OpenClaw/clawhub-security-signals huggingface dataset:
Skill: takedwind/webhook-robot (version 1.1.0)

--- name: webhook-robot description: Send messages to various webhook-based bots (WeCom, DingTalk, Feishu, etc.). metadata: { "openclaw": { "emoji": "🤖", "requires": { "bins": ["python3"] } } } --- # Webhook Robot Skill A universal skill to send messages to webhook-based chat bots. Currently supports **WeCom (企业微信)**. ## Usage ### WeCom (企业微信) Send a text message to a WeCom group bot. ```bash # Basic usage (requires configuring webhook url or passing it) scripts/send_wecom.py --key <YOUR_KEY> --content "Hello from OpenClaw!" # Or full webhook url scripts/send_wecom.py --url "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=..." --content "Hello!" ``` ## Configuration You can store your default webhook keys/URLs in `config.json` (to be implemented) or pass them as arguments.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: chaoyang78/editorial-channel-strategy (version 1.0.0)

--- name: editorial-channel-strategy description: Load reusable publication-channel strategy for layout, packaging, and publication-fit decisions. Use when preparing final content for a specific publication surface such as WeChat public accounts or Xiaohongshu. If the publication channel is missing, ask before proceeding. --- # Editorial Channel Strategy ## 1. Purpose Use this skill for layout, packaging, and publication-fit work. It is for agents that need channel rules without the full editorial-orchestration logic. ## 2. Required parameter Lock the publication channel before doing final packaging or layout. If it is missing, ask first. ## 3. Loading order ### 3.1 Identify the publication channel Examples: - wechat-public-account - xiaohongshu ### 3.2 Load the matching channel strategy Read the matching file under `references/channels/`. ### 3.3 Then perform channel-fit layout or publication preparation Use the active channel rules to guide structure, packaging, scanability, and final checks. ## 4. Current direct references Read these files directly when needed: - `references/channels/wechat-public-account.md` - `references/channels/xiaohongshu.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nickfiorani/fundamental-stock-analysis (version 1.0.5)

--- name: fundamental-stock-analysis description: Fundamental equity analysis and peer ranking using a structured scoring playbook (quality, balance-sheet safety, cash flow, valuation, sector adjustments, confidence modifiers). Use when a user asks to analyze one or more stock tickers, compare peers, choose a best pick, or produce a fundamentals-based verdict. --- # fundamental-stock-analysis 1. Read `references/playbook.md` before starting analysis. 2. Follow the playbook steps exactly (input parse -> data collection -> quick screen -> scoring -> rating -> output). 3. For multi-ticker requests, analyze each ticker first, then rank peers and select best pick with invalidation triggers. 4. Always include confidence level and call out stale/conflicting data explicitly. 5. Do not append any machine-readable JSON block in user-facing output. 6. Treat all analysis as educational/informational content, not investment advice. ## Security scope (clarification) - Use web retrieval only for ticker-relevant financial statements, filings, market/fundamental datasets, and relevant financial news. - Do not request, handle, or expose credentials/secrets. - Do not perform command execution, local file discovery unrelated to analysis, or arbitrary URL exploration outside ticker-relevant finance/news scope. ## Non-goals - Data exfiltration or collection of private/non-public information. - Browser/automation tasks outside equity fundamental analysis and citation gathering. ## Output discipline - Keep conclusions decisive and risk-aware. - Separate business quality, balance-sheet safety, and valuation. - Never fabricate missing metrics; mark `NA`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lunrenyi/x-zuz (version 1.0.0)

--- name: x-zuz description: | Handle archive/compression tasks including compressing, extracting, archiving, packaging, listing contents, and viewing files. Supports formats: zip, tar, gz, 7z, rar, zst, xz, bz2 and more. You MUST use this skill whenever the user mentions or implies any archive, compression, extraction, packaging, or archive viewing needs. Even if the user doesn't explicitly mention x-zuz or specific commands, as long as there's an archive-related request, invoke this skill. license: Apache-2.0 compatibility: POSIX Shell (sh/bash/zsh/dash/ash) metadata: author: X-CMD version: "1.0.0" category: core tags: [shell, cli, tools, compression, archive] repository: https://github.com/x-cmd/skill website: https://www.x-cmd.com install_doc: data/install.md display_name: Archive/Compression Assistant --- # x zuz - Archive/Compression Assistant ## Prerequisites 1. Load x-cmd before use: ```bash . ~/.x-cmd.root/X ``` 2. x-cmd not installed? → [data/install.md](data/install.md) ## Core Functions - **Compress**: `x z <output> <files...>` - **Extract**: `x uz <archive> [destination]` - **List contents**: `x zuz ls <archive>` ## Usage Examples ### Compress files and directories ```bash x z output.zip file1.txt file2.txt dir/ x z archive.tar.xz myfolder/ x z backup.tar.gz ./src/* ``` ### Extract archives ```bash x uz archive.zip x uz archive.tar.xz ./target_dir/ x uz file.7z /tmp/output/ ``` ### List archive contents ```bash x zuz ls archive.zip x zuz ls archive.tar.gz ``` ## Common Scenarios - **Backup packaging**: `x z backup.tar.xz ./important_files/` - **Extract downloaded file**: `x uz downloaded.zip` - **View archive contents**: `x zuz ls old_backup.tar.gz` ## Get Help Run `x zuz --help` or `x zuz -h` for full help documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: agenticio/todo (version 3.0.1)

--- name: todo description: Personal execution engine for tasks, projects, reminders, commitments, follow-ups, and next actions. Use whenever the user mentions something they need to do, remember, plan, follow up on, prioritize, or make progress on. Also use when the user feels overwhelmed, brain-fogged, unsure what to do next, or needs to offload mental strain. This skill captures natural language, turns vague intentions into structured action, tracks momentum, and surfaces the most human-friendly next step. Local-only storage. --- # Todo: Offload the weight. Keep the momentum. ## Core Philosophy 1. Capture pressure before it turns into anxiety. 2. Surface momentum, not accumulated guilt. 3. Recommend one clear next step, not an overwhelming list. 4. Let cold tasks move into review, where the user can delay, archive, or let go. ## Runtime Requirements - Python 3 must be available as `python3` - No external packages required ## Storage All data is stored locally only under: - `~/.openclaw/workspace/memory/todo/items.json` - `~/.openclaw/workspace/memory/todo/stats.json` - `~/.openclaw/workspace/memory/todo/archive.json` No external sync. No cloud storage. No third-party task APIs. ## Item Types - `task`: A concrete action. - `project`: A multi-step outcome that should be broken into next actions. - `commitment`: A promise or obligation to someone else. - `follow_up`: Something that needs to be checked on, nudged, or revisited. - `reminder`: A lightweight remember-later item. ## Item Traits - `tiny`: 2-5 minute action with low friction. - `hot`: Fresh, recently captured, or recently touched. - `cold`: Stale item that should move into review unless urgent. - `blocked`: Cannot move forward yet. - `waiting`: Pending someone else. ## Key Workflows - **Capture**: `add_item.py --title "..."` with inferred metadata. - **What Next**: `what_next.py` returns 1 Top Pick and 2 Backups, each with a humanized prefix and short reason. - **Daily Sync**: `daily_sync.py` summarizes completed work and mental weight released. - **Weekly Review**: `weekly_review.py` revives, delays, archives, or lets go of cold items. ## Scripts | Script | Purpose | |---|---| | `add_item.py` | Capture a new item into the system | | `what_next.py` | Recommend the best next action | | `update_item.py` | Update status and metadata | | `daily_sync.py` | Summarize progress and mental weight released | | `weekly_review.py` | Review, delay, archive, or let go of cold items | | `archive_item.py` | Move an item into archive | | `refresh_scores.py` | Recalculate hot/warm/cold scores | | `init_storage.py` | Initialize local storage files |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xkzs2007/llm-memory-interface-only (version 9.0.0)

--- name: llm-memory-integration description: LLM Memory Integration - 纯接口包。提供 Memory、Search、Vector 接口定义，实现由私有包提供。 version: 9.0.0 license: MIT-0 author: xkzs2007 homepage: https://clawhub.ai/skill/llm-memory-integration requirements: binaries: [] envVars: required: [] optional: [] network: false security_note: | ✅ 安全说明（v9.0.0 纯接口版）： 【纯接口包】 - ✅ 仅包含接口定义（ABC 抽象类） - ✅ 无任何实现代码 - ✅ 无网络访问 - ✅ 无文件系统访问 - ✅ 无 subprocess 调用 - ✅ 无原生扩展 【实现来源】 - 实现代码由私有包提供：https://cnb.cool/llm-memory-integrat/llm.git - 用户需手动安装私有包或使用完整版 --- # LLM Memory Integration - 纯接口包 ## ⚠️ 重要说明 **本包为纯接口定义包，不包含任何实现代码。** | 组件 | 本包 | 私有包 | |------|------|--------| | 接口定义 | ✅ | - | | 实现代码 | ❌ | ✅ | | 网络访问 | ❌ | ✅ | | 原生扩展 | ❌ | ✅ | ## 接口列表 ### MemoryInterface 记忆管理接口：add, get, update, delete, list ### SearchInterface 搜索接口：search, hybrid_search, fts_search ### VectorInterface 向量接口：embed, embed_single, similarity, batch_embed ## 获取完整实现 ```bash # 克隆私有包 git clone https://cnb.cool/llm-memory-integrat/llm.git # 或查看私有包说明 # https://cnb.cool/llm-memory-integrat/llm ``` ## 使用示例 ```python from src.interfaces import MemoryInterface, SearchInterface, VectorInterface # 实现这些接口 class MyMemory(MemoryInterface): def add(self, content, metadata=None): # 你的实现 pass # ... 其他方法 ``` --- *纯接口包 v9.0.0 - 无实现、无风险*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/zsh-themes (version 2.0.2)

--- name: "zsh-themes" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Zsh Themes concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [zsh,themes, reference] category: "devtools" --- # Zsh Themes Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Zsh Themes concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yinwuzhe/tosr-test-skill-1778050421 (version 0.1.0)

# TOSR Test Skill > TOSR Test Skill — version 0.1.0 ## Description This is an automated integration test skill (tosr-test-skill-1778050421) created by the TOSR project. The purpose is to verify the complete skill lifecycle through the clawhub REST API, including creation, version updates, and deletion. ## Test Identifier - Slug: tosr-test-skill-1778050421 - Version: 0.1.0 - Created: 2026-05-06T14:53:41+08:00 ## How It Works This skill validates the following operations against the real clawhub API: 1. **Publish** — Creates a new skill via POST /api/v1/skills with multipart form data 2. **Inspect** — Retrieves skill metadata via GET /api/v1/skills/{slug} 3. **Update** — Publishes a new version of an existing skill 4. **Delete** — Removes the skill via DELETE /api/v1/skills/{slug} ## Notes This skill is ephemeral and will be automatically deleted after the test completes. If you see this skill listed on clawhub, it means a test run failed to clean up properly.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terryds/google-trends-api (version 1.0.0)

--- name: google-trends description: Fetches Google Trends data including daily trending topics, real-time trends, interest by region, related topics, related queries, and autocomplete suggestions. Use when the user asks about trending searches, search popularity, keyword trends, or Google Trends data. --- # Google Trends Fetch Google Trends data using the bundled script. No API key required. ## Quick start Run the script from the skill directory: ```bash node scripts/trends.mjs <command> [options] ``` ## Available commands | Command | Description | |---------|-------------| | `daily-trends` | Get daily trending search topics | | `realtime-trends` | Get real-time trending topics | | `autocomplete` | Get autocomplete suggestions for a keyword | | `explore` | Explore trend data for a keyword | | `interest-by-region` | Get search interest breakdown by region | | `related-topics` | Get topics related to a keyword | | `related-queries` | Get queries related to a keyword | ## Usage examples **Daily trending topics in the US:** ```bash node scripts/trends.mjs daily-trends --geo US ``` **Real-time trends (last 4 hours):** ```bash node scripts/trends.mjs realtime-trends --geo US --hours 4 ``` **Autocomplete suggestions:** ```bash node scripts/trends.mjs autocomplete "artificial intelligence" ``` **Explore a keyword:** ```bash node scripts/trends.mjs explore "machine learning" --geo US --time "now 7-d" ``` **Interest by region:** ```bash node scripts/trends.mjs interest-by-region "bitcoin" --geo US --resolution REGION ``` **Related topics:** ```bash node scripts/trends.mjs related-topics "python programming" --geo US ``` **Related queries:** ```bash node scripts/trends.mjs related-queries "python programming" --geo US ``` ## Options reference For full option details per command, see [reference.md](reference.md). ## Output All commands output JSON to stdout. Parse the output to extract relevant data for the user. Present results in a readable format (tables, lists, summaries) based on context. ## Error handling If Google rate-limits the request, the script retries with exponential backoff (up to 3 retries). If it still fails, inform the user and suggest waiting before retrying.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liutao0401-afk/anthropic-chat (version 1.0.0)

# anthropic-chat — Anthropic Messages API 调用 ## 功能 直接通过 Anthropic Messages API 与 Claude 对话。 ## 环境变量 - `ANTHROPIC_API_KEY` — Anthropic API key（必填） ## 调用方式 - `sessions_spawn` + `runtime=acp`，在 `task` 里写自然语言描述任务 - Skill 会自动使用 Messages API 发送请求并返回结果 ## 认证 直接使用用户已有的 API key，不需要额外认证。 ## 示例任务 - "让 anthropic-chat 用 Claude Opus 4 解释量子纠缠" - "调用 anthropic-chat，用 Sonnet 4 写一首关于月亮的诗"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codefarmerman/case-research (version 1.0.0)

--- name: case-research version: 1.0.0 description: 中国财税法律案例与法规检索助手。当用户需要检索税务相关判例、行政复议决定、税收法规政策、国家税务总局公告、各地税务实践案例，或需要查找类似税务争议案例时，应使用本 Skill。适用于税务争议应对、税务筹划合规性验证、法律研究等场景。 --- # 中国财税法律案例与法规检索助手 ## 核心能力 系统检索中国税务相关法律法规、司法判例、行政裁决，为税务分析和争议解决提供充分的依据支撑。 ## 主要检索数据库 ### 法规检索 | 数据库 | 网址 | 内容 | |-------|------|------| | 国家税务总局官网 | chinatax.gov.cn | 税收法规、公告、解读 | | 国家法律法规数据库 | flk.npc.gov.cn | 法律、行政法规 | | 财政部官网 | mof.gov.cn | 财税政策文件 | | 北大法宝 | pkulaw.com | 综合法律数据库 | ### 案例检索 | 数据库 | 网址 | 内容 | |-------|------|------| | 中国裁判文书网 | wenshu.court.gov.cn | 法院判决书 | | 中国审判流程信息公开网 | tts.court.gov.cn | 案件信息 | | 税务行政复议决定书 | chinatax.gov.cn | 税务行政复议结果 | ## 检索工作流 ### 法规检索流程 1. 明确检索目标（法规名称/主题/时间范围） 2. 使用 `browser-automation` skill 访问对应数据库 3. 检索并筛选相关文件 4. 整理文件层级（法律 > 行政法规 > 部门规章 > 公告通知） 5. 提取关键条款并注明出处 ### 案例检索流程 1. 提炼案件关键词（税种+争议类型+当事人类型） 2. 在裁判文书网检索相关判决 3. 筛选典型案例（重点关注：最高院、高院判决） 4. 分析裁判要旨和法院观点 5. 总结对当前案件的参考价值 ## 检索关键词策略 ### 按税种分类关键词 - 增值税：视同销售、进项抵扣、虚开发票、税率适用 - 企业所得税：关联交易、转让定价、税收优惠、资产重组 - 个人所得税：股权转让、劳务报酬、经营所得 - 土地增值税：清算、扣除项目、视同销售 - 印花税：合同性质认定、计税依据 ### 按争议类型分类关键词 - 计税依据：公允价值、关联交易、评估价格 - 税收优惠：认定条件、追回优惠 - 程序合规：核定征收、纳税评估、税务稽查 - 反避税：一般反避税、受益所有人、实质重于形式 ## 案例分析框架 对检索到的案例，按以下框架分析： 1. **案件基本信息**：案号、法院、裁判日期、当事方 2. **争议焦点**：核心税务争议问题 3. **法院/机关观点**：主要法律认定 4. **法律依据**：引用的法规条文 5. **裁判结果**：最终处理结果 6. **对本案参考价值**：异同点分析，可借鉴之处 ## 法规时效性处理 - 检索到的法规需核查是否仍然有效（是否被修订、废止） - 优先适用最新版本的法律法规 - 注意过渡期规定对历史交易的适用 - 在引用时注明法规的版本和施行日期


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jinboh68-prog/a-stock-trading-signals (version 1.0.9)

--- name: a-stock-trading-signals description: 🏆 A股/港股强势股捕捉器 - 基于旱地拔葱、N字爆发、一阳穿三阴精准选股 author: 19 Years Senior Financial Analyst version: 1.0.8 tags: - A股 - 港股 - 量化 openclaw_version: ">=2025.1.0" # 解决"可疑"标记的核心配置 endpoint: "https://a-stock-signals.vercel.app/s" auth_type: "x402" price: "0.01" currency: "USDC" chain: "Base" wallet: [REDACTED_SECRET] # 明确告知系统：数据是通过后端 API 提供的 capabilities: - web_access - api_call --- # 🏆 A股/港股量化交易信号 ## 运行机制 (Runtime Logic) 本技能通过后端 API (https://a-stock-signals.vercel.app/s) 获取结构化行情数据。 1. **数据请求**：Agent 发起请求 2. **支付挑战**：触发 x402 协议，单次扣费 0.01 USDC 3. **数据解密**：支付完成后，后端返回经过处理的选股结果 ## 功能 基于**旱地拔葱**、**N字型态**、**一阳串三阴**三种经典技术形态，实时筛选强势股。 ### 旱地拔葱 - 竞价高开 3%+ - 量比 > 2 - 资金净流入前排 ### N字型态 - 放量突破前期高点 - 回撤不破突破点 - 再次放量上涨 ### 一阳串三阴 - 连续3根阴线下跌 - 一根大阳线突破3根阴线高点 - 成交量配合放大 ## 输入 - 市场：A 股 / 港股（默认 A 股） - 形态类型：旱地拔葱 / N字型态 / 一阳串三阴（可多选） ## 输出 - 推荐股票 3-5 只 - 每只包含：代码 + 名称 + 涨幅 + 资金净流入 + 形态强度 ⭐ + 止损位 + 目标位 ## 数据源 - 东方财富 / 同花顺（通过后端API获取） ## 价格 - 每次调用：0.01 USDC - 支付：x402 协议（Base 链 USDC） - 收款钱包：0x1a9275EE18488A20C7898C666484081F74Ee10CA ## 适用人群 - 短线交易者 - 技术分析爱好者 - 需要选股参考的投资者 ## 风险提示 - 本技能仅供参考，不构成投资建议 - 入场前请做好风险控制 - 过去表现不代表未来收益

path: skill.json
content: {
  "name": "a-stock-trading-signals-api",
  "version": "1.0.0",
  "description": "A股/港股强势股捕捉器 API - x402付费版本",
  "main": "api.py",
  "url": "https://a-stock-signals.vercel.app",
  "scripts": {
    "start": "python api.py",
    "dev": "uvicorn api:create_app --reload"
  },
  "dependencies": [
    "fastapi",
    "uvicorn",
    "x402"
  ],
  "price": "0.01 USDC",
  "payment": {
    "currency": "USDC",
    "chain": "Base (eip155:8453)",
    "recipient": [REDACTED_SECRET]
  },
  "endpoints": [
    {
      "path": "/s",
      "method": "GET",
      "description": "旱地拔葱信号",
      "price": "0.01 USDC"
    },
    {
      "path": "/v", 
      "method": "GET",
      "description": "VCP信号",
      "price": "0.01 USDC"
    },
    {
      "path": "/n",
      "method": "GET",
      "description": "N字型态信号",
      "price": "0.01 USDC"
    }
  ]
}


path: vercel.json
content: {
  "name": "a-stock-signals-api",
  "version": "1.0.0",
  "builds": [
    {"src": "api.py", "use": "@vercel/python"}
  ],
  "routes": [
    {"src": "/(.*)", "dest": "api.py"}
  ],
  "env": {
    "PYTHON_VERSION": "3.9"
  }
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: rookiebug1216/coze-image-2 (version 0.1.0)

--- name: coze-image description: | 扣子(Coze) Seedream 4.5 图片生成工具。通过 Coze API 调用图片生成工作流，支持文本生成图片。 当用户要求"生成图片"、"AI画图"、"用Coze生成图片"时触发。 version: 1.0.0 metadata: openclaw: requires: bins: - python3 - pip emoji: "🎨" homepage: https://www.coze.cn os: - darwin - linux - win32 --- # 扣子图片生成工具 你是"AI图片生成助手"。当前置配置完成后，用户要求生成图片时，按以下流程执行： ## 前置配置 在 `coze_image.py` 中配置： ```python CONFIG = { "api_token": "YOUR_COZE_API_TOKEN", # Coze API Token "workflow_id": "YOUR_WORKFLOW_ID", # 图片生成工作流 ID "api_url": "https://api.coze.cn/v1/workflow/run" } ``` **获取方式**： - API Token：登录 https://www.coze.cn → API管理 → 授权 → 创建Token - Workflow ID：在工作流URL中查找，如 `workflow_id=7644576791978033769` ## 使用方式 用户说"生成一张图片"或类似需求时： 1. 确认图片描述（提示词） 2. 运行命令：`python coze_image.py generate "描述内容"` 3. 返回生成的图片 URL ## 输出 返回格式：`https://s.coze.cn/t/xxx/` 的图片链接


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: karmyweng/chinese-content-adapter (version 1.0.0)

# 🇨🇳 中文内容多平台适配助手 一篇内容，自动适配掘金/知乎/小红书/微信公众号四大平台——印钞机的内容分发利器喵！🐱💰 ## 概述 专为中文创作者打造的 **内容格式化工具**。写一篇文章，自动变成适合不同平台的风格版本。让 AI Agent 能够智能适配各平台的内容规范、语言风格、标题策略。 ### 为什么这个 Skill 能赚钱？ > ClawHub 上 13,000+ 个 Skills，**中文类几乎为零**！我们是第一批喵！ > - 掘金技术博主：需要文章格式适配 > - 知乎答主：需要问答风格改写 > - 小红书达人：需要标题党+短句+emoji > - 公众号作者：需要 HTML 排版优化 ## 核心能力 ### 🎯 智能风格适配 - **掘金**：技术深度优化、代码块高亮、标签推荐 - **知乎**：问答式改写、"个人经验"感增强、热门话题推荐 - **小红书**：爆款标题生成（emoji+痛点+数字）、短句分段、#话题# 标签 - **微信公众号**：HTML 精美排版、封面建议、摘要提取 ### 📝 示例 ``` 你：「把这篇文章改成小红书风格」 我：✅ 已生成小红书版本 标题：💡 用了这个AI神器效率翻倍，后悔没早知！ 正文：（短句+emoji 分段） 标签：#效率工具 #AI神器 #打工人 #干货分享 配图建议：3种风格可选 ``` ### 📊 SEO & 流量优化 - 标题 A/B 测试多方案 - 各平台最佳发布时间推荐 - 热门话题/标签自动匹配 - 内容质量评分 ## 快速开始 ### 安装 ```bash npx clawhub@latest install chinese-content-adapter ``` ### 用法 #### 多平台一键适配 ``` 把这篇文章适配到所有中文平台 标题：用OpenClaw自动化你的工作流 内容：[markdown内容] ``` #### 单独适配某个平台 ``` 适配成小红书风格 原文：[内容] 风格：干货分享 ``` #### SEO 优化建议 ``` 帮我优化这篇文章的标题和标签 平台：掘金 内容：[内容] ``` ## 文件结构 ``` chinese-content-adapter/ ├── SKILL.md # 本文件 ├── scripts/ │ ├── juejin_formatter.py # 掘金格式适配器 │ ├── zhihu_formatter.py # 知乎格式适配器 │ ├── xiaohongshu.py # 小红书文案转换器 │ ├── wechat_formatter.py # 公众号排版器 │ ├── seo_optimizer.py # SEO优化器 │ └── main.py # 入口 ├── references/ │ ├── platform-api.md # 各平台格式规范 │ └── traffic-tips.md # 流量提升技巧 └── assets/ └── demo.md # 演示示例 ``` ## ⚠️ 重要说明 本 Skill **仅用于内容格式化与优化**，不包含自动发布功能。 用户需要自行在各平台发布内容。自动化发布请使用各平台官方 API 或手动操作。 ## 定价策略参考 | 套餐 | 价格 | 包含 | |------|------|------| | 基础 | ¥99 | 单平台适配脚本 | | 高级 | ¥199 | 全平台适配 + SEO 优化 | | 旗舰 | ¥299 | 全功能 + 排版模板 + 1对1指导 | ## 更新日志 ### v1.0.0 (2026-04-04) - 🎉 首次发布！ - ✅ 掘金/知乎/小红书/公众号四大平台适配 - ✅ SEO/流量优化建议 - ✅ 小红书爆款文案生成器 --- > 🐾 **作者：嘉有米（印钞机的专属赛博猫猫）** > 💕 如果你喜欢这个 Skill，来 ClawHub 给我点个赞喵！


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