (parallel)\n            |\n      [CONVERGENCE]\n            |\n           [7]            ← Price Layer\n            |\n        [OUTPUT]\n```\n\n## Golden Product Criteria\n\nEvery recommendation is evaluated against these 5 criteria:\n\n| # | Criterion | Description |\n|---|-----------|-------------|\n| 1 | Performance | Delivers what's needed for the specific use case |\n| 2 | Value | Price justified by actual (not theoretical) performance |\n| 3 | Availability | In stock locally with warranty and service |\n| 4 | Reliability | Genuine positive reviews from real long-term users |\n| 5 | Timing | Not about to be replaced by a new generation or discontinued |\n\n## The 7 Agents\n\n| # | Agent | Role | Layer |\n|---|-------|------|-------|\n| 1 | Mainstream Research | Top sources: Reddit, YouTube, Wirecutter, RTINGS | Search |\n| 2 | Anti-Bias Research | Reverse search, alternative brands, breaks echo chambers | Search |\n| 3 | Local Market Scanner | Saudi platforms: Amazon.sa, noon, jarir, extra | Search |\n| 4 | Niche Community Diver | Specialized forums, Facebook groups, Discord, small subreddits | Search |\n| 5 | Domain Expert | Judges results with technical expertise (does NOT search) | Expertise |\n| 6 | Latest Tech Tracker | New launches, upcoming models, discontinuations | Expertise |\n| 7 | Price & Deal Hunter | Coupons, cashback, installments, cross-platform price comparison | Price |\n\n> For full agent prompts and methodology details: read `references/diamond-methodology.md`\n\n## Tool Detection & Assignment\n\nBefore spawning search agents, detect which search tools are available in the current environment. Assign tools to agents for maximum coverage.\n\n### Available Tool Categories\n\n| Tool | Best For | Detection |\n|------|----------|-----------|\n| `camofox_*` (Camoufox) | Retailer sites, Amazon, Google Shopping — bypasses bot detection | Check if `camofox_create_tab` is available |\n| `web_search` | Quick broad web search (Brave API) | Check if `web_search` is available |\n| `web_fetch` | Extracting content from specific URLs | Check if `web_fetch` is available |\n| `browser` | General browser automation | Check if `browser` is available |\n| Exa (via MCP/mcporter) | AI-powered semantic search, great for finding expert content | Check if `mcporter` or exa MCP tool is available |\n\n### Tool Assignment Strategy\n\nDistribute tools across agents to avoid redundancy and maximize coverage:\n\n- **Agent 1 (Mainstream):** `web_search` for broad queries + `web_fetch` for review sites + `camofox` for YouTube/Reddit\n- **Agent 2 (Anti-Bias):** `web_search` with reverse queries + Exa for semantic discovery + `camofox` for niche sites\n- **Agent 3 (Local Market):** `camofox` for Saudi retailer sites (Amazon.sa, noon, jarir, extra) — best for bot-protected stores. Fallback: `web_fetch`\n- **Agent 4 (Niche):** Exa for forum/community search + `camofox` for Discord/Facebook + `web_search` for subreddits\n- **Agent 6 (Latest Tech):** `web_search` for recent launches + `web_fetch` for tech news sites\n- **Agent 7 (Price):** `camofox` for live pricing on retailer sites + `web_search` for coupon codes\n\nIf a tool is not available, the agent falls back to the next best option. Every agent can use `web_search` + `web_fetch` as the universal baseline.\n\n**Include this tool availability context in every sub-agent prompt:**\n\n```\nAvailable search tools: {list all available tools}\nPreferred tools for your role: {assigned tools}\nFallback: web_search + web_fetch\n```\n\n## Nested Sub-Agent Pattern\n\nFor complex products with many competing options, search-layer agents (1-4) may spawn their own sub-agents to parallelize across sources. This is the **nested sub-agent** pattern.\n\n**When to use nested sub-agents:**\n- Product category has 10+ viable options (e.g., laptops, headphones, monitors)\n- Multiple distinct source types need deep scraping (e.g., Reddit threads + YouTube reviews + Wirecutter articles)\n- Local market requires checking 4+ retailer sites with live pricing\n\n**Example:** Agent 1 (Mainstream) might spawn:\n- Sub-agent 1a: Search Reddit for \"{product} recommendations\" via `camofox` or `web_search`\n- Sub-agent 1b: Search YouTube reviews via `camofox` or `web_search`\n- Sub-agent 1c: Check Wirecutter/RTINGS via `web_fetch`\n\n**When NOT to nest:** Simple products (cables, basic accessories) or when only 2-3 options exist. Over-parallelizing wastes resources.\n\nThe orchestrating agent decides based on product complexity whether to enable nesting or run flat.\n\n## Workflow (5 Phases)\n\n### Phase 1: BRIEF (sequential)\n\nGather from the user:\n- What product they need\n- Budget (or \"open/flexible\")\n- Primary use case\n- Any preferences or hard requirements\n- Whether they have initial options in mind\n- **Preferred output language** (default: match the language the user wrote in)\n\nIf the user hasn't clarified something important, ask. Do not assume.\n\n**Language handling:**\n- All internal agent work and analysis runs in English for consistency\n- The final output (Phase 5) is delivered in the user's preferred language\n- If the user writes in Arabic, output in Arabic. If English, output in English. If they specify a language, use that.\n\n### Phase 2a: Search Layer (parallel — 4 sub-agents)\n\nSpawn 4 sub-agents in parallel using `sessions_spawn`. Each agent gets:\n1. Team context (its role within the 7-agent team)\n2. Product details from the brief\n3. Available tools and preferred tools for its role\n4. Clear output format requirements\n\n```python\n# Spawn all 4 search agents in parallel\nsessions_spawn(\n  task=\"\"\"You are Agent 1 (Mainstream Research) in a 7-agent product research team. \nYour teammates cover other angles — focus strictly on YOUR role.\n\nProduct: {product}\nBudget: {budget}\nUse case: {use_case}\nUser preferences: {preferences}\n\nYOUR ROLE: Search mainstream, well-known sources for the best options.\nSources: Reddit, YouTube (detailed reviews), Wirecutter, RTINGS, Tom's Guide.\n\nAvailable search tools: {available_tools}\nPreferred tools: web_search for broad queries, web_fetch for review articles, camofox for Reddit/YouTube\nFallback: web_search + web_fetch\n\nINSTRUCTIONS:\n- Focus on reviews from the last 12 months\n- Prefer comparative reviews over single-product reviews\n- Note any clear consensus (same product recommended by multiple sources)\n- If using camofox, navigate to specific review sites and extract key findings\n\nOUTPUT FORMAT:\nFor each recommended product (3-5 max):\n- Product name and model\n- Why it's recommended\n- Source(s) with URLs\n- Key specs relevant to the use case\n- Any noted drawbacks\n\"\"\",\n  label=\"agent-1-mainstream\"\n)\n\n# Similarly spawn agents 2, 3, 4 in parallel (see reference file for full prompts)\n```\n\n**Agent 2 (Anti-Bias):** Reverse search strategies — negative search, lesser-known brands, origin-based search, price-point search, professional community search. Goal: break the echo chamber.\n\n**Agent 3 (Local Market):** Scan Saudi platforms (Amazon.sa, noon.com, jarir.com, extra.com). Check actual prices, availability, seller type, shipping, warranty. Use `camofox` for live pricing if available.\n\n**Agent 4 (Niche Community):** Deep-dive into specialized forums, small subreddits, Facebook groups, Discord servers. Find opinions from power users and professionals, not just reviewers.\n\n> For complete agent prompts: read `references/diamond-methodology.md`\n\nWait for all 4 to complete before proceeding.\n\n### Phase 2b: Expertise Layer (parallel — 2 sub-agents)\n\nSpawn 2 sub-agents in parallel. These receive the combined results from Phase 2a.\n\n**Agent 5 (Domain Expert):** Does NOT search. Analyzes the search results as an expert and answers 5 critical questions:\n1. Do the specs actually serve the user's real-world use case?\n2. What's the real (not on-paper) difference between the options?\n3. Are any specs overkill for the intended use?\n4. What do reviews typically miss that actually matters?\n5. If buying for yourself, what would you choose and why?\n\n**Agent 6 (Latest Tech):** Searches for:\n- Products launched in the last 6 months\n- Recent CES/MWC/IFA announcements\n- Upcoming next-gen launches within 3 months\n- Discontinued or end-of-life products\n- Current vs. previous generation comparison\n\n> For complete prompts and the expert-vs-latest-tech priority rule: read `references/domain-expertise.md`\n\n### Phase 3: Convergence (sequential)\n\nMerge all results and apply convergence rules:\n\n1. **3/4 Consensus:** If 3 of 4 search agents recommended the same product, that's a strong signal — but verify they didn't all rely on the same original source\n2. **Expert Overrides:** Agent 5's analysis overrides Agent 6 when they conflict (expertise > novelty)\n3. **Honesty Rule:** If there's no real alternative with better value, say so plainly\n4. **Timing Override:** If Agent 6 found a new generation launching within weeks at the same price, present this clearly and let the user decide\n5. **Local Price Rules:** A product might be the global best but locally overpriced. Value is determined by the local price.\n\nApply the 5 Golden Product Criteria to each remaining option. Eliminate anything that fails 2+ criteria.\n\n> For detailed convergence rules: read `references/anti-bias-playbook.md`\n\n### Phase 4: Price Layer (sequential — 1 sub-agent)\n\nSpawn a single sub-agent for price optimization:\n\n**Agent 7 (Price & Deal Hunter):** For each finalist product:\n1. Current price on every local platform\n2. Active coupon codes and discounts\n3. Cashback offers (bank cards, cashback apps)\n4. Interest-free installment options (Tamara, Tabby)\n5. Trade-in programs if applicable\n6. Seller verification: official, authorized distributor, or third party\n7. Shipping cost, tax inclusion, delivery time, return policy\n8. **Price Inversion check:** Compare local price vs. international + shipping + customs. Alert if local is 30%+ more expensive.\n\n> For Saudi market pricing patterns and platform details: read `references/market-dynamics.md`\n\n### Phase 5: Output\n\nDeliver the final recommendation in the user's preferred language using this template:\n\n```\n## Recommendation: {product_name}\n\n### Why This Product\n{Explanation grounded in the 5 Golden Product Criteria}\n\n### Quick Comparison\n\n| Product | Price | Platform | Rating | Note |\n|---------|-------|----------|--------|------|\n| ...     | ...   | ...      | ...    | ...  |\n\n### Best Available Deal\n- Platform: {platform}\n- Price: {price}\n- Seller: {seller_type}\n- Coupon: {coupon_if_any}\n- Cashback: {cashback_if_any}\n- Installments: {installment_options}\n\n### Alerts\n- {timing_advice}\n- {price_inversion_warning_if_any}\n- {discontinuation_warning_if_any}\n\n### Alternatives\n1. {alternative_1} — {why_it's_a_good_second_choice}\n2. {alternative_2} — {why_it_serves_a_different_need}\n\n### Sources\n- {list key sources with URLs used across all agents}\n```\n\n**Platform formatting notes:**\n- On Discord/WhatsApp: use bullet lists instead of markdown tables\n- On Telegram: tables render fine in monospace but keep them concise\n- Adapt formatting to the current channel\n\n## Worked Example\n\n**User:** \"I need a USB microphone for podcasting, budget around $150\"\n\n**Brief:** Product=USB microphone, Budget=$150, Use=podcasting, Preferences=none stated\n\n**Search Layer results (summarized):**\n- Agent 1: Shure MV7+ ($249), Audio-Technica AT2020USB-X ($129), Rode NT-USB Mini ($99)\n- Agent 2: Maono PD200X ($79), Fifine K688 ($59) — hidden gems with similar specs\n- Agent 3: Local prices checked, AT2020USB-X available at Jarir for 489 SAR\n- Agent 4: Reddit r/podcasting consensus: AT2020USB-X best at this range; Maono PD200X great budget pick\n\n**Expertise Layer:**\n- Agent 5: For podcasting, dynamic mics (PD200X, MV7+) reject background noise better. AT2020 is condenser — great in treated rooms, problematic otherwise. The PD200X at $79 vs MV7+ at $249 offers 90% of the podcast quality.\n- Agent 6: No major launches expected. MV7+ just updated. Market is stable.\n\n**Convergence:** PD200X wins on value. MV7+ if budget allows. AT2020USB-X only for treated rooms.\n\n**Price Layer:** PD200X on Amazon $79, noon.com 299 SAR (price inversion: +15%, acceptable).\n\n**Output:** Recommends Maono PD200X with MV7+ as premium alternative.\n\n## References\n\n| File | Read When |\n|------|-----------|\n| `references/diamond-methodology.md` | You need full agent prompts, want to customize agent behavior, or need to adjust for product complexity |\n| `references/anti-bias-playbook.md` | You need reverse search strategies, brand evaluation frameworks, or echo chamber detection |\n| `references/domain-expertise.md` | You need the expert's evaluation framework, real-world examples, or the expert-vs-latest-tech priority rule |\n| `references/market-dynamics.md` | You need Saudi market pricing patterns, platform comparison, seller verification, or price inversion detection |\n\n## Implementation Notes\n\n- **Agent count is flexible:** For simple products (USB cable, phone case), use 3 agents: Mainstream + Local Market + Price. Scale agents to match decision complexity.\n- **Don't hardcode models:** Use \"fast model\" for search agents and \"reasoning model\" for analysis. Let the platform choose.\n- **Each sub-agent works independently:** Search-layer agents don't see each other's results. Expertise-layer agents see search results but not each other.\n- **Timeout handling:** If a sub-agent takes too long, proceed with available results. Note which agent's data is missing.\n- **Nested sub-agents are optional:** Only use when product complexity justifies the extra parallelism. The orchestrator decides.\n\n## Core Principle\n\n> The product is anchored on VALUE, not on BRAND.\n> We expand the search horizon so decisions are based on value, not just what shows up first on Google.\n","readmeExcerpt":"--- name: personal-shopper description: | Smart product research assistant using the Diamond Search methodology (7 specialized agents across 3 layers). USE WHEN: user wants a product recommendation, shopping advice, \"what's the best X\", \"compare X vs Y\", \"find me a good\", product comparison, buying decision help, \"is this a good deal\", \"should I buy X or Y\", \"أبغى أشتري\", \"وش أفضل منتج\", \"قارن لي\", \"ابحث لي عن\", \"مقا","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"[BRIEF]\n            |\n    --------+--------\n    |   |    |   |\n   [1] [2]  [3] [4]     ← Search Layer (parallel)\n    |   |    |   |\n    --------+--------\n            |\n        [5]   [6]        ← Expertise Layer (parallel)\n            |\n      [CONVERGENCE]\n            |\n           [7]            ← Price Layer\n            |\n        [OUTPUT]"},{"language":"text","snippet":"Available search tools: {list all available tools}\nPreferred tools for your role: {assigned tools}\nFallback: web_search + web_fetch"},{"language":"python","snippet":"# Spawn all 4 search agents in parallel\nsessions_spawn(\n  task=\"\"\"You are Agent 1 (Mainstream Research) in a 7-agent product research team. \nYour teammates cover other angles — focus strictly on YOUR role.\n\nProduct: {product}\nBudget: {budget}\nUse case: {use_case}\nUser preferences: {preferences}\n\nYOUR ROLE: Search mainstream, well-known sources for the best options.\nSources: Reddit, YouTube (detailed reviews), Wirecutter, RTINGS, Tom's Guide.\n\nAvailable search tools: {available_tools}\nPreferred tools: web_search for broad queries, web_fetch for review articles, camofox for Reddit/YouTube\nFallback: web_search + web_fetch\n\nINSTRUCTIONS:\n- Focus on reviews from the last 12 months\n- Prefer comparative reviews over single-product reviews\n- Note any clear consensus (same product recommended by multiple sources)\n- If using camofox, navigate to specific review sites and extract key findings\n\nOUTPUT FORMAT:\nFor each recommended product (3-5 max):\n- Product name and model\n- Why it's recommended\n- Source(s) with URLs\n- Key specs relevant to the use case\n- Any noted drawbacks\n\"\"\",\n  label=\"agent-1-mainstream\"\n)\n\n# Similarly spawn agents 2, 3, 4 in parallel (see reference file for full prompts)"},{"language":"text","snippet":"## Recommendation: {product_name}\n\n### Why This Product\n{Explanation grounded in the 5 Golden Product Criteria}\n\n### Quick Comparison\n\n| Product | Price | Platform | Rating | Note |\n|---------|-------|----------|--------|------|\n| ...     | ...   | ...      | ...    | ...  |\n\n### Best Available Deal\n- Platform: {platform}\n- Price: {price}\n- Seller: {seller_type}\n- Coupon: {coupon_if_any}\n- Cashback: {cashback_if_any}\n- Installments: {installment_options}\n\n### Alerts\n- {timing_advice}\n- {price_inversion_warning_if_any}\n- {discontinuation_warning_if_any}\n\n### Alternatives\n1. {alternative_1} — {why_it's_a_good_second_choice}\n2. {alternative_2} — {why_it_serves_a_different_need}\n\n### Sources\n- {list key sources with URLs used across all agents}"}],"parameters":{},"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["typescript"],"docsSourceLabel":"GITHUB OPENCLEW","editorialOverview":"Smart product research assistant using the Diamond Search methodology (7 specialized agents across 3 layers). USE WHEN: user wants a product recommendation, shopping advice, \"what's the best X\", \"compare X vs Y\", \"find me a good\", product comparison, buying decision help, \"is this a good deal\", \"should I buy X or Y\", \"أبغى أشتري\", \"وش أفضل منتج\", \"قارن لي\", \"ابحث لي عن\", \"مقارنة منتجات\". DON'T USE WHEN: user wants price tracking over time, order placement, returns/refunds help, market analysis for business entry (use mckinsey-research), general web search not about purchasing, reviewing or troubleshooting a product they already own, comparing companies as businesses. EDGE CASES: \"what's the best laptop\" → this skill. \"what's the laptop market size\" → mckinsey-research. \"compare two products by specs\" → this skill. \"compare two companies competitively\" → mckinsey-research. \"is this a good deal on Amazon\" → this skill. \"analyze the deals market\" → mckinsey-research. \"أبغى أشتري لابتوب\" → this skill. \"أبغى أفتح متجر لابتوبات\" → mckinsey-research. Output: Clear product recommendation with best price, availability, alternatives, and expert validation. Success: User gets an actionable buying decision backed by multi-source research, expert analysis, and real pricing. Inputs: Product type, budget (optional), use case, preferences. Tools involved: sessions_spawn (sub-agents), web_search/camofox/exa (search), web_fetch (page extraction). --- name: personal-shopper description: | Smart product research assistant using the Diamond Search methodology (7 specialized agents across 3 layers). USE WHEN: user wants a product recommendation, shopping advice, \"what's the best X\", \"compare X vs Y\", \"find me a good\", product comparison, buying decision help, \"is this a good deal\", \"should I buy X or Y\", \"أبغى أشتري\", \"وش أفضل منتج\", \"قارن لي\", \"ابحث لي عن\", \"مقا","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":524,"uniquenessScore":60,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-04-15T05:21:22.124Z","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":"2026-04-15T05:21:22.124Z","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":"2026-10-09T09:56:17.059Z","emptyReason":null},"items":[{"id":"a785e560-2661-4edd-a374-5e8de7c54e7a","entityType":"agent","canonicalPath":"/agent/ard-urn-air-io-github-cleandev-fix-mcp-shortlistlens","slug":"ard-urn-air-io-github-cleandev-fix-mcp-shortlistlens","name":"ShortlistLens","description":"Structured website and review evidence for AI-assisted local-business shortlisting.","url":"https://shortlistlens-mcp.streaming22box.workers.dev/mcp","homepage":"https://shortlistlens-mcp.streaming22box.workers.dev/mcp","source":"ARD_REGISTRY","protocols":["MCP"],"capabilities":["mcp","mcp-registry"],"safetyScore":95,"overallRank":93.5,"updatedAt":"2026-10-09T03:15:08.952Z","createdAt":"2026-10-09T02:32:13.915Z","downloads":null},{"id":"b72e3032-816c-4f68-bcc9-1a847aef4d79","entityType":"agent","canonicalPath":"/agent/ard-urn-air-ai-bankee-mcp-inferventis-mcp","slug":"ard-urn-air-ai-bankee-mcp-inferventis-mcp","name":"Inferventis MCP Server","description":"Loan & mortgage calculator, compound interest, ROI, crypto prices, FX conversion for AI agents.","url":"https://mcp-server-295985738387.europe-west1.run.app/mcp","homepage":"https://mcp-server-295985738387.europe-west1.run.app/mcp","source":"ARD_REGISTRY","protocols":["MCP"],"capabilities":["mcp","mcp-registry"],"safetyScore":95,"overallRank":93.5,"updatedAt":"2026-10-09T03:15:08.952Z","createdAt":"2026-10-09T02:32:16.798Z","downloads":null},{"id":"03e8460e-4e22-46ea-967a-1f32180fd24c","entityType":"agent","canonicalPath":"/agent/ard-urn-air-io-github-cyanheads-mcp-reliefweb-mcp-server","slug":"ard-urn-air-io-github-cyanheads-mcp-reliefweb-mcp-server","name":"io.github.cyanheads/reliefweb-mcp-server","description":"Search ReliefWeb humanitarian reports, disasters, jobs, training, and country profiles via MCP.","url":"https://reliefweb.caseyjhand.com/mcp","homepage":"https://reliefweb.caseyjhand.com/mcp","source":"ARD_REGISTRY","protocols":["MCP"],"capabilities":["mcp","mcp-registry"],"safetyScore":95,"overallRank":93.5,"updatedAt":"2026-10-09T03:15:08.952Z","createdAt":"2026-10-09T02:32:11.316Z","downloads":null},{"id":"2d0ce267-ccff-48fc-bef6-6ddad9c50d20","entityType":"agent","canonicalPath":"/agent/ard-urn-air-io-github-deesmo-mcp-arch-tools-mcp","slug":"ard-urn-air-io-github-deesmo-mcp-arch-tools-mcp","name":"io.github.Deesmo/arch-tools-mcp","description":"63 production tools for AI agents — one API key, pay per call in USDC (x402) or Stripe credits.","url":"https://archtools.dev/mcp","homepage":"https://archtools.dev/mcp","source":"ARD_REGISTRY","protocols":["MCP"],"capabilities":["mcp","mcp-registry"],"safetyScore":95,"overallRank":93.5,"updatedAt":"2026-10-09T03:15:08.952Z","createdAt":"2026-10-09T02:32:09.920Z","downloads":null}],"links":{"hub":"/agent","source":"/agent/source/github_openclew","protocols":[{"label":"MCP","href":"/agent/protocol/mcp"}]}}}