Skill: gechengling/clawhub-skill-optimizer (version 2.0.0)

--- name: ClawHub Skill Growth Engine description: 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. version: 2.0.0 --- # ClawHub Skill Growth Engine / ClawHub技能热度增长引擎 > **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. > > **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。 ## Trigger Keywords / 触发关键词 Immediately activate when user mentions: - ClawHub / clawhub 优化 / 技能优化 / 热度提升 - 下载量提升 / stars / 下载量 / 曝光量 - 评论分析 / 用户反馈 / review 分析 / feedback - 热点追踪 / 热搜 / trending / 热点挖掘 - 标题优化 / description 优化 / SEO / 关键词优化 - skill 改进 / 技能改进 / 提升关注度 - GitHub stars 策略 / stars 增长 / trending 技巧 ## Section 0: Latest ClawHub Platform Updates (2026-05-11) | 更新日期 | 平台/趋势 | 对技能优化的影响 | 推荐动作 | |---------|-----------|----------------|---------| | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | --- ## Core Capabilities / 核心能力 ### 1. User Review & Feedback Analysis Engine / 用户评论与反馈分析引擎 Analyze user reviews, feedback, and usage data to extract actionable improvement suggestions. **Analysis Dimensions:** | Dimension | What It Detects | Action | |-----------|----------------|--------| | **Feature Requests** | Users asking for capabilities the skill lacks | Add missing modules to SKILL.md | | **Pain Points** | Frustration or confusion signals in reviews | Simplify instructions, add examples | | **Competitor Mentions** | Users comparing to other tools | Add differentiation points | | **Localization Gaps** | Non-Chinese users struggling (language barriers) | Add English README + bilingual docs | | **Pricing/Access Issues** | Access friction, download barriers | Optimize onboarding flow | | **Emotional Signals** | Excitement/disappointment in wording | Prioritize highly-praised features | | **Video/Social Requests** | "能不能出个视频教程" / "想要小红书推广" | Add video script + thumbnail prompts | | **MCP/Integration Gaps** | "能否对接XX工具" / "支持MCP吗" | Add MCP integration section | | **Long Context Needs** | "处理长文档时卡住" / "支持XX万字吗" | Add context window optimization | **Review Analysis Code:** ```python import re from collections import Counter def analyze_reviews(reviews: list[str]) -> dict: """ Analyze user reviews and extract actionable insights. reviews: list of review texts Returns: dict with categorized insights """ positive_keywords = [ "great", "amazing", "love", "perfect", "useful", "helpful", "强大", "好用", "实用", "完美", "赞", "棒", "优秀" ] negative_keywords = [ "confusing", "broken", "bug", "missing", "wrong", "复杂", "难用", "没用", "问题", "错误", "缺东西" ] feature_request_patterns = [ r"wish.*could", r"would be nice", r"should have", r"建议", r"希望有", r"能否加入", r"期待" ] results = { "positive_signals": [], "negative_signals": [], "feature_requests": [], "keywords": Counter() } for review in reviews: text_lower = review.lower() # Detect sentiment signals for kw in positive_keywords: if kw in text_lower: results["positive_signals"].append(review) break for kw in negative_keywords: if kw in text_lower: results["negative_signals"].append(review) break # Detect feature requests for pattern in feature_request_patterns: if re.search(pattern, text_lower): results["feature_requests"].append(review) break # Word frequency (simple tokenizer) words = re.findall(r'\b\w{3,}\b', text_lower) results["keywords"].update(w for w in words if len(w) > 3) return results ``` **Output Format:** ```markdown ## Review Analysis Report ### 🔥 Top 5 Praised Features 1. [Feature] — mentioned X times 2. ... ### 💡 Top 5 Feature Requests 1. [Request] — mentioned X times → Priority: HIGH/MEDIUM/LOW 2. ... ### ⚠️ Top 5 Pain Points 1. [Pain point] — urgency: CRITICAL/HIGH/MEDIUM 2. ... ### 📊 Keyword Frequency (Top 20) | Keyword | Count | Sentiment | |---------|-------|-----------| | XXX | 123 | Positive | | ... | ... | ... | ### 🎯 Recommended Actions 1. **[HIGH]** Add [missing feature] to address [request] 2. **[MEDIUM]** Simplify [confusing part] based on [pain point] 3. **[LOW]** Add [example/tutorial] to reduce confusion ``` --- ### 2. Trending Topic Tracker / 全网热点追踪引擎 Monitor trending topics across 40+ platforms to identify hot keywords that can boost skill visibility. **Supported Data Sources:** | Source | API/Endpoint | Data | Use Case | |--------|-------------|------|----------| | Weibo Hot Search | `uapis.cn` | Real-time热搜 | China trending | | Zhihu Hot | `uapis.cn` | 知乎热榜 | Tech discussions | | Bilibili Trending | `uapis.cn` | B站热搜 | Youth/tech audience | | GitHub Trending | `github.com/trending` | GitHub热门 | Developer tools | | WeChat Index | Tencent API | 微信指数 | China ecosystem | | Baidu Index | `index.baidu.com` | 百度指数 | Search trends | | Google Trends | `trends.google.com` | Global trends | International | | Product Hunt | `producthunt.com` | PH热榜 | Global startup tools | **Trending Data Fetching Code:** ```python import requests import json def fetch_weibo_trending(limit: int = 20) -> list[dict]: """Fetch real-time Weibo hot search topics.""" url = "https://uapis.cn/api/hotboard" params = {"type": "weibo", "limit": limit} try: resp = requests.get(url, params=params, timeout=10) data = resp.json() return [ {"rank": i+1, "title": item.get("title", ""), "hot": item.get("hot", ""), "url": item.get("url", "")} for i, item in enumerate(data.get("data", [])[:limit]) ] except Exception as e: return [{"error": str(e)}] def fetch_github_trending(lang: str = "python", limit: int = 10) -> list[dict]: """Fetch GitHub trending repositories.""" url = f"https://api.github.com/search/repositories" params = { "q": f"language:{lang}+created:>2025-01-01", "sort": "stars", "order": "desc", "per_page": limit } headers = {"Accept": "application/vnd.github.v3+json"} try: resp = requests.get(url, params=params, headers=headers, timeout=10) data = resp.json() return [ {"name": item["name"], "stars": item["stargazers_count"], "description": item["description"], "url": item["html_url"]} for item in data.get("items", [])[:limit] ] except Exception as e: return [{"error": str(e)}] def google_trends_suggestions(keyword: str) -> list[str]: """Get related queries from Google Trends.""" # Using pytrends library from pytrends.request import TrendReq pytrends = TrendReq(hl='en-US', tz=360) pytrends.build_payload([keyword], cat=0, timeframe='today 3-m', geo='') related = pytrends.related_queries() suggestions = [] for kw_list in related.values(): for item in kw_list.get('top', []) if kw_list else []: suggestions.append(item['query']) return suggestions[:10] ``` **Trending Keyword Mapping for Skills:** ```python TRENDING_MAPPING = { # 2026 AI/LLM trends → skill keyword suggestions "DeepSeek": ["DeepSeek", "LLM", "AI agent", "Chinese AI", "open-source LLM"], "AI Agent": ["AI Agent", "workflow automation", "autonomous AI", "MCP"], "Claude": ["Claude", "Anthropic", "context window", "reasoning", "long context"], "MCP": ["MCP", "Model Context Protocol", "tool integration", "AI agent tools"], "Stock Market": ["A-share", "quantitative trading", "technical analysis", "缠论", "量化"], "Insurance": ["insurance tech", "insurtech", "risk management", "C-ROSS", "NFRA"], "Content Creation": ["AI video", "short video", "social media AI", "video SEO", "thumbnail"], "Productivity": ["workflow automation", "efficiency", "productivity tools", "RAG", "long context"], "Compliance": ["bank compliance", "NFRA", "Basel III", "AML", "PIPL", "regulatory"], "Actuarial": ["actuarial pricing", "C-ROSS II", "IFRS 17", "HKFRS 17", "life table 2025"], } def map_trending_to_skill(trending_topics: list[str], skill_tags: list[str]) -> list[dict]: """Map trending topics to skill tags for SEO boost.""" suggestions = [] for topic in trending_topics: for trend, keywords in TRENDING_MAPPING.items(): if trend.lower() in topic.lower(): for kw in keywords: if kw not in skill_tags: suggestions.append({ "trend": topic, "suggested_tag": kw, "priority": "HIGH" if len(suggestions) < 5 else "MEDIUM" }) return suggestions[:10] ``` --- ### 3. SEO Title & Description Optimizer / SEO标题与描述优化器 Rewrite skill titles and descriptions using proven SEO frameworks to maximize search visibility and click-through rate. **Title Optimization Framework:** | Principle | English Example | Chinese Example | |-----------|----------------|----------------| | **Front-load value** | "AI Insurance Claims Analyzer" | "保险理赔AI专家" | | **Include keyword** | "Stock Technical Analysis" | "A股技术分析" | | **Show outcome** | "Increase Downloads 10x" | "提升下载量" | | **Use numbers** | "5-Step Process" | "7大核心能力" | | **Be specific** | "China Insurance C-ROSS Actuarial" | "偿二代精算定价" | | **Evoke emotion** | "Stop Losing Money" | "告别选号盲目" | **Description Structure (AIDA Framework):** ``` A - Attention: [Bold hook: "The ONLY ClawHub skill that..."] I - Interest: [Specific problem + your unique solution] D - Desire: [Concrete results: "Used by 500+ analysts"] A - Action: [Clear CTA: "Install now and..."] ``` **Title Rewrite Examples:** | Original (Chinese) | Optimized (English) | Optimized (Chinese) | Stars Impact | |--------------------|--------------------|--------------------|--------------| | 招投标文书助手 | Enterprise Bid Document AI | 企业招投标文书AI助手 | ⭐⭐⭐ | | 保险反欺诈 | Insurance Anti-Fraud Pro | 保险反欺诈分析专家 | ⭐⭐⭐⭐ | | 缠论技术分析 | Chanlun Technical Analysis Engine | 缠论技术分析引擎 | ⭐⭐⭐⭐ | | 彩票预测 | Lottery Data Analysis & Number Generator | 彩票数据分析选号助手 | ⭐⭐ | **Tag Optimization:** ```python def optimize_tags(current_tags: list[str], trending_keywords: list[str], competitors: list[str]) -> dict: """ Optimize skill tags for maximum discoverability. """ must_have = ["clawhub", "skill", "ai-agent"] # Always include high_value = ["python", "api", "automation", "analysis", "tool"] trending = [kw for kw in trending_keywords if kw not in current_tags][:5] competitor_tags = [t for t in competitors if t not in current_tags][:3] optimized = must_have + high_value + trending + competitor_tags optimized = list(dict.fromkeys(optimized))[:20] # Dedupe, max 20 return { "current_tags": current_tags, "recommended_tags": optimized, "new_tags_added": [t for t in optimized if t not in current_tags], "tags_removed": [t for t in current_tags if t not in optimized], "seo_score_improvement": f"+{len([t for t in optimized if t not in current_tags]) * 5}%", } ``` --- ### 4. Video Thumbnail & AI Preview Generation / AI视频缩略图生成与预览优化 Generate compelling skill preview visuals and AI video scripts for social media promotion. **Thumbnail Prompt Framework:** | Platform | Thumbnail Style | Prompt Template | |----------|---------------|----------------| | 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" | | 抖音 | 强冲击+情绪化画面 | "Explosion effect, bold text 'XX', dramatic lighting, 9:16 vertical, trending color palette" | | B站 | 知识感+人物出镜 | "Clean desk setup, person pointing at screen, code editor visible, warm lighting, 16:9" | | 微信 | 简约商务风 | "Minimal flat design, skill icon centered, subtle gradient, 2:1 ratio, Chinese + English title" | **AI Thumbnail Generation Code:** ```python from openai import OpenAI import json def generate_thumbnail_prompt(skill_name: str, target_platform: str, highlight: str, style: str = "modern") -> str: """Generate optimized thumbnail prompt for skill promotion.""" platform_configs = { "小红书": {"ratio": "3:4", "color": "vibrant coral + white", "text_pos": "top-center"}, "抖音": {"ratio": "9:16", "color": "neon purple + cyan", "text_pos": "center"}, "B站": {"ratio": "16:9", "color": "dark blue + gold", "text_pos": "bottom-left"}, "微信": {"ratio": "2:1", "color": "minimal white + blue", "text_pos": "center-bottom"} } cfg = platform_configs.get(target_platform, platform_configs["小红书"]) return ( f"Professional skill preview thumbnail for '{skill_name}', " f"highlight: {highlight}. Style: {style}. " f"Aspect ratio {cfg['ratio']}, color scheme {cfg['color']}. " f"Large bold text '{skill_name}' at {cfg['text_pos']}. " f"Clean, modern, high contrast, suitable for {target_platform}." ) def generate_video_script(skill_name: str, skill_slug: str, duration_sec: int = 60) -> dict: """ Generate 1-minute video script with 4 acts (15s each). Returns dict with act breakdown and AI image prompts. """ return { "title": f"【技能推荐】{skill_name} — 3分钟上手指南", "duration": f"{duration_sec}秒", "hook": f"XX秒就能搞定的{skill_name}技能，效率提升10倍！", "acts": [ {"act": 1, "seconds": "0-15s", "scene": "Hook — 痛点场景", "narration": f"还在为XX问题头疼？{skill_name}帮你一键解决！", "visual_prompt": f"Stressed person at desk, messy data, red alerts, warm lighting, cinematic"}, {"act": 2, "seconds": "15-30s", "scene": "Demo — 技能展示", "narration": "看，这是它的核心功能，我只需要输入XX，就能得到XX。", "visual_prompt": f"Screen recording UI of {skill_name}, clean interface, smooth animation, cursor clicking"}, {"act": 3, "seconds": "30-45s", "scene": "Result — 效果对比", "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！", "visual_prompt": f"Split screen: left messy slow process, right clean fast result, dramatic contrast lighting"}, {"act": 4, "seconds": "45-60s", "scene": "CTA — 行动号召", "narration": f"安装命令：npx clawhub install @yourname/{skill_slug}，马上试试！", "visual_prompt": f"Large text overlay '{skill_name}', install command shown, QR code, clean blue gradient"} ] } ``` **Video Script Template (Markdown):** ```markdown ## 🎬 {Skill Name} 视频推广脚本 ({duration}秒) ### Act 1: 钩子 (0-{d1}s) - **画面**: [visual_prompt] - **配音**: {hook_narration} - **字幕**: [大字突出痛点关键词] ### Act 2: 演示 ({d1}-{d2}s) - **画面**: [screen recording showing skill in action] - **配音**: [step-by-step usage walkthrough] - **字幕**: [关键操作步骤] ### Act 3: 效果 ({d2}-{d3}s) - **画面**: Before/After comparison, data visualization - **配音**: [quantified improvement] - **字幕**: [数字: 效率提升XX倍/节省XX时间] ### Act 4: 行动号召 ({d3}-{duration}s) - **画面**: Install command + QR code - **配音**: [enthusiastic CTA] - **字幕**: npx clawhub install @yourname/{slug} ``` --- ### 4b. Cross-Platform Social Syndication Strategy / 跨平台社交媒体推广策略 Design multi-platform promotion campaigns for maximum reach. **Platform-Specific SEO Matrix:** | Platform | Title Style | Description Length | Keyword Density | CTA Format | |----------|-------------|-------------------|-----------------|------------| | 小红书 | 中文感叹句，含数字 | 300-500字 | 高 | 评论区置顶安装命令 | | 抖音 | 悬念式/对比式 | 视频字幕为主 | 中 | 评论区引导 | | B站 | 知识干货型 | 800-2000字 | 低 | 简介区链接 | | 知乎 | 深度分析型 | 1000-3000字 | 低 | 专栏文章链接 | | 微信公众号 | 商务正式型 | 500-1000字 | 中 | 文末二维码 | **Social Proof Framework:** ```python SOCIAL_PROOF_TYPES = { "download_count": {"format": "🔥 X万次安装", "impact": "HIGH"}, "star_count": {"format": "⭐ X千Star", "impact": "HIGH"}, "user_testimonial": {"format": "用户说：'...'", "impact": "VERY_HIGH"}, "media_mention": {"format": "被XX媒体报道", "impact": "MEDIUM"}, "award_badge": {"format": "🏆 ClawHub热门技能", "impact": "MEDIUM"}, "update_freshness": {"format": "✅ 今日更新", "impact": "HIGH"}, } def build_social_proof_badge(skill_data: dict) -> str: """Build multi-element social proof string.""" badges = [] if skill_data.get("downloads", 0) > 1000: badges.append(f"🔥 {skill_data['downloads']//1000}万+安装") if skill_data.get("stars", 0) > 100: badges.append(f"⭐ {skill_data['stars']//1000}千Star") if skill_data.get("last_updated_days", 99) < 7: badges.append("✅ 近期更新") if skill_data.get("is_top_rated"): badges.append("🏆 热门推荐") return " | ".join(badges) if badges else "" ``` **A/B Testing Framework for Titles:** ```python def generate_title_variants(original: str, skill_domain: str) -> list[dict]: """Generate 3-5 title variants for A/B testing.""" templates = { "insurance": [ f"【保险人必装】{original} — 效率提升10倍", f"保险{original}专家版：XX分钟搞定XX", f"不想加班？{original}让保险工作自动化", ], "bank": [ f"银行人专属{original}，合规效率双提升", f"【合规必备】{original} — 银保监合规利器", ], "trading": [ f"{original}：XX个指标一键分析，炒股不迷茫", f"看盘神器！{original}让技术分析零门槛", ], "default": [ f"【AI工具】{original} — 3分钟上手教程", f"{original}：提升效率XX倍的秘密武器", ] } variants = templates.get(skill_domain, templates["default"]) return [{"variant": v, "approach": t} for v, t in zip(variants, [ "数字+感叹词", "领域专属词", "痛点引导" ][:len(variants)])] ``` --- ### 5. GitHub-Style Stars Growth Strategy / GitHub式Star增长策略 Apply proven open-source project growth tactics to ClawHub skills. **Strategy Framework:** | Strategy | Implementation | Expected Impact | |----------|---------------|----------------| | **README Quality** | First 5 lines = summary. Clear "What/Why/How". Screenshots. | ⭐⭐⭐⭐ | | **Keyword SEO** | Title + first 2 lines contain main keywords. README H1-H3 structure. | ⭐⭐⭐⭐⭐ | | **Demo/Preview** | Short video or GIF showing the skill in action | ⭐⭐⭐⭐ | | **Cross-posting** | Share on Zhihu, Weibo, Bilibili with skill link | ⭐⭐⭐ | | **Community Building** | Create WeChat group / QQ group for skill users | ⭐⭐⭐ | | **Regular Updates** | Version updates with changelog. "Updated 2 days ago" signal. | ⭐⭐⭐⭐ | | **Comparison Content** | "vs [competitor]" articles to attract their users | ⭐⭐⭐ | | **Trending Integration** | Tie skill to current hot topics (AI agents, DeepSeek, etc.) | ⭐⭐⭐⭐⭐ | | **Multi-language** | English README = global audience 10x | ⭐⭐⭐⭐⭐ | **README Bilingual Template:** ```markdown # [English Title] / [中文标题] <!-- English (for international users - put FIRST) --> > **English Description**: One powerful sentence describing the skill's core value. > Built for [target user]. Solves [specific problem]. ## ✨ Features / Features / 核心功能 - ✅ Feature 1 with specific metric or result - ✅ Feature 2 — [why it matters] - ✅ Feature 3 ## 🚀 Quick Start ```bash # Install npx clawhub install @yourname/your-skill # Use /your-skill [command] ``` ## 📖 Documentation Full docs at [link] or continue reading below. --- <!-- 中文部分（放在英文后面，供国内用户阅读） --> > **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]。 ## 🎯 核心功能 - ✅ 功能1 — [具体效果/数据] - ✅ 功能2 — [为什么有用] - ✅ 功能3 ## ⚡ 快速上手 1. 安装：`npx clawhub install @yourname/your-skill` 2. 使用：`/your-skill [命令]` 3. 查看文档见下方 ## 📚 详细文档 [详细内容...] ``` --- ### 5. GitHub-Style Stars Growth Strategy / GitHub式Star增长策略（更新：2026视频+社媒推广） Apply proven open-source project growth tactics to ClawHub skills. **Strategy Framework (2026 Updated):** | Strategy | 2026 Implementation | Expected Impact | |----------|-------------------|----------------| | **README Quality** | First 5 lines = summary. Clear "What/Why/How". Screenshots. | ⭐⭐⭐⭐ | | **Keyword SEO** | Title + first 2 lines contain main keywords. README H1-H3 structure. | ⭐⭐⭐⭐⭐ | | **Demo Video** | Short video or GIF showing the skill in action (小红书/抖音/B站) | ⭐⭐⭐⭐⭐ | | **Video Thumbnail** | AI-generated preview thumbnail with bold text + high contrast | ⭐⭐⭐⭐ | | **Cross-posting** | Share on 知乎/微博/小红书/B站 with skill link | ⭐⭐⭐ | | **Community Building** | Create WeChat group / QQ group for skill users | ⭐⭐⭐ | | **Regular Updates** | Version updates with changelog. "Updated 2 days ago" signal. | ⭐⭐⭐⭐ | | **Comparison Content** | "vs [competitor]" articles to attract their users | ⭐⭐⭐ | | **Trending Integration** | Tie skill to current hot topics (AI agents, DeepSeek, MCP, etc.) | ⭐⭐⭐⭐⭐ | | **Multi-language** | English README = global audience 10x | ⭐⭐⭐⭐⭐ | | **Social Proof Badge** | Downloads + stars count displayed in title | ⭐⭐⭐⭐ | --- ### 6. Full Skill Optimization Report / 全流程技能优化报告 Generate a complete optimization report combining all analysis: ```markdown # 🎯 ClawHub Skill Optimization Report **Skill**: [skill-name] **Generated**: [timestamp] **Analyzer**: ClawHub Skill Growth Engine v2.0.0 --- ## 📊 Current Status | Metric | Current | Target | Gap | |--------|---------|--------|-----| | Downloads | XXX | 1,000+ | +XXX | | Stars | XX | 100+ | +XX | | Description Length | XXX chars | 200-300 | OK | | Tags Count | X | 10-15 | Add X | | Has English README | No | Yes | MISSING | | Last Updated | YYYY-MM-DD | < 30 days | STALE | --- ## 🔥 Trending Keywords to Integrate | # | Trending Topic | Relevant Tag | Priority | Integration | |---|---------------|-------------|---------|-------------| | 1 | [topic] | [tag] | HIGH | Add to description | | 2 | [topic] | [tag] | MEDIUM | Add to tags | | ... | ... | ... | ... | ... | --- ## 📝 Title & Description Rewrite ### Current **Title**: [old title] **Description**: [old description] ### Optimized **Title (EN)**: [optimized English title] **Title (CN)**: [optimized Chinese title] **Description (EN)**: > [SEO-optimized English description - AIDA framework] **Description (CN)**: > [优化后的中文描述] ### Tags Optimization **Current**: [tag1, tag2, ...] **Add**: [new tags] **Remove**: [obsolete tags] --- ## 💬 Review Analysis Findings ### Top Requests from Users 1. [Request 1] → Add to SKILL.md priority section 2. [Request 2] → Add FAQ section 3. [Request 3] → Create tutorial ### Pain Points to Fix 1. [Pain point 1] → Rewrite confusing section 2. [Pain point 2] → Add troubleshooting guide --- ## 📋 Action Plan (Priority Order) | # | Action | Type | Impact | Effort | |---|--------|------|--------|--------| | 1 | Add English README | Content | ⭐⭐⭐⭐⭐ | Low | | 2 | Rewrite title with trending keyword | SEO | ⭐⭐⭐⭐⭐ | Low | | 3 | Add missing feature from reviews | Feature | ⭐⭐⭐⭐ | Medium | | 4 | Cross-post on [platform] | Promotion | ⭐⭐⭐⭐ | High | | ... | ... | ... | ... | ... | --- ## ✅ Checklist for Publishing (v2.0) - [ ] Title contains main keyword + 2026 trending keyword (AI Agent/MCP/etc.) - [ ] Description is 200-300 characters (English), first 30 chars include core keyword (Chinese) - [ ] Tags include trending + evergreen keywords (AI Agent, MCP, workflow automation, etc.) - [ ] English README added (English FIRST, Chinese SECOND) - [ ] Video thumbnail prompt generated for each target platform - [ ] 60-second video script completed (if promoting on social media) - [ ] Cross-platform social post plan drafted (小红书/抖音/B站/知乎) - [ ] Social proof badges added (download count, stars, recent update) - [ ] Changelog updated with v2.0 changes - [ ] Version bumped to X.X.X - [ ] All reference files checked for broken links - [ ] Bilingual trigger keywords in SKILL.md ``` --- ## Workflow / 工作流程 ``` User Input: Current skill details / user reviews / trending goal ↓ [Step 1] Analyze Reviews → Extract pain points + requests [Step 2] Fetch Trending Data → Map to skill keywords (AI Agent/MCP/video SEO) [Step 3] SEO Rewrite → Title + description + tags + 2026 trending keywords [Step 4] Generate Video Assets → Thumbnail prompt + 60s video script [Step 5] Cross-Platform Plan → Platform-specific SEO + social syndication [Step 6] GitHub Stars Strategy → README + promotion plan + social proof [Step 7] Generate Full Report → Actionable checklist ↓ User confirms changes ↓ Apply: Update SKILL.md + README.md + tags + thumbnail prompts + changelog ``` --- ## Reference Files | File | Content | |------|---------| | `references/seo_optimization_guide.md` | Full SEO framework + keyword research methods + 2026 trending | | `references/trending_topic_tracker.md` | Trending APIs + code + keyword mapping (updated with MCP/video SEO) | | `references/review_analysis_templates.md` | Review analysis templates + sentiment scoring | | `references/video_seo_guide.md` | Video thumbnail prompts + platform-specific SEO + 60s script templates |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)