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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
- Code blocks in this document are illustrative teaching examples — they show the logic and structure of an approach; nothing here is executed, and no scripts, binaries, or installers are bundled with this skill.
- No persistent storage, background execution, or credential collection. To be precise about network access: this skill itself makes no network calls. The source endpoints and Python snippets shown later are illustrative material — if a user chooses to run them in their own environment, that traffic originates from their machine and goes to third-party platforms, not from this skill.
- 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，本技能自身不发起这些请求。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
- 预览后再落地：本技能产出的优化报告、改写文案、标签清单在写入任何文件之前，必须先输出预览（条目数、涉及字段、是否含未核实数据），经用户明确确认后再保存；未经预览与确认不落盘
- 不代为修改文件：不修改任何 SKILL.md、README 或元数据文件；本技能只输出建议与素材文本，编辑动作由用户完成
- 不代为发布与发帖：不在任何平台发帖、不代为提交技能更新
- 不保证效果：不承诺任何下载量、Star 数或排名结果；所有预期影响均为经验性判断，实际效果因平台算法而异
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 (2026-10-12)#
更新日期平台/趋势对技能优化的影响推荐动作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2026-05AI 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2026-05MCP (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2026-05OpenClaw 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2026-03Long 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 RAG2026-03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新增·2026-09安全扫描对「触发词过宽」的判定更常见泛化触发词会被要求收窄触发词收窄到具体任务并附不触发清单新增·2026-09反例文本会被当作正文一并扫描为说明「不要这样做」写出的命令会被判为真实指令反例不写出可直接复制的完整命令新增·2026-09「只改版本号」型更新被明确识别无实质内容增长的更新难通过每次更新至少补一组示例与一个表格维度新增·2026-10表格结构完整性影响可读性评分缺列的表格行会被判为格式缺陷提交前检查每个表块各行分隔符数量一致
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。
Section 0b: 优化前的一致性自检（发布前必做）#
自检项检查方式不一致的后果修复优先级能力声明 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，进入待审最高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高版本号一致frontmatter 版本与线上最新版本是否衔接发布被拒或版本回退最高篇幅完整度是否只改版本号就提交被判定无实质更新，审核不通过最高链接有效性正文引用的文件路径是否真实存在断链，影响使用体验中术语时效机构名、法规名是否为现行表述专业性受损，可能触发合规审查中表格结构每个表块各行分隔符数量是否与表头一致缺列行渲染错乱中反例写法安全类反例是否写出可直接复制的完整命令被扫描器当作正文指令高反例与禁令自洽示例文本是否与本文自身的禁止清单冲突使用者照抄示例绕过筛查高
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:
DimensionWhat It DetectsAction误判提示Feature RequestsUsers asking for capabilities the skill lacksAdd missing modules to SKILL.md个别请求不等于普遍需求，看出现次数Pain PointsFrustration or confusion signals in reviewsSimplify instructions, add examples区分「没看懂」与「不好用」Competitor MentionsUsers comparing to other toolsAdd differentiation points只提名字不算对比，看是否指出差异Localization GapsNon-Chinese users struggling (language barriers)Add English README + bilingual docs英文优先是本地化取舍，不是安全能力Pricing/Access IssuesAccess friction, download barriersOptimize onboarding flow部分问题在平台侧，技能改不了Emotional SignalsExcitement/disappointment in wordingPrioritize highly-praised features情绪强度不等于需求广度Video/Social Requests能不能出个视频教程 / 想要小红书推广Add video script + thumbnail prompts先确认内容已定稿再推广MCP/Integration Gaps能否对接XX工具 / 支持MCP吗Add MCP integration section确认是否真有对接能力再写Long Context Needs处理长文档时卡住 / 支持XX万字吗Add context window optimization不承诺超出实际能力的长度Review Gaps评论数过少或集中在单一渠道扩大反馈收集面样本过小时结论不可靠
评论分析示例（三条真实形态的评论）：
评论原文（已脱敏）判定维度动作优先级功能挺全的，就是刚开始不知道怎么用上手门槛（Pain Point）在开头补一段最短上手路径高能不能支持导出成表格Feature Request评估后决定是否加入能力清单中和另外那个 XX 技能比，这个好在 XXCompetitor Mention把该差异点写进 description中
一条容易被忽略的规则： 评论分析结论要标注样本量。三条评论里两条提到同一个问题，与三百条评论里二十条提到同一问题，结论强度完全不同。不标注样本量的分析容易把个别意见当成普遍需求。
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:
SourceAPI/EndpointDataUse Case调用时的隐私提示Weibo Hot Searchuapis.cnReal-time热搜China trending请求含查询词与来源 IPZhihu Hotuapis.cn知乎热榜Tech discussions同上Bilibili Trendinguapis.cnB站热搜Youth/tech audience同上GitHub Trendinggithub.com/trendingGitHub热门Developer tools未鉴权请求受速率限制WeChat IndexTencent API微信指数China ecosystem通常需要平台侧授权Baidu Indexindex.baidu.com百度指数Search trends请求含查询词与来源 IPGoogle Trendstrends.google.comGlobal trendsInternational跨境传输，需评估合规性Product Huntproducthunt.comPH热榜Global startup tools跨境传输，需评估合规性
外部调用提示（E1）：上表中的地址仅用于说明「可以从哪些方向取趋势数据」，本技能不会替用户发起这些请求。若你在自己的环境中运行后面的示例代码，查询词、来源 IP 与时间戳会发送到对应第三方平台，请事先确认所在组织的数据出境与第三方调用规定允许这样做。
示例（仅示意取数逻辑，不代用户执行）：
步骤做什么注意1确定要追踪的关键词集合关键词本身也可能含敏感信息2选定 2–3 个来源交叉验证单一来源的趋势容易误判3记录取数时间与口径便于下次对比，也便于复盘4只用聚合结果，不保存原始明细减少不必要的数据留存5趋势词与技能实际能力对照后再写入无对应能力的热点词会带来负面评价
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:
PrincipleEnglish ExampleChinese Example常见误用Front-load value"AI Insurance Claims Analyzer"保险理赔AI专家把品牌名放在最前Include keyword"Stock Technical Analysis"A股技术分析堆砌不相关热词Show outcome"Increase Downloads 10x"提升下载量承诺无法验证的倍数Use numbers"5-Step Process"7大核心能力数字与实际内容不符Be specific"China Insurance C-ROSS Actuarial"偿二代精算定价生僻到无人搜索Evoke emotion"Stop Losing Money"告别选号盲目制造焦虑或夸大State the boundary"Reference Framework, Not Advice"参考框架，非投资建议只用情绪词替代定位
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⭐⭐银行合规审查Bank Compliance Review Assistant银行合规审查助手⭐⭐⭐⭐知识库管理Financial Knowledge Base Manager金融行业知识库管家⭐⭐⭐
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:
PlatformThumbnail StylePrompt 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}@<版本号>，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}@<版本号>
**关于固定版本（RP1）：** 上面示例中的安装命令一律带 `<版本号>` 占位，这是一个有意为之的写法——安装固定版本可以确保拿到经过审查的那一版，避免上游变更在你不知情的情况下进入工作环境。请把占位符替换为你要推荐的具体版本号，不要留空，也不要改用不指定版本的写法。同理，命令行工具本身也应使用你所在机构已登记的固定版本。
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:
PlatformTitle StyleDescription LengthKeyword DensityCTA Format发布前确认小红书中文感叹句，含数字300-500字高评论区置顶安装命令内容已定稿再发抖音悬念式/对比式视频字幕为主中评论区引导字幕与口播一致B站知识干货型800-2000字低简介区链接链接可访问知乎深度分析型1000-3000字低专栏文章链接声明利益关系微信公众号商务正式型500-1000字中文末二维码已过内部审核
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} — 监管合规利器",
],
"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:
StrategyImplementationExpected Impact落地前提README QualityFirst 5 lines = summary. Clear What/Why/How. Screenshots.⭐⭐⭐⭐截图为当前版本Keyword SEOTitle + first 2 lines contain main keywords. README H1-H3 structure.⭐⭐⭐⭐⭐关键词与内容相符Demo/PreviewShort video or GIF showing the skill in action⭐⭐⭐⭐演示不夸大能力Cross-postingShare on Zhihu, Weibo, Bilibili with skill link⭐⭐⭐各平台规则允许Community BuildingCreate WeChat group / QQ group for skill users⭐⭐⭐有持续维护能力Regular UpdatesVersion updates with changelog. Updated recently signal.⭐⭐⭐⭐每次更新确有实质内容Comparison Contentvs [competitor] articles to attract their users⭐⭐⭐对比客观、不贬损Trending IntegrationTie skill to current hot topics (AI agents, DeepSeek, etc.)⭐⭐⭐⭐⭐热点与能力真实相关Multi-languageEnglish README = global audience 10x⭐⭐⭐⭐⭐英文部分与中文一致Boundary clarityState what the skill does not do⭐⭐⭐⭐声明与正文无矛盾
关于「英文在前」的说明（语言与本地化取舍）： README 模板把英文部分放在前面，是为了照顾跨语言检索与海外读者，这是一项 内容组织上的本地化取舍，不是安全能力，也不代表本技能优先服务某一语种的使用者。若你的受众主要在国内，完全可以把中文部分前置——本技能不做强制要求。
需要保持一致的是：两种语言描述的是同一个技能、同一组能力。中英文内容出现能力描述不一致，比语种顺序更容易引发问题。
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@<版本号>
# 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@<版本号> - 使用：
/your-skill [命令] - 查看文档见下方
📚 详细文档#
[详细内容...]
text
---
> **⚠️ 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。
**2026 年补充要点**
| Strategy | 2026 Implementation | Expected Impact | 前提 |
|----------|-------------------|----------------|------|
| **Demo Video** | Short video or GIF showing the skill in action (小红书/抖音/B站) | ⭐⭐⭐⭐⭐ | 内容已定稿 |
| **Video Thumbnail** | AI-generated preview thumbnail with bold text + high contrast | ⭐⭐⭐⭐ | 缩略图与内容一致 |
| **Social Proof Badge** | Downloads + stars count displayed in title | ⭐⭐⭐⭐ | 数字真实可核 |
| **Update Cadence** | 固定节奏更新并在描述中体现更新时间 | ⭐⭐⭐⭐ | 每次确有实质更新 |
| **Boundary Line** | 在描述中明确本技能不做什么 | ⭐⭐⭐⭐ | 与正文能力声明一致 |
---
### 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 v2.1.3
---
## 📊 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 (v2.1)
- [ ] Title contains main keyword + current 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 this version's changes
- [ ] Version bumped to X.X.X
- [ ] **Ability declaration matches the body** (no "no code" claim alongside code blocks)
- [ ] **No broken internal path references** in the body
- [ ] **Terminology is current** (regulator names, regulation names, versions)
- [ ] Bilingual trigger keywords in SKILL.md
---
## 报告落地清单 / From Report to Action
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）：
| 顺序 | 动作 | 为什么放在这个位置 |
|-----|------|-----------------|
| 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 |
| 2 | 补内容示例与边界说明 | 内容是留存的基础，先于推广 |
| 3 | 调整标题与描述关键词 | 有了内容支撑，关键词才站得住 |
| 4 | 更新标签与触发词 | 影响被发现，但不影响满意度 |
| 5 | 制作视频 / 缩略图素材 | 素材依赖已定稿的内容 |
| 6 | 跨平台分发 | 最后一步，避免把未定稿内容传出去 |
**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。
---
## Reference Files
| 类型 | 内容 |
|------|------|
| SEO 优化指南 | 完整 SEO 框架 + 关键词研究方法 + 当期热点方向 |
| 热点追踪手册 | 数据源清单 + 检索逻辑 + 关键词映射方法 |
| 评论分析模板 | 评论分类模板 + 情感打分口径 |
| 视频 SEO 指南 | 各平台缩略图提示词 + 视频脚本模板 |
> 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。
常见误用与纠偏 / Common Mistakes#
误用表现纠偏动作自检问题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，再谈关键词读完能说出它是做什么的吗？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本次比上一版多了什么？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）声明与正文逐条对得上吗？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）这个说法能被验证吗？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，改为描述性引用这个路径随包发布吗？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这个名称还是现行的吗？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这次改动能归因吗？反例写成命令为说明「不要这样做」而写出完整可复制命令反例只描述做法，不写出完整命令这段文本能被照抄执行吗？示例与禁令冲突示例文本里出现本文禁止清单中的表述示例与禁止清单逐条比对示例是否违反了自己的禁令？
内容质量评分表（发布前自评）#
维度权重5 分标准3 分标准1 分标准快速检查定位清晰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读首段能否复述结构完整20%有痛点、能力、示例、边界、免责缺 1–2 项只有能力罗列五项是否齐全示例可操作20%每个能力配 2 组以上场景示例只有 1 组示例无示例数每个模块的示例数声明一致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逐条比对声明与正文术语时效10%机构名、法规名、版本号均现行个别过时多处过时检索机构名与法规名触发精准10%触发词只覆盖职责范围有个别泛化词大量通用词是否有不触发清单表格结构加分项每个表块各行分隔符数量一致个别行缺列多处缺列逐表数分隔符
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。
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
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。
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