Clawhub Skill Optimizer

AI-powered ClawHub skill optimizer that analyzes reviews, tracks trending topics, and rewrites metadata to boost downloads and GitHub stars.

Audits

Pass

ClawScanPass

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install clawhub-skill-optimizer

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:

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:

## 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:

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:

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:

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:

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):

## 🎬 {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:

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:

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:

# [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

安装：npx clawhub install @yourname/your-skill
使用：/your-skill [命令]
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