Clawhub Skill Optimizer

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

ClawHub Skill Growth Engine / ClawHub技能热度增长引擎

⚠️ SECURITY NOTICE / 安全声明

• Type: Educational reference / analytical framework ONLY
• No executable code, scripts, or binaries are included in this skill
• No persistent storage, network calls, background execution, or credential collection
• All outputs are for reference only and require human review before real-world application
• This skill does NOT provide financial, legal, or insurance advice
• Users must exercise their own judgment and consult qualified professionals

⚠️ 数据安全警告

• 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
• 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
• 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
• 不收集、不存储用户的任何平台数据、技能代码或个人信息
• 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
• 不保证任何优化策略能带来具体的下载量或Star数增长
• 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

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

⚠️ 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。

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：

• "您需要ClawHub技能优化建议吗？"
• 仅在用户明确确认后，才提供具体分析和建议

激活关键词（需用户确认后生效）：

• ClawHub 优化 / clawhub 技能增长 / 技能热度提升
• 技能下载量提升 / 技能曝光优化 / 技能SEO
• 评论分析 / 用户反馈分析 / review 分析
• 热点追踪 / 热搜分析 / 趋势挖掘
• 标题优化 / description 优化 / 关键词优化
• skill 改进 / 技能改进 / 提升关注度

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):

text

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: clawhub-skill-optimizer
                          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...]

text

---

### 5. GitHub-Style Stars Growth Strategy

> **⚠️ 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。

/ 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

text

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