{"id":"3fb4976c-c544-47a0-b8f1-2df655fd7814","entityType":"agent","slug":"clawhub-gechengling-clawhub-skill-optimizer","name":"Clawhub Skill Optimizer","canonicalUrl":"https://www.xpersona.co/agent/clawhub-gechengling-clawhub-skill-optimizer","canonicalPath":"/agent/clawhub-gechengling-clawhub-skill-optimizer","generatedAt":"2026-10-11T03:55:08.489Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-11T01:49:24.778Z","emptyReason":null},"description":"AI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to boost downloads and GitHub stars. Skill: Clawhub Skill Optimizer Owner: gechengling Summary: AI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to boost downloads and GitHub stars. Tags: ai-agent:1.0.0, chinese-market:1.0.0, clawhub:1.0.0, clawhub-skill-optimizer:2.1.2, downloads:1.0.0, github-strategy:1.0.0, latest:2.1.2, optimization:1.0.0, review-analysis:1.0.0, seo:1.0.0, skill-growth:1.0.0, st","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. 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GitHub-Style Stars Growth Strategy'整节重复（合并为单节并保留操作提示）；修复示例代码语法错误（generate_video_script 参数 skill_slug 缺类型声明与逗号）；修复安全声明与正文含代码示例的自相矛盾（'No executable code'改为说明代码块为教学示例）；Section 0 平台动态更新至 2026-09-15 并扩充至11项；新增 Section 0b 发布前一致性自检表；新增常见误用与纠偏表、内容质量评分表（六维加权）、报告落地清单；更新过时术语（银保监→监管合规表述）；内部文件路径引用改为出版友好表述；发布检查清单扩充至15项\n\nv2.1.1 | 2026-06-02T15:21:03.793Z | auto\n\n- Added explicit user confirmation step before providing optimization suggestions, enhancing privacy and control.\n- Clarified that all Python code is for educational reference only and does not execute automatically.\n- Expanded data privacy and external API usage warnings, especially for trending topic tracking.\n- Updated capabilities to include code-examples-reference.\n- Removed the obsolete skill-card.md file.\n\nv2.1.0 | 2026-06-02T14:38:59.489Z | auto\n\n- Added stricter, more explicit trigger rules—skill now activates only for ClawHub-specific optimization requests, not generic SEO or trending queries.\n- Improved security and privacy notice: clarified data handling, no code execution, and no collection/storage of user data.\n- Updated version from 2.0.0 to 2.0.1 and removed skill-card.md file.\n- No changes to core optimization engines or analysis capabilities; documentation and guardrails only.\n\nv2.0.2 | 2026-06-01T22:34:52.458Z | user\n\nSecurity compliance: fixed garbled text, added capability declarations and advisory-only disclaimers; enriched content with detailed steps, rules, and report templates\n\nv2.0.1 | 2026-06-01T15:10:51.386Z | user\n\nSecurity compliance update: added capability declarations and advisory-only disclaimers to meet ClawHub security scan requirements\n\nv2.0.0 | 2026-05-11T07:02:11.944Z | auto\n\nClawHub Skill Growth Engine v2.0.0 introduces major platform-driven growth optimizations and new feature support:\n\n- Added 2026 platform and SEO trends, including AI Agent, MCP (Model Context Protocol), and Long Context RAG support.\n- Now recommends video scripts, thumbnail prompt generation, and cross-platform social media syndication strategies for skill promotion.\n- Updated review analysis to detect requests for video tutorials, MCP integration, long-context handling, and social marketing.\n- Expanded trigger keywords and optimized description for video SEO, Chinese core terms, and social media integration.\n- Included a \"Latest ClawHub Platform Updates\" section for actionable, real-time growth insights.\n\nv1.0.0 | 2026-05-04T15:02:58.393Z | user\n\nInitial release: ClawHub Skill Growth Engine — SEO title/description optimizer, real-time trending topic tracker (40+ platforms), GitHub-style stars strategy, bilingual README generator, review analysis engine with sentiment scoring. Built for ClawHub skill creators who want to maximize downloads and GitHub-style stars.\n\nArchive index:\n\nArchive v2.1.2: 3 files, 16723 bytes\n\nFiles: skill-card.md (2199b), SKILL.md (34987b), _meta.json (142b)\n\nFile v2.1.2:SKILL.md\n\n---\nname: ClawHub Skill Growth Engine\ndescription: AI-powered ClawHub skill growth optimizer v2.0 — analyzes reviews, tracks 2026 trending topics (AI Agent, MCP, video SEO), rewrites titles/descriptions for maximum downloads and stars. Supports video thumbnail prompts, cross-platform social syndication, and growth metrics tracking. Triggers: clawhub optimization, skill growth, SEO, stars, downloads, review analysis, trending keywords, skill improvement, GitHub stars strategy, video SEO, social media integration, thumbnail generation.\nslug: clawhub-skill-optimizer\nversion: 2.1.2\nallowed-tools: []\ncapabilities:\n  - educational-reference\n  - advisory-only\n  - requires-human-review\n  - code-examples-reference\n---\n\n# ClawHub Skill Growth Engine / ClawHub技能热度增长引擎\n\n> **⚠️ SECURITY NOTICE / 安全声明**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **Code blocks in this document are illustrative teaching examples** — they show the logic and\n>   structure of an approach; nothing here is executed, and no scripts, binaries, or installers\n>   are bundled with this skill.\n> - **No persistent storage, network calls, background execution, or credential collection**\n> - **All outputs are for reference only and require human review before real-world application**\n> - **This skill does NOT provide financial, legal, or insurance advice**\n> - **Users must exercise their own judgment and consult qualified professionals**\n>\n> **⚠️ 数据安全警告**\n> - 本技能仅提供ClawHub技能优化策略的参考框架，**不执行任何代码或脚本**\n> - 文中的Python代码为**教学参考示例**，展示逻辑概念，不会自动执行\n> - 文中的API调用（如Google Trends、微博热搜、GitHub Trending）为**外部服务引用**，用户如实际调用需自行评估数据隐私风险：查询关键词、IP地址、时间戳等信息将被发送至第三方平台\n> - 不收集、不存储用户的任何平台数据、技能代码或个人信息\n> - 热度分析和SEO建议基于公开信息，实际效果因平台算法变化而异\n> - 不保证任何优化策略能带来具体的下载量或Star数增长\n> - **用户确认要求**：所有涉及修改技能元数据或发布社交媒体内容的建议，均需用户自行手动操作并确认\n\n\n\n> **English:** AI-powered growth engine for ClawHub skills — analyze user reviews, track global trending topics, and rewrite your skill metadata (title, description, tags) to maximize downloads and GitHub-style stars.\n>\n> **中文:** ClawHub技能热度增长引擎——分析用户评论、追踪全网热点、优化技能标题与描述，一站式提升下载量与Star数。\n\n## Trigger Keywords / 触发关键词\n\n**⚠️ 精确触发规则**：仅当用户明确提到ClawHub技能增长/优化需求时激活。日常对话中提及\"SEO\"、\"下载量\"、\"stars\"、\"热度\"等通用词汇时**不会自动触发**，必须与**ClawHub技能优化**直接关联。\n\n**用户确认规则**：匹配以下关键词时，需先向用户确认后再进入优化模式：\n- \"您需要ClawHub技能优化建议吗？\"\n- 仅在用户明确确认后，才提供具体分析和建议\n\n激活关键词（需用户确认后生效）：\n\n- ClawHub 优化 / clawhub 技能增长 / 技能热度提升\n- 技能下载量提升 / 技能曝光优化 / 技能SEO\n- 评论分析 / 用户反馈分析 / review 分析\n- 热点追踪 / 热搜分析 / 趋势挖掘\n- 标题优化 / description 优化 / 关键词优化\n- skill 改进 / 技能改进 / 提升关注度\n\n## Section 0: Latest ClawHub Platform Updates (2026-09-15)\n\n| 更新日期 | 平台/趋势 | 对技能优化的影响 | 推荐动作 |\n|---------|-----------|----------------|---------|\n| 2026-09 | 技能数量持续增长，同类竞争加剧 | 仅靠关键词堆砌难以获得持续曝光 | 内容深度 + 差异化定位优先于词藻 |\n| 2026-08 | 平台对技能内容与声明一致性审查趋严 | 声明与正文矛盾会被标记待审 | 发布前核对能力声明与正文是否一致 |\n| 2026-07 | 中文技能检索权重继续提升 | 中文 description 前 30 字含核心关键词更有效 | 中文关键词前置，避免先写英文长句 |\n| 2026-06 | 长上下文与文档处理类需求稳定 | 长文档类技能更看重结构化输出示例 | 补充输入输出示例与边界说明 |\n| 2026-05 | AI Agent 成为 ClawHub 下载量最大品类 | 标题含 \"AI Agent\" 可提升曝光 | 含 AI Agent 关键词的技能优先更新 |\n| 2026-05 | MCP (Model Context Protocol) 生态活跃 | MCP 相关技能搜索量显著增长 | 技能 description 加入 MCP 关键词 |\n| 2026-05 | OpenClaw 版本迭代，技能生态繁荣 | 竞争加剧 | 差异化描述 + 社媒推广成刚需 |\n| 2026-04 | 视频内容（小红书/抖音/B站）成推广主战场 | 带视频演示的技能互动更高 | 技能增加视频脚本 + 缩略图提示词 |\n| 2026-04 | 中国市场（DeepSeek/通义/Kimi）热度高 | 中文技能 SEO 权重提升 | 中文 description 前 30 字含核心关键词 |\n| 2026-03 | Long Context RAG（100K–2M token）成热点 | 长文档分析类技能需求爆发 | 精算/财报/法律类技能 description 强化 RAG |\n| 2026-03 | 银行保险监管合规类技能需求稳定增长 | 合规类技能长尾流量稳定 | 合规类技能持续更新法规版本 |\n\n> 说明：以上为平台与生态层面的观察与经验总结，具体平台规则与算法**以官方最新发布为准**。\n\n### Section 0b: 优化前的一致性自检（发布前必做）\n\n| 自检项 | 检查方式 | 不一致的后果 |\n|-------|---------|-------------|\n| 能力声明 vs 正文 | 声明\"无代码\"时正文是否真的没有代码块 | 被判定声明与内容矛盾，进入待审 |\n| 触发词范围 | 触发词是否只覆盖本技能职责范围 | 触发过宽会被要求收窄 |\n| 版本号一致 | frontmatter 版本与线上最新版本是否衔接 | 发布被拒或版本回退 |\n| 篇幅完整度 | 是否只改版本号就提交 | 被判定无实质更新，审核不通过 |\n| 链接有效性 | 正文引用的文件路径是否真实存在 | 断链，影响使用体验 |\n| 术语时效 | 机构名、法规名是否为现行表述 | 专业性受损，可能触发合规审查 |\n\n---\n\n## Core Capabilities / 核心能力\n\n### 1. User Review & Feedback Analysis Engine\n/ 用户评论与反馈分析引擎\n\nAnalyze user reviews, feedback, and usage data to extract actionable improvement suggestions.\n\n**Analysis Dimensions:**\n\n| Dimension | What It Detects | Action |\n|-----------|----------------|--------|\n| **Feature Requests** | Users asking for capabilities the skill lacks | Add missing modules to SKILL.md |\n| **Pain Points** | Frustration or confusion signals in reviews | Simplify instructions, add examples |\n| **Competitor Mentions** | Users comparing to other tools | Add differentiation points |\n| **Localization Gaps** | Non-Chinese users struggling (language barriers) | Add English README + bilingual docs |\n| **Pricing/Access Issues** | Access friction, download barriers | Optimize onboarding flow |\n| **Emotional Signals** | Excitement/disappointment in wording | Prioritize highly-praised features |\n| **Video/Social Requests** | \"能不能出个视频教程\" / \"想要小红书推广\" | Add video script + thumbnail prompts |\n| **MCP/Integration Gaps** | \"能否对接XX工具\" / \"支持MCP吗\" | Add MCP integration section |\n| **Long Context Needs** | \"处理长文档时卡住\" / \"支持XX万字吗\" | Add context window optimization |\n\n**Review Analysis Code:**\n\n```python\nimport re\nfrom collections import Counter\n\ndef analyze_reviews(reviews: list[str]) -> dict:\n    \"\"\"\n    Analyze user reviews and extract actionable insights.\n    reviews: list of review texts\n    Returns: dict with categorized insights\n    \"\"\"\n    positive_keywords = [\n        \"great\", \"amazing\", \"love\", \"perfect\", \"useful\", \"helpful\",\n        \"强大\", \"好用\", \"实用\", \"完美\", \"赞\", \"棒\", \"优秀\"\n    ]\n    negative_keywords = [\n        \"confusing\", \"broken\", \"bug\", \"missing\", \"wrong\",\n        \"复杂\", \"难用\", \"没用\", \"问题\", \"错误\", \"缺东西\"\n    ]\n    feature_request_patterns = [\n        r\"wish.*could\", r\"would be nice\", r\"should have\",\n        r\"建议\", r\"希望有\", r\"能否加入\", r\"期待\"\n    ]\n\n    results = {\n        \"positive_signals\": [],\n        \"negative_signals\": [],\n        \"feature_requests\": [],\n        \"keywords\": Counter()\n    }\n\n    for review in reviews:\n        text_lower = review.lower()\n        # Detect sentiment signals\n        for kw in positive_keywords:\n            if kw in text_lower:\n                results[\"positive_signals\"].append(review)\n                break\n        for kw in negative_keywords:\n            if kw in text_lower:\n                results[\"negative_signals\"].append(review)\n                break\n        # Detect feature requests\n        for pattern in feature_request_patterns:\n            if re.search(pattern, text_lower):\n                results[\"feature_requests\"].append(review)\n                break\n        # Word frequency (simple tokenizer)\n        words = re.findall(r'\\b\\w{3,}\\b', text_lower)\n        results[\"keywords\"].update(w for w in words if len(w) > 3)\n\n    return results\n```\n\n**Output Format:**\n\n```markdown\n## Review Analysis Report\n\n### 🔥 Top 5 Praised Features\n1. [Feature] — mentioned X times\n2. ...\n\n### 💡 Top 5 Feature Requests\n1. [Request] — mentioned X times → Priority: HIGH/MEDIUM/LOW\n2. ...\n\n### ⚠️ Top 5 Pain Points\n1. [Pain point] — urgency: CRITICAL/HIGH/MEDIUM\n2. ...\n\n### 📊 Keyword Frequency (Top 20)\n| Keyword | Count | Sentiment |\n|---------|-------|-----------|\n| XXX     | 123   | Positive  |\n| ...     | ...   | ...       |\n\n### 🎯 Recommended Actions\n1. **[HIGH]** Add [missing feature] to address [request]\n2. **[MEDIUM]** Simplify [confusing part] based on [pain point]\n3. **[LOW]** Add [example/tutorial] to reduce confusion\n```\n\n---\n\n### 2. Trending Topic Tracker\n/ 全网热点追踪引擎\n\nMonitor trending topics across 40+ platforms to identify hot keywords that can boost skill visibility.\n\n**Supported Data Sources:**\n\n| Source | API/Endpoint | Data | Use Case |\n|--------|-------------|------|----------|\n| Weibo Hot Search | `uapis.cn` | Real-time热搜 | China trending |\n| Zhihu Hot | `uapis.cn` | 知乎热榜 | Tech discussions |\n| Bilibili Trending | `uapis.cn` | B站热搜 | Youth/tech audience |\n| GitHub Trending | `github.com/trending` | GitHub热门 | Developer tools |\n| WeChat Index | Tencent API | 微信指数 | China ecosystem |\n| Baidu Index | `index.baidu.com` | 百度指数 | Search trends |\n| Google Trends | `trends.google.com` | Global trends | International |\n| Product Hunt | `producthunt.com` | PH热榜 | Global startup tools |\n\n**Trending Data Fetching Code:**\n\n```python\nimport requests\nimport json\n\ndef fetch_weibo_trending(limit: int = 20) -> list[dict]:\n    \"\"\"Fetch real-time Weibo hot search topics.\"\"\"\n    url = \"https://uapis.cn/api/hotboard\"\n    params = {\"type\": \"weibo\", \"limit\": limit}\n    try:\n        resp = requests.get(url, params=params, timeout=10)\n        data = resp.json()\n        return [\n            {\"rank\": i+1, \"title\": item.get(\"title\", \"\"),\n             \"hot\": item.get(\"hot\", \"\"), \"url\": item.get(\"url\", \"\")}\n            for i, item in enumerate(data.get(\"data\", [])[:limit])\n        ]\n    except Exception as e:\n        return [{\"error\": str(e)}]\n\ndef fetch_github_trending(lang: str = \"python\", limit: int = 10) -> list[dict]:\n    \"\"\"Fetch GitHub trending repositories.\"\"\"\n    url = f\"https://api.github.com/search/repositories\"\n    params = {\n        \"q\": f\"language:{lang}+created:>2025-01-01\",\n        \"sort\": \"stars\", \"order\": \"desc\", \"per_page\": limit\n    }\n    headers = {\"Accept\": \"application/vnd.github.v3+json\"}\n    try:\n        resp = requests.get(url, params=params, headers=headers, timeout=10)\n        data = resp.json()\n        return [\n            {\"name\": item[\"name\"], \"stars\": item[\"stargazers_count\"],\n             \"description\": item[\"description\"], \"url\": item[\"html_url\"]}\n            for item in data.get(\"items\", [])[:limit]\n        ]\n    except Exception as e:\n        return [{\"error\": str(e)}]\n\ndef google_trends_suggestions(keyword: str) -> list[str]:\n    \"\"\"Get related queries from Google Trends.\"\"\"\n    # Using pytrends library\n    from pytrends.request import TrendReq\n    pytrends = TrendReq(hl='en-US', tz=360)\n    pytrends.build_payload([keyword], cat=0, timeframe='today 3-m', geo='')\n    related = pytrends.related_queries()\n    suggestions = []\n    for kw_list in related.values():\n        for item in kw_list.get('top', []) if kw_list else []:\n            suggestions.append(item['query'])\n    return suggestions[:10]\n```\n\n**Trending Keyword Mapping for Skills:**\n\n```python\nTRENDING_MAPPING = {\n    # 2026 AI/LLM trends → skill keyword suggestions\n    \"DeepSeek\": [\"DeepSeek\", \"LLM\", \"AI agent\", \"Chinese AI\", \"open-source LLM\"],\n    \"AI Agent\": [\"AI Agent\", \"workflow automation\", \"autonomous AI\", \"MCP\"],\n    \"Claude\": [\"Claude\", \"Anthropic\", \"context window\", \"reasoning\", \"long context\"],\n    \"MCP\": [\"MCP\", \"Model Context Protocol\", \"tool integration\", \"AI agent tools\"],\n    \"Stock Market\": [\"A-share\", \"quantitative trading\", \"technical analysis\", \"缠论\", \"量化\"],\n    \"Insurance\": [\"insurance tech\", \"insurtech\", \"risk management\", \"C-ROSS\", \"NFRA\"],\n    \"Content Creation\": [\"AI video\", \"short video\", \"social media AI\", \"video SEO\", \"thumbnail\"],\n    \"Productivity\": [\"workflow automation\", \"efficiency\", \"productivity tools\", \"RAG\", \"long context\"],\n    \"Compliance\": [\"bank compliance\", \"NFRA\", \"Basel III\", \"AML\", \"PIPL\", \"regulatory\"],\n    \"Actuarial\": [\"actuarial pricing\", \"C-ROSS II\", \"IFRS 17\", \"HKFRS 17\", \"life table 2025\"],\n}\n\ndef map_trending_to_skill(trending_topics: list[str], skill_tags: list[str]) -> list[dict]:\n    \"\"\"Map trending topics to skill tags for SEO boost.\"\"\"\n    suggestions = []\n    for topic in trending_topics:\n        for trend, keywords in TRENDING_MAPPING.items():\n            if trend.lower() in topic.lower():\n                for kw in keywords:\n                    if kw not in skill_tags:\n                        suggestions.append({\n                            \"trend\": topic,\n                            \"suggested_tag\": kw,\n                            \"priority\": \"HIGH\" if len(suggestions) < 5 else \"MEDIUM\"\n                        })\n    return suggestions[:10]\n```\n\n---\n\n### 3. SEO Title & Description Optimizer\n/ SEO标题与描述优化器\n\nRewrite skill titles and descriptions using proven SEO frameworks to maximize search visibility and click-through rate.\n\n**Title Optimization Framework:**\n\n| Principle | English Example | Chinese Example |\n|-----------|----------------|----------------|\n| **Front-load value** | \"AI Insurance Claims Analyzer\" | \"保险理赔AI专家\" |\n| **Include keyword** | \"Stock Technical Analysis\" | \"A股技术分析\" |\n| **Show outcome** | \"Increase Downloads 10x\" | \"提升下载量\" |\n| **Use numbers** | \"5-Step Process\" | \"7大核心能力\" |\n| **Be specific** | \"China Insurance C-ROSS Actuarial\" | \"偿二代精算定价\" |\n| **Evoke emotion** | \"Stop Losing Money\" | \"告别选号盲目\" |\n\n**Description Structure (AIDA Framework):**\n\n```\nA - Attention:    [Bold hook: \"The ONLY ClawHub skill that...\"]\nI - Interest:     [Specific problem + your unique solution]\nD - Desire:       [Concrete results: \"Used by 500+ analysts\"]\nA - Action:       [Clear CTA: \"Install now and...\"]\n```\n\n**Title Rewrite Examples:**\n\n| Original (Chinese) | Optimized (English) | Optimized (Chinese) | Stars Impact |\n|--------------------|--------------------|--------------------|--------------|\n| 招投标文书助手 | Enterprise Bid Document AI | 企业招投标文书AI助手 | ⭐⭐⭐ |\n| 保险反欺诈 | Insurance Anti-Fraud Pro | 保险反欺诈分析专家 | ⭐⭐⭐⭐ |\n| 缠论技术分析 | Chanlun Technical Analysis Engine | 缠论技术分析引擎 | ⭐⭐⭐⭐ |\n| 彩票预测 | Lottery Data Analysis & Number Generator | 彩票数据分析选号助手 | ⭐⭐ |\n\n**Tag Optimization:**\n\n```python\ndef optimize_tags(current_tags: list[str], trending_keywords: list[str],\n                  competitors: list[str]) -> dict:\n    \"\"\"\n    Optimize skill tags for maximum discoverability.\n    \"\"\"\n    must_have = [\"clawhub\", \"skill\", \"ai-agent\"]  # Always include\n    high_value = [\"python\", \"api\", \"automation\", \"analysis\", \"tool\"]\n    trending = [kw for kw in trending_keywords if kw not in current_tags][:5]\n    competitor_tags = [t for t in competitors if t not in current_tags][:3]\n\n    optimized = must_have + high_value + trending + competitor_tags\n    optimized = list(dict.fromkeys(optimized))[:20]  # Dedupe, max 20\n\n    return {\n        \"current_tags\": current_tags,\n        \"recommended_tags\": optimized,\n        \"new_tags_added\": [t for t in optimized if t not in current_tags],\n        \"tags_removed\": [t for t in current_tags if t not in optimized],\n        \"seo_score_improvement\": f\"+{len([t for t in optimized if t not in current_tags]) * 5}%\",\n    }\n```\n\n---\n\n### 4. Video Thumbnail & AI Preview Generation\n/ AI视频缩略图生成与预览优化\n\nGenerate compelling skill preview visuals and AI video scripts for social media promotion.\n\n**Thumbnail Prompt Framework:**\n\n| Platform | Thumbnail Style | Prompt Template |\n|----------|---------------|----------------|\n| 小红书 | 高对比度大字+真人/产品图 | \"Bold Chinese text 'XX', close-up screenshot of [UI], gradient background #hex, 3:4 ratio\" |\n| 抖音 | 强冲击+情绪化画面 | \"Explosion effect, bold text 'XX', dramatic lighting, 9:16 vertical, trending color palette\" |\n| B站 | 知识感+人物出镜 | \"Clean desk setup, person pointing at screen, code editor visible, warm lighting, 16:9\" |\n| 微信 | 简约商务风 | \"Minimal flat design, skill icon centered, subtle gradient, 2:1 ratio, Chinese + English title\" |\n\n**AI Thumbnail Generation Code:**\n\n```python\nfrom openai import OpenAI\nimport json\n\ndef generate_thumbnail_prompt(skill_name: str, target_platform: str,\n                               highlight: str, style: str = \"modern\") -> str:\n    \"\"\"Generate optimized thumbnail prompt for skill promotion.\"\"\"\n    platform_configs = {\n        \"小红书\": {\"ratio\": \"3:4\", \"color\": \"vibrant coral + white\", \"text_pos\": \"top-center\"},\n        \"抖音\": {\"ratio\": \"9:16\", \"color\": \"neon purple + cyan\", \"text_pos\": \"center\"},\n        \"B站\": {\"ratio\": \"16:9\", \"color\": \"dark blue + gold\", \"text_pos\": \"bottom-left\"},\n        \"微信\": {\"ratio\": \"2:1\", \"color\": \"minimal white + blue\", \"text_pos\": \"center-bottom\"}\n    }\n    cfg = platform_configs.get(target_platform, platform_configs[\"小红书\"])\n\n    return (\n        f\"Professional skill preview thumbnail for '{skill_name}', \"\n        f\"highlight: {highlight}. Style: {style}. \"\n        f\"Aspect ratio {cfg['ratio']}, color scheme {cfg['color']}. \"\n        f\"Large bold text '{skill_name}' at {cfg['text_pos']}. \"\n        f\"Clean, modern, high contrast, suitable for {target_platform}.\"\n    )\n\ndef generate_video_script(skill_name: str, skill_slug: str,\n                          duration_sec: int = 60) -> dict:\n    \"\"\"\n    Generate 1-minute video script with 4 acts (15s each).\n    Returns dict with act breakdown and AI image prompts.\n    \"\"\"\n    return {\n        \"title\": f\"【技能推荐】{skill_name} — 3分钟上手指南\",\n        \"duration\": f\"{duration_sec}秒\",\n        \"hook\": f\"XX秒就能搞定的{skill_name}技能，效率提升10倍！\",\n        \"acts\": [\n            {\"act\": 1, \"seconds\": \"0-15s\",\n             \"scene\": \"Hook — 痛点场景\",\n             \"narration\": f\"还在为XX问题头疼？{skill_name}帮你一键解决！\",\n             \"visual_prompt\": f\"Stressed person at desk, messy data, red alerts, warm lighting, cinematic\"},\n            {\"act\": 2, \"seconds\": \"15-30s\",\n             \"scene\": \"Demo — 技能展示\",\n             \"narration\": \"看，这是它的核心功能，我只需要输入XX，就能得到XX。\",\n             \"visual_prompt\": f\"Screen recording UI of {skill_name}, clean interface, smooth animation, cursor clicking\"},\n            {\"act\": 3, \"seconds\": \"30-45s\",\n             \"scene\": \"Result — 效果对比\",\n             \"narration\": \"对比一下：原来要XX分钟，现在只要XX秒，效率提升太明显了！\",\n             \"visual_prompt\": f\"Split screen: left messy slow process, right clean fast result, dramatic contrast lighting\"},\n            {\"act\": 4, \"seconds\": \"45-60s\",\n             \"scene\": \"CTA — 行动号召\",\n             \"narration\": f\"安装命令：npx clawhub install @yourname/{skill_slug}，马上试试！\",\n             \"visual_prompt\": f\"Large text overlay '{skill_name}', install command shown, QR code, clean blue gradient\"}\n        ]\n    }\n```\n\n**Video Script Template (Markdown):**\n\n```markdown\n## 🎬 {Skill Name} 视频推广脚本 ({duration}秒)\n\n### Act 1: 钩子 (0-{d1}s)\n- **画面**: [visual_prompt]\n- **配音**: {hook_narration}\n- **字幕**: [大字突出痛点关键词]\n\n### Act 2: 演示 ({d1}-{d2}s)\n- **画面**: [screen recording showing skill in action]\n- **配音**: [step-by-step usage walkthrough]\n- **字幕**: [关键操作步骤]\n\n### Act 3: 效果 ({d2}-{d3}s)\n- **画面**: Before/After comparison, data visualization\n- **配音**: [quantified improvement]\n- **字幕**: [数字: 效率提升XX倍/节省XX时间]\n\n### Act 4: 行动号召 ({d3}-{duration}s)\n- **画面**: Install command + QR code\n- **配音**: [enthusiastic CTA]\n- **字幕**: npx clawhub install @yourname/{slug}\n```\n\n---\n\n### 4b. Cross-Platform Social Syndication Strategy\n/ 跨平台社交媒体推广策略\n\nDesign multi-platform promotion campaigns for maximum reach.\n\n**Platform-Specific SEO Matrix:**\n\n| Platform | Title Style | Description Length | Keyword Density | CTA Format |\n|----------|-------------|-------------------|-----------------|------------|\n| 小红书 | 中文感叹句，含数字 | 300-500字 | 高 | 评论区置顶安装命令 |\n| 抖音 | 悬念式/对比式 | 视频字幕为主 | 中 | 评论区引导 |\n| B站 | 知识干货型 | 800-2000字 | 低 | 简介区链接 |\n| 知乎 | 深度分析型 | 1000-3000字 | 低 | 专栏文章链接 |\n| 微信公众号 | 商务正式型 | 500-1000字 | 中 | 文末二维码 |\n\n**Social Proof Framework:**\n\n```python\nSOCIAL_PROOF_TYPES = {\n    \"download_count\": {\"format\": \"🔥 X万次安装\", \"impact\": \"HIGH\"},\n    \"star_count\": {\"format\": \"⭐ X千Star\", \"impact\": \"HIGH\"},\n    \"user_testimonial\": {\"format\": \"用户说：'...'\", \"impact\": \"VERY_HIGH\"},\n    \"media_mention\": {\"format\": \"被XX媒体报道\", \"impact\": \"MEDIUM\"},\n    \"award_badge\": {\"format\": \"🏆 ClawHub热门技能\", \"impact\": \"MEDIUM\"},\n    \"update_freshness\": {\"format\": \"✅ 今日更新\", \"impact\": \"HIGH\"},\n}\n\ndef build_social_proof_badge(skill_data: dict) -> str:\n    \"\"\"Build multi-element social proof string.\"\"\"\n    badges = []\n    if skill_data.get(\"downloads\", 0) > 1000:\n        badges.append(f\"🔥 {skill_data['downloads']//1000}万+安装\")\n    if skill_data.get(\"stars\", 0) > 100:\n        badges.append(f\"⭐ {skill_data['stars']//1000}千Star\")\n    if skill_data.get(\"last_updated_days\", 99) < 7:\n        badges.append(\"✅ 近期更新\")\n    if skill_data.get(\"is_top_rated\"):\n        badges.append(\"🏆 热门推荐\")\n    return \" | \".join(badges) if badges else \"\"\n```\n\n**A/B Testing Framework for Titles:**\n\n```python\ndef generate_title_variants(original: str, skill_domain: str) -> list[dict]:\n    \"\"\"Generate 3-5 title variants for A/B testing.\"\"\"\n    templates = {\n        \"insurance\": [\n            f\"【保险人必装】{original} — 效率提升10倍\",\n            f\"保险{original}专家版：XX分钟搞定XX\",\n            f\"不想加班？{original}让保险工作自动化\",\n        ],\n        \"bank\": [\n            f\"银行人专属{original}，合规效率双提升\",\n            f\"【合规必备】{original} — 监管合规利器\",\n        ],\n        \"trading\": [\n            f\"{original}：XX个指标一键分析，炒股不迷茫\",\n            f\"看盘神器！{original}让技术分析零门槛\",\n        ],\n        \"default\": [\n            f\"【AI工具】{original} — 3分钟上手教程\",\n            f\"{original}：提升效率XX倍的秘密武器\",\n        ]\n    }\n    variants = templates.get(skill_domain, templates[\"default\"])\n    return [{\"variant\": v, \"approach\": t} for v, t in zip(variants, [\n        \"数字+感叹词\", \"领域专属词\", \"痛点引导\"\n    ][:len(variants)])]\n```\n\n---\n\n### 5. GitHub-Style Stars Growth Strategy\n/ GitHub式Star增长策略\n\nApply proven open-source project growth tactics to ClawHub skills.\n\n**Strategy Framework:**\n\n| Strategy | Implementation | Expected Impact |\n|----------|---------------|----------------|\n| **README Quality** | First 5 lines = summary. Clear \"What/Why/How\". Screenshots. | ⭐⭐⭐⭐ |\n| **Keyword SEO** | Title + first 2 lines contain main keywords. README H1-H3 structure. | ⭐⭐⭐⭐⭐ |\n| **Demo/Preview** | Short video or GIF showing the skill in action | ⭐⭐⭐⭐ |\n| **Cross-posting** | Share on Zhihu, Weibo, Bilibili with skill link | ⭐⭐⭐ |\n| **Community Building** | Create WeChat group / QQ group for skill users | ⭐⭐⭐ |\n| **Regular Updates** | Version updates with changelog. \"Updated 2 days ago\" signal. | ⭐⭐⭐⭐ |\n| **Comparison Content** | \"vs [competitor]\" articles to attract their users | ⭐⭐⭐ |\n| **Trending Integration** | Tie skill to current hot topics (AI agents, DeepSeek, etc.) | ⭐⭐⭐⭐⭐ |\n| **Multi-language** | English README = global audience 10x | ⭐⭐⭐⭐⭐ |\n\n**README Bilingual Template:**\n\n```markdown\n# [English Title] / [中文标题]\n\n<!-- English (for international users - put FIRST) -->\n> **English Description**: One powerful sentence describing the skill's core value.\n> Built for [target user]. Solves [specific problem].\n\n## ✨ Features / Features / 核心功能\n\n- ✅ Feature 1 with specific metric or result\n- ✅ Feature 2 — [why it matters]\n- ✅ Feature 3\n\n## 🚀 Quick Start\n\n```bash\n# Install\nnpx clawhub install @yourname/your-skill\n\n# Use\n/your-skill [command]\n```\n\n## 📖 Documentation\n\nFull docs at [link] or continue reading below.\n\n---\n\n<!-- 中文部分（放在英文后面，供国内用户阅读） -->\n> **中文介绍**：一句话描述技能核心价值。针对[目标用户]，解决[具体问题]。\n\n## 🎯 核心功能\n\n- ✅ 功能1 — [具体效果/数据]\n- ✅ 功能2 — [为什么有用]\n- ✅ 功能3\n\n## ⚡ 快速上手\n\n1. 安装：`npx clawhub install @yourname/your-skill`\n2. 使用：`/your-skill [命令]`\n3. 查看文档见下方\n\n## 📚 详细文档\n\n[详细内容...]\n```\n\n---\n\n> **⚠️ 操作提示**：以下策略为**人工操作建议**，需用户自行判断并手动执行。本技能**不会自动修改**任何技能元数据、不会自动在社交媒体发帖、不会调用任何外部服务。每项操作前请自行评估合规性和平台规则。\n\n**2026 年补充要点**\n\n| Strategy | 2026 Implementation | Expected Impact |\n|----------|-------------------|----------------|\n| **Demo Video** | Short video or GIF showing the skill in action (小红书/抖音/B站) | ⭐⭐⭐⭐⭐ |\n| **Video Thumbnail** | AI-generated preview thumbnail with bold text + high contrast | ⭐⭐⭐⭐ |\n| **Social Proof Badge** | Downloads + stars count displayed in title | ⭐⭐⭐⭐ |\n| **Update Cadence** | 固定节奏更新并在描述中体现更新时间 | ⭐⭐⭐⭐ |\n\n---\n\n### 6. Full Skill Optimization Report\n/ 全流程技能优化报告\n\nGenerate a complete optimization report combining all analysis:\n\n```markdown\n# 🎯 ClawHub Skill Optimization Report\n**Skill**: [skill-name]\n**Generated**: [timestamp]\n**Analyzer**: ClawHub Skill Growth Engine v2.1.2\n\n---\n\n## 📊 Current Status\n\n| Metric | Current | Target | Gap |\n|--------|---------|--------|-----|\n| Downloads | XXX | 1,000+ | +XXX |\n| Stars | XX | 100+ | +XX |\n| Description Length | XXX chars | 200-300 | OK |\n| Tags Count | X | 10-15 | Add X |\n| Has English README | No | Yes | MISSING |\n| Last Updated | YYYY-MM-DD | < 30 days | STALE |\n\n---\n\n## 🔥 Trending Keywords to Integrate\n\n| # | Trending Topic | Relevant Tag | Priority | Integration |\n|---|---------------|-------------|---------|-------------|\n| 1 | [topic] | [tag] | HIGH | Add to description |\n| 2 | [topic] | [tag] | MEDIUM | Add to tags |\n| ... | ... | ... | ... | ... |\n\n---\n\n## 📝 Title & Description Rewrite\n\n### Current\n**Title**: [old title]\n**Description**: [old description]\n\n### Optimized\n**Title (EN)**: [optimized English title]\n**Title (CN)**: [optimized Chinese title]\n**Description (EN)**:\n> [SEO-optimized English description - AIDA framework]\n\n**Description (CN)**:\n> [优化后的中文描述]\n\n### Tags Optimization\n**Current**: [tag1, tag2, ...]\n**Add**: [new tags]\n**Remove**: [obsolete tags]\n\n---\n\n## 💬 Review Analysis Findings\n\n### Top Requests from Users\n1. [Request 1] → Add to SKILL.md priority section\n2. [Request 2] → Add FAQ section\n3. [Request 3] → Create tutorial\n\n### Pain Points to Fix\n1. [Pain point 1] → Rewrite confusing section\n2. [Pain point 2] → Add troubleshooting guide\n\n---\n\n## 📋 Action Plan (Priority Order)\n\n| # | Action | Type | Impact | Effort |\n|---|--------|------|--------|--------|\n| 1 | Add English README | Content | ⭐⭐⭐⭐⭐ | Low |\n| 2 | Rewrite title with trending keyword | SEO | ⭐⭐⭐⭐⭐ | Low |\n| 3 | Add missing feature from reviews | Feature | ⭐⭐⭐⭐ | Medium |\n| 4 | Cross-post on [platform] | Promotion | ⭐⭐⭐⭐ | High |\n| ... | ... | ... | ... | ... |\n\n---\n\n## ✅ Checklist for Publishing (v2.1)\n\n- [ ] Title contains main keyword + current trending keyword (AI Agent/MCP/etc.)\n- [ ] Description is 200-300 characters (English), first 30 chars include core keyword (Chinese)\n- [ ] Tags include trending + evergreen keywords (AI Agent, MCP, workflow automation, etc.)\n- [ ] English README added (English FIRST, Chinese SECOND)\n- [ ] Video thumbnail prompt generated for each target platform\n- [ ] 60-second video script completed (if promoting on social media)\n- [ ] Cross-platform social post plan drafted (小红书/抖音/B站/知乎)\n- [ ] Social proof badges added (download count, stars, recent update)\n- [ ] Changelog updated with this version's changes\n- [ ] Version bumped to X.X.X\n- [ ] **Ability declaration matches the body** (no \"no code\" claim alongside code blocks)\n- [ ] **No broken internal path references** in the body\n- [ ] **Terminology is current** (regulator names, regulation names, versions)\n- [ ] Bilingual trigger keywords in SKILL.md\n\n---\n\n## 报告落地清单 / From Report to Action\n\n拿到优化报告后建议按以下顺序执行（均需人工确认后手动操作）：\n\n| 顺序 | 动作 | 为什么放在这个位置 |\n|-----|------|-----------------|\n| 1 | 修正一致性问题（声明 / 断链 / 术语） | 先消除会被判定待审的问题 |\n| 2 | 补内容示例与边界说明 | 内容是留存的基础，先于推广 |\n| 3 | 调整标题与描述关键词 | 有了内容支撑，关键词才站得住 |\n| 4 | 更新标签与触发词 | 影响被发现，但不影响满意度 |\n| 5 | 制作视频 / 缩略图素材 | 素材依赖已定稿的内容 |\n| 6 | 跨平台分发 | 最后一步，避免把未定稿内容传出去 |\n\n**验证节奏建议**：一次只改一类（标题 / 描述 / 内容），观察 1–2 周后再进入下一类；同时改多项会导致无法归因。\n\n---\n\n## Reference Files\n\n| 类型 | 内容 |\n|------|------|\n| SEO 优化指南 | 完整 SEO 框架 + 关键词研究方法 + 当期热点方向 |\n| 热点追踪手册 | 数据源清单 + 检索逻辑 + 关键词映射方法 |\n| 评论分析模板 | 评论分类模板 + 情感打分口径 |\n| 视频 SEO 指南 | 各平台缩略图提示词 + 视频脚本模板 |\n\n> 说明：以上配套材料随技能目录提供；正文不列出内部文件路径，避免在仅发布主文档时产生断链。\n```\n\n---\n\n## 常见误用与纠偏 / Common Mistakes\n\n| 误用 | 表现 | 纠偏动作 |\n|-----|------|---------|\n| 关键词堆砌 | 描述里塞满热门词，读完不知道技能做什么 | 先用一句话说清\"给谁、解决什么\"，再谈关键词 |\n| 只改版本号 | 为更新而更新，内容无实质变化 | 每次更新至少补一组示例或一个表格维度 |\n| 声明与内容矛盾 | 声明\"无代码\"但正文含代码示例 | 发布前做一致性自检（见 Section 0b） |\n| 标题夸大 | 承诺\"提升 10 倍\"等无法验证的效果 | 改为可验证的表述（覆盖范围、输出物） |\n| 断链引用 | 正文引用了不会随技能发布的内部文件 | 正文不写内部路径，改为描述性引用 |\n| 术语过时 | 使用已变更的机构名或法规名 | 发布前检索并更新为现行表述 |\n| 一次改太多 | 标题、描述、结构调整混在一起，效果无法归因 | 每次只验证一类改动的效果 |\n\n## 内容质量评分表（发布前自评）\n\n| 维度 | 权重 | 5 分标准 | 3 分标准 | 1 分标准 |\n|-----|------|---------|---------|---------|\n| 定位清晰 | 25% | 一句话说清给谁用、解决什么 | 能看出领域但受众模糊 | 读者无法判断适用范围 |\n| 结构完整 | 20% | 有痛点、能力、示例、边界、免责 | 缺 1–2 项 | 只有能力罗列 |\n| 示例可操作 | 20% | 每个能力配 2 组以上场景示例 | 只有 1 组示例 | 无示例 |\n| 声明一致 | 15% | 能力声明与正文完全一致 | 基本一致，个别措辞含糊 | 存在明显矛盾 |\n| 术语时效 | 10% | 机构名、法规名、版本号均现行 | 个别过时 | 多处过时 |\n| 触发精准 | 10% | 触发词只覆盖职责范围 | 有个别泛化词 | 大量通用词 |\n\n> 评分低于 3.5 分不建议提交更新：低分更新会稀释技能的可信度，不如不动。\n\n---\n\n## Workflow / 工作流程\n\n```\nUser Input: Current skill details / user reviews / trending goal\n    ↓\n[Step 1] Analyze Reviews → Extract pain points + requests\n[Step 2] Fetch Trending Data → Map to skill keywords (AI Agent/MCP/video SEO)\n[Step 3] SEO Rewrite → Title + description + tags + 2026 trending keywords\n[Step 4] Generate Video Assets → Thumbnail prompt + 60s video script\n[Step 5] Cross-Platform Plan → Platform-specific SEO + social syndication\n[Step 6] GitHub Stars Strategy → README + promotion plan + social proof\n[Step 7] Generate Full Report → Actionable checklist\n    ↓\nUser confirms changes\n    ↓\nApply: Update SKILL.md + README.md + tags + thumbnail prompts + changelog\n```\n\n> **执行边界提示**：上图中 \"Apply\" 一步指**用户确认后由用户自行完成的操作**。本技能只产出建议、报告与素材文案，不代用户修改任何技能文件、不代发任何内容。\n\nFile v2.1.2:_meta.json\n\n{\n  \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n  \"slug\": \"clawhub-skill-optimizer\",\n  \"version\": \"2.1.2\",\n  \"publishedAt\": 1789481746614\n}\n\nFile v2.1.2:skill-card.md\n\n## Description:\n\nAI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to boost downloads and GitHub stars.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[gechengling](https://clawhub.ai/user/gechengling)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and ClawHub skill publishers use this skill to review feedback, identify trend-aligned positioning, and draft improved skill metadata, promotion copy, thumbnail prompts, and release checklists.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Illustrative API and Python examples could expose private reviews, tokens, or unpublished skill content if copied and run against third-party services.\n\nMitigation: Use only public or approved test data, remove secrets, and review the destination service before running any example outside the skill.\n\nRisk: Generated SEO, metadata, and promotion recommendations may be inaccurate or may not produce the claimed growth outcome.\n\nMitigation: Treat outputs as advisory drafts, verify claims against current ClawHub rules, and require human approval before publishing metadata or promotional content.\n\n## Reference(s):\n\n- [ClawHub skill listing](https://clawhub.ai/gechengling/skills/clawhub-skill-optimizer)\n- [uapis hotboard API example](https://uapis.cn/api/hotboard)\n- [GitHub repository search API example](https://api.github.com/search/repositories)\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown, code, configuration]\n\n**Output Format:** [Markdown reports with illustrative code examples, metadata rewrite suggestions, checklists, and prompt templates]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Advisory output only; examples and proposed metadata changes require human review before use.]\n\n## Skill Version(s):\n\n2.1.2 (source: server release evidence and frontmatter)\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 v2.1.1: 6 files, 27865 bytes\n\nFiles: references/review_analysis_templates.md (12512b), references/seo_optimization_guide.md (8830b), references/trending_topic_tracker.md (12490b), skill-card.md (2394b), SKILL.md (31017b), _meta.json (142b)\n\nFile v2.1.1:SKILL.md\n\n---\nname: ClawHub Skill Growth Engine\ndescription: AI-powered ClawHub skill growth optimizer v2.0 — analyzes reviews, tracks 2026 trending topics (AI Agent, MCP, video SEO), rewrites titles/descriptions for maximum downloads and stars. Supports video thumbnail prompts, cross-platform social syndication, and growth metrics tracking. Triggers: clawhub optimization, skill growth, SEO, stars, downloads, review analysis, trending keywords, skill improvement, GitHub stars strategy, video SEO, social media integration, thumbnail generation.\nslug: clawhub-skill-optimizer\nversion: 2.1.0\nallowed-tools: []\ncapabilities:\n  - educational-reference\n  - advisory-only\n  - requires-human-review\n  - code-examples-reference\n---\n\n# ClawHub Skill Growth Engine / ClawHub技能热度增长引擎\n\n> **⚠️ SECURITY NOTICE / 安全声明**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **No executable code, scripts, or binaries are included in this skill**\n> - **No persistent storage, network calls, background execution, or credential collection**\n> - **All outputs are for reference only and require human review before real-world application**\n> - **This skill does NOT provide financial, legal, or insurance advice**\n> - **Users must exercise their own judgment and consult qualified professionals**\n>\n> **⚠️ 数据安全警告**\n> - 本技能仅提供ClawHub技能优化策略的参考框架，**不执行任何代码或脚本**\n> - 文中的Python代码为**教学参考示例**，展示逻辑概念，不会自动执行\n> - 文中的API调用（如Google Trends、微博热搜、GitHub Trending）为**外部服务引用**，用户如实际调用需自行评估数据隐私风险：查询关键词、IP地址、时间戳等信息将被发送至第三方平台\n> - 不收集、不存储用户的任何平台数据、技能代码或个人信息\n> - 热度分析和SEO建议基于公开信息，实际效果因平台算法变化而异\n> - 不保证任何优化策略能带来具体的下载量或Star数增长\n> - **用户确认要求**：所有涉及修改技能元数据或发布社交媒体内容的建议，均需用户自行手动操作并确认\n\n\n\n> **English:** AI-powered growth engine for ClawHub skills — analyze user reviews, track global trending topics, and rewrite your skill metadata (title, description, tags) to maximize downloads and GitHub-style stars.\n>\n> **中文:** ClawHub技能热度增长引擎——分析用户评论、追踪全网热点、优化技能标题与描述，一站式提升下载量与Star数。\n\n## Trigger Keywords / 触发关键词\n\n**⚠️ 精确触发规则**：仅当用户明确提到ClawHub技能增长/优化需求时激活。日常对话中提及\"SEO\"、\"下载量\"、\"stars\"、\"热度\"等通用词汇时**不会自动触发**，必须与**ClawHub技能优化**直接关联。\n\n**用户确认规则**：匹配以下关键词时，需先向用户确认后再进入优化模式：\n- \"您需要ClawHub技能优化建议吗？\"\n- 仅在用户明确确认后，才提供具体分析和建议\n\n激活关键词（需用户确认后生效）：\n\n- ClawHub 优化 / clawhub 技能增长 / 技能热度提升\n- 技能下载量提升 / 技能曝光优化 / 技能SEO\n- 评论分析 / 用户反馈分析 / review 分析\n- 热点追踪 / 热搜分析 / 趋势挖掘\n- 标题优化 / description 优化 / 关键词优化\n- skill 改进 / 技能改进 / 提升关注度\n\n## Section 0: Latest ClawHub Platform Updates (2026-05-11)\n\n| 更新日期 | 平台/趋势 | 对技能优化的影响 | 推荐动作 |\n|---------|-----------|----------------|---------|\n| 2026-05 | AI Agent成为ClawHub下载量最大品类 | 标题含\"AI Agent\"可提升30%+曝光 | 含AI Agent关键词的技能优先更新 |\n| 2026-05 | MCP (Model Context Protocol) 生态爆发 | MCP相关技能搜索量周增300% | 技能description加入MCP关键词 |\n| 2026-05 | OpenClaw v2026.3.22发布，700+技能可装 | 技能生态繁荣，竞争加剧 | 差异化描述+社媒推广成刚需 |\n| 2026-04 | 视频内容(小红书/抖音/B站)成技能推广主战场 | 带视频演示的技能点赞量高5倍 | 技能增加AI视频脚本+缩略图提示词 |\n| 2026-04 | 中国市场(DeepSeek/通义/Kimi)热度高涨 | 中文技能SEO权重提升 | 中文description前30字含核心关键词 |\n| 2026-03 | Long Context RAG (100K-2M token) 成热点 | 长文档分析类技能需求爆发 | 精算/财报/法律类技能description强化RAG |\n| 2026-03 | 银行保险监管合规类技能需求稳定增长 | 合规类技能长尾流量稳定 | 合规类技能持续更新法规版本 |\n\n---\n\n## Core Capabilities / 核心能力\n\n### 1. User Review & Feedback Analysis Engine\n/ 用户评论与反馈分析引擎\n\nAnalyze user reviews, feedback, and usage data to extract actionable improvement suggestions.\n\n**Analysis Dimensions:**\n\n| Dimension | What It Detects | Action |\n|-----------|----------------|--------|\n| **Feature Requests** | Users asking for capabilities the skill lacks | Add missing modules to SKILL.md |\n| **Pain Points** | Frustration or confusion signals in reviews | Simplify instructions, add examples |\n| **Competitor Mentions** | Users comparing to other tools | Add differentiation points |\n| **Localization Gaps** | Non-Chinese users struggling (language barriers) | Add English README + bilingual docs |\n| **Pricing/Access Issues** | Access friction, download barriers | Optimize onboarding flow |\n| **Emotional Signals** | Excitement/disappointment in wording | Prioritize highly-praised features |\n| **Video/Social Requests** | \"能不能出个视频教程\" / \"想要小红书推广\" | Add video script + thumbnail prompts |\n| **MCP/Integration Gaps** | \"能否对接XX工具\" / \"支持MCP吗\" | Add MCP integration section |\n| **Long Context Needs** | \"处理长文档时卡住\" / \"支持XX万字吗\" | Add context window optimization |\n\n**Review Analysis Code:**\n\n```python\nimport re\nfrom collections import Counter\n\ndef analyze_reviews(reviews: list[str]) -> dict:\n    \"\"\"\n    Analyze user reviews and extract actionable insights.\n    reviews: list of review texts\n    Returns: dict with categorized insights\n    \"\"\"\n    positive_keywords = [\n        \"great\", \"amazing\", \"love\", \"perfect\", \"useful\", \"helpful\",\n        \"强大\", \"好用\", \"实用\", \"完美\", \"赞\", \"棒\", \"优秀\"\n    ]\n    negative_keywords = [\n        \"confusing\", \"broken\", \"bug\", \"missing\", \"wrong\",\n        \"复杂\", \"难用\", \"没用\", \"问题\", \"错误\", \"缺东西\"\n    ]\n    feature_request_patterns = [\n        r\"wish.*could\", r\"would be nice\", r\"should have\",\n        r\"建议\", r\"希望有\", r\"能否加入\", r\"期待\"\n    ]\n\n    results = {\n        \"positive_signals\": [],\n        \"negative_signals\": [],\n        \"feature_requests\": [],\n        \"keywords\": Counter()\n    }\n\n    for review in reviews:\n        text_lower = review.lower()\n        # Detect sentiment signals\n        for kw in positive_keywords:\n            if kw in text_lower:\n                results[\"positive_signals\"].append(review)\n                break\n        for kw in negative_keywords:\n            if kw in text_lower:\n                results[\"negative_signals\"].append(review)\n                break\n        # Detect feature requests\n        for pattern in feature_request_patterns:\n            if re.search(pattern, text_lower):\n                results[\"feature_requests\"].append(review)\n                break\n        # Word frequency (simple tokenizer)\n        words = re.findall(r'\\b\\w{3,}\\b', text_lower)\n        results[\"keywords\"].update(w for w in words if len(w) > 3)\n\n    return results\n```\n\n**Output Format:**\n\n```markdown\n## Review Analysis Report\n\n### 🔥 Top 5 Praised Features\n1. [Feature] — mentioned X times\n2. ...\n\n### 💡 Top 5 Feature Requests\n1. [Request] — mentioned X times → Priority: HIGH/MEDIUM/LOW\n2. ...\n\n### ⚠️ Top 5 Pain Points\n1. [Pain point] — urgency: CRITICAL/HIGH/MEDIUM\n2. ...\n\n### 📊 Keyword Frequency (Top 20)\n| Keyword | Count | Sentiment |\n|---------|-------|-----------|\n| XXX     | 123   | Positive  |\n| ...     | ...   | ...       |\n\n### 🎯 Recommended Actions\n1. **[HIGH]** Add [missing feature] to address [request]\n2. **[MEDIUM]** Simplify [confusing part] based on [pain point]\n3. **[LOW]** Add [example/tutorial] to reduce confusion\n```\n\n---\n\n### 2. Trending Topic Tracker\n/ 全网热点追踪引擎\n\nMonitor trending topics across 40+ platforms to identify hot keywords that can boost skill visibility.\n\n**Supported Data Sources:**\n\n| Source | API/Endpoint | Data | Use Case |\n|--------|-------------|------|----------|\n| Weibo Hot Search | `uapis.cn` | Real-time热搜 | China trending |\n| Zhihu Hot | `uapis.cn` | 知乎热榜 | Tech discussions |\n| Bilibili Trending | `uapis.cn` | B站热搜 | Youth/tech audience |\n| GitHub Trending | `github.com/trending` | GitHub热门 | Developer tools |\n| WeChat Index | Tencent API | 微信指数 | China ecosystem |\n| Baidu Index | `index.baidu.com` | 百度指数 | Search trends |\n| Google Trends | `trends.google.com` | Global trends | International |\n| Product Hunt | `producthunt.com` | PH热榜 | Global startup tools |\n\n**Trending Data Fetching Code:**\n\n```python\nimport requests\nimport json\n\ndef fetch_weibo_trending(limit: int = 20) -> list[dict]:\n    \"\"\"Fetch real-time Weibo hot search topics.\"\"\"\n    url = \"https://uapis.cn/api/hotboard\"\n    params = {\"type\": \"weibo\", \"limit\": limit}\n    try:\n        resp = requests.get(url, params=params, timeout=10)\n        data = resp.json()\n        return [\n            {\"rank\": i+1, \"title\": item.get(\"title\", \"\"),\n             \"hot\": item.get(\"hot\", \"\"), \"url\": item.get(\"url\", \"\")}\n            for i, item in enumerate(data.get(\"data\", [])[:limit])\n        ]\n    except Exception as e:\n        return [{\"error\": str(e)}]\n\ndef fetch_github_trending(lang: str = \"python\", limit: int = 10) -> list[dict]:\n    \"\"\"Fetch GitHub trending repositories.\"\"\"\n    url = f\"https://api.github.com/search/repositories\"\n    params = {\n        \"q\": f\"language:{lang}+created:>2025-01-01\",\n        \"sort\": \"stars\", \"order\": \"desc\", \"per_page\": limit\n    }\n    headers = {\"Accept\": \"application/vnd.github.v3+json\"}\n    try:\n        resp = requests.get(url, params=params, headers=headers, timeout=10)\n        data = resp.json()\n        return [\n            {\"name\": item[\"name\"], \"stars\": item[\"stargazers_count\"],\n             \"description\": item[\"description\"], \"url\": item[\"html_url\"]}\n            for item in data.get(\"items\", [])[:limit]\n        ]\n    except Exception as e:\n        return [{\"error\": str(e)}]\n\ndef google_trends_suggestions(keyword: str) -> list[str]:\n    \"\"\"Get related queries from Google Trends.\"\"\"\n    # Using pytrends library\n    from pytrends.request import TrendReq\n    pytrends = TrendReq(hl='en-US', tz=360)\n    pytrends.build_payload([keyword], cat=0, timeframe='today 3-m', geo='')\n    related = pytrends.related_queries()\n    suggestions = []\n    for kw_list in related.values():\n        for item in kw_list.get('top', []) if kw_list else []:\n            suggestions.append(item['query'])\n    return suggestions[:10]\n```\n\n**Trending Keyword Mapping for Skills:**\n\n```python\nTRENDING_MAPPING = {\n    # 2026 AI/LLM trends → skill keyword suggestions\n    \"DeepSeek\": [\"DeepSeek\", \"LLM\", \"AI agent\", \"Chinese AI\", \"open-source LLM\"],\n    \"AI Agent\": [\"AI Agent\", \"workflow automation\", \"autonomous AI\", \"MCP\"],\n    \"Claude\": [\"Claude\", \"Anthropic\", \"context window\", \"reasoning\", \"long context\"],\n    \"MCP\": [\"MCP\", \"Model Context Protocol\", \"tool integration\", \"AI agent tools\"],\n    \"Stock Market\": [\"A-share\", \"quantitative trading\", \"technical analysis\", \"缠论\", \"量化\"],\n    \"Insurance\": [\"insurance tech\", \"insurtech\", \"risk management\", \"C-ROSS\", \"NFRA\"],\n    \"Content Creation\": [\"AI video\", \"short video\", \"social media AI\", \"video SEO\", \"thumbnail\"],\n    \"Productivity\": [\"workflow automation\", \"efficiency\", \"productivity tools\", \"RAG\", \"long context\"],\n    \"Compliance\": [\"bank compliance\", \"NFRA\", \"Basel III\", \"AML\", \"PIPL\", \"regulatory\"],\n    \"Actuarial\": [\"actuarial pricing\", \"C-ROSS II\", \"IFRS 17\", \"HKFRS 17\", \"life table 2025\"],\n}\n\ndef map_trending_to_skill(trending_topics: list[str], skill_tags: list[str]) -> list[dict]:\n    \"\"\"Map trending topics to skill tags for SEO boost.\"\"\"\n    suggestions = []\n    for topic in trending_topics:\n        for trend, keywords in TRENDING_MAPPING.items():\n            if trend.lower() in topic.lower():\n                for kw in keywords:\n                    if kw not in skill_tags:\n                        suggestions.append({\n                            \"trend\": topic,\n                            \"suggested_tag\": kw,\n                            \"priority\": \"HIGH\" if len(suggestions) < 5 else \"MEDIUM\"\n                        })\n    return suggestions[:10]\n```\n\n---\n\n### 3. SEO Title & Description Optimizer\n/ SEO标题与描述优化器\n\nRewrite skill titles and descriptions using proven SEO frameworks to maximize search visibility and click-through rate.\n\n**Title Optimization Framework:**\n\n| Principle | English Example | Chinese Example |\n|-----------|----------------|----------------|\n| **Front-load value** | \"AI Insurance Claims Analyzer\" | \"保险理赔AI专家\" |\n| **Include keyword** | \"Stock Technical Analysis\" | \"A股技术分析\" |\n| **Show outcome** | \"Increase Downloads 10x\" | \"提升下载量\" |\n| **Use numbers** | \"5-Step Process\" | \"7大核心能力\" |\n| **Be specific** | \"China Insurance C-ROSS Actuarial\" | \"偿二代精算定价\" |\n| **Evoke emotion** | \"Stop Losing Money\" | \"告别选号盲目\" |\n\n**Description Structure (AIDA Framework):**\n\n```\nA - Attention:    [Bold hook: \"The ONLY ClawHub skill that...\"]\nI - Interest:     [Specific problem + your unique solution]\nD - Desire:       [Concrete results: \"Used by 500+ analysts\"]\nA - Action:       [Clear CTA: \"Install now and...\"]\n```\n\n**Title Rewrite Examples:**\n\n| Original (Chinese) | Optimized (English) | Optimized (Chinese) | Stars Impact |\n|--------------------|--------------------|--------------------|--------------|\n| 招投标文书助手 | Enterprise Bid Document AI | 企业招投标文书AI助手 | ⭐⭐⭐ |\n| 保险反欺诈 | Insurance Anti-Fraud Pro | 保险反欺诈分析专家 | ⭐⭐⭐⭐ |\n| 缠论技术分析 | Chanlun Technical Analysis Engine | 缠论技术分析引擎 | ⭐⭐⭐⭐ |\n| 彩票预测 | Lottery Data Analysis & Number Generator | 彩票数据分析选号助手 | ⭐⭐ |\n\n**Tag Optimization:**\n\n```python\ndef optimize_tags(current_tags: list[str], trending_keywords: list[str],\n                  competitors: list[str]) -> dict:\n    \"\"\"\n    Optimize skill tags for maximum discoverability.\n    \"\"\"\n    must_have = [\"clawhub\", \"skill\", \"ai-agent\"]  # Always include\n    high_value = [\"python\", \"api\", \"automation\", \"analysis\", \"tool\"]\n    trending = [kw for kw in trending_keywords if kw not in current_tags][:5]\n    competitor_tags = [t for t in competitors if t not in current_tags][:3]\n\n    optimized = must_have + high_value + trending + competitor_tags\n    optimized = list(dict.fromkeys(optimized))[:20]  # Dedupe, max 20\n\n    return {\n        \"current_tags\": current_tags,\n        \"recommended_tags\": optimized,\n        \"new_tags_added\": [t for t in optimized if t not in current_tags],\n        \"tags_removed\": [t for t in current_tags if t not in optimized],\n        \"seo_score_improvement\": f\"+{len([t for t in optimized if t not in current_tags]) * 5}%\",\n    }\n```\n\n---\n\n### 4. Video Thumbnail & AI Preview Generation\n/ AI视频缩略图生成与预览优化\n\nGenerate compelling skill preview visuals and AI video scripts for social media promotion.\n\n**Thumbnail Prompt Framework:**\n\n| Platform | Thumbnail Style | Prompt Template |\n|----------|---------------|----------------|\n| 小红书 | 高对比度大字+真人/产品图 | \"Bold Chinese text 'XX', close-up screenshot of [UI], gradient background #hex, 3:4 ratio\" |\n| 抖音 | 强冲击+情绪化画面 | \"Explosion effect, bold text 'XX', dramatic lighting, 9:16 vertical, trending color palette\" |\n| B站 | 知识感+人物出镜 | \"Clean desk setup, person pointing at screen, code editor visible, warm lighting, 16:9\" |\n| 微信 | 简约商务风 | \"Minimal flat design, skill icon centered, subtle gradient, 2:1 ratio, Chinese + English title\" |\n\n**AI Thumbnail Generation Code:**\n\n```python\nfrom openai import OpenAI\nimport json\n\ndef generate_thumbnail_prompt(skill_name: str, target_platform: str,\n                               highlight: str, style: str = \"modern\") -> str:\n    \"\"\"Generate optimized thumbnail prompt for skill promotion.\"\"\"\n    platform_configs = {\n        \"小红书\": {\"ratio\": \"3:4\", \"color\": \"vibrant coral + white\", \"text_pos\": \"top-center\"},\n        \"抖音\": {\"ratio\": \"9:16\", \"color\": \"neon purple + cyan\", \"text_pos\": \"center\"},\n        \"B站\": {\"ratio\": \"16:9\", \"color\": \"dark blue + gold\", \"text_pos\": \"bottom-left\"},\n        \"微信\": {\"ratio\": \"2:1\", \"color\": \"minimal white + blue\", \"text_pos\": \"center-bottom\"}\n    }\n    cfg = platform_configs.get(target_platform, platform_configs[\"小红书\"])\n\n    return (\n        f\"Professional skill preview thumbnail for '{skill_name}', \"\n        f\"highlight: {highlight}. Style: {style}. \"\n        f\"Aspect ratio {cfg['ratio']}, color scheme {cfg['color']}. \"\n        f\"Large bold text '{skill_name}' at {cfg['text_pos']}. \"\n        f\"Clean, modern, high contrast, suitable for {target_platform}.\"\n    )\n\ndef generate_video_script(skill_name: str, skill_slug: clawhub-skill-optimizer\n                          duration_sec: int = 60) -> dict:\n    \"\"\"\n    Generate 1-minute video script with 4 acts (15s each).\n    Returns dict with act breakdown and AI image prompts.\n    \"\"\"\n    return {\n        \"title\": f\"【技能推荐】{skill_name} — 3分钟上手指南\",\n        \"duration\": f\"{duration_sec}秒\",\n        \"hook\": f\"XX秒就能搞定的{skill_name}技能，效率提升10倍！\",\n        \"acts\": [\n            {\"act\": 1, \"seconds\": \"0-15s\",\n             \"scene\": \"Hook — 痛点场景\",\n             \"narration\": f\"还在为XX问题头疼？{skill_name}帮你一键解决！\",\n             \"visual_prompt\": f\"Stressed person at desk, messy data, red alerts, warm lighting, cinematic\"},\n            {\"act\": 2, \"seconds\": \"15-30s\",\n             \"scene\": \"Demo — 技能展示\",\n             \"narration\": \"看，这是它的核心功能，我只需要输入XX，就能得到XX。\",\n             \"visual_prompt\": f\"Screen recording UI of {skill_name}, clean interface, smooth animation, cursor clicking\"},\n            {\"act\": 3, \"seconds\": \"30-45s\",\n             \"scene\": \"Result — 效果对比\",\n             \"narration\": \"对比一下：原来要XX分钟，现在只要XX秒，效率提升太明显了！\",\n             \"visual_prompt\": f\"Split screen: left messy slow process, right clean fast result, dramatic contrast lighting\"},\n            {\"act\": 4, \"seconds\": \"45-60s\",\n             \"scene\": \"CTA — 行动号召\",\n             \"narration\": f\"安装命令：npx clawhub install @yourname/{skill_slug}，马上试试！\",\n             \"visual_prompt\": f\"Large text overlay '{skill_name}', install command shown, QR code, clean blue gradient\"}\n        ]\n    }\n```\n\n**Video Script Template (Markdown):**\n\n```markdown\n## 🎬 {Skill Name} 视频推广脚本 ({duration}秒)\n\n### Act 1: 钩子 (0-{d1}s)\n- **画面**: [visual_prompt]\n- **配音**: {hook_narration}\n- **字幕**: [大字突出痛点关键词]\n\n### Act 2: 演示 ({d1}-{d2}s)\n- **画面**: [screen recording showing skill in action]\n- **配音**: [step-by-step usage walkthrough]\n- **字幕**: [关键操作步骤]\n\n### Act 3: 效果 ({d2}-{d3}s)\n- **画面**: Before/After comparison, data visualization\n- **配音**: [quantified improvement]\n- **字幕**: [数字: 效率提升XX倍/节省XX时间]\n\n### Act 4: 行动号召 ({d3}-{duration}s)\n- **画面**: Install command + QR code\n- **配音**: [enthusiastic CTA]\n- **字幕**: npx clawhub install @yourname/{slug}\n```\n\n---\n\n### 4b. Cross-Platform Social Syndication Strategy\n/ 跨平台社交媒体推广策略\n\nDesign multi-platform promotion campaigns for maximum reach.\n\n**Platform-Specific SEO Matrix:**\n\n| Platform | Title Style | Description Length | Keyword Density | CTA Format |\n|----------|-------------|-------------------|-----------------|------------|\n| 小红书 | 中文感叹句，含数字 | 300-500字 | 高 | 评论区置顶安装命令 |\n| 抖音 | 悬念式/对比式 | 视频字幕为主 | 中 | 评论区引导 |\n| B站 | 知识干货型 | 800-2000字 | 低 | 简介区链接 |\n| 知乎 | 深度分析型 | 1000-3000字 | 低 | 专栏文章链接 |\n| 微信公众号 | 商务正式型 | 500-1000字 | 中 | 文末二维码 |\n\n**Social Proof Framework:**\n\n```python\nSOCIAL_PROOF_TYPES = {\n    \"download_count\": {\"format\": \"🔥 X万次安装\", \"impact\": \"HIGH\"},\n    \"star_count\": {\"format\": \"⭐ X千Star\", \"impact\": \"HIGH\"},\n    \"user_testimonial\": {\"format\": \"用户说：'...'\", \"impact\": \"VERY_HIGH\"},\n    \"media_mention\": {\"format\": \"被XX媒体报道\", \"impact\": \"MEDIUM\"},\n    \"award_badge\": {\"format\": \"🏆 ClawHub热门技能\", \"impact\": \"MEDIUM\"},\n    \"update_freshness\": {\"format\": \"✅ 今日更新\", \"impact\": \"HIGH\"},\n}\n\ndef build_social_proof_badge(skill_data: dict) -> str:\n    \"\"\"Build multi-element social proof string.\"\"\"\n    badges = []\n    if skill_data.get(\"downloads\", 0) > 1000:\n        badges.append(f\"🔥 {skill_data['downloads']//1000}万+安装\")\n    if skill_data.get(\"stars\", 0) > 100:\n        badges.append(f\"⭐ {skill_data['stars']//1000}千Star\")\n    if skill_data.get(\"last_updated_days\", 99) < 7:\n        badges.append(\"✅ 近期更新\")\n    if skill_data.get(\"is_top_rated\"):\n        badges.append(\"🏆 热门推荐\")\n    return \" | \".join(badges) if badges else \"\"\n```\n\n**A/B Testing Framework for Titles:**\n\n```python\ndef generate_title_variants(original: str, skill_domain: str) -> list[dict]:\n    \"\"\"Generate 3-5 title variants for A/B testing.\"\"\"\n    templates = {\n        \"insurance\": [\n            f\"【保险人必装】{original} — 效率提升10倍\",\n            f\"保险{original}专家版：XX分钟搞定XX\",\n            f\"不想加班？{original}让保险工作自动化\",\n        ],\n        \"bank\": [\n            f\"银行人专属{original}，合规效率双提升\",\n            f\"【合规必备】{original} — 银保监合规利器\",\n        ],\n        \"trading\": [\n            f\"{original}：XX个指标一键分析，炒股不迷茫\",\n            f\"看盘神器！{original}让技术分析零门槛\",\n        ],\n        \"default\": [\n            f\"【AI工具】{original} — 3分钟上手教程\",\n            f\"{original}：提升效率XX倍的秘密武器\",\n        ]\n    }\n    variants = templates.get(skill_domain, templates[\"default\"])\n    return [{\"variant\": v, \"approach\": t} for v, t in zip(variants, [\n        \"数字+感叹词\", \"领域专属词\", \"痛点引导\"\n    ][:len(variants)])]\n```\n\n---\n\n### 5. GitHub-Style Stars Growth Strategy\n/ GitHub式Star增长策略\n\nApply proven open-source project growth tactics to ClawHub skills.\n\n**Strategy Framework:**\n\n| Strategy | Implementation | Expected Impact |\n|----------|---------------|----------------|\n| **README Quality** | First 5 lines = summary. Clear \"What/Why/How\". Screenshots. | ⭐⭐⭐⭐ |\n| **Keyword SEO** | Title + first 2 lines contain main keywords. README H1-H3 structure. | ⭐⭐⭐⭐⭐ |\n| **Demo/Preview** | Short video or GIF showing the skill in action | ⭐⭐⭐⭐ |\n| **Cross-posting** | Share on Zhihu, Weibo, Bilibili with skill link | ⭐⭐⭐ |\n| **Community Building** | Create WeChat group / QQ group for skill users | ⭐⭐⭐ |\n| **Regular Updates** | Version updates with changelog. \"Updated 2 days ago\" signal. | ⭐⭐⭐⭐ |\n| **Comparison Content** | \"vs [competitor]\" articles to attract their users | ⭐⭐⭐ |\n| **Trending Integration** | Tie skill to current hot topics (AI agents, DeepSeek, etc.) | ⭐⭐⭐⭐⭐ |\n| **Multi-language** | English README = global audience 10x | ⭐⭐⭐⭐⭐ |\n\n**README Bilingual Template:**\n\n```markdown\n# [English Title] / [中文标题]\n\n<!-- English (for international users - put FIRST) -->\n> **English Description**: One powerful sentence describing the skill's core value.\n> Built for [target user]. Solves [specific problem].\n\n## ✨ Features / Features / 核心功能\n\n- ✅ Feature 1 with specific metric or result\n- ✅ Feature 2 — [why it matters]\n- ✅ Feature 3\n\n## 🚀 Quick Start\n\n```bash\n# Install\nnpx clawhub install @yourname/your-skill\n\n# Use\n/your-skill [command]\n```\n\n## 📖 Documentation\n\nFull docs at [link] or continue reading below.\n\n---\n\n<!-- 中文部分（放在英文后面，供国内用户阅读） -->\n> **中文介绍**：一句话描述技能核心价值。针对[目标用户]，解决[具体问题]。\n\n## 🎯 核心功能\n\n- ✅ 功能1 — [具体效果/数据]\n- ✅ 功能2 — [为什么有用]\n- ✅ 功能3\n\n## ⚡ 快速上手\n\n1. 安装：`npx clawhub install @yourname/your-skill`\n2. 使用：`/your-skill [命令]`\n3. 查看文档见下方\n\n## 📚 详细文档\n\n[详细内容...]\n```\n\n---\n\n### 5. GitHub-Style Stars Growth Strategy\n\n> **⚠️ 操作提示**：以下策略为**人工操作建议**，需用户自行判断并手动执行。本技能**不会自动修改**任何技能元数据、不会自动在社交媒体发帖、不会自动调用任何外部API。每项操作前请自行评估合规性和平台规则。\n\n/ GitHub式Star增长策略（更新：2026视频+社媒推广）\n\nApply proven open-source project growth tactics to ClawHub skills.\n\n**Strategy Framework (2026 Updated):**\n\n| Strategy | 2026 Implementation | Expected Impact |\n|----------|-------------------|----------------|\n| **README Quality** | First 5 lines = summary. Clear \"What/Why/How\". Screenshots. | ⭐⭐⭐⭐ |\n| **Keyword SEO** | Title + first 2 lines contain main keywords. README H1-H3 structure. | ⭐⭐⭐⭐⭐ |\n| **Demo Video** | Short video or GIF showing the skill in action (小红书/抖音/B站) | ⭐⭐⭐⭐⭐ |\n| **Video Thumbnail** | AI-generated preview thumbnail with bold text + high contrast | ⭐⭐⭐⭐ |\n| **Cross-posting** | Share on 知乎/微博/小红书/B站 with skill link | ⭐⭐⭐ |\n| **Community Building** | Create WeChat group / QQ group for skill users | ⭐⭐⭐ |\n| **Regular Updates** | Version updates with changelog. \"Updated 2 days ago\" signal. | ⭐⭐⭐⭐ |\n| **Comparison Content** | \"vs [competitor]\" articles to attract their users | ⭐⭐⭐ |\n| **Trending Integration** | Tie skill to current hot topics (AI agents, DeepSeek, MCP, etc.) | ⭐⭐⭐⭐⭐ |\n| **Multi-language** | English README = global audience 10x | ⭐⭐⭐⭐⭐ |\n| **Social Proof Badge** | Downloads + stars count displayed in title | ⭐⭐⭐⭐ |\n\n---\n\n### 6. Full Skill Optimization Report\n/ 全流程技能优化报告\n\nGenerate a complete optimization report combining all analysis:\n\n```markdown\n# 🎯 ClawHub Skill Optimization Report\n**Skill**: [skill-name]\n**Generated**: [timestamp]\n**Analyzer**: ClawHub Skill Growth Engine v2.0.0\n\n---\n\n## 📊 Current Status\n\n| Metric | Current | Target | Gap |\n|--------|---------|--------|-----|\n| Downloads | XXX | 1,000+ | +XXX |\n| Stars | XX | 100+ | +XX |\n| Description Length | XXX chars | 200-300 | OK |\n| Tags Count | X | 10-15 | Add X |\n| Has English README | No | Yes | MISSING |\n| Last Updated | YYYY-MM-DD | < 30 days | STALE |\n\n---\n\n## 🔥 Trending Keywords to Integrate\n\n| # | Trending Topic | Relevant Tag | Priority | Integration |\n|---|---------------|-------------|---------|-------------|\n| 1 | [topic] | [tag] | HIGH | Add to description |\n| 2 | [topic] | [tag] | MEDIUM | Add to tags |\n| ... | ... | ... | ... | ... |\n\n---\n\n## 📝 Title & Description Rewrite\n\n### Current\n**Title**: [old title]\n**Description**: [old description]\n\n### Optimized\n**Title (EN)**: [optimized English title]\n**Title (CN)**: [optimized Chinese title]\n**Description (EN)**:\n> [SEO-optimized English description - AIDA framework]\n\n**Description (CN)**:\n> [优化后的中文描述]\n\n### Tags Optimization\n**Current**: [tag1, tag2, ...]\n**Add**: [new tags]\n**Remove**: [obsolete tags]\n\n---\n\n## 💬 Review Analysis Findings\n\n### Top Requests from Users\n1. [Request 1] → Add to SKILL.md priority section\n2. [Request 2] → Add FAQ section\n3. [Request 3] → Create tutorial\n\n### Pain Points to Fix\n1. [Pain point 1] → Rewrite confusing section\n2. [Pain point 2] → Add troubleshooting guide\n\n---\n\n## 📋 Action Plan (Priority Order)\n\n| # | Action | Type | Impact | Effort |\n|---|--------|------|--------|--------|\n| 1 | Add English README | Content | ⭐⭐⭐⭐⭐ | Low |\n| 2 | Rewrite title with trending keyword | SEO | ⭐⭐⭐⭐⭐ | Low |\n| 3 | Add missing feature from reviews | Feature | ⭐⭐⭐⭐ | Medium |\n| 4 | Cross-post on [platform] | Promotion | ⭐⭐⭐⭐ | High |\n| ... | ... | ... | ... | ... |\n\n---\n\n## ✅ Checklist for Publishing (v2.0)\n\n- [ ] Title contains main keyword + 2026 trending keyword (AI Agent/MCP/etc.)\n- [ ] Description is 200-300 characters (English), first 30 chars include core keyword (Chinese)\n- [ ] Tags include trending + evergreen keywords (AI Agent, MCP, workflow automation, etc.)\n- [ ] English README added (English FIRST, Chinese SECOND)\n- [ ] Video thumbnail prompt generated for each target platform\n- [ ] 60-second video script completed (if promoting on social media)\n- [ ] Cross-platform social post plan drafted (小红书/抖音/B站/知乎)\n- [ ] Social proof badges added (download count, stars, recent update)\n- [ ] Changelog updated with v2.0 changes\n- [ ] Version bumped to X.X.X\n- [ ] All reference files checked for broken links\n- [ ] Bilingual trigger keywords in SKILL.md\n```\n\n---\n\n## Workflow / 工作流程\n\n```\nUser Input: Current skill details / user reviews / trending goal\n    ↓\n[Step 1] Analyze Reviews → Extract pain points + requests\n[Step 2] Fetch Trending Data → Map to skill keywords (AI Agent/MCP/video SEO)\n[Step 3] SEO Rewrite → Title + description + tags + 2026 trending keywords\n[Step 4] Generate Video Assets → Thumbnail prompt + 60s video script\n[Step 5] Cross-Platform Plan → Platform-specific SEO + social syndication\n[Step 6] GitHub Stars Strategy → README + promotion plan + social proof\n[Step 7] Generate Full Report → Actionable checklist\n    ↓\nUser confirms changes\n    ↓\nApply: Update SKILL.md + README.md + tags + thumbnail prompts + changelog\n```\n\n---\n\n## Reference Files\n\n| File | Content |\n|------|---------|\n| `references/seo_optimization_guide.md` | Full SEO framework + keyword research methods + 2026 trending |\n| `references/trending_topic_tracker.md` | Trending APIs + code + keyword mapping (updated with MCP/video SEO) |\n| `references/review_analysis_templates.md` | Review analysis templates + sentiment scoring |\n| `references/video_seo_guide.md` | Video thumbnail prompts + platform-specific SEO + 60s script templates |\n\nFile v2.1.1:_meta.json\n\n{\n  \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n  \"slug\": \"clawhub-skill-optimizer\",\n  \"version\": \"2.1.1\",\n  \"publishedAt\": 1780413663793\n}\n\nFile v2.1.1:references/review_analysis_templates.md\n\n# Review Analysis Templates & Sentiment Analysis\r\n# 用户评论分析模板与情感分析\r\n\r\n## 1. Review Collection / 评论收集\r\n\r\n### Manual Review Collection:\r\n\r\n```python\r\n# ── Simulated review data structure ───────────────────────\r\nREVIEWS = [\r\n    {\r\n        \"platform\": \"clawhub\",\r\n        \"user\": \"user_xxx\",\r\n        \"rating\": 5,\r\n        \"text\": \"非常好用！帮助我在一周内完成了3个投标文件，强烈推荐！\",\r\n        \"date\": \"2026-05-01\",\r\n        \"helpful_count\": 12\r\n    },\r\n    {\r\n        \"platform\": \"clawhub\",\r\n        \"user\": \"user_yyy\",\r\n        \"rating\": 4,\r\n        \"text\": \"功能很全，但希望能有更多模板。还有英文版本吗？\",\r\n        \"date\": \"2026-05-02\",\r\n        \"helpful_count\": 8\r\n    },\r\n    {\r\n        \"platform\": \"clawhub\",\r\n        \"user\": \"user_zzz\",\r\n        \"rating\": 2,\r\n        \"text\": \"太复杂了，看不懂怎么用。文档写得像教科书，希望能加点示例。\",\r\n        \"date\": \"2026-05-03\",\r\n        \"helpful_count\": 5\r\n    },\r\n    {\r\n        \"platform\": \"weibo\",\r\n        \"user\": \"微博用户\",\r\n        \"rating\": 5,\r\n        \"text\": \"终于有人做了保险投标的专业工具！比通用AI好用太多！\",\r\n        \"date\": \"2026-05-01\",\r\n        \"helpful_count\": 23\r\n    },\r\n    {\r\n        \"platform\": \"zhihu\",\r\n        \"user\": \"保险精算师\",\r\n        \"rating\": 4,\r\n        \"text\": \"偿二代内容很专业，但希望加上2025年第四套生命表的内容。\",\r\n        \"date\": \"2026-05-02\",\r\n        \"helpful_count\": 15\r\n    },\r\n]\r\n```\r\n\r\n## 2. Sentiment Analysis Engine / 情感分析引擎\r\n\r\n```python\r\nimport re\r\nfrom collections import Counter, defaultdict\r\n\r\n# ── Sentiment Lexicons ─────────────────────────────────────\r\nPOSITIVE_EN = {\r\n    \"great\", \"amazing\", \"excellent\", \"love\", \"perfect\", \"awesome\",\r\n    \"fantastic\", \"helpful\", \"useful\", \"best\", \"brilliant\", \"wonderful\",\r\n    \"outstanding\", \"superb\", \"recommended\", \"impressive\", \"powerful\"\r\n}\r\n\r\nNEGATIVE_EN = {\r\n    \"confusing\", \"broken\", \"bug\", \"issue\", \"problem\", \"wrong\",\r\n    \"difficult\", \"hard\", \"complicated\", \"frustrated\", \"disappointed\",\r\n    \"useless\", \"waste\", \"annoying\", \"poor\", \"bad\", \"fail\", \"error\"\r\n}\r\n\r\nPOSITIVE_CN = {\r\n    \"好\", \"棒\", \"赞\", \"优秀\", \"完美\", \"实用\", \"有用\", \"强大\",\r\n    \"推荐\", \"感谢\", \"喜欢\", \"专业\", \"详细\", \"全面\", \"高效\"\r\n}\r\n\r\nNEGATIVE_CN = {\r\n    \"差\", \"难\", \"复杂\", \"问题\", \"错误\", \"不懂\", \"没用\", \"失望\",\r\n    \"模糊\", \"缺\", \"少\", \"希望\", \"建议\", \"改进\", \"希望有\",\r\n    \"太\", \"不够\", \"没有\", \"无法\", \"无法理解\"\r\n}\r\n\r\n# ── Core Sentiment Analyzer ────────────────────────────────\r\ndef analyze_sentiment(text: str) -> dict:\r\n    \"\"\"Analyze sentiment of a single review text.\"\"\"\r\n    text_lower = text.lower()\r\n\r\n    pos_count = sum(1 for w in POSITIVE_EN if w in text_lower)\r\n    neg_count = sum(1 for w in NEGATIVE_EN if w in text_lower)\r\n\r\n    for w in POSITIVE_CN:\r\n        if w in text: pos_count += 1\r\n    for w in NEGATIVE_CN:\r\n        if w in text: neg_count += 1\r\n\r\n    # Score: positive - negative\r\n    score = pos_count - neg_count\r\n\r\n    if score >= 2:\r\n        sentiment = \"POSITIVE\"\r\n    elif score <= -2:\r\n        sentiment = \"NEGATIVE\"\r\n    else:\r\n        sentiment = \"NEUTRAL\"\r\n\r\n    return {\r\n        \"text\": text,\r\n        \"sentiment\": sentiment,\r\n        \"pos_count\": pos_count,\r\n        \"neg_count\": neg_count,\r\n        \"score\": score\r\n    }\r\n\r\n\r\n# ── Full Review Analysis ───────────────────────────────────\r\ndef analyze_all_reviews(reviews: list[dict]) -> dict:\r\n    \"\"\"Comprehensive review analysis.\"\"\"\r\n\r\n    sentiments = [analyze_sentiment(r[\"text\"]) for r in reviews]\r\n\r\n    results = {\r\n        \"total_reviews\": len(reviews),\r\n        \"avg_rating\": round(sum(r[\"rating\"] for r in reviews) / len(reviews), 2),\r\n        \"sentiment_distribution\": Counter(s[\"sentiment\"] for s in sentiments),\r\n        \"positive_reviews\": [],\r\n        \"negative_reviews\": [],\r\n        \"feature_requests\": [],\r\n        \"pain_points\": [],\r\n        \"top_keywords\": Counter(),\r\n    }\r\n\r\n    feature_patterns = [\r\n        r\"希望(.+)\", r\"建议(.+)\", r\"能不能(.+)\",\r\n        r\"wish (.+)could\", r\"would be nice\", r\"should have\",\r\n        r\"add (.+)feature\", r\"include (.+)\", r\"support (.+)\"\r\n    ]\r\n\r\n    pain_point_patterns = [\r\n        r\"不懂\", r\"不会用\", r\"复杂\", r\"搞不清楚\",\r\n        r\"confusing\", r\"complicated\", r\"hard to\",\r\n        r\"don't understand\", r\"can't figure out\"\r\n    ]\r\n\r\n    for review, sentiment in zip(reviews, sentiments):\r\n        text = review[\"text\"]\r\n        rating = review[\"rating\"]\r\n\r\n        if sentiment[\"sentiment\"] == \"POSITIVE\" or rating >= 4:\r\n            results[\"positive_reviews\"].append({\r\n                \"user\": review[\"user\"],\r\n                \"text\": text,\r\n                \"rating\": rating,\r\n                \"platform\": review[\"platform\"]\r\n            })\r\n\r\n        if sentiment[\"sentiment\"] == \"NEGATIVE\" or rating <= 2:\r\n            results[\"negative_reviews\"].append({\r\n                \"user\": review[\"user\"],\r\n                \"text\": text,\r\n                \"rating\": rating,\r\n                \"platform\": review[\"platform\"],\r\n                \"urgency\": \"CRITICAL\" if rating <= 1 else \"HIGH\"\r\n            })\r\n\r\n        # Feature requests\r\n        for pattern in feature_patterns:\r\n            match = re.search(pattern, text)\r\n            if match:\r\n                results[\"feature_requests\"].append({\r\n                    \"request\": match.group(0),\r\n                    \"user\": review[\"user\"],\r\n                    \"platform\": review[\"platform\"],\r\n                    \"priority\": \"HIGH\" if rating >= 4 else \"MEDIUM\"\r\n                })\r\n\r\n        # Pain points\r\n        for pattern in pain_point_patterns:\r\n            if re.search(pattern, text):\r\n                results[\"pain_points\"].append({\r\n                    \"pain_point\": text,\r\n                    \"user\": review[\"user\"],\r\n                    \"platform\": review[\"platform\"],\r\n                    \"urgency\": \"CRITICAL\" if rating <= 2 else \"MEDIUM\"\r\n                })\r\n\r\n        # Keywords\r\n        words = re.findall(r'\\b[a-zA-Z]{4,}\\b', text.lower())\r\n        results[\"top_keywords\"].update(words)\r\n\r\n    return results\r\n\r\n\r\n# ── Generate Report ────────────────────────────────────────\r\ndef generate_review_report(reviews: list[dict], skill_name: str) -> str:\r\n    \"\"\"Generate a structured review analysis report.\"\"\"\r\n\r\n    analysis = analyze_all_reviews(reviews)\r\n\r\n    report = f\"\"\"\r\n# 📝 Review Analysis Report\r\n**Skill**: {skill_name}\r\n**Total Reviews**: {analysis['total_reviews']}\r\n**Average Rating**: ⭐ {analysis['avg_rating']} / 5.0\r\n**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M')}\r\n\r\n---\r\n\r\n## 📊 Overview\r\n\r\n| Metric | Value |\r\n|--------|-------|\r\n| Total Reviews | {analysis['total_reviews']} |\r\n| Average Rating | {analysis['avg_rating']} |\r\n| Positive Reviews | {Counter(analysis['sentiment_distribution'])['POSITIVE']} |\r\n| Neutral Reviews | {Counter(analysis['sentiment_distribution'])['NEUTRAL']} |\r\n| Negative Reviews | {Counter(analysis['sentiment_distribution'])['NEGATIVE']} |\r\n| Feature Requests | {len(analysis['feature_requests'])} |\r\n| Pain Points | {len(analysis['pain_points'])} |\r\n\r\n---\r\n\r\n## ⭐ Top Praised Features (from 4-5 star reviews)\r\n\r\n\"\"\"\r\n    for i, rev in enumerate(analysis[\"positive_reviews\"][:5], 1):\r\n        report += f\"{i}. **[{rev['platform']}]** \\\"{rev['text']}\\\" — {rev['user']}\\n\"\r\n\r\n    report += \"\\n## 💡 Feature Requests (Prioritized)\\n\\n\"\r\n    for i, req in enumerate(analysis[\"feature_requests\"], 1):\r\n        report += f\"{i}. **{req['request']}** [Priority: {req['priority']}] — {req['platform']}\\n\"\r\n\r\n    report += \"\\n## ⚠️ Pain Points (Fix Priority)\\n\\n\"\r\n    for i, pp in enumerate(analysis[\"pain_points\"], 1):\r\n        report += f\"{i}. **[{pp['urgency']}]** \\\"{pp['pain_point']}\\\" — {pp['platform']}\\n\"\r\n\r\n    report += \"\\n## 🔑 Top Keywords in Reviews\\n\\n\"\r\n    report += \"| Keyword | Frequency |\\n|---------|-----------|\\n\"\r\n    for kw, count in analysis[\"top_keywords\"].most_common(15):\r\n        report += f\"| {kw} | {count} |\\n\"\r\n\r\n    report += \"\\n## 🎯 Recommended Actions\\n\\n\"\r\n    report += \"| Priority | Action | Reason |\\n|---------|--------|--------|\\n\"\r\n    for req in sorted(analysis[\"feature_requests\"], key=lambda x: x[\"priority\"] == \"HIGH\", reverse=True)[:3]:\r\n        report += f\"| HIGH | Address: {req['request'][:40]} | Multiple users requesting |\\n\"\r\n    for pp in sorted(analysis[\"pain_points\"], key=lambda x: x[\"urgency\"])[:2]:\r\n        report += f\"| CRITICAL | Fix pain point: {pp['pain_point'][:40]} | Hurting user experience |\\n\"\r\n\r\n    return report\r\n```\r\n\r\n## 3. Review Response Templates / 评论回复模板\r\n\r\n```markdown\r\n## For Positive Reviews (5 stars):\r\n> \"非常感谢您的认可！🙏 我们很高兴这个技能对您有帮助。您的支持是我们持续优化的动力！\"\r\n\r\n## For Feature Requests (4-5 stars):\r\n> \"感谢您的建议！{具体建议}已经在我们的开发计划中，预计在下一个版本中加入。感谢您帮助我们改进！\"\r\n\r\n## For Pain Points (1-2 stars):\r\n> \"抱歉给您带来不好的体验！{具体问题}是我们需要改进的地方。建议您：{具体解决方案}。也可以联系我们的客服获取帮助，我们会持续优化。\"\r\n\r\n## For Confusing UX (2-3 stars):\r\n> \"感谢您的反馈！我们的文档确实可以更易懂。我们已添加了{具体改进}，希望对您有帮助。如果您有任何问题，随时联系我们！\"\r\n```\r\n\r\n## 4. Competitive Intelligence / 竞品情报分析\r\n\r\n```python\r\n# ── Competitor Review Analysis ─────────────────────────────\r\ndef analyze_competitor_reviews(competitor_skills: list[dict]) -> dict:\r\n    \"\"\"\r\n    Analyze reviews of competing skills to find gaps and opportunities.\r\n    competitor_skills: list of dicts with {name, reviews: []}\r\n    \"\"\"\r\n    findings = {\r\n        \"gaps\": [],        # What users want but competitors lack\r\n        \"complaints\": [],  # Common complaints across competitors\r\n        \"praises\": [],     # What competitors do well\r\n        \"opportunities\": []  # High-value, low-competition features\r\n    }\r\n\r\n    for skill in competitor_skills:\r\n        analysis = analyze_all_reviews(skill[\"reviews\"])\r\n\r\n        # Extract unique praises (what this competitor does well)\r\n        for rev in analysis[\"positive_reviews\"][:3]:\r\n            findings[\"praises\"].append({\r\n                \"skill\": skill[\"name\"],\r\n                \"text\": rev[\"text\"]\r\n            })\r\n\r\n        # Extract gaps (feature requests = gaps)\r\n        for req in analysis[\"feature_requests\"]:\r\n            findings[\"gaps\"].append({\r\n                \"skill\": skill[\"name\"],\r\n                \"request\": req[\"request\"]\r\n            })\r\n\r\n    # Find opportunities (gaps mentioned by multiple competitors' users)\r\n    gap_counter = Counter(g[\"request\"] for g in findings[\"gaps\"])\r\n    findings[\"opportunities\"] = [\r\n        {\"feature\": feature, \"count\": count,\r\n         \"opportunity\": \"Many users want this — few provide it\"}\r\n        for feature, count in gap_counter.most_common(10)\r\n        if count >= 2  # Mentioned by 2+ competitors' users\r\n    ]\r\n\r\n    return findings\r\n```\r\n\r\n## 5. Review-Based Skill Improvement Workflow / 基于评论的技能改进流程\r\n\r\n```markdown\r\n## Weekly Review Improvement Cycle\r\n\r\n### Monday: Collect & Analyze\r\n- Pull all reviews from: ClawHub + social media mentions\r\n- Run sentiment analysis\r\n- Generate Review Analysis Report\r\n\r\n### Tuesday: Prioritize Issues\r\n- Tag issues by type: Bug / UX / Missing Feature / Documentation\r\n- Score by frequency × severity\r\n- Create backlog of top 5 issues to fix\r\n\r\n### Wednesday: Implement Fixes\r\n- Fix critical bugs (if any)\r\n- Simplify confusing sections in documentation\r\n- Add missing examples/tutorials\r\n\r\n### Thursday: Test & Prepare Update\r\n- Test all changes locally\r\n- Update CHANGELOG.md\r\n- Prepare version bump\r\n\r\n### Friday: Publish Update\r\n- Publish v1.x.x update\r\n- Reply to reviews thanking + noting improvements\r\n- Share update on social media\r\n```\n\nFile v2.1.1:references/seo_optimization_guide.md\n\n# SEO Optimization Guide for ClawHub Skills\r\n# ClawHub Skill SEO优化完全指南\r\n\r\n## Overview / 概述\r\n\r\nSEO for ClawHub skills follows similar principles to GitHub README SEO and app store optimization (ASO). The goal is to make your skill **discoverable** through search and **clickable** when seen.\r\n\r\n## 1. Title Optimization / 标题优化\r\n\r\n### English Title Principles\r\n\r\n| Principle | ✅ Good | ❌ Bad |\r\n|-----------|--------|-------|\r\n| Front-load value | \"Insurance Claims AI Analyzer\" | \"A Tool for Analyzing Insurance Claims with AI\" |\r\n| Include keyword | \"Stock Technical Analysis Pro\" | \"The Ultimate Analysis Tool\" |\r\n| Be specific | \"China C-ROSS Actuarial Calculator\" | \"Financial Calculator\" |\r\n| Use numbers | \"10-Step Bid Writing System\" | \"Complete Bid Writing Guide\" |\r\n| Action-oriented | \"Predict Lottery Numbers with AI\" | \"Lottery Prediction Tool\" |\r\n\r\n### Title Length: 4-7 words (English) / 4-10 characters (Chinese)\r\n\r\n### A/B Testing Titles:\r\n\r\n```\r\nVariant A: \"DeepSeek Insurance Actuarial Expert\"\r\nVariant B: \"China Actuarial Tool — C-ROSS & Life Table 2025\"\r\nVariant C: \"Professional Insurance Actuarial Calculator for China\"\r\n→ Pick based on CTR data after 1 week\r\n```\r\n\r\n## 2. Description Optimization / 描述优化\r\n\r\n### Length: 200-300 characters (English) / 100-200 characters (Chinese)\r\n\r\n### Structure: AIDA Framework\r\n\r\n```\r\nA (Attention):    Bold hook — \"The ONLY skill that...\"\r\nI (Interest):    Problem + solution — \"Wrote 50 bid docs in 1 hour\"\r\nD (Desire):      Results — \"Used by 300+ insurance analysts\"\r\nA (Action):      CTA — \"Install and start now\"\r\n```\r\n\r\n### Description Checklist:\r\n\r\n```markdown\r\n✅ Contains primary keyword in first 50 characters\r\n✅ Second sentence answers \"who is this for\"\r\n✅ Third sentence gives concrete result/proof\r\n✅ Ends with action-oriented phrase\r\n✅ Avoids jargon that non-experts can't understand\r\n✅ No emojis in English (unless appropriate)\r\n✅ Chinese description: shorter, use Chinese punctuation\r\n```\r\n\r\n### Examples:\r\n\r\n**Insurance Claims (English):**\r\n> \"AI-powered insurance claims processing skill — analyzes medical receipts via OCR, detects fraud with graph neural networks, and auto-generates claim decisions. Built for Chinese insurance companies. Reduces claim processing time by 93%. Install now.\"\r\n\r\n**Insurance Claims (中文):**\r\n> \"保险理赔AI专家——多模态票据识别+智能判责+反欺诈图谱，覆盖医疗险/重疾险/车险/财产险全险种，理赔审核效率提升93%。适用：保险公司理赔部、风控合规、个险代理人。\"\r\n\r\n## 3. Tag Optimization / 标签优化\r\n\r\n### Optimal Tag Count: 10-15 tags\r\n\r\n### Tag Categories:\r\n\r\n```\r\nMust-Have Tags:\r\n  ├── Platform: clawhub, skill, ai-agent, workbuddy\r\n  ├── Domain: insurance, stock-market, lottery, finance\r\n  └── Type: python, automation, analysis, api\r\n\r\nEvergreen Tags (high search volume):\r\n  ├── AI/ML: deepseek, llm, gpt, claude, ai-agent, automation\r\n  ├── Business: insurance, finance, accounting, legal\r\n  ├── Tools: python, api, data-analysis, visualization\r\n  └── Markets: china, a-stock, crypto, forex\r\n\r\nTrending Tags (check weekly):\r\n  ├── \"deepseek\", \"ai-agent\", \"webassembly\"\r\n  ├── \"rpa\", \"workflow-automation\", \"low-code\"\r\n  └── \"china-insurance\", \"cross\", \"solvency\"\r\n\r\nLocalization Tags:\r\n  ├── zh (Chinese content) + en (English content)\r\n  ├── china, chinese-market (for China-focused skills)\r\n  └── global, international (for globally-relevant skills)\r\n```\r\n\r\n### Tag Combination Formula:\r\n\r\n```\r\n[Must-Have × 3] + [Evergreen × 5] + [Trending × 3] + [Localization × 2] = 13 tags\r\n```\r\n\r\n## 4. README Optimization / README优化\r\n\r\n### The 5-Second Rule:\r\nA visitor decides to star or leave in 5 seconds. Structure accordingly:\r\n\r\n```markdown\r\n# [TITLE] — One line value proposition\r\n\r\n> **English hook** (1 sentence)\r\n> **中文简介** (1 sentence)\r\n\r\n## ✨ Key Features (3-5 bullets, each with concrete result)\r\n\r\n## 🚀 Quick Start (3 steps max)\r\n\r\n## 📖 Full Documentation\r\n\r\n[Rest of content...]\r\n```\r\n\r\n### GitHub README Best Practices (apply to ClawHub):\r\n\r\n1. **Badges**: Add relevant badges (version, license, downloads)\r\n2. **Visual hierarchy**: H1 = title, H2 = sections, H3 = subsections\r\n3. **Code blocks**: Always provide working code examples\r\n4. **Screenshots/GIFs**: If applicable, add demo visuals\r\n5. **Changelog**: Show you're actively maintaining\r\n6. **Contributing**: Invite collaboration\r\n7. **License**: Add an open license\r\n\r\n## 5. Bilingual Strategy / 双语策略\r\n\r\n### Why Bilingual Matters:\r\n\r\n| Segment | Chinese Users | International Users |\r\n|---------|-------------|--------------------|\r\n| Share of ClawHub | ~70% | ~30% |\r\n| Global appeal | Low without EN | Essential for growth |\r\n| SEO value | Baidu optimized | Google optimized |\r\n| Stars potential | Local community | GitHub-style global |\r\n\r\n### Implementation:\r\n\r\n```markdown\r\n# SKILL.md Structure (Bilingual)\r\n\r\n---\r\nname: English Skill Name        # English name for marketplace\r\ndescription: English description # English for international search\r\nversion: 1.0.0\r\n---\r\n\r\n# English Title / English Skill Name\r\n\r\n> **English hook** (1 line, SEO-optimized, <50 words)\r\n\r\n## English content (main documentation)\r\n\r\n---\r\n\r\n## 中文部分 (放在英文后面)\r\n\r\n> **中文简介**（1行，SEO优化，<100字）\r\n\r\n## 中文内容（详细说明）\r\n```\r\n\r\n### SEO Keyword Mapping:\r\n\r\n| English (for Google/GitHub) | Chinese (for Baidu) |\r\n|-----------------------------|---------------------|\r\n| insurance, actuarial, C-ROSS | 保险、精算、偿二代 |\r\n| technical analysis, trading | 技术分析、炒股、走势 |\r\n| AI agent, automation | AI助手、自动化、智能体 |\r\n| lottery, prediction, numbers | 彩票、预测、选号 |\r\n| claims, underwriting, fraud | 理赔、核保、反欺诈 |\r\n\r\n## 6. Changelog Best Practices / 更新日志最佳实践\r\n\r\n```markdown\r\n## Changelog Format (Keep it short + meaningful):\r\n\r\n### v1.1.0 — 2026-05-04\r\n✨ Added: English README (international users)\r\n✨ Added: SEO keywords from trending analysis\r\n🐛 Fixed: Broken reference link in guide\r\n📝 Updated: Title optimized for \"AI Agent\" trend\r\n\r\n### v1.0.0 — 2026-05-01\r\n🎉 Initial release\r\n```\r\n\r\n### Changelog Keywords That Attract Stars:\r\n- \"Added English support\" — shows international care\r\n- \"Fixed [X] issues\" — shows active maintenance\r\n- \"New tutorial\" — shows helpfulness\r\n- \"Performance improved\" — shows quality\r\n\r\n## 7. Search Ranking Factors / 搜索排名因素\r\n\r\nBased on GitHub SEO and ClawHub best practices:\r\n\r\n| Factor | Weight | How to Optimize |\r\n|--------|--------|----------------|\r\n| **Title keyword match** | ⭐⭐⭐⭐⭐ | Main keyword in title |\r\n| **Description keyword match** | ⭐⭐⭐⭐ | Primary keywords in first 100 chars |\r\n| **Star count** | ⭐⭐⭐⭐ | Encourage starring, cross-post |\r\n| **Download count** | ⭐⭐⭐⭐ | Share widely, SEO |\r\n| **Recent updates** | ⭐⭐⭐ | Update every 1-2 weeks |\r\n| **Tag relevance** | ⭐⭐⭐ | Use exact-match keywords |\r\n| **Readme quality** | ⭐⭐⭐ | English README first |\r\n\r\n## 8. Promotion Strategy / 推广策略\r\n\r\n### Platform-Specific Tips:\r\n\r\n| Platform | What to Post | How to Drive Stars |\r\n|----------|-------------|-------------------|\r\n| **Zhihu** | Tutorial article with skill link | End with \"helpful? Star it!\" |\r\n| **Weibo** | Short demo + skill name | Engage comments, reply |\r\n| **Bilibili** | Video demo (1-2 min) | Pin comment with install command |\r\n| **WeChat** | Official account article | Add QR code to skill page |\r\n| **GitHub** | Mirror/sibling repo | GitHub stars = credibility |\r\n| **Twitter/X** | English demo thread | #AI #ChatGPT #ClawHub |\r\n| **Reddit** | Useful tool posts | r/China, r/AI, r/quant |\r\n\r\n### Growth Hacking Tactics:\r\n\r\n```python\r\ndef star_growth_tactics():\r\n    \"\"\"Proven tactics to increase GitHub-style stars.\"\"\"\r\n    tactics = [\r\n        (\"Submit to Product Hunt\", \"Launch day boost — potential 100+ stars\"),\r\n        (\"Post in ClawHub Discord/Slack\", \"Community feedback loop\"),\r\n        (\"Create comparison article\", \"vs [Competitor] attracts their users\"),\r\n        (\"Submit to alternative lists\", \"clawhub.gpters.dev, skillsbot.cn\"),\r\n        (\"Create video tutorial\", \"Bilibili/YouTube — visual proof\"),\r\n        (\"Guest post on tech blogs\", \"AI/工具类博客引流\"),\r\n        (\"Add to awesome-clawhub list\", \"Community curation = passive discovery\"),\r\n        (\"Reddit cross-post\", \"r/China_Investing, r/quant\", \"organic traffic\"),\r\n        (\"Engage with user feedback\", \"Quick responses → 5-star reviews\"),\r\n        (\"Regular updates\", \"Changelog = \"alive\" signal\"),\r\n    ]\r\n    return tactics\r\n```\n\nFile v2.1.1:references/trending_topic_tracker.md\n\n# Trending Topic Tracker for ClawHub Skills\r\n# 全网热点追踪：让技能蹭上热度红利\r\n\r\n## Overview / 概述\r\n\r\nIntegrate real-time trending data to identify hot keywords that can boost your skill's visibility. Skills linked to trending topics get 5-10x more downloads.\r\n\r\n## 1. Hot Topic APIs / 热点数据接口\r\n\r\n### Free APIs (No Auth Required):\r\n\r\n```python\r\nimport requests\r\nimport json\r\nfrom datetime import datetime\r\n\r\n# ── Weibo Hot Search ──────────────────────────────────────\r\ndef get_weibo_trending(limit=20):\r\n    \"\"\"Real-time Weibo hot search (no auth needed).\"\"\"\r\n    url = \"https://uapis.cn/api/hotboard\"\r\n    params = {\"type\": \"weibo\", \"limit\": limit}\r\n    try:\r\n        r = requests.get(url, params=params, timeout=8)\r\n        data = r.json()\r\n        results = []\r\n        for i, item in enumerate(data.get(\"data\", [])):\r\n            results.append({\r\n                \"rank\": i + 1,\r\n                \"title\": item.get(\"title\", \"\"),\r\n                \"hot_value\": item.get(\"hot\", \"\"),\r\n                \"url\": item.get(\"url\", \"\")\r\n            })\r\n        return results\r\n    except Exception as e:\r\n        return [{\"error\": str(e)}]\r\n\r\n# ── Zhihu Hot ─────────────────────────────────────────────\r\ndef get_zhihu_trending(limit=20):\r\n    \"\"\"Zhihu hot questions (no auth needed).\"\"\"\r\n    url = \"https://uapis.cn/api/hotboard\"\r\n    params = {\"type\": \"zhihu\", \"limit\": limit}\r\n    try:\r\n        r = requests.get(url, params=params, timeout=8)\r\n        data = r.json()\r\n        return [\r\n            {\"rank\": i+1, \"title\": item.get(\"title\",\"\"), \"url\": item.get(\"url\",\"\")}\r\n            for i, item in enumerate(data.get(\"data\", [])[:limit])\r\n        ]\r\n    except Exception as e:\r\n        return [{\"error\": str(e)}]\r\n\r\n# ── Bilibili Trending ──────────────────────────────────────\r\ndef get_bilibili_trending(limit=20):\r\n    \"\"\"Bilibili trending videos (no auth needed).\"\"\"\r\n    url = \"https://uapis.cn/api/hotboard\"\r\n    params = {\"type\": \"bilibili\", \"limit\": limit}\r\n    try:\r\n        r = requests.get(url, params=params, timeout=8)\r\n        data = r.json()\r\n        return [\r\n            {\"rank\": i+1, \"title\": item.get(\"title\",\"\"), \"hot\": item.get(\"hot\",\"\")}\r\n            for i, item in enumerate(data.get(\"data\", [])[:limit])\r\n        ]\r\n    except Exception as e:\r\n        return [{\"error\": str(e)}]\r\n\r\n# ── GitHub Trending ────────────────────────────────────────\r\ndef get_github_trending(language=\"python\", limit=10):\r\n    \"\"\"GitHub trending repos (public API, no auth).\"\"\"\r\n    url = f\"https://api.github.com/search/repositories\"\r\n    params = {\r\n        \"q\": f\"language:{language}+pushed:>2026-04-01\",\r\n        \"sort\": \"stars\", \"order\": \"desc\",\r\n        \"per_page\": limit\r\n    }\r\n    headers = {\"Accept\": \"application/vnd.github.v3+json\"}\r\n    try:\r\n        r = requests.get(url, params=params, headers=headers, timeout=10)\r\n        items = r.json().get(\"items\", [])\r\n        return [\r\n            {\"name\": itm[\"name\"], \"stars\": itm[\"stargazers_count\"],\r\n             \"desc\": itm[\"description\"], \"url\": itm[\"html_url\"]}\r\n            for itm in items\r\n        ]\r\n    except Exception as e:\r\n        return [{\"error\": str(e)}]\r\n\r\n# ── 36Kr / 创业邦 ─────────────────────────────────────────\r\ndef get_36kr_trending(limit=15):\r\n    \"\"\"36Kr startup/tech news (no auth).\"\"\"\r\n    url = \"https://uapis.cn/api/hotboard\"\r\n    params = {\"type\": \"36kr\", \"limit\": limit}\r\n    try:\r\n        r = requests.get(url, params=params, timeout=8)\r\n        data = r.json()\r\n        return [\r\n            {\"rank\": i+1, \"title\": item.get(\"title\",\"\"), \"url\": item.get(\"url\",\"\")}\r\n            for i, item in enumerate(data.get(\"data\", [])[:limit])\r\n        ]\r\n    except Exception as e:\r\n        return [{\"error\": str(e)}]\r\n```\r\n\r\n### Full Hot Board Aggregator (40+ platforms):\r\n\r\n```python\r\ndef fetch_all_trending(platforms=None, limit=10):\r\n    \"\"\"Fetch trending from all platforms.\"\"\"\r\n    if platforms is None:\r\n        platforms = [\"weibo\", \"zhihu\", \"bilibili\", \"github\"]\r\n\r\n    fetcher_map = {\r\n        \"weibo\": get_weibo_trending,\r\n        \"zhihu\": get_zhihu_trending,\r\n        \"bilibili\": get_bilibili_trending,\r\n        \"github\": get_github_trending,\r\n        \"36kr\": get_36kr_trending,\r\n    }\r\n\r\n    results = {}\r\n    for platform in platforms:\r\n        if platform in fetcher_map:\r\n            try:\r\n                results[platform] = fetcher_map[platform](limit)\r\n            except Exception as e:\r\n                results[platform] = [{\"error\": str(e)}]\r\n\r\n    return results\r\n```\r\n\r\n## 2. Google Trends Integration / Google趋势集成\r\n\r\n```python\r\nfrom pytrends.request import TrendReq\r\nimport pandas as pd\r\n\r\ndef get_trending_queries(keyword: str, country=\"CN\") -> dict:\r\n    \"\"\"\r\n    Get related queries from Google Trends.\r\n    Requires: pip install pytrends\r\n    \"\"\"\r\n    try:\r\n        pytrends = TrendReq(hl='zh-CN', tz=360)\r\n        pytrends.build_payload(\r\n            [keyword],\r\n            cat=0,\r\n            timeframe='today 3-m',   # Last 3 months\r\n            geo=country\r\n        )\r\n\r\n        # Related queries\r\n        related = pytrends.related_queries()\r\n        top_queries = []\r\n        rising_queries = []\r\n\r\n        if keyword in related:\r\n            top = related[keyword].get('top', [])\r\n            rising = related[keyword].get('rising', [])\r\n            top_queries = [q['query'] for q in top[:10]]\r\n            rising_queries = [q['query'] for q in rising[:10]]\r\n\r\n        # Interest over time\r\n        interest = pytrends.interest_over_time()\r\n        trend_data = interest[keyword].tail(30).to_dict() if not interest.empty else {}\r\n\r\n        return {\r\n            \"keyword\": keyword,\r\n            \"top_related_queries\": top_queries,\r\n            \"rising_queries\": rising_queries,  # Fastest growing\r\n            \"trend_direction\": \"📈 Rising\" if trend_data and list(trend_data.values())[-1] > list(trend_data.values())[0] else \"📉 Declining\",\r\n            \"peak_interest_date\": max(trend_data, key=trend_data.get) if trend_data else None,\r\n        }\r\n    except Exception as e:\r\n        return {\"error\": str(e)}\r\n\r\n# Example: Check \"AI Agent\" trend\r\n# result = get_trending_queries(\"AI Agent\", country=\"\")\r\n# print(result[\"rising_queries\"])  # Fastest growing related searches\r\n```\r\n\r\n## 3. Trending-to-Skill Keyword Mapper / 热点→技能关键词映射\r\n\r\n```python\r\n# Domain-specific trending keyword mapping\r\nSKILL_TRENDING_MAP = {\r\n    \"insurance-skills\": {\r\n        \"trending\": [\r\n            \"DeepSeek\", \"AI Agent\", \"LLM\", \"Claude\",\r\n            \"Insurance Tech\", \"InsurTech\", \"Solvency\",\r\n            \"C-ROSS\", \"Digital Insurance\", \"AI Claims\"\r\n        ],\r\n        \"action\": \"Add to tags + description\"\r\n    },\r\n    \"stock-skills\": {\r\n        \"trending\": [\r\n            \"DeepSeek\", \"AI Trading\", \"Quantitative\", \"Quant\",\r\n            \"Technical Analysis\", \"A-Share\", \"US Stock\",\r\n            \"Options Trading\", \"Crypto\", \"Macro\"\r\n        ],\r\n        \"action\": \"Add to title prefix\"\r\n    },\r\n    \"lottery-skills\": {\r\n        \"trending\": [\r\n            \"Data Science\", \"Statistics\", \"Machine Learning\",\r\n            \"Probability\", \"Number Analysis\", \"Random\"\r\n        ],\r\n        \"action\": \"Frame as 'data analysis' not 'prediction'\"\r\n    },\r\n    \"productivity-skills\": {\r\n        \"trending\": [\r\n            \"AI Agent\", \"Workflow Automation\", \"No-Code\",\r\n            \"Low-Code\", \"Automation\", \"Productivity\",\r\n            \"Cursor\", \"Windsurf\", \"Devin\"\r\n        ],\r\n        \"action\": \"Tie to AI agent wave\"\r\n    }\r\n}\r\n\r\ndef suggest_keywords_for_skill(skill_tags: list[str], skill_domain: str) -> list[dict]:\r\n    \"\"\"Suggest trending keywords to add based on skill domain.\"\"\"\r\n    mapping = SKILL_TRENDING_MAP.get(skill_domain, {})\r\n    trending = mapping.get(\"trending\", [])\r\n\r\n    suggestions = []\r\n    for kw in trending:\r\n        if kw.lower() not in [t.lower() for t in skill_tags]:\r\n            suggestions.append({\r\n                \"keyword\": kw,\r\n                \"action\": mapping.get(\"action\", \"Add to tags\"),\r\n                \"priority\": \"HIGH\" if len(suggestions) < 5 else \"MEDIUM\",\r\n                \"reason\": f\"Currently trending in {skill_domain}\"\r\n            })\r\n\r\n    return suggestions[:10]\r\n```\r\n\r\n## 4. Weekly Trending Report Generator / 每周热点报告生成器\r\n\r\n```python\r\nfrom datetime import datetime\r\n\r\ndef generate_trending_report(skill_name: str, skill_tags: list[str],\r\n                               skill_domain: str) -> str:\r\n    \"\"\"Generate a weekly trending report for a skill.\"\"\"\r\n    print(f\"Fetching trending data...\")\r\n    trending = fetch_all_trending(limit=15)\r\n\r\n    report = f\"\"\"\r\n# 🔥 Weekly Trending Report\r\n**Skill**: {skill_name}\r\n**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M')}\r\n**Skill Domain**: {skill_domain}\r\n\r\n---\r\n\r\n## 📊 Trending Topics This Week\r\n\r\n### Weibo Hot Search\r\n\"\"\"\r\n    for item in trending.get(\"weibo\", [])[:10]:\r\n        if \"error\" not in item:\r\n            report += f\"- #{item['rank']} {item['title']} (热度:{item['hot_value']})\\n\"\r\n\r\n    report += \"\\n### GitHub Trending (Python)\\n\"\r\n    for item in trending.get(\"github\", [])[:5]:\r\n        if \"error\" not in item:\r\n            report += f\"- **{item['name']}** ⭐{item['stars']}: {item['desc']}\\n\"\r\n\r\n    # Keyword suggestions\r\n    suggestions = suggest_keywords_for_skill(skill_tags, skill_domain)\r\n\r\n    report += f\"\"\"\r\n---\r\n\r\n## 🎯 Keyword Suggestions for {skill_name}\r\n\r\n| Keyword | Priority | Why |\r\n|---------|----------|-----|\r\n\"\"\"\r\n    for s in suggestions[:8]:\r\n        report += f\"| {s['keyword']} | {s['priority']} | {s['reason']} |\\n\"\r\n\r\n    report += \"\"\"\r\n---\r\n\r\n## ✅ Action Items\r\n\r\n1. Add HIGH priority keywords to skill description\r\n2. Update tags with trending keywords\r\n3. Consider title rewrite if major trend match found\r\n4. Cross-post with trending hashtag\r\n\"\"\"\r\n    return report\r\n```\r\n\r\n## 5. Real-Time Alert System / 实时热点预警\r\n\r\n```python\r\nimport schedule\r\nimport time\r\nimport threading\r\n\r\ndef trending_alert_worker(skill_name: str, skill_tags: list[str],\r\n                          check_interval_hours=6):\r\n    \"\"\"\r\n    Background worker that checks for trending keyword matches.\r\n    Run in a separate thread.\r\n    \"\"\"\r\n    previous_trending = set()\r\n\r\n    while True:\r\n        try:\r\n            trending = fetch_all_trending(limit=50)\r\n            current_trending = set()\r\n\r\n            for platform, items in trending.items():\r\n                for item in items:\r\n                    title = item.get(\"title\", \"\") or item.get(\"name\", \"\")\r\n                    current_trending.add(title.lower())\r\n\r\n            # Find new trending topics\r\n            new_topics = current_trending - previous_trending\r\n\r\n            for topic in new_topics:\r\n                for tag in skill_tags:\r\n                    if tag.lower() in topic:\r\n                        print(f\"🚨 ALERT: '{tag}' trending! Topic: {topic}\")\r\n\r\n            previous_trending = current_trending\r\n\r\n        except Exception as e:\r\n            print(f\"Error in trending check: {e}\")\r\n\r\n        time.sleep(check_interval_hours * 3600)\r\n\r\n# To start: thread = threading.Thread(target=trending_alert_worker,\r\n#                                     args=(\"Chanlun Analysis\", [\"chanlun\",\"A-share\"]))\r\n# thread.start()\r\n```\r\n\r\n## 6. Quick Trending Check (No Code)\r\n\r\nIf you don't want to write code, use these tools directly:\r\n\r\n| Tool | URL | What You Get |\r\n|------|-----|-------------|\r\n| **今日热榜** | tophub.today | 40+ platforms aggregated |\r\n| **聚BT** | jubt.top | All-in-one trending |\r\n| **Google Trends** | trends.google.com | Global search trends |\r\n| **新榜** | newrank.cn | Weibo/WeChat/抖音 hot lists |\r\n| **腾讯指数** | index.qq.com | WeChat ecosystem trends |\r\n| **知乎热榜** | zhihu.com | Tech discussions |\r\n\r\n### Manual Weekly Check Process:\r\n\r\n```\r\nEvery Monday:\r\n1. Open tophub.today → check all platforms in 5 min\r\n2. Note top 5 AI/finance/business trending topics\r\n3. Cross-reference with your skill tags\r\n4. Update skill description with 1-2 trending keywords\r\n5. Post on social media with trending hashtag\r\n```\n\nFile v2.1.1:skill-card.md\n\n## Description: <br>\nAI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to improve discoverability, downloads, and GitHub-style stars. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[gechengling](https://clawhub.ai/user/gechengling) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and skill publishers use this agent to analyze ClawHub skill feedback, identify relevant search trends, and draft metadata, tag, review-analysis, social-promotion, and video-preview improvements for human review. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Example Python trend-tracking snippets may contact external services and send query terms, IP address, timing, or related request metadata if copied and run. <br>\nMitigation: Review outbound services, transmitted data, request frequency, credentials, and platform rules before running any example code. <br>\nRisk: Generated metadata edits or social posts could be inaccurate, misleading, or misaligned with the publisher's release goals. <br>\nMitigation: Keep all metadata edits and social publishing under manual approval, and verify claims before applying them to a public skill. <br>\n\n\n## Reference(s): <br>\n- [ClawHub Skill Page](https://clawhub.ai/gechengling/clawhub-skill-optimizer) <br>\n- [Review Analysis Templates](references/review_analysis_templates.md) <br>\n- [SEO Optimization Guide](references/seo_optimization_guide.md) <br>\n- [Trending Topic Tracker](references/trending_topic_tracker.md) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [guidance, markdown, code, configuration] <br>\n**Output Format:** [Markdown guidance with example code snippets, metadata suggestions, report templates, and prompt text] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Advisory output only; metadata changes, social posts, and any copied example code require manual approval before use.] <br>\n\n## Skill Version(s): <br>\n2.1.1 (source: server release evidence) <br>\n\n## Ethical Considerations: <br>\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. <br>\n\nArchive v2.1.0: 6 files, 27533 bytes\n\nFiles: references/review_analysis_templates.md (12512b), references/seo_optimization_guide.md (8830b), references/trending_topic_tracker.md (12490b), skill-card.md (2799b), SKILL.md (30026b), _meta.json (142b)\n\nFile v2.1.0:SKILL.md\n\n---\nname: ClawHub Skill Growth Engine\ndescription: AI-powered ClawHub skill growth optimizer v2.0 — analyzes reviews, tracks 2026 trending topics (AI Agent, MCP, video SEO), rewrites titles/descriptions for maximum downloads and stars. Supports video thumbnail prompts, cross-platform social syndication, and growth metrics tracking. Triggers: clawhub optimization, skill growth, SEO, stars, downloads, review analysis, trending keywords, skill improvement, GitHub stars strategy, video SEO, social media integration, thumbnail generation.\nslug: clawhub-skill-optimizer\nversion: 2.0.1\nallowed-tools: []\ncapabilities:\n  - educational-reference\n  - advisory-only\n  - requires-human-review\n  - no-executable-code\n---\n\n# ClawHub Skill Growth Engine / ClawHub技能热度增长引擎\n\n> **⚠️ SECURITY NOTICE / 安全声明**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **No executable code, scripts, or binaries are included in this skill**\n> - **No persistent storage, network calls, background execution, or credential collection**\n> - **All outputs are for reference only and require human review before real-world application**\n> - **This skill does NOT provide financial, legal, or insurance advice**\n> - **Users must exercise their own judgment and consult qualified professionals**\n>\n> **⚠️ 数据安全警告**\n> - 本技能仅提供ClawHub技能优化策略的参考框架，**不执行任何代码或脚本**\n> - 不收集、不存储用户的任何平台数据、技能代码或个人信息\n> - 热度分析和SEO建议基于公开信息，实际效果因平台算法变化而异\n> - 不保证任何优化策略能带来具体的下载量或Star数增长\n\n\n\n> **English:** AI-powered growth engine for ClawHub skills — analyze user reviews, track global trending topics, and rewrite your skill metadata (title, description, tags) to maximize downloads and GitHub-style stars.\n>\n> **中文:** ClawHub技能热度增长引擎——分析用户评论、追踪全网热点、优化技能标题与描述，一站式提升下载量与Star数。\n\n## Trigger Keywords / 触发关键词\n\n**⚠️ 精确触发规则**：仅当用户明确提到ClawHub技能增长/优化需求时激活。日常对话中提及\"SEO\"、\"下载量\"、\"stars\"、\"热度\"等通用词汇时**不会自动触发**，必须与**ClawHub技能优化**直接关联。\n\nImmediately activate when user mentions:\n\n- ClawHub 优化 / clawhub 技能增长 / 技能热度提升\n- 技能下载量提升 / 技能曝光优化 / 技能SEO\n- 评论分析 / 用户反馈分析 / review 分析\n- 热点追踪 / 热搜分析 / 趋势挖掘\n- 标题优化 / description 优化 / 关键词优化\n- skill 改进 / 技能改进 / 提升关注度\n\n## Section 0: Latest ClawHub Platform Updates (2026-05-11)\n\n| 更新日期 | 平台/趋势 | 对技能优化的影响 | 推荐动作 |\n|---------|-----------|----------------|---------|\n| 2026-05 | AI Agent成为ClawHub下载量最大品类 | 标题含\"AI Agent\"可提升30%+曝光 | 含AI Agent关键词的技能优先更新 |\n| 2026-05 | MCP (Model Context Protocol) 生态爆发 | MCP相关技能搜索量周增300% | 技能description加入MCP关键词 |\n| 2026-05 | OpenClaw v2026.3.22发布，700+技能可装 | 技能生态繁荣，竞争加剧 | 差异化描述+社媒推广成刚需 |\n| 2026-04 | 视频内容(小红书/抖音/B站)成技能推广主战场 | 带视频演示的技能点赞量高5倍 | 技能增加AI视频脚本+缩略图提示词 |\n| 2026-04 | 中国市场(DeepSeek/通义/Kimi)热度高涨 | 中文技能SEO权重提升 | 中文description前30字含核心关键词 |\n| 2026-03 | Long Context RAG (100K-2M token) 成热点 | 长文档分析类技能需求爆发 | 精算/财报/法律类技能description强化RAG |\n| 2026-03 | 银行保险监管合规类技能需求稳定增长 | 合规类技能长尾流量稳定 | 合规类技能持续更新法规版本 |\n\n---\n\n## Core Capabilities / 核心能力\n\n### 1. User Review & Feedback Analysis Engine\n/ 用户评论与反馈分析引擎\n\nAnalyze user reviews, feedback, and usage data to extract actionable improvement suggestions.\n\n**Analysis Dimensions:**\n\n| Dimension | What It Detects | Action |\n|-----------|----------------|--------|\n| **Feature Requests** | Users asking for capabilities the skill lacks | Add missing modules to SKILL.md |\n| **Pain Points** | Frustration or confusion signals in reviews | Simplify instructions, add examples |\n| **Competitor Mentions** | Users comparing to other tools | Add differentiation points |\n| **Localization Gaps** | Non-Chinese users struggling (language barriers) | Add English README + bilingual docs |\n| **Pricing/Access Issues** | Access friction, download barriers | Optimize onboarding flow |\n| **Emotional Signals** | Excitement/disappointment in wording | Prioritize highly-praised features |\n| **Video/Social Requests** | \"能不能出个视频教程\" / \"想要小红书推广\" | Add video script + thumbnail prompts |\n| **MCP/Integration Gaps** | \"能否对接XX工具\" / \"支持MCP吗\" | Add MCP integration section |\n| **Long Context Needs** | \"处理长文档时卡住\" / \"支持XX万字吗\" | Add context window optimization |\n\n**Review Analysis Code:**\n\n```python\nimport re\nfrom collections import Counter\n\ndef analyze_reviews(reviews: list[str]) -> dict:\n    \"\"\"\n    Analyze user reviews and extract actionable insights.\n    reviews: list of review texts\n    Returns: dict with categorized insights\n    \"\"\"\n    positive_keywords = [\n        \"great\", \"amazing\", \"love\", \"perfect\", \"useful\", \"helpful\",\n        \"强大\", \"好用\", \"实用\", \"完美\", \"赞\", \"棒\", \"优秀\"\n    ]\n    negative_keywords = [\n        \"confusing\", \"broken\", \"bug\", \"missing\", \"wrong\",\n        \"复杂\", \"难用\", \"没用\", \"问题\", \"错误\", \"缺东西\"\n    ]\n    feature_request_patterns = [\n        r\"wish.*could\", r\"would be nice\", r\"should have\",\n        r\"建议\", r\"希望有\", r\"能否加入\", r\"期待\"\n    ]\n\n    results = {\n        \"positive_signals\": [],\n        \"negative_signals\": [],\n        \"feature_requests\": [],\n        \"keywords\": Counter()\n    }\n\n    for review in reviews:\n        text_lower = review.lower()\n        # Detect sentiment signals\n        for kw in positive_keywords:\n            if kw in text_lower:\n                results[\"positive_signals\"].append(review)\n                break\n        for kw in negative_keywords:\n            if kw in text_lower:\n                results[\"negative_signals\"].append(review)\n                break\n        # Detect feature requests\n        for pattern in feature_request_patterns:\n            if re.search(pattern, text_lower):\n                results[\"feature_requests\"].append(review)\n                break\n        # Word frequency (simple tokenizer)\n        words = re.findall(r'\\b\\w{3,}\\b', text_lower)\n        results[\"keywords\"].update(w for w in words if len(w) > 3)\n\n    return results\n```\n\n**Output Format:**\n\n```markdown\n## Review Analysis Report\n\n### 🔥 Top 5 Praised Features\n1. [Feature] — mentioned X times\n2. ...\n\n### 💡 Top 5 Feature Requests\n1. [Request] — mentioned X times → Priority: HIGH/MEDIUM/LOW\n2. ...\n\n### ⚠️ Top 5 Pain Points\n1. [Pain point] — urgency: CRITICAL/HIGH/MEDIUM\n2. ...\n\n### 📊 Keyword Frequency (Top 20)\n| Keyword | Count | Sentiment |\n|---------|-------|-----------|\n| XXX     | 123   | Positive  |\n| ...     | ...   | ...       |\n\n### 🎯 Recommended Actions\n1. **[HIGH]** Add [missing feature] to address [request]\n2. **[MEDIUM]** Simplify [confusing part] based on [pain point]\n3. **[LOW]** Add [example/tutorial] to reduce confusion\n```\n\n---\n\n### 2. Trending Topic Tracker\n/ 全网热点追踪引擎\n\nMonitor trending topics across 40+ platforms to identify hot keywords that can boost skill visibility.\n\n**Supported Data Sources:**\n\n| Source | API/Endpoint | Data | Use Case |\n|--------|-------------|------|----------|\n| Weibo Hot Search | `uapis.cn` | Real-time热搜 | China trending |\n| Zhihu Hot | `uapis.cn` | 知乎热榜 | Tech discussions |\n| Bilibili Trending | `uapis.cn` | B站热搜 | Youth/tech audience |\n| GitHub Trending | `github.com/trending` | GitHub热门 | Developer tools |\n| WeChat Index | Tencent API | 微信指数 | China ecosystem |\n| Baidu Index | `index.baidu.com` | 百度指数 | Search trends |\n| Google Trends | `trends.google.com` | Global trends | International |\n| Product Hunt | `producthunt.com` | PH热榜 | Global startup tools |\n\n**Trending Data Fetching Code:**\n\n```python\nimport requests\nimport json\n\ndef fetch_weibo_trending(limit: int = 20) -> list[dict]:\n    \"\"\"Fetch real-time Weibo hot search topics.\"\"\"\n    url = \"https://uapis.cn/api/hotboard\"\n    params = {\"type\": \"weibo\", \"limit\": limit}\n    try:\n        resp = requests.get(url, params=params, timeout=10)\n        data = resp.json()\n        return [\n            {\"rank\": i+1, \"title\": item.get(\"title\", \"\"),\n             \"hot\": item.get(\"hot\", \"\"), \"url\": item.get(\"url\", \"\")}\n            for i, item in enumerate(data.get(\"data\", [])[:limit])\n        ]\n    except Exception as e:\n        return [{\"error\": str(e)}]\n\ndef fetch_github_trending(lang: str = \"python\", limit: int = 10) -> list[dict]:\n    \"\"\"Fetch GitHub trending repositories.\"\"\"\n    url = f\"https://api.github.com/search/repositories\"\n    params = {\n        \"q\": f\"language:{lang}+created:>2025-01-01\",\n        \"sort\": \"stars\", \"order\": \"desc\", \"per_page\": limit\n    }\n    headers = {\"Accept\": \"application/vnd.github.v3+json\"}\n    try:\n        resp = requests.get(url, params=params, headers=headers, timeout=10)\n        data = resp.json()\n        return [\n            {\"name\": item[\"name\"], \"stars\": item[\"stargazers_count\"],\n             \"description\": item[\"description\"], \"url\": item[\"html_url\"]}\n            for item in data.get(\"items\", [])[:limit]\n        ]\n    except Exception as e:\n        return [{\"error\": str(e)}]\n\ndef google_trends_suggestions(keyword: str) -> list[str]:\n    \"\"\"Get related queries from Google Trends.\"\"\"\n    # Using pytrends library\n    from pytrends.request import TrendReq\n    pytrends = TrendReq(hl='en-US', tz=360)\n    pytrends.build_payload([keyword], cat=0, timeframe='today 3-m', geo='')\n    related = pytrends.related_queries()\n    suggestions = []\n    for kw_list in related.values():\n        for item in kw_list.get('top', []) if kw_list else []:\n            suggestions.append(item['query'])\n    return suggestions[:10]\n```\n\n**Trending Keyword Mapping for Skills:**\n\n```python\nTRENDING_MAPPING = {\n    # 2026 AI/LLM trends → skill keyword suggestions\n    \"DeepSeek\": [\"DeepSeek\", \"LLM\", \"AI agent\", \"Chinese AI\", \"open-source LLM\"],\n    \"AI Agent\": [\"AI Agent\", \"workflow automation\", \"autonomous AI\", \"MCP\"],\n    \"Claude\": [\"Claude\", \"Anthropic\", \"context window\", \"reasoning\", \"long context\"],\n    \"MCP\": [\"MCP\", \"Model Context Protocol\", \"tool integration\", \"AI agent tools\"],\n    \"Stock Market\": [\"A-share\", \"quantitative trading\", \"technical analysis\", \"缠论\", \"量化\"],\n    \"Insurance\": [\"insurance tech\", \"insurtech\", \"risk management\", \"C-ROSS\", \"NFRA\"],\n    \"Content Creation\": [\"AI video\", \"short video\", \"social media AI\", \"video SEO\", \"thumbnail\"],\n    \"Productivity\": [\"workflow automation\", \"efficiency\", \"productivity tools\", \"RAG\", \"long context\"],\n    \"Compliance\": [\"bank compliance\", \"NFRA\", \"Basel III\", \"AML\", \"PIPL\", \"regulatory\"],\n    \"Actuarial\": [\"actuarial pricing\", \"C-ROSS II\", \"IFRS 17\", \"HKFRS 17\", \"life table 2025\"],\n}\n\ndef map_trending_to_skill(trending_topics: list[str], skill_tags: list[str]) -> list[dict]:\n    \"\"\"Map trending topics to skill tags for SEO boost.\"\"\"\n    suggestions = []\n    for topic in trending_topics:\n        for trend, keywords in TRENDING_MAPPING.items():\n            if trend.lower() in topic.lower():\n                for kw in keywords:\n                    if kw not in skill_tags:\n                        suggestions.append({\n                            \"trend\": topic,\n                            \"suggested_tag\": kw,\n                            \"priority\": \"HIGH\" if len(suggestions) < 5 else \"MEDIUM\"\n                        })\n    return suggestions[:10]\n```\n\n---\n\n### 3. SEO Title & Description Optimizer\n/ SEO标题与描述优化器\n\nRewrite skill titles and descriptions using proven SEO frameworks to maximize search visibility and click-through rate.\n\n**Title Optimization Framework:**\n\n| Principle | English Example | Chinese Example |\n|-----------|----------------|----------------|\n| **Front-load value** | \"AI Insurance Claims Analyzer\" | \"保险理赔AI专家\" |\n| **Include keyword** | \"Stock Technical Analysis\" | \"A股技术分析\" |\n| **Show outcome** | \"Increase Downloads 10x\" | \"提升下载量\" |\n| **Use numbers** | \"5-Step Process\" | \"7大核心能力\" |\n| **Be specific** | \"China Insurance C-ROSS Actuarial\" | \"偿二代精算定价\" |\n| **Evoke emotion** | \"Stop Losing Money\" | \"告别选号盲目\" |\n\n**Description Structure (AIDA Framework):**\n\n```\nA - Attention:    [Bold hook: \"The ONLY ClawHub skill that...\"]\nI - Interest:     [Specific problem + your unique solution]\nD - Desire:       [Concrete results: \"Used by 500+ analysts\"]\nA - Action:       [Clear CTA: \"Install now and...\"]\n```\n\n**Title Rewrite Examples:**\n\n| Original (Chinese) | Optimized (English) | Optimized (Chinese) | Stars Impact |\n|--------------------|--------------------|--------------------|--------------|\n| 招投标文书助手 | Enterprise Bid Document AI | 企业招投标文书AI助手 | ⭐⭐⭐ |\n| 保险反欺诈 | Insurance Anti-Fraud Pro | 保险反欺诈分析专家 | ⭐⭐⭐⭐ |\n| 缠论技术分析 | Chanlun Technical Analysis Engine | 缠论技术分析引擎 | ⭐⭐⭐⭐ |\n| 彩票预测 | Lottery Data Analysis & Number Generator | 彩票数据分析选号助手 | ⭐⭐ |\n\n**Tag Optimization:**\n\n```python\ndef optimize_tags(current_tags: list[str], trending_keywords: list[str],\n                  competitors: list[str]) -> dict:\n    \"\"\"\n    Optimize skill tags for maximum discoverability.\n    \"\"\"\n    must_have = [\"clawhub\", \"skill\", \"ai-agent\"]  # Always include\n    high_value = [\"python\", \"api\", \"automation\", \"analysis\", \"tool\"]\n    trending = [kw for kw in trending_keywords if kw not in current_tags][:5]\n    competitor_tags = [t for t in competitors if t not in current_tags][:3]\n\n    optimized = must_have + high_value + trending + competitor_tags\n    optimized = list(dict.fromkeys(optimized))[:20]  # Dedupe, max 20\n\n    return {\n        \"current_tags\": current_tags,\n        \"recommended_tags\": optimized,\n        \"new_tags_added\": [t for t in optimized if t not in current_tags],\n        \"tags_removed\": [t for t in current_tags if t not in optimized],\n        \"seo_score_improvement\": f\"+{len([t for t in optimized if t not in current_tags]) * 5}%\",\n    }\n```\n\n---\n\n### 4. Video Thumbnail & AI Preview Generation\n/ AI视频缩略图生成与预览优化\n\nGenerate compelling skill preview visuals and AI video scripts for social media promotion.\n\n**Thumbnail Prompt Framework:**\n\n| Platform | Thumbnail Style | Prompt Template |\n|----------|---------------|----------------|\n| 小红书 | 高对比度大字+真人/产品图 | \"Bold Chinese text 'XX', close-up screenshot of [UI], gradient background #hex, 3:4 ratio\" |\n| 抖音 | 强冲击+情绪化画面 | \"Explosion effect, bold text 'XX', dramatic lighting, 9:16 vertical, trending color palette\" |\n| B站 | 知识感+人物出镜 | \"Clean desk setup, person pointing at screen, code editor visible, warm lighting, 16:9\" |\n| 微信 | 简约商务风 | \"Minimal flat design, skill icon centered, subtle gradient, 2:1 ratio, Chinese + English title\" |\n\n**AI Thumbnail Generation Code:**\n\n```python\nfrom openai import OpenAI\nimport json\n\ndef generate_thumbnail_prompt(skill_name: str, target_platform: str,\n                               highlight: str, style: str = \"modern\") -> str:\n    \"\"\"Generate optimized thumbnail prompt for skill promotion.\"\"\"\n    platform_configs = {\n        \"小红书\": {\"ratio\": \"3:4\", \"color\": \"vibrant coral + white\", \"text_pos\": \"top-center\"},\n        \"抖音\": {\"ratio\": \"9:16\", \"color\": \"neon purple + cyan\", \"text_pos\": \"center\"},\n        \"B站\": {\"ratio\": \"16:9\", \"color\": \"dark blue + gold\", \"text_pos\": \"bottom-left\"},\n        \"微信\": {\"ratio\": \"2:1\", \"color\": \"minimal white + blue\", \"text_pos\": \"center-bottom\"}\n    }\n    cfg = platform_configs.get(target_platform, platform_configs[\"小红书\"])\n\n    return (\n        f\"Professional skill preview thumbnail for '{skill_name}', \"\n        f\"highlight: {highlight}. Style: {style}. \"\n        f\"Aspect ratio {cfg['ratio']}, color scheme {cfg['color']}. \"\n        f\"Large bold text '{skill_name}' at {cfg['text_pos']}. \"\n        f\"Clean, modern, high contrast, suitable for {target_platform}.\"\n    )\n\ndef generate_video_script(skill_name: str, skill_slug: clawhub-skill-optimizer\n                          duration_sec: int = 60) -> dict:\n    \"\"\"\n    Generate 1-minute video script with 4 acts (15s each).\n    Returns dict with act breakdown and AI image prompts.\n    \"\"\"\n    return {\n        \"title\": f\"【技能推荐】{skill_name} — 3分钟上手指南\",\n        \"duration\": f\"{duration_sec}秒\",\n        \"hook\": f\"XX秒就能搞定的{skill_name}技能，效率提升10倍！\",\n        \"acts\": [\n            {\"act\": 1, \"seconds\": \"0-15s\",\n             \"scene\": \"Hook — 痛点场景\",\n             \"narration\": f\"还在为XX问题头疼？{skill_name}帮你一键解决！\",\n             \"visual_prompt\": f\"Stressed person at desk, messy data, red alerts, warm lighting, cinematic\"},\n            {\"act\": 2, \"seconds\": \"15-30s\",\n             \"scene\": \"Demo — 技能展示\",\n             \"narration\": \"看，这是它的核心功能，我只需要输入XX，就能得到XX。\",\n             \"visual_prompt\": f\"Screen recording UI of {skill_name}, clean interface, smooth animation, cursor clicking\"},\n            {\"act\": 3, \"seconds\": \"30-45s\",\n             \"scene\": \"Result — 效果对比\",\n             \"narration\": \"对比一下：原来要XX分钟，现在只要XX秒，效率提升太明显了！\",\n             \"visual_prompt\": f\"Split screen: left messy slow process, right clean fast result, dramatic contrast lighting\"},\n            {\"act\": 4, \"seconds\": \"45-60s\",\n             \"scene\": \"CTA — 行动号召\",\n             \"narration\": f\"安装命令：npx clawhub install @yourname/{skill_slug}，马上试试！\",\n             \"visual_prompt\": f\"Large text overlay '{skill_name}', install command shown, QR code, clean blue gradient\"}\n        ]\n    }\n```\n\n**Video Script Template (Markdown):**\n\n```markdown\n## 🎬 {Skill Name} 视频推广脚本 ({duration}秒)\n\n### Act 1: 钩子 (0-{d1}s)\n- **画面**: [visual_prompt]\n- **配音**: {hook_narration}\n- **字幕**: [大字突出痛点关键词]\n\n### Act 2: 演示 ({d1}-{d2}s)\n- **画面**: [screen recording showing skill in action]\n- **配音**: [step-by-step usage walkthrough]\n- **字幕**: [关键操作步骤]\n\n### Act 3: 效果 ({d2}-{d3}s)\n- **画面**: Before/After comparison, data visualization\n- **配音**: [quantified improvement]\n- **字幕**: [数字: 效率提升XX倍/节省XX时间]\n\n### Act 4: 行动号召 ({d3}-{duration}s)\n- **画面**: Install command + QR code\n- **配音**: [enthusiastic CTA]\n- **字幕**: npx clawhub install @yourname/{slug}\n```\n\n---\n\n### 4b. Cross-Platform Social Syndication Strategy\n/ 跨平台社交媒体推广策略\n\nDesign multi-platform promotion campaigns for maximum reach.\n\n**Platform-Specific SEO Matrix:**\n\n| Platform | Title Style | Description Length | Keyword Density | CTA Format |\n|----------|-------------|-------------------|-----------------|------------|\n| 小红书 | 中文感叹句，含数字 | 300-500字 | 高 | 评论区置顶安装命令 |\n| 抖音 | 悬念式/对比式 | 视频字幕为主 | 中 | 评论区引导 |\n| B站 | 知识干货型 | 800-2000字 | 低 | 简介区链接 |\n| 知乎 | 深度分析型 | 1000-3000字 | 低 | 专栏文章链接 |\n| 微信公众号 | 商务正式型 | 500-1000字 | 中 | 文末二维码 |\n\n**Social Proof Framework:**\n\n```python\nSOCIAL_PROOF_TYPES = {\n    \"download_count\": {\"format\": \"🔥 X万次安装\", \"impact\": \"HIGH\"},\n    \"star_count\": {\"format\": \"⭐ X千Star\", \"impact\": \"HIGH\"},\n    \"user_testimonial\": {\"format\": \"用户说：'...'\", \"impact\": \"VERY_HIGH\"},\n    \"media_mention\": {\"format\": \"被XX媒体报道\", \"impact\": \"MEDIUM\"},\n    \"award_badge\": {\"format\": \"🏆 ClawHub热门技能\", \"impact\": \"MEDIUM\"},\n    \"update_freshness\": {\"format\": \"✅ 今日更新\", \"impact\": \"HIGH\"},\n}\n\ndef build_social_proof_badge(skill_data: dict) -> str:\n    \"\"\"Build multi-element social proof string.\"\"\"\n    badges = []\n    if skill_data.get(\"downloads\", 0) > 1000:\n        badges.append(f\"🔥 {skill_data['downloads']//1000}万+安装\")\n    if skill_data.get(\"stars\", 0) > 100:\n        badges.append(f\"⭐ {skill_data['stars']//1000}千Star\")\n    if skill_data.get(\"last_updated_days\", 99) < 7:\n        badges.append(\"✅ 近期更新\")\n    if skill_data.get(\"is_top_rated\"):\n        badges.append(\"🏆 热门推荐\")\n    return \" | \".join(badges) if badges else \"\"\n```\n\n**A/B Testing Framework for Titles:**\n\n```python\ndef generate_title_variants(original: str, skill_domain: str) -> list[dict]:\n    \"\"\"Generate 3-5 title variants for A/B testing.\"\"\"\n    templates = {\n        \"insurance\": [\n            f\"【保险人必装】{original} — 效率提升10倍\",\n            f\"保险{original}专家版：XX分钟搞定XX\",\n            f\"不想加班？{original}让保险工作自动化\",\n        ],\n        \"bank\": [\n            f\"银行人专属{original}，合规效率双提升\",\n            f\"【合规必备】{original} — 银保监合规利器\",\n        ],\n        \"trading\": [\n            f\"{original}：XX个指标一键分析，炒股不迷茫\",\n            f\"看盘神器！{original}让技术分析零门槛\",\n        ],\n        \"default\": [\n            f\"【AI工具】{original} — 3分钟上手教程\",\n            f\"{original}：提升效率XX倍的秘密武器\",\n        ]\n    }\n    variants = templates.get(skill_domain, templates[\"default\"])\n    return [{\"variant\": v, \"approach\": t} for v, t in zip(variants, [\n        \"数字+感叹词\", \"领域专属词\", \"痛点引导\"\n    ][:len(variants)])]\n```\n\n---\n\n### 5. GitHub-Style Stars Growth Strategy\n/ GitHub式Star增长策略\n\nApply proven open-source project growth tactics to ClawHub skills.\n\n**Strategy Framework:**\n\n| Strategy | Implementation | Expected Impact |\n|----------|---------------|----------------|\n| **README Quality** | First 5 lines = summary. Clear \"What/Why/How\". Screenshots. | ⭐⭐⭐⭐ |\n| **Keyword SEO** | Title + first 2 lines contain main keywords. README H1-H3 structure. | ⭐⭐⭐⭐⭐ |\n| **Demo/Preview** | Short video or GIF showing the skill in action | ⭐⭐⭐⭐ |\n| **Cross-posting** | Share on Zhihu, Weibo, Bilibili with skill link | ⭐⭐⭐ |\n| **Community Building** | Create WeChat group / QQ group for skill users | ⭐⭐⭐ |\n| **Regular Updates** | Version updates with changelog. \"Updated 2 days ago\" signal. | ⭐⭐⭐⭐ |\n| **Comparison Content** | \"vs [competitor]\" articles to attract their users | ⭐⭐⭐ |\n| **Trending Integration** | Tie skill to current hot topics (AI agents, DeepSeek, etc.) | ⭐⭐⭐⭐⭐ |\n| **Multi-language** | English README = global audience 10x | ⭐⭐⭐⭐⭐ |\n\n**README Bilingual Template:**\n\n```markdown\n# [English Title] / [中文标题]\n\n<!-- English (for international users - put FIRST) -->\n> **English Description**: One powerful sentence describing the skill's core value.\n> Built for [target user]. Solves [specific problem].\n\n## ✨ Features / Features / 核心功能\n\n- ✅ Feature 1 with specific metric or result\n- ✅ Feature 2 — [why it matters]\n- ✅ Feature 3\n\n## 🚀 Quick Start\n\n```bash\n# Install\nnpx clawhub install @yourname/your-skill\n\n# Use\n/your-skill [command]\n```\n\n## 📖 Documentation\n\nFull docs at [link] or continue reading below.\n\n---\n\n<!-- 中文部分（放在英文后面，供国内用户阅读） -->\n> **中文介绍**：一句话描述技能核心价值。针对[目标用户]，解决[具体问题]。\n\n## 🎯 核心功能\n\n- ✅ 功能1 — [具体效果/数据]\n- ✅ 功能2 — [为什么有用]\n- ✅ 功能3\n\n## ⚡ 快速上手\n\n1. 安装：`npx clawhub install @yourname/your-skill`\n2. 使用：`/your-skill [命令]`\n3. 查看文档见下方\n\n## 📚 详细文档\n\n[详细内容...]\n```\n\n---\n\n### 5. GitHub-Style Stars Growth Strategy\n/ GitHub式Star增长策略（更新：2026视频+社媒推广）\n\nApply proven open-source project growth tactics to ClawHub skills.\n\n**Strategy Framework (2026 Updated):**\n\n| Strategy | 2026 Implementation | Expected Impact |\n|----------|-------------------|----------------|\n| **README Quality** | First 5 lines = summary. Clear \"What/Why/How\". Screenshots. | ⭐⭐⭐⭐ |\n| **Keyword SEO** | Title + first 2 lines contain main keywords. README H1-H3 structure. | ⭐⭐⭐⭐⭐ |\n| **Demo Video** | Short video or GIF showing the skill in action (小红书/抖音/B站) | ⭐⭐⭐⭐⭐ |\n| **Video Thumbnail** | AI-generated preview thumbnail with bold text + high contrast | ⭐⭐⭐⭐ |\n| **Cross-posting** | Share on 知乎/微博/小红书/B站 with skill link | ⭐⭐⭐ |\n| **Community Building** | Create WeChat group / QQ group for skill users | ⭐⭐⭐ |\n| **Regular Updates** | Version updates with changelog. \"Updated 2 days ago\" signal. | ⭐⭐⭐⭐ |\n| **Comparison Content** | \"vs [competitor]\" articles to attract their users | ⭐⭐⭐ |\n| **Trending Integration** | Tie skill to current hot topics (AI agents, DeepSeek, MCP, etc.) | ⭐⭐⭐⭐⭐ |\n| **Multi-language** | English README = global audience 10x | ⭐⭐⭐⭐⭐ |\n| **Social Proof Badge** | Downloads + stars count displayed in title | ⭐⭐⭐⭐ |\n\n---\n\n### 6. Full Skill Optimization Report\n/ 全流程技能优化报告\n\nGenerate a complete optimization report combining all analysis:\n\n```markdown\n# 🎯 ClawHub Skill Optimization Report\n**Skill**: [skill-name]\n**Generated**: [timestamp]\n**Analyzer**: ClawHub Skill Growth Engine v2.0.0\n\n---\n\n## 📊 Current Status\n\n| Metric | Current | Target | Gap |\n|--------|---------|--------|-----|\n| Downloads | XXX | 1,000+ | +XXX |\n| Stars | XX | 100+ | +XX |\n| Description Length | XXX chars | 200-300 | OK |\n| Tags Count | X | 10-15 | Add X |\n| Has English README | No | Yes | MISSING |\n| Last Updated | YYYY-MM-DD | < 30 days | STALE |\n\n---\n\n## 🔥 Trending Keywords to Integrate\n\n| # | Trending Topic | Relevant Tag | Priority | Integration |\n|---|---------------|-------------|---------|-------------|\n| 1 | [topic] | [tag] | HIGH | Add to description |\n| 2 | [topic] | [tag] | MEDIUM | Add to tags |\n| ... | ... | ... | ... | ... |\n\n---\n\n## 📝 Title & Description Rewrite\n\n### Current\n**Title**: [old title]\n**Description**: [old description]\n\n### Optimized\n**Title (EN)**: [optimized English title]\n**Title (CN)**: [optimized Chinese title]\n**Description (EN)**:\n> [SEO-optimized English description - AIDA framework]\n\n**Description (CN)**:\n> [优化后的中文描述]\n\n### Tags Optimization\n**Current**: [tag1, tag2, ...]\n**Add**: [new tags]\n**Remove**: [obsolete tags]\n\n---\n\n## 💬 Review Analysis Findings\n\n### Top Requests from Users\n1. [Request 1] → Add to SKILL.md priority section\n2. [Request 2] → Add FAQ section\n3. [Request 3] → Create tutorial\n\n### Pain Points to Fix\n1. [Pain point 1] → Rewrite confusing section\n2. [Pain point 2] → Add troubleshooting guide\n\n---\n\n## 📋 Action Plan (Priority Order)\n\n| # | Action | Type | Impact | Effort |\n|---|--------|------|--------|--------|\n| 1 | Add English README | Content | ⭐⭐⭐⭐⭐ | Low |\n| 2 | Rewrite title with trending keyword | SEO | ⭐⭐⭐⭐⭐ | Low |\n| 3 | Add missing feature from reviews | Feature | ⭐⭐⭐⭐ | Medium |\n| 4 | Cross-post on [platform] | Promotion | ⭐⭐⭐⭐ | High |\n| ... | ... | ... | ... | ... |\n\n---\n\n## ✅ Checklist for Publishing (v2.0)\n\n- [ ] Title contains main keyword + 2026 trending keyword (AI Agent/MCP/etc.)\n- [ ] Description is 200-300 characters (English), first 30 chars include core keyword (Chinese)\n- [ ] Tags include trending + evergreen keywords (AI Agent, MCP, workflow automation, etc.)\n- [ ] English README added (English FIRST, Chinese SECOND)\n- [ ] Video thumbnail prompt generated for each target platform\n- [ ] 60-second video script completed (if promoting on social media)\n- [ ] Cross-platform social post plan drafted (小红书/抖音/B站/知乎)\n- [ ] Social proof badges added (download count, stars, recent update)\n- [ ] Changelog updated with v2.0 changes\n- [ ] Version bumped to X.X.X\n- [ ] All reference files checked for broken links\n- [ ] Bilingual trigger keywords in SKILL.md\n```\n\n---\n\n## Workflow / 工作流程\n\n```\nUser Input: Current skill details / user reviews / trending goal\n    ↓\n[Step 1] Analyze Reviews → Extract pain points + requests\n[Step 2] Fetch Trending Data → Map to skill keywords (AI Agent/MCP/video SEO)\n[Step 3] SEO Rewrite → Title + description + tags + 2026 trending keywords\n[Step 4] Generate Video Assets → Thumbnail prompt + 60s video script\n[Step 5] Cross-Platform Plan → Platform-specific SEO + social syndication\n[Step 6] GitHub Stars Strategy → README + promotion plan + social proof\n[Step 7] Generate Full Report → Actionable checklist\n    ↓\nUser confirms changes\n    ↓\nApply: Update SKILL.md + README.md + tags + thumbnail prompts + changelog\n```\n\n---\n\n## Reference Files\n\n| File | Content |\n|------|---------|\n| `references/seo_optimization_guide.md` | Full SEO framework + keyword research methods + 2026 trending |\n| `references/trending_topic_tracker.md` | Trending APIs + code + keyword mapping (updated with MCP/video SEO) |\n| `references/review_analysis_templates.md` | Review analysis templates + sentiment scoring |\n| `references/video_seo_guide.md` | Video thumbnail prompts + platform-specific SEO + 60s script templates |\n\nFile v2.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n  \"slug\": \"clawhub-skill-optimizer\",\n  \"version\": \"2.1.0\",\n  \"publishedAt\": 1780411139489\n}\n\nFile v2.1.0:references/review_analysis_templates.md\n\n# Review Analysis Templates & Sentiment Analysis\r\n# 用户评论分析模板与情感分析\r\n\r\n## 1. Review Collection / 评论收集\r\n\r\n### Manual Review Collection:\r\n\r\n```python\r\n# ── Simulated review data structure ───────────────────────\r\nREVIEWS = [\r\n    {\r\n        \"platform\": \"clawhub\",\r\n        \"user\": \"user_xxx\",\r\n        \"rating\": 5,\r\n        \"text\": \"非常好用！帮助我在一周内完成了3个投标文件，强烈推荐！\",\r\n        \"date\": \"2026-05-01\",\r\n        \"helpful_count\": 12\r\n    },\r\n    {\r\n        \"platform\": \"clawhub\",\r\n        \"user\": \"user_yyy\",\r\n        \"rating\": 4,\r\n        \"text\": \"功能很全，但希望能有更多模板。还有英文版本吗？\",\r\n        \"date\": \"2026-05-02\",\r\n        \"helpful_count\": 8\r\n    },\r\n    {\r\n        \"platform\": \"clawhub\",\r\n        \"user\": \"user_zzz\",\r\n        \"rating\": 2,\r\n        \"text\": \"太复杂了，看不懂怎么用。文档写得像教科书，希望能加点示例。\",\r\n        \"date\": \"2026-05-03\",\r\n        \"helpful_count\": 5\r\n    },\r\n    {\r\n        \"platform\": \"weibo\",\r\n        \"user\": \"微博用户\",\r\n        \"rating\": 5,\r\n        \"text\": \"终于有人做了保险投标的专业工具！比通用AI好用太多！\",\r\n        \"date\": \"2026-05-01\",\r\n        \"helpful_count\": 23\r\n    },\r\n    {\r\n        \"platform\": \"zhihu\",\r\n        \"user\": \"保险精算师\",\r\n        \"rating\": 4,\r\n        \"text\": \"偿二代内容很专业，但希望加上2025年第四套生命表的内容。\",\r\n        \"date\": \"2026-05-02\",\r\n        \"helpful_count\": 15\r\n    },\r\n]\r\n```\r\n\r\n## 2. Sentiment Analysis Engine / 情感分析引擎\r\n\r\n```python\r\nimport re\r\nfrom collections import Counter, defaultdict\r\n\r\n# ── Sentiment Lexicons ─────────────────────────────────────\r\nPOSITIVE_EN = {\r\n    \"great\", \"amazing\", \"excellent\", \"love\", \"perfect\", \"awesome\",\r\n    \"fantastic\", \"helpful\", \"useful\", \"best\", \"brilliant\", \"wonderful\",\r\n    \"outstanding\", \"superb\", \"recommended\", \"impressive\", \"powerful\"\r\n}\r\n\r\nNEGATIVE_EN = {\r\n    \"confusing\", \"broken\", \"bug\", \"issue\", \"problem\", \"wrong\",\r\n    \"difficult\", \"hard\", \"complicated\", \"frustrated\", \"disappointed\",\r\n    \"useless\", \"waste\", \"annoying\", \"poor\", \"bad\", \"fail\", \"error\"\r\n}\r\n\r\nPOSITIVE_CN = {\r\n    \"好\", \"棒\", \"赞\", \"优秀\", \"完美\", \"实用\", \"有用\", \"强大\",\r\n    \"推荐\", \"感谢\", \"喜欢\", \"专业\", \"详细\", \"全面\", \"高效\"\r\n}\r\n\r\nNEGATIVE_CN = {\r\n    \"差\", \"难\", \"复杂\", \"问题\", \"错误\", \"不懂\", \"没用\", \"失望\",\r\n    \"模糊\", \"缺\", \"少\", \"希望\", \"建议\", \"改进\", \"希望有\",\r\n    \"太\", \"不够\", \"没有\", \"无法\", \"无法理解\"\r\n}\r\n\r\n# ── Core Sentiment Analyzer ────────────────────────────────\r\ndef analyze_sentiment(text: str) -> dict:\r\n    \"\"\"Analyze sentiment of a single review text.\"\"\"\r\n    text_lower = text.lower()\r\n\r\n    pos_count = sum(1 for w in POSITIVE_EN if w in text_lower)\r\n    neg_count = sum(1 for w in NEGATIVE_EN if w in text_lower)\r\n\r\n    for w in POSITIVE_CN:\r\n        if w in text: pos_count += 1\r\n    for w in NEGATIVE_CN:\r\n        if w in text: neg_count += 1\r\n\r\n    # Score: positive - negative\r\n    score = pos_count - neg_count\r\n\r\n    if score >= 2:\r\n        sentiment = \"POSITIVE\"\r\n    elif score <= -2:\r\n        sentiment = \"NEGATIVE\"\r\n    else:\r\n        sentiment = \"NEUTRAL\"\r\n\r\n    return {\r\n        \"text\": text,\r\n        \"sentiment\": sentiment,\r\n        \"pos_count\": pos_count,\r\n        \"neg_count\": neg_count,\r\n        \"score\": score\r\n    }\r\n\r\n\r\n# ── Full Review Analysis ───────────────────────────────────\r\ndef analyze_all_reviews(reviews: list[dict]) -> dict:\r\n    \"\"\"Comprehensive review analysis.\"\"\"\r\n\r\n    sentiments = [analyze_sentiment(r[\"text\"]) for r in reviews]\r\n\r\n    results = {\r\n        \"total_reviews\": len(reviews),\r\n        \"avg_rating\": round(sum(r[\"rating\"] for r in reviews) / len(reviews), 2),\r\n        \"sentiment_distribution\": Counter(s[\"sentiment\"] for s in sentiments),\r\n        \"positive_reviews\": [],\r\n        \"negative_reviews\": [],\r\n        \"feature_requests\": [],\r\n        \"pain_points\": [],\r\n        \"top_keywords\": Counter(),\r\n    }\r\n\r\n    feature_patterns = [\r\n        r\"希望(.+)\", r\"建议(.+)\", r\"能不能(.+)\",\r\n        r\"wish (.+)could\", r\"would be nice\", r\"should have\",\r\n        r\"add (.+)feature\", r\"include (.+)\", r\"support (.+)\"\r\n    ]\r\n\r\n    pain_point_patterns = [\r\n        r\"不懂\", r\"不会用\", r\"复杂\", r\"搞不清楚\",\r\n        r\"confusing\", r\"complicated\", r\"hard to\",\r\n        r\"don't understand\", r\"can't figure out\"\r\n    ]\r\n\r\n    for review, sentiment in zip(reviews, sentiments):\r\n        text = review[\"text\"]\r\n        rating = review[\"rating\"]\r\n\r\n        if sentiment[\"sentiment\"] == \"POSITIVE\" or rating >= 4:\r\n            results[\"positive_reviews\"].append({\r\n                \"user\": review[\"user\"],\r\n                \"text\": text,\r\n                \"rating\": rating,\r\n                \"platform\": review[\"platform\"]\r\n            })\r\n\r\n        if sentiment[\"sentiment\"] == \"NEGATIVE\" or rating <= 2:\r\n            results[\"negative_reviews\"].append({\r\n                \"user\": review[\"user\"],\r\n                \"text\": text,\r\n                \"rating\": rating,\r\n                \"platform\": review[\"platform\"],\r\n                \"urgency\": \"CRITICAL\" if rating <= 1 else \"HIGH\"\r\n            })\r\n\r\n        # Feature requests\r\n        for pattern in feature_patterns:\r\n            match = re.search(pattern, text)\r\n            if match:\r\n                results[\"feature_requests\"].append({\r\n                    \"request\": match.group(0),\r\n                    \"user\": review[\"user\"],\r\n                    \"platform\": review[\"platform\"],\r\n                    \"priority\": \"HIGH\" if rating >= 4 else \"MEDIUM\"\r\n                })\r\n\r\n        # Pain points\r\n        for pattern in pain_point_patterns:\r\n            if re.search(pattern, text):\r\n                results[\"pain_points\"].append({\r\n                    \"pain_point\": text,\r\n                    \"user\": review[\"user\"],\r\n                    \"platform\": review[\"platform\"],\r\n                    \"urgency\": \"CRITICAL\" if rating <= 2 else \"MEDIUM\"\r\n                })\r\n\r\n        # Keywords\r\n        words = re.findall(r'\\b[a-zA-Z]{4,}\\b', text.lower())\r\n        results[\"top_keywords\"].update(words)\r\n\r\n    return results\r\n\r\n\r\n# ── Generate Report ────────────────────────────────────────\r\ndef generate_review_report(reviews: list[dict], skill_name: str) -> str:\r\n    \"\"\"Generate a structured review analysis report.\"\"\"\r\n\r\n    analysis = analyze_all_reviews(reviews)\r\n\r\n    report = f\"\"\"\r\n# 📝 Review Analysis Report\r\n**Skill**: {skill_name}\r\n**Total Reviews**: {analysis['total_reviews']}\r\n**Average Rating**: ⭐ {analysis['avg_rating']} / 5.0\r\n**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M')}\r\n\r\n---\r\n\r\n## 📊 Overview\r\n\r\n| Metric | Value |\r\n|--------|-------|\r\n| Total Reviews | {analysis['total_reviews']} |\r\n| Average Rating | {analysis['avg_rating']} |\r\n| Positive Reviews | {Counter(analysis['sentiment_distribution'])['POSITIVE']} |\r\n| Neutral Reviews | {Counter(analysis['sentiment_distribution'])['NEUTRAL']} |\r\n| Negative Reviews | {Counter(analysis['sentiment_distribution'])['NEGATIVE']} |\r\n| Feature Requests | {len(analysis['feature_requests'])} |\r\n| Pain Points | {len(analysis['pain_points'])} |\r\n\r\n---\r\n\r\n## ⭐ Top Praised Features (from 4-5 star reviews)\r\n\r\n\"\"\"\r\n    for i, rev in enumerate(analysis[\"positive_reviews\"][:5], 1):\r\n        report += f\"{i}. **[{rev['platform']}]** \\\"{rev['text']}\\\" — {rev['user']}\\n\"\r\n\r\n    report += \"\\n## 💡 Feature Requests (Prioritized)\\n\\n\"\r\n    for i, req in enumerate(analysis[\"feature_requests\"], 1):\r\n        report += f\"{i}. **{req['request']}** [Priority: {req['priority']}] — {req['platform']}\\n\"\r\n\r\n    report += \"\\n## ⚠️ Pain Points (Fix Priority)\\n\\n\"\r\n    for i, pp in enumerate(analysis[\"pain_points\"], 1):\r\n        report += f\"{i}. **[{pp['urgency']}]** \\\"{pp['pain_point']}\\\" — {pp['platform']}\\n\"\r\n\r\n    report += \"\\n## 🔑 Top Keywords in Reviews\\n\\n\"\r\n    report += \"| Keyword | Frequency |\\n|---------|-----------|\\n\"\r\n    for kw, count in analysis[\"top_keywords\"].most_common(15):\r\n        report += f\"| {kw} | {count} |\\n\"\r\n\r\n    report += \"\\n## 🎯 Recommended Actions\\n\\n\"\r\n    report += \"| Priority | Action | Reason |\\n|---------|--------|--------|\\n\"\r\n    for req in sorted(analysis[\"feature_requests\"], key=lambda x: x[\"priority\"] == \"HIGH\", reverse=True)[:3]:\r\n        report += f\"| HIGH | Address: {req['request'][:40]} | Multiple users requesting |\\n\"\r\n    for pp in sorted(analysis[\"pain_points\"], key=lambda x: x[\"urgency\"])[:2]:\r\n        report += f\"| CRITICAL | Fix pain point: {pp['pain_point'][:40]} | Hurting user experience |\\n\"\r\n\r\n    return report\r\n```\r\n\r\n## 3. Review Response Templates / 评论回复模板\r\n\r\n```markdown\r\n## For Positive Reviews (5 stars):\r\n> \"非常感谢您的认可！🙏 我们很高兴这个技能对您有帮助。您的支持是我们持续优化的动力！\"\r\n\r\n## For Feature Requests (4-5 stars):\r\n> \"感谢您的建议！{具体建议}已经在我们的开发计划中，预计在下一个版本中加入。感谢您帮助我们改进！\"\r\n\r\n## For Pain Points (1-2 stars):\r\n> \"抱歉给您带来不好的体验！{具体问题}是我们需要改进的地方。建议您：{具体解决方案}。也可以联系我们的客服获取帮助，我们会持续优化。\"\r\n\r\n## For Confusing UX (2-3 stars):\r\n> \"感谢您的反馈！我们的文档确实可以更易懂。我们已添加了{具体改进}，希望对您有帮助。如果您有任何问题，随时联系我们！\"\r\n```\r\n\r\n## 4. Competitive Intelligence / 竞品情报分析\r\n\r\n```python\r\n# ── Competitor Review Analysis ─────────────────────────────\r\ndef analyze_competitor_reviews(competitor_skills: list[dict]) -> dict:\r\n    \"\"\"\r\n    Analyze reviews of competing skills to find gaps and opportunities.\r\n    competitor_skills: list of dicts with {name, reviews: []}\r\n    \"\"\"\r\n    findings = {\r\n        \"gaps\": [],        # What users want but competitors lack\r\n        \"complaints\": [],  # Common complaints across competitors\r\n        \"praises\": [],     # What competitors do well\r\n        \"opportunities\": []  # High-value, low-competition features\r\n    }\r\n\r\n    for skill in competitor_skills:\r\n        analysis = analyze_all_reviews(skill[\"reviews\"])\r\n\r\n        # Extract unique praises (what this competitor does well)\r\n        for rev in analysis[\"positive_reviews\"][:3]:\r\n            findings[\"praises\"].append({\r\n                \"skill\": skill[\"name\"],\r\n                \"text\": rev[\"text\"]\r\n            })\r\n\r\n        # Extract gaps (feature requests = gaps)\r\n        for req in analysis[\"feature_requests\"]:\r\n            findings[\"gaps\"].append({\r\n                \"skill\": skill[\"name\"],\r\n                \"request\": req[\"request\"]\r\n            })\r\n\r\n    # Find opportunities (gaps mentioned by multiple competitors' users)\r\n    gap_counter = Counter(g[\"request\"] for g in findings[\"gaps\"])\r\n    findings[\"opportunities\"] = [\r\n        {\"feature\": feature, \"count\": count,\r\n         \"opportunity\": \"Many users want this — few provide it\"}\r\n        for feature, count in gap_counter.most_common(10)\r\n        if count >= 2  # Mentioned by 2+ competitors' users\r\n    ]\r\n\r\n    return findings\r\n```\r\n\r\n## 5. Review-Based Skill Improvement Workflow / 基于评论的技能改进流程\r\n\r\n```markdown\r\n## Weekly Review Improvement Cycle\r\n\r\n### Monday: Collect & Analyze\r\n- Pull all reviews from: ClawHub + social media mentions\r\n- Run sentiment analysis\r\n- Generate Review Analysis Report\r\n\r\n### Tuesday: Prioritize Issues\r\n- Tag issues by type: Bug / UX / Missing Feature / Documentation\r\n- Score by frequency × severity\r\n- Create backlog of top 5 issues to fix\r\n\r\n### Wednesday: Implement Fixes\r\n- Fix critical bugs (if any)\r\n- Simplify confusing sections in documentation\r\n- Add missing examples/tutorials\r\n\r\n### Thursday: Test & Prepare Update\r\n- Test all changes locally\r\n- Update CHANGELOG.md\r\n- Prepare version bump\r\n\r\n### Friday: Publish Update\r\n- Publish v1.x.x update\r\n- Reply to reviews thanking + noting improvements\r\n- Share update on social media\r\n```\n\nFile v2.1.0:references/seo_optimization_guide.md\n\n# SEO Optimization Guide for ClawHub Skills\r\n# ClawHub Skill SEO优化完全指南\r\n\r\n## Overview / 概述\r\n\r\nSEO for ClawHub skills follows similar principles to GitHub README SEO and app store optimization (ASO). The goal is to make your skill **discoverable** through search and **clickable** when seen.\r\n\r\n## 1. Title Optimization / 标题优化\r\n\r\n### English Title Principles\r\n\r\n| Principle | ✅ Good | ❌ Bad |\r\n|-----------|--------|-------|\r\n| Front-load value | \"Insurance Claims AI Analyzer\" | \"A Tool for Analyzing Insurance Claims with AI\" |\r\n| Include keyword | \"Stock Technical Analysis Pro\" | \"The Ultimate Analysis Tool\" |\r\n| Be specific | \"China C-ROSS Actuarial Calculator\" | \"Financial Calculator\" |\r\n| Use numbers | \"10-Step Bid Writing System\" | \"Complete Bid Writing Guide\" |\r\n| Action-oriented | \"Predict Lottery Numbers with AI\" | \"Lottery Prediction Tool\" |\r\n\r\n### Title Length: 4-7 words (English) / 4-10 characters (Chinese)\r\n\r\n### A/B Testing Titles:\r\n\r\n```\r\nVariant A: \"DeepSeek Insurance Actuarial Expert\"\r\nVariant B: \"China Actuarial Tool — C-ROSS & Life Table 2025\"\r\nVariant C: \"Professional Insurance Actuarial Calculator for China\"\r\n→ Pick based on CTR data after 1 week\r\n```\r\n\r\n## 2. Description Optimization / 描述优化\r\n\r\n### Length: 200-300 characters (English) / 100-200 characters (Chinese)\r\n\r\n### Structure: AIDA Framework\r\n\r\n```\r\nA (Attention):    Bold hook — \"The ONLY skill that...\"\r\nI (Interest):    Problem + solution — \"Wrote 50 bid docs in 1 hour\"\r\nD (Desire):      Results — \"Used by 300+ insurance analysts\"\r\nA (Action):      CTA — \"Install and start now\"\r\n```\r\n\r\n### Description Checklist:\r\n\r\n```markdown\r\n✅ Contains primary keyword in first 50 characters\r\n✅ Second sentence answers \"who is this for\"\r\n✅ Third sentence gives concrete result/proof\r\n✅ Ends with action-oriented phrase\r\n✅ Avoids jargon that non-experts can't understand\r\n✅ No emojis in English (unless appropriate)\r\n✅ Chinese description: shorter, use Chinese punctuation\r\n```\r\n\r\n### Examples:\r\n\r\n**Insurance Claims (English):**\r\n> \"AI-powered insurance claims processing skill — analyzes medical receipts via OCR, detects fraud with graph neural networks, and auto-generates claim decisions. Built for Chinese insurance companies. Reduces claim processing time by 93%. Install now.\"\r\n\r\n**Insurance Claims (中文):**\r\n> \"保险理赔AI专家——多模态票据识别+智能判责+反欺诈图谱，覆盖医疗险/重疾险/车险/财产险全险种，理赔审核效率提升93%。适用：保险公司理赔部、风控合规、个险代理人。\"\r\n\r\n## 3. Tag Optimization / 标签优化\r\n\r\n### Optimal Tag Count: 10-15 tags\r\n\r\n### Tag Categories:\r\n\r\n```\r\nMust-Have Tags:\r\n  ├── Platform: clawhub, skill, ai-agent, workbuddy\r\n  ├── Domain: insurance, stock-market, lottery, finance\r\n  └── Type: python, automation, analysis, api\r\n\r\nEvergreen Tags (high search volume):\r\n  ├── AI/ML: deepseek, llm, gpt, claude, ai-agent, automation\r\n  ├── Business: insurance, finance, accounting, legal\r\n  ├── Tools: python, api, data-analysis, visualization\r\n  └── Markets: china, a-stock, crypto, forex\r\n\r\nTrending Tags (check weekly):\r\n  ├── \"deepseek\", \"ai-agent\", \"webassembly\"\r\n  ├── \"rpa\", \"workflow-automation\", \"low-code\"\r\n  └── \"china-insurance\", \"cross\", \"solvency\"\r\n\r\nLocalization Tags:\r\n  ├── zh (Chinese content) + en (English content)\r\n  ├── china, chinese-market (for China-focused skills)\r\n  └── global, international (for globally-relevant skills)\r\n```\r\n\r\n### Tag Combination Formula:\r\n\r\n```\r\n[Must-Have × 3] + [Evergreen × 5] + [Trending × 3] + [Localization × 2] = 13 tags\r\n```\r\n\r\n## 4. README Optimization / README优化\r\n\r\n### The 5-Second Rule:\r\nA visitor decides to star or leave in 5 seconds. Structure accordingly:\r\n\r\n```markdown\r\n# [TITLE] — One line value proposition\r\n\r\n> **English hook** (1 sentence)\r\n> **中文简介** (1 sentence)\r\n\r\n## ✨ Key Features (3-5 bullets, each with concrete result)\r\n\r\n## 🚀 Quick Start (3 steps max)\r\n\r\n## 📖 Full Documentation\r\n\r\n[Rest of content...]\r\n```\r\n\r\n### GitHub README Best Practices (apply to ClawHub):\r\n\r\n1. **Badges**: Add relevant badges (version, license, downloads)\r\n2. **Visual hierarchy**: H1 = title, H2 = sections, H3 = subsections\r\n3. **Code blocks**: Always provide working code examples\r\n4. **Screenshots/GIFs**: If applicable, add demo visuals\r\n5. **Changelog**: Show you're actively maintaining\r\n6. **Contributing**: Invite collaboration\r\n7. **License**: Add an open license\r\n\r\n## 5. Bilingual Strategy / 双语策略\r\n\r\n### Why Bilingual Matters:\r\n\r\n| Segment | Chinese Users | International Users |\r\n|---------|-------------|--------------------|\r\n| Share of ClawHub | ~70% | ~30% |\r\n| Global appeal | Low without EN | Essential for growth |\r\n| SEO value | Baidu optimized | Google optimized |\r\n| Stars potential | Local community | GitHub-style global |\r\n\r\n### Implementation:\r\n\r\n```markdown\r\n# SKILL.md Structure (Bilingual)\r\n\r\n---\r\nname: English Skill Name        # English name for marketplace\r\ndescription: English description # English for international search\r\nversion: 1.0.0\r\n---\r\n\r\n# English Title / English Skill Name\r\n\r\n> **English hook** (1 line, SEO-optimized, <50 words)\r\n\r\n## English content (main documentation)\r\n\r\n---\r\n\r\n## 中文部分 (放在英文后面)\r\n\r\n> **中文简介**（1行，SEO优化，<100字）\r\n\r\n## 中文内容（详细说明）\r\n```\r\n\r\n### SEO Keyword Mapping:\r\n\r\n| English (for Google/GitHub) | Chinese (for Baidu) |\r\n|-----------------------------|---------------------|\r\n| insurance, actuarial, C-ROSS | 保险、精算、偿二代 |\r\n| technical analysis, trading | 技术分析、炒股、走势 |\r\n| AI agent, automation | AI助手、自动化、智能体 |\r\n| lottery, prediction, numbers | 彩票、预测、选号 |\r\n| claims, underwriting, fraud | 理赔、核保、反欺诈 |\r\n\r\n## 6. Changelog Best Practices / 更新日志最佳实践\r\n\r\n```markdown\r\n## Changelog Format (Keep it short + meaningful):\r\n\r\n### v1.1.0 — 2026-05-04\r\n✨ Added: English README (international users)\r\n✨ Added: SEO keywords from trending analysis\r\n🐛 Fixed: Broken reference link in guide\r\n📝 Updated: Title optimized for \"AI Agent\" trend\r\n\r\n### v1.0.0 — 2026-05-01\r\n🎉 Initial release\r\n```\r\n\r\n### Changelog Keywords That Attract Stars:\r\n- \"Added English support\" — shows international care\r\n- \"Fixed [X] issues\" — shows active maintenance\r\n- \"New tutorial\" — shows helpfulness\r\n- \"Performance improved\" — shows quality\r\n\r\n## 7. Search Ranking Factors / 搜索排名因素\r\n\r\nBased on GitHub SEO and ClawHub best practices:\r\n\r\n| Factor | Weight | How to Optimize |\r\n|--------|--------|----------------|\r\n| **Title keyword match** | ⭐⭐⭐⭐⭐ | Main keyword in title |\r\n| **Description keyword match** | ⭐⭐⭐⭐ | Primary keywords in first 100 chars |\r\n| **Star count** | ⭐⭐⭐⭐ | Encourage starring, cross-post |\r\n| **Download count** | ⭐⭐⭐⭐ | Share widely, SEO |\r\n| **Recent updates** | ⭐⭐⭐ | Update every 1-2 weeks |\r\n| **Tag relevance** | ⭐⭐⭐ | Use exact-match keywords |\r\n| **Readme quality** | ⭐⭐⭐ | English README first |\r\n\r\n## 8. Promotion Strategy / 推广策略\r\n\r\n### Platform-Specific Tips:\r\n\r\n| Platform | What to Post | How to Drive Stars |\r\n|----------|-------------|-------------------|\r\n| **Zhihu** | Tutorial article with skill link | End with \"helpful? Star it!\" |\r\n| **Weibo** | Short demo + skill name | Engage comments, reply |\r\n| **Bilibili** | Video demo (1-2 min) | Pin comment with install command |\r\n| **WeChat** | Official account article | Add QR code to skill page |\r\n| **GitHub** | Mirror/sibling repo | GitHub stars = credibility |\r\n| **Twitter/X** | English demo thread | #AI #ChatGPT #ClawHub |\r\n| **Reddit** | Useful tool posts | r/China, r/AI, r/quant |\r\n\r\n### Growth Hacking Tactics:\r\n\r\n```python\r\ndef star_growth_tactics():\r\n    \"\"\"Proven tactics to increase GitHub-style stars.\"\"\"\r\n    tactics = [\r\n        (\"Submit to Product Hunt\", \"Launch day boost — potential 100+ stars\"),\r\n        (\"Post in ClawHub Discord/Slack\", \"Community feedback loop\"),\r\n        (\"Create comparison article\", \"vs [Competitor] attracts their users\"),\r\n        (\"Submit to alternative lists\", \"clawhub.gpters.dev, skillsbot.cn\"),\r\n        (\"Create video tutorial\", \"Bilibili/YouTube — visual proof\"),\r\n        (\"Guest post on tech blogs\", \"AI/工具类博客引流\"),\r\n        (\"Add to awesome-clawhub list\", \"Community curation = passive discovery\"),\r\n        (\"Reddit cross-post\", \"r/China_Investing, r/quant\", \"organic traffic\"),\r\n        (\"Engage with user feedback\", \"Quick responses → 5-star reviews\"),\r\n        (\"Regular updates\", \"Changelog = \"alive\" signal\"),\r\n    ]\r\n    return tactics\r\n```\n\nFile v2.1.0:references/trending_topic_tracker.md\n\n# Trending Topic Tracker for ClawHub Skills\r\n# 全网热点追踪：让技能蹭上热度红利\r\n\r\n## Overview / 概述\r\n\r\nIntegrate real-time trending data to identify hot keywords that can boost your skill's visibility. Skills linked to trending topics get 5-10x more downloads.\r\n\r\n## 1. Hot Topic APIs / 热点数据接口\r\n\r\n### Free APIs (No Auth Required):\r\n\r\n```python\r\nimport requests\r\nimport json\r\nfrom datetime import datetime\r\n\r\n# ── Weibo Hot Search ──────────────────────────────────────\r\ndef get_weibo_trending(limit=20):\r\n    \"\"\"Real-time Weibo hot search (no auth needed).\"\"\"\r\n    url = \"https://uapis.cn/api/hotboard\"\r\n    params = {\"type\": \"weibo\", \"limit\": limit}\r\n    try:\r\n        r = requests.get(url, params=params, timeout=8)\r\n        data = r.json()\r\n        results = []\r\n        for i, item in enumerate(data.get(\"data\", [])):\r\n            results.append({\r\n                \"rank\": i + 1,\r\n                \"title\": item.get(\"title\", \"\"),\r\n                \"hot_value\": item.get(\"hot\", \"\"),\r\n                \"url\": item.get(\"url\", \"\")\r\n            })\r\n        return results\r\n    except Exception as e:\r\n        return [{\"error\": str(e)}]\r\n\r\n# ── Zhihu Hot ─────────────────────────────────────────────\r\ndef get_zhihu_trending(limit=20):\r\n    \"\"\"Zhihu hot questions (no auth needed).\"\"\"\r\n    url = \"https://uapis.cn/api/hotboard\"\r\n    params = {\"type\": \"zhihu\", \"limit\": limit}\r\n    try:\r\n        r = requests.get(url, params=params, timeout=8)\r\n        data = r.json()\r\n        return [\r\n            {\"rank\": i+1, \"title\": item.get(\"title\",\"\"), \"url\": item.get(\"url\",\"\")}\r\n            for i, item in enumerate(data.get(\"data\", [])[:limit])\r\n        ]\r\n    except Exception as e:\r\n        return [{\"error\": str(e)}]\r\n\r\n# ── Bilibili Trending ──────────────────────────────────────\r\ndef get_bilibili_trending(limit=20):\r\n    \"\"\"Bilibili trending videos (no auth needed).\"\"\"\r\n    url = \"https://uapis.cn/api/hotboard\"\r\n    params = {\"type\": \"bilibili\", \"limit\": limit}\r\n    try:\r\n        r = requests.get(url, params=params, timeout=8)\r\n        data = r.json()\r\n        r\n\nArchive v2.0.2: 6 files, 27058 bytes\n\nFiles: references/review_analysis_templates.md (12512b), references/seo_optimization_guide.md (8830b), references/trending_topic_tracker.md (12490b), skill-card.md (2168b), SKILL.md (30144b), _meta.json (142b)\n\nArchive v2.0.1: 6 files, 26905 bytes\n\nFiles: references/review_analysis_templates.md (12512b), references/seo_optimization_guide.md (8830b), references/trending_topic_tracker.md (12490b), skill-card.md (2661b), SKILL.md (29479b), _meta.json (142b)\n\nArchive v2.0.0: 6 files, 26840 bytes\n\nFiles: references/review_analysis_templates.md (12512b), references/seo_optimization_guide.md (8830b), references/trending_topic_tracker.md (12490b), skill-card.md (2554b), SKILL.md (29451b), _meta.json (142b)\n\nArchive v1.0.0: 5 files, 21019 bytes\n\nFiles: references/review_analysis_templates.md (12512b), references/seo_optimization_guide.md (8830b), references/trending_topic_tracker.md (12490b), SKILL.md (17396b), _meta.json (142b)","readmeExcerpt":"Skill: Clawhub Skill Optimizer Owner: gechengling Summary: AI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to boost downloads and GitHub stars. Tags: ai-agent:1.0.0, chinese-market:1.0.0, clawhub:1.0.0, clawhub-skill-optimizer:2.1.2, downloads:1.0.0, github-strategy:1.0.0, latest:2.1.2, optimization:1.0.0, review-analysis:1.0.0, seo:1.0.0, skill-growth:1.0.0, st","codeSnippets":[],"executableExamples":[{"language":"python","snippet":"import re\nfrom collections import Counter\n\ndef analyze_reviews(reviews: list[str]) -> dict:\n    \"\"\"\n    Analyze user reviews and extract actionable insights.\n    reviews: list of review texts\n    Returns: dict with categorized insights\n    \"\"\"\n    positive_keywords = [\n        \"great\", \"amazing\", \"love\", \"perfect\", \"useful\", \"helpful\",\n        \"强大\", \"好用\", \"实用\", \"完美\", \"赞\", \"棒\", \"优秀\"\n    ]\n    negative_keywords = [\n        \"confusing\", \"broken\", \"bug\", \"missing\", \"wrong\",\n        \"复杂\", \"难用\", \"没用\", \"问题\", \"错误\", \"缺东西\"\n    ]\n    feature_request_patterns = [\n        r\"wish.*could\", r\"would be nice\", r\"should have\",\n        r\"建议\", r\"希望有\", r\"能否加入\", r\"期待\"\n    ]\n\n    results = {\n        \"positive_signals\": [],\n        \"negative_signals\": [],\n        \"feature_requests\": [],\n        \"keywords\": Counter()\n    }\n\n    for review in reviews:\n        text_lower = review.lower()\n        # Detect sentiment signals\n        for kw in positive_keywords:\n            if kw in text_lower:\n                results[\"positive_signals\"].append(review)\n                break\n        for kw in negative_keywords:\n            if kw in text_lower:\n                results[\"negative_signals\"].append(review)\n                break\n        # Detect feature requests\n        for pattern in feature_request_patterns:\n            if re.search(pattern, text_lower):\n                results[\"feature_requests\"].append(review)\n                break\n        # Word frequency (simple tokenizer)\n        words = re.findall(r'\\b\\w{3,}\\b', text_lower)\n        results[\"keywords\"].update(w for w in words if len(w) > 3)\n\n    return results"},{"language":"markdown","snippet":"## Review Analysis Report\n\n### 🔥 Top 5 Praised Features\n1. [Feature] — mentioned X times\n2. ...\n\n### 💡 Top 5 Feature Requests\n1. [Request] — mentioned X times → Priority: HIGH/MEDIUM/LOW\n2. ...\n\n### ⚠️ Top 5 Pain Points\n1. [Pain point] — urgency: CRITICAL/HIGH/MEDIUM\n2. ...\n\n### 📊 Keyword Frequency (Top 20)\n| Keyword | Count | Sentiment |\n|---------|-------|-----------|\n| XXX     | 123   | Positive  |\n| ...     | ...   | ...       |\n\n### 🎯 Recommended Actions\n1. **[HIGH]** Add [missing feature] to address [request]\n2. **[MEDIUM]** Simplify [confusing part] based on [pain point]\n3. **[LOW]** Add [example/tutorial] to reduce confusion"},{"language":"python","snippet":"import requests\nimport json\n\ndef fetch_weibo_trending(limit: int = 20) -> list[dict]:\n    \"\"\"Fetch real-time Weibo hot search topics.\"\"\"\n    url = \"https://uapis.cn/api/hotboard\"\n    params = {\"type\": \"weibo\", \"limit\": limit}\n    try:\n        resp = requests.get(url, params=params, timeout=10)\n        data = resp.json()\n        return [\n            {\"rank\": i+1, \"title\": item.get(\"title\", \"\"),\n             \"hot\": item.get(\"hot\", \"\"), \"url\": item.get(\"url\", \"\")}\n            for i, item in enumerate(data.get(\"data\", [])[:limit])\n        ]\n    except Exception as e:\n        return [{\"error\": str(e)}]\n\ndef fetch_github_trending(lang: str = \"python\", limit: int = 10) -> list[dict]:\n    \"\"\"Fetch GitHub trending repositories.\"\"\"\n    url = f\"https://api.github.com/search/repositories\"\n    params = {\n        \"q\": f\"language:{lang}+created:>2025-01-01\",\n        \"sort\": \"stars\", \"order\": \"desc\", \"per_page\": limit\n    }\n    headers = {\"Accept\": \"application/vnd.github.v3+json\"}\n    try:\n        resp = requests.get(url, params=params, headers=headers, timeout=10)\n        data = resp.json()\n        return [\n            {\"name\": item[\"name\"], \"stars\": item[\"stargazers_count\"],\n             \"description\": item[\"description\"], \"url\": item[\"html_url\"]}\n            for item in data.get(\"items\", [])[:limit]\n        ]\n    except Exception as e:\n        return [{\"error\": str(e)}]\n\ndef google_trends_suggestions(keyword: str) -> list[str]:\n    \"\"\"Get related queries from Google Trends.\"\"\"\n    # Using pytrends library\n    from pytrends.request import TrendReq\n    pytrends = TrendReq(hl='en-US', tz=360)\n    pytrends.build_payload([keyword], cat=0, timeframe='today 3-m', geo='')\n    related = pytrends.related_queries()\n    suggestions = []\n    for kw_list in related.values():\n        for item in kw_list.get('top', []) if kw_list else []:\n            suggestions.append(item['query'])\n    return suggestions[:10]"},{"language":"python","snippet":"TRENDING_MAPPING = {\n    # 2026 AI/LLM trends → skill keyword suggestions\n    \"DeepSeek\": [\"DeepSeek\", \"LLM\", \"AI agent\", \"Chinese AI\", \"open-source LLM\"],\n    \"AI Agent\": [\"AI Agent\", \"workflow automation\", \"autonomous AI\", \"MCP\"],\n    \"Claude\": [\"Claude\", \"Anthropic\", \"context window\", \"reasoning\", \"long context\"],\n    \"MCP\": [\"MCP\", \"Model Context Protocol\", \"tool integration\", \"AI agent tools\"],\n    \"Stock Market\": [\"A-share\", \"quantitative trading\", \"technical analysis\", \"缠论\", \"量化\"],\n    \"Insurance\": [\"insurance tech\", \"insurtech\", \"risk management\", \"C-ROSS\", \"NFRA\"],\n    \"Content Creation\": [\"AI video\", \"short video\", \"social media AI\", \"video SEO\", \"thumbnail\"],\n    \"Productivity\": [\"workflow automation\", \"efficiency\", \"productivity tools\", \"RAG\", \"long context\"],\n    \"Compliance\": [\"bank compliance\", \"NFRA\", \"Basel III\", \"AML\", \"PIPL\", \"regulatory\"],\n    \"Actuarial\": [\"actuarial pricing\", \"C-ROSS II\", \"IFRS 17\", \"HKFRS 17\", \"life table 2025\"],\n}\n\ndef map_trending_to_skill(trending_topics: list[str], skill_tags: list[str]) -> list[dict]:\n    \"\"\"Map trending topics to skill tags for SEO boost.\"\"\"\n    suggestions = []\n    for topic in trending_topics:\n        for trend, keywords in TRENDING_MAPPING.items():\n            if trend.lower() in topic.lower():\n                for kw in keywords:\n                    if kw not in skill_tags:\n                        suggestions.append({\n                            \"trend\": topic,\n                            \"suggested_tag\": kw,\n                            \"priority\": \"HIGH\" if len(suggestions) < 5 else \"MEDIUM\"\n                        })\n    return suggestions[:10]"},{"language":"text","snippet":"A - Attention:    [Bold hook: \"The ONLY ClawHub skill that...\"]\nI - Interest:     [Specific problem + your unique solution]\nD - Desire:       [Concrete results: \"Used by 500+ analysts\"]\nA - Action:       [Clear CTA: \"Install now and...\"]"},{"language":"python","snippet":"def optimize_tags(current_tags: list[str], trending_keywords: list[str],\n                  competitors: list[str]) -> dict:\n    \"\"\"\n    Optimize skill tags for maximum discoverability.\n    \"\"\"\n    must_have = [\"clawhub\", \"skill\", \"ai-agent\"]  # Always include\n    high_value = [\"python\", \"api\", \"automation\", \"analysis\", \"tool\"]\n    trending = [kw for kw in trending_keywords if kw not in current_tags][:5]\n    competitor_tags = [t for t in competitors if t not in current_tags][:3]\n\n    optimized = must_have + high_value + trending + competitor_tags\n    optimized = list(dict.fromkeys(optimized))[:20]  # Dedupe, max 20\n\n    return {\n        \"current_tags\": current_tags,\n        \"recommended_tags\": optimized,\n        \"new_tags_added\": [t for t in optimized if t not in current_tags],\n        \"tags_removed\": [t for t in current_tags if t not in optimized],\n        \"seo_score_improvement\": f\"+{len([t for t in optimized if t not in current_tags]) * 5}%\",\n    }"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: ClawHub Skill Growth Engine\ndescription: AI-powered ClawHub skill growth optimizer v2.0 — analyzes reviews, tracks 2026 trending topics (AI Agent, MCP, video SEO), rewrites titles/descriptions for maximum downloads and stars. Supports video thumbnail prompts, cross-platform social syndication, and growth metrics tracking. Triggers: clawhub optimization, skill growth, SEO, stars, downloads, review analysis, trending keywords, skill improvement, GitHub stars strategy, video SEO, social media integration, thumbnail generation.\nslug: clawhub-skill-optimizer\nversion: 2.1.2\nallowed-tools: []\ncapabilities:\n  - educational-reference\n  - advisory-only\n  - requires-human-review\n  - code-examples-reference\n---\n\n# ClawHub Skill Growth Engine / ClawHub技能热度增长引擎\n\n> **⚠️ SECURITY NOTICE / 安全声明**\n> - **Type:** Educational reference / analytical framework ONLY\n> - **Code blocks in this document are illustrative teaching examples** — they show the logic and\n>   structure of an approach; nothing here is executed, and no scripts, binaries, or installers\n>   are bundled with this skill.\n> - **No persistent storage, network calls, background execution, or credential collection**\n> - **All outputs are for reference only and require human review before real-world application**\n> - **This skill does NOT provide financial, legal, or insurance advice**\n> - **Users must exercise their own judgment and consult qualified professionals**\n>\n> **⚠️ 数据安全警告**\n> - 本技能仅提供ClawHub技能优化策略的参考框架，**不执行任何代码或脚本**\n> - 文中的Python代码为**教学参考示例**，展示逻辑概念，不会自动执行\n> - 文中的API调用（如Google Trends、微博热搜、GitHub Trending）为**外部服务引用**，用户如实际调用需自行评估数据隐私风险：查询关键词、IP地址、时间戳等信息将被发送至第三方平台\n> - 不收集、不存储用户的任何平台数据、技能代码或个人信息\n> - 热度分析和SEO建议基于公开信息，实际效果因平台算法变化而异\n> - 不保证任何优化策略能带来具体的下载量或Star数增长\n> - **用户确认要求**：所有涉及修改技能元数据或发布社交媒体内容的建议，均需用户自行手动操作并确认\n\n\n\n> **English:** AI-powered growth engine for ClawHub skills — analyze user reviews, track global trending topics, and rewrite your skill metadata (title, description, tags) to maximize downloads and GitHub-style stars.\n>\n> **中文:** ClawHub技能热度增长引擎——分析用户评论、追踪全网热点、优化技能标题与描述，一站式提升下载量与Star数。\n\n## Trigger Keywords / 触发关键词\n\n**⚠️ 精确触发规则**：仅当用户明确提到ClawHub技能增长/优化需求时激活。日常对话中提及\"SEO\"、\"下载量\"、\"stars\"、\"热度\"等通用词汇时**不会自动触发**，必须与**ClawHub技能优化**直接关联。\n\n**用户确认规则**：匹配以下关键词时，需先向用户确认后再进入优化模式：\n- \"您需要ClawHub技能优化建议吗？\"\n- 仅在用户明确确认后，才提供具体分析和建议\n\n激活关键词（需用户确认后生效）：\n\n- ClawHub 优化 / clawhub 技能增长 / 技能热度提升\n- 技能下载量提升 / 技能曝光优化 / 技能SEO\n- 评论分析 / 用户反馈分析 / review 分析\n- 热点追踪 / 热搜分析 / 趋势挖掘\n- 标题优化 / description 优化 / 关键词优化\n- skill 改进 / 技能改进 / 提升关注度\n\n## Section 0: Latest ClawHub Platform Updates (2026-09-15)\n\n| 更新日期 | 平台/趋势 | 对技能优化的影响 | 推荐动作 |\n|---------|-----------|----------------|---------|\n| 2026-09 | 技能数量持续增长，同类竞争加剧 | 仅靠关键词堆砌难以获得持续曝光 | 内容深度 + 差异化定位优先于词藻 |\n| 2026-08 | 平台对技能内容与声明一致性审查趋严 | 声明与正文矛盾会被标记待审 | 发布前核对能力声明与正文是否一致 |\n| 2026-07 | 中文技能检索权重继续提升 | 中文 description 前 30 字含核心关键词更有效 | 中文关键词前置，避免先写英文长句 |\n| 2026-06 | 长上下文与文档处理类需求稳定 | 长文档类技能更看重结构化输出示例 | 补充输入输出示例与边界说明 |\n| 2026-05 | AI Agent 成为 ClawHub 下载量最大品类 | 标题含 \"AI Agent\" 可提升曝光 | 含"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn74e704j3ygjcygnpf02rdvd185js13\",\n  \"slug\": \"clawhub-skill-optimizer\",\n  \"version\": \"2.1.2\",\n  \"publishedAt\": 1789481746614\n}"},{"path":"skill-card.md","content":"## Description:\n\nAI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to boost downloads and GitHub stars.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[gechengling](https://clawhub.ai/user/gechengling)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and ClawHub skill publishers use this skill to review feedback, identify trend-aligned positioning, and draft improved skill metadata, promotion copy, thumbnail prompts, and release checklists.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Illustrative API and Python examples could expose private reviews, tokens, or unpublished skill content if copied and run against third-party services.\n\nMitigation: Use only public or approved test data, remove secrets, and review the destination service before running any example outside the skill.\n\nRisk: Generated SEO, metadata, and promotion recommendations may be inaccurate or may not produce the claimed growth outcome.\n\nMitigation: Treat outputs as advisory drafts, verify claims against current ClawHub rules, and require human approval before publishing metadata or promotional content.\n\n## Reference(s):\n\n- [ClawHub skill listing](https://clawhub.ai/gechengling/skills/clawhub-skill-optimizer)\n- [uapis hotboard API example](https://uapis.cn/api/hotboard)\n- [GitHub repository search API example](https://api.github.com/search/repositories)\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown, code, configuration]\n\n**Output Format:** [Markdown reports with illustrative code examples, metadata rewrite suggestions, checklists, and prompt templates]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Advisory output only; examples and proposed metadata changes require human review before use.]\n\n## Skill Version(s):\n\n2.1.2 (source: server release evidence and frontmatter)\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":"AI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to boost downloads and GitHub stars. Skill: Clawhub Skill Optimizer Owner: gechengling Summary: AI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to boost downloads and GitHub stars. Tags: ai-agent:1.0.0, chinese-market:1.0.0, clawhub:1.0.0, clawhub-skill-optimizer:2.1.2, downloads:1.0.0, github-strategy:1.0.0, latest:2.1.2, optimization:1.0.0, review-analysis:1.0.0, seo:1.0.0, skill-growth:1.0.0, st","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":1230,"uniquenessScore":49,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-11T01:49:24.778Z","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":"2026-10-11T01:49:24.778Z","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":"2026-10-11T03:55:08.489Z","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. 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