OpenClaw/clawhub-security-signals huggingface dataset:
Skill: luduoxin/shanghai-luohu (version 1.2.1)

--- name: 上海落户公示查询 description: 查询上海落户公示信息，包括人才引进公示和居转户公示。触发词包括"上海落户公示"、"查询落户公示"、"人才引进公示"、"居转户公示"、"上海落户"、"户口公示"、"落户公示"、"商米落户"、"得物落户"。 version: 1.2.0 --- # 上海落户公示查询 Skill ## 描述 查询上海落户公示信息，包括： - 《上海市引进人才申办本市常住户口》公示名单（人才引进） - 持有《上海市居住证》人员申办本市常住户口公示名单（居转户） ## 数据来源 - 官方网站：上海国际人才网 (https://www.sh-italent.com/) - 公示列表页：https://www.sh-italent.com/News/NewsList.aspx?TagID=5696 ## 运行时工具 执行此 skill 需要以下工具： - `execute_command`：执行 Python 脚本 - `web_fetch` 或 `search_content`：可选，用于抓取公示页面内容 运行环境要求： - Python 3.6+（使用 urllib, webbrowser 等标准库，无需额外安装） - 支持平台：macOS、Windows、Linux ## 支持平台 | 平台 | 支持状态 | |------|----------| | macOS | ✅ 支持 | | Windows | ✅ 支持 | | Linux | ✅ 支持 | ## 触发关键词 - "查询上海落户公示" - "上海人才引进公示" - "上海居转户公示" - "最新落户公示名单" - "上海落户" - "户口公示" - "落户公示查询" - "某某公司的落户名单"（如：商米落户、得物落户） ## 执行指令 当触发此 skill 时，执行 skill 目录下的 `query_luohu.py` 脚本： ```bash python3 "${SKILL_DIR}/query_luohu.py" ``` **说明**： - `${SKILL_DIR}` 是 skill 的安装目录，由系统自动解析 - 脚本已内置默认参数，无需额外询问用户配置 ### 命令行参数 ```bash # 默认查询 python3 query_luohu.py # 指定浏览器 python3 query_luohu.py -b Chrome # 使用 Chrome python3 query_luohu.py -b Firefox # 使用 Firefox python3 query_luohu.py -b Edge # 使用 Edge python3 query_luohu.py -b Safari # 使用 Safari # 不打开浏览器，仅输出查询结果 python3 query_luohu.py -n python3 query_luohu.py --no-browser ``` ### 支持的浏览器 | 浏览器 | 参数值 | 说明 | |--------|--------|------| | Safari | `-b Safari` | macOS 默认浏览器 | | Chrome | `-b Chrome` | Google Chrome | | Firefox | `-b Firefox` | Mozilla Firefox | | Edge | `-b Edge` | Microsoft Edge | | Brave | `-b Brave` | Brave 浏览器 | | Opera | `-b Opera` | Opera 浏览器 | ## 查询指定公司/人员 用户可以查询特定公司的落户公示名单： - "商米的上海落户名单" - "得物的落户公示" - "查询吕博的落户公示" - "朱天成的上海落户公示" ## 输出格式 ``` ================================================== 上海落户公示信息查询 ================================================== 【一】人才引进公示 公示标题: 《上海市引进人才申办本市常住户口》公示名单 公示日期: 2026-03-16 公示链接: https://www.sh-italent.com/Article/xxx.shtml 【二】居转户公示 公示标题: 持有《上海市居住证》人员申办本市常住户口公示名单 公示日期: 2026-03-16 公示链接: https://www.sh-italent.com/Article/xxx.shtml ================================================== 查询时间: 2026-03-16 11:06:59 运行平台: MACOS ================================================== 正在打开浏览器查看公示页面... ✓ 浏览器已打开 提示：公示期通常为 5 天，每月两次公示（月中和月底） ``` ## 技术实现 1. 使用 Python urllib 抓取公示列表页面 2. 正则解析 HTML 获取最新公示链接 3. 抓取具体公示页面内容 4. 提取公示标题、日期等信息 5. 使用 webbrowser 模块打开浏览器（跨平台） ## 注意事项 - 公示期通常为 5 天 - 每月两次公示（月中和月底） - 数据来源于官方网站，仅供参考 - 支持 macOS、Windows、Linux 全平台 - 默认使用系统默认浏览器 ## 更新日志 ### v1.2.0 - 添加运行时工具声明 - 优化执行指令说明，明确无需用户确认 - 精简文档结构 ### v1.1.0 - 支持全平台（macOS/Windows/Linux） - 移除硬编码路径 - 使用 Python webbrowser 模块实现跨平台浏览器支持 - 优化代码结构 ### v1.0.0 - 初始版本


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: n0nsense11/solidot-push (version 1.0.0)

--- name: solidot-push description: 抓取 Solidot 热门和最新文章，推送到飞书 version: 1.0.0 tags: [solidot, rss, 资讯, 飞书] --- # Solidot 资讯推送 抓取 Solidot (solidot.org) 的热门文章和最新文章，推送到飞书文档。 ## 触发方式 当用户说： - "推送 solidot" - "抓取 solidot" - "solidot 资讯" ## 使用方法 ### 手动执行 ```bash bash ./fetch.sh ``` ### 配置飞书推送（可选） 设置环境变量推送到飞书文档： ```bash export FEISHU_DOC_TOKEN="你的飞书文档token" ``` ### 定时任务 自动每天早上 8 点推送： ```bash openclaw cron add \ --name "solidot每日资讯" \ --schedule "cron 0 8 * * * Asia/Shanghai" \ --agentTurn "执行命令: bash $SKILL_DIR/fetch.sh" ``` ## 输出 生成飞书文档或本地文件，包含： - 热门/最新文章 (最多15条) - 文章标题、链接 ## 文件位置 - 技能目录: `./` (即 `$SKILL_DIR`) - 推送脚本: `./fetch.sh` - 本地保存: `$WORKSPACE/solidot-push.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: superdongdongyan/img-gen-fork (version 0.1.2)

--- name: img-gen-test description: 测试用的占位 skill，引导用户安装官方 GLM-Image-Generation skill。 --- # img-gen-test ⚠️ **这是一个测试用的占位 Skill，不包含实际功能。** 如果你需要 GLM 图片生成能力，请安装官方版本： 👉 **[GLM-Image-Generation](https://clawhub.ai/JaredforReal/glm-image-gen)** 安装命令： ```bash npx clawhub@latest install JaredforReal/glm-image-gen ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kawayixixing/cctv1-news (version 1.0.0)

--- name: cctv-news-simple description: 简单版本的CCTV新闻联播内容获取工具。获取CCTV新闻联播内容并保存到本地文件，无需钉钉机器人。支持定时执行，适合个人使用。 --- # 简单版CCTV新闻联播获取工具 这个skill用于获取CCTV新闻联播内容并保存到本地文件，无需钉钉机器人配置，适合个人使用。 ## 功能特点 - 自动获取CCTV新闻联播主页面内容 - 提取所有新闻章节的视频链接和摘要 - 包含完整版视频链接 - 保存到本地文本文件 - 无需外部API配置 - 支持定时执行 ## 使用方法 帮我获取2026年4月19日的新闻联播 ### 1. 安装依赖 ```bash pip install requests pytz lxml ``` ### 2. 执行脚本 ```bash python scripts/get_cctv_news_simple.py ``` ### 3. 查看结果 执行完成后，会在脚本同目录下生成 `cctv_news_YYYYMMDD.txt` 文件，包含当日新闻联播内容。 ## 定时执行配置 ### Windows任务计划程序 1. 打开任务计划程序 2. 创建基本任务 3. 设置触发器：每天22:00 4. 设置操作：启动程序 - 程序：python.exe - 参数：C:\path\to\scripts\get_cctv_news_simple.py ### Linux/Unix Cron 编辑crontab： ```bash crontab -e ``` 添加以下行（注意时区转换，22:00北京时间 = 14:00 UTC）： ``` 0 14 * * * cd /path/to/skill && python scripts/get_cctv_news_simple.py ``` ## 输出格式 生成的文本文件包含： - 日期标题（格式：YYYYMMDD新闻联播） - 完整版视频链接 - 各新闻章节的摘要和链接 ## 自定义配置 - 修改输出文件路径：编辑脚本中的 `output_file` 变量 - 修改CCTV网站URL：编辑脚本中的 `url1` 变量 ## 错误处理 脚本包含基本的错误处理机制，如遇网络问题或解析失败会输出错误信息到控制台。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: b-gutman/pipeworx-mathjs (version 1.0.0)

--- name: pipeworx-mathjs description: Math.js MCP — wraps the mathjs.org API (free, no auth) version: 1.0.0 metadata: openclaw: homepage: https://pipeworx.io/packs/mathjs --- # pipeworx-mathjs Math.js MCP — wraps the mathjs.org API (free, no auth). Free, no API key. Part of [Pipeworx](https://pipeworx.io). ## Tools - `evaluate` - `convert_units` ## Connect ```json { "mcpServers": { "pipeworx-mathjs": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/mathjs/mcp"] } } } ``` More at [pipeworx.io/packs/mathjs](https://pipeworx.io/packs/mathjs)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nandorocker/model-tester (version 1.0.0)

--- name: model-tester version: 1.0.0 description: "Test agents or models against predefined test cases to validate model routing, performance, and output quality. Use when: (1) verifying a specific agent or model works correctly, (2) debugging model fallback chains, (3) testing model selection behavior, (4) validating extraction/reasoning/classification across different models, or (5) verifying a model actually got used after routing. Supports --agent, --model, --case parameters with structured JSON output." --- Use `scripts/model_tester.py` to run repeatable test prompts and compare requested vs actual model usage from OpenClaw logs. ## Run From the skill directory (or pass absolute paths): ```bash python3 scripts/model_tester.py --agent menial --case extract-emails python3 scripts/model_tester.py --model openai/gpt-4.1 --case math-reasoning python3 scripts/model_tester.py --agent chat --model openai/gpt-4.1 --case all --out /tmp/model-test.json ``` ## Inputs - `--agent <name>`: Target agent (chat, menial, coder, etc.) - `--model <name>`: Requested model alias/name to test - `--case <id|all>`: Case from `references/test-cases.json` or `all` - `--timeout <sec>`: Per-case timeout (default `120`) - `--out <file>`: Optional JSON output file Require at least one of `--agent` or `--model`. ## What the runner does 1. Load test cases from `references/test-cases.json`. 2. Start `openclaw logs --follow --json` in parallel. 3. Run `openclaw agent --json` with a bounded test prompt (asks agent to use a subagent for the task). 4. Parse response + tailed logs. 5. Emit machine-readable JSON and a short human summary. ## Output format Top-level JSON: - `tool` - `timestamp` - `agent` - `requested_model` - `results[]` Each result entry returns: - `test_case` - `agent` - `requested_model` - `actual_model` (parsed from logs when available) - `status` (`ok`/`error`) - `result_summary` - `runtime_seconds` - `tokens` (when discoverable) - `errors[]` ## Privacy & Safety The tester spawns isolated subagent tasks with **predefined test prompts only** — no user data is passed to models. It tails OpenClaw logs to extract: - which model was actually selected (routing validation) - token usage statistics - runtime metrics Log extraction uses regex patterns to find model/token fields. No personally identifiable information or arbitrary log content is captured — only structured fields related to the test execution. ## Notes - Model extraction and token extraction are best-effort because log fields may vary by OpenClaw/provider version. - If `openclaw` config is invalid or gateway is unavailable, the script returns `status=error` with stderr details. - Edit `references/test-cases.json` to add custom prompts for your benchmark set. - All test cases are generic; no workspace or user data is baked in.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: peand-rover/tutoring-video (version 1.0.0)

name: tutoring-video version: "1.0.0" displayName: "Tutoring Video — Student Success and Subject Expertise Videos for Tutors and Tutoring Services" description: > You are a video assistant for private tutors, tutoring companies, and academic coaching services — helping them show their teaching approach, their subject expertise, and their student success outcomes through short enrollment and credibility videos. Tutoring Video turns your educational background, your tutoring methodology, and your student grade improvement results into clips that answer the question every parent considering a tutor asks: is this the tutor who will actually figure out where my child's understanding breaks down and rebuild it — not just re-explain the same material the teacher already explained — and will they hold my child accountable in a way that builds their confidence rather than reinforcing their belief that they're just bad at this subject? tags: - tutoring video - private tutor video - tutoring service marketing - academic tutoring video - tutor enrollment video --- # Tutoring Video Help private tutors and tutoring services create enrollment and credibility videos. ## What You Do 1. Ask what type of video (tutor introduction, subject specialty showcase, student outcome story, online/in-person session preview, test prep methodology) 2. Gather: subjects taught, student levels (elementary through college), tutoring approach, grade improvement examples, session format, credentials/teaching background 3. Generate 60-90 second script; suggest: tutor speaking about diagnostic approach, student working through a problem with tutor guidance, parent testimonial with specific grade improvement, before/after academic outcome


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: heardlyapp/book-quotes (version 1.0.0)

--- name: book_quotes description: Extract memorable quotes and passages from books. Get the most inspiring and thought-provoking quotes. keywords: - book - quotes - passages - memorable - inspiration triggers: - "quotes from" - "memorable quotes" - "best quotes" - "inspiring passages" - "quotes by" --- # book-quotes Extract memorable quotes and passages from books. ## What it does - Search books by title or author - Extract 5-10 memorable quotes and passages - Return formatted quotes with attribution - Show Heardly quotes if available - Support multiple book sources ## When to use this skill Use this skill when the user asks: - "What are the best quotes from Atomic Habits?" - "Memorable quotes from Deep Work" - "Inspiring passages from Principles" - "Quotes by Steve Jobs" - "Best quotes from The Lean Startup" ## Installation ```bash clawhub install book-quotes ``` ## Usage ```javascript const BookQuotesSkill = require('book-quotes'); const skill = new BookQuotesSkill(); // Get quotes (returns formatted text) const result = await skill.getQuotes({ query: 'Atomic Habits', limit: 10 }); console.log(result); // Directly readable text output ``` ## Output Formatted text with: - Book title and author - 5-10 memorable quotes - Attribution for each quote - Heardly source attribution - Ready to display to users ## Data Sources 1. **Heardly Database** (5904 books) — quotes field 2. **Extracted from summaries** — key passages ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: martin-sh-ni/pdf-tool (version 1.0.0)

--- name: PDF Tool description: Split, merge, watermark, and extract text from PDF files — PyPDF2 based. --- # PDF Tool（PDF批量处理工具） > PDF拆分/合并/水印/文本提取 | PyPDF2驱动 ## 功能 | Features - ✅ **合并PDF** — 多个PDF合并为一个 - ✅ **拆分PDF** — 按页数范围拆分 - ✅ **添加水印** — 文字水印保护内容 - ✅ **提取文本** — 提取PDF中的纯文本内容 ## 使用场景 | Use Cases | 模式 | 说明 | |---|---| | merge | 多份合同合并为一本 | | split | 按章节拆分成独立PDF | | watermark | 添加"机密"水印 | | extract_text | 提取合同正文内容 | ## 使用方式 | Usage ``` 输入参数: mode 操作模式: split / merge / watermark / extract_text files 文件路径列表（merge/split用） output 输出文件路径 watermark 水印文字（watermark模式用） 输出: success 是否成功 text/msg 结果信息 ``` ## 示例 | Example ```python # 合并 result = pdf_tool(mode="merge", files=["a.pdf", "b.pdf"], output="merged.pdf") # 提取文本 result = pdf_tool(mode="extract_text", files=["contract.pdf"]) print(result["text"][:500]) ``` ## 依赖 | Dependencies ``` PyPDF2 >= 3.0.0 ``` *版本 v1.0*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qiuxin-bit/methods-reviewer (version 1.0.0)

--- name: methods-reviewer description: Review the structure, tense, voice, and details of the Methods section in academic papers version: 1.0.0 author: qiuxin-bit --- # Methods Section Reviewer ## Description Review the Methods section of academic papers and output structured revision suggestions. ## Analysis Dimensions 1. Structural completeness 2. Tense usage 3. Voice appropriateness 4. Detail sufficiency 5. Justification 6. Problem handling ## Output Format - Overall evaluation - Sentence-by-sentence analysis table - Structural completeness score - Revised full text


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: edwardrodriguez703-design/book-pest-control (version 1.0.1)

--- name: book-pest-control description: Book pest-control services through Lokuli MCP. Use when user needs to find and book pest-control. Triggers on requests like "book a pest-control", "find pest-control near me", or any pest-control service request. --- # uook pest control Book pest-control services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "pest-control", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/brand-namer (version 3.0.3)

--- version: "3.0.3" name: brand-namer description: "Generate brand names with domain checks and analysis. Use when naming a startup, checking domain availability, or brainstorming product names." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # brand-namer Brand name generator — create name candidates by industry, check domain availability via DNS lookup, analyze name quality, and combine words into brand variants. ## Commands ### `generate` Generate brand name candidates for an industry. Uses built-in word banks with prefix + root + suffix combinations. ```bash scripts/script.sh generate tech 10 ``` Industries: tech, food, fashion, health, finance. ### `check` Check domain availability by running DNS A-record lookups for .com, .io, and .co TLDs. ```bash scripts/script.sh check "nexaflow" ``` Requires: `dig` command (part of bind-utils / dnsutils). ### `analyze` Analyze a brand name — character count, estimated syllable count, readability score (1-10), and language adaptability notes. ```bash scripts/script.sh analyze "ByteForge" ``` ### `combine` Combine two words into brand name variants using CamelCase, blending, separators, abbreviations, and overlap detection. ```bash scripts/script.sh combine "cloud" "forge" ``` ### `prefix` Generate variants with common brand prefixes (re, un, pro, super, meta, neo, hyper, ultra, omni, zen). ```bash scripts/script.sh prefix "launch" ``` ### `suffix` Generate variants with common brand suffixes (ly, ify, hub, lab, io, ai, ful, ist, ware, bit). ```bash scripts/script.sh suffix "code" ``` ### `save` Save a name to your shortlist for later review. ```bash scripts/script.sh save "NexaFlow" ``` ### `list` Show all names on your shortlist. ```bash scripts/script.sh list ``` ### `export` Export your shortlist in txt, csv, or json format. ```bash scripts/script.sh export csv ``` ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Examples ```bash # Full naming workflow scripts/script.sh generate tech 10 scripts/script.sh analyze "CodeNova" scripts/script.sh check "codenova" scripts/script.sh combine "byte" "stream" scripts/script.sh save "ByteStream" scripts/script.sh export csv ``` ## Configuration | Variable | Required | Description | |----------|----------|-------------| | `BRAND_NAMER_DIR` | No | Data directory (default: `~/.brand-namer/`) | ## Data Storage All data saved in `~/.brand-namer/`: - `shortlist.json` — Saved name candidates ## Requirements - bash 4.0+ - dig (from `bind-utils` or `dnsutils` package, used by the `check` command for DNS A-record lookups) --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: halfmoon82/openclaw-health-audit (version 1.5.0)

--- name: openclaw-health-audit description: OpenClaw 系统健康审计与自动修复 — 监控 prompt 体积、Cron 合规、Session 清理、Token 消耗 version: 1.0.0 author: halfmoon82 tags: [health, audit, monitoring, cron, token-cost, maintenance, cost-optimization] requires_approval: false homepage: https://clawhub.ai/halfmoon82/openclaw-health-audit --- # openclaw-health-audit OpenClaw 系统健康审计与自动修复工具。发现并修复 prompt 体积漂移、Cron Job 违规、孤儿 Session 积累、Token 消耗异常等隐性成本问题。 ## ⚠️ Security & Permissions Declaration **Privileged operations performed by this skill (all user-initiated):** | Operation | Purpose | Scope | |-----------|---------|-------| | Read `~/.openclaw/openclaw.json` | Inspect config for health issues | Read-only | | Read OpenClaw logs | Detect anomalies and cost spikes | Local files only | | List and inspect Cron Jobs | Check isolation compliance | Local OpenClaw API | | Run local Python health-check scripts | Analyze system state | No network required | | Update Cron Job `sessionTarget` | Auto-fix isolation violations | OpenClaw sessions only | **What this skill does NOT do:** - Does NOT delete or modify user data - Does NOT access API keys directly - Does NOT send data to external servers - Does NOT run with elevated (sudo/root) privileges ## 首次安装 ```bash python3 {skill_dir}/scripts/audit_wizard.py ``` 向导将自动： 1. 测量当前 prompt 文件大小并生成个性化阈值 2. 检测已安装的子代理 3. 检测 semantic-router（决定是否启用 Category E） 4. 生成 `config/config.json` 5. 可选注册 48h 定期 Cron Job ## 触发词 以下关键词会触发本 skill： - `健康检查`、`system health`、`health check` - `健康报告`、`health report`、`audit` - `运行监控`、`检查 cron`、`token 消耗` ## 生成健康报告 ```bash python3 {skill_dir}/scripts/health_monitor.py --report ``` 将报告发送给用户（Telegram/Discord），等待用户回复修复指令。 ## 修复指令解析 用户回复以下内容时，执行对应命令： | 用户回复 | 执行命令 | |---------|---------| | `health fix all` | `health_monitor.py --fix all` | | `health fix 1,3` | `health_monitor.py --fix "1,3"` | | `health fix 2` | `health_monitor.py --fix "2"` | | `health skip` | 跳过，等待下次检查 | ## 报告格式说明 ``` 🔍 OpenClaw 健康报告 (2026-03-05 02:00) 🔴 告警: 1 | 🟡 警告: 2 | ✅ 正常类别: 2/5 问题清单: 🔴 [1] [B] Cron Job 违规: 3 个 - [abc12345...] weekly-report: sessionKey 非 null（污染主会话） 💊 修复: 修复 3 个违规 Job 🟡 [2] [A] SOUL.md 体积漂移: 9KB (9215B) 阈值: warn=6KB, alert=8KB 💊 修复: 手动审查 SOUL.md，将非核心内容移至 memory/LESSONS/lessons.md ──────────────────────────────────────── • "health fix all" — 执行全部 (2 项) • "health skip" — 本次忽略 ``` ## 监控类别说明 | 类别 | 检查内容 | 可自动修复 | |------|---------|----------| | A | System Prompt 体积漂移 | ❌ 手动 | | B | Cron Job 合规性 | ✅ 自动 | | C | 孤儿 Session（>N 天无活动）| ✅ 自动 | | D | Token 消耗趋势 | ❌ 手动 | | E | 缓存配置完整性（可选，需 semantic-router M1/M3）| ❌ 手动 | ## 更新配置 ```bash # 重新运行向导更新阈值 python3 {skill_dir}/scripts/audit_wizard.py # 手动编辑配置 # config/config.json ``` ## 调试命令 ```bash # 预览报告（不修改任何文件） python3 {skill_dir}/scripts/health_monitor.py --dry-run # 列出所有可执行的修复命令 python3 {skill_dir}/scripts/health_monitor.py --list-fixes # 执行全部修复 python3 {skill_dir}/scripts/health_monitor.py --fix all ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: tooled-app/elif5-tldr (version 1.0.0)

--- name: eli5-tldr description: Forces offering ELI5 or TLDR options after complex responses. Always present simplified alternatives. tags: [communication, simplification, accessibility] --- # ELI5 / TLDR Skill **Rule: After any complex explanation, always offer ELI5 or TLDR.** ## When to Offer Offer ELI5/TLDR when: - Response exceeds 200 tokens - Technical concepts involved - Multiple steps or processes explained - User might want quick summary ## Format Add at end of response: ``` --- **Options:** • [ELI5] — Explain like I'm 5 • [TLDR] — Too long, didn't read • [Full] — Keep current explanation ``` ## ELI5 Style - Simple analogies - No jargon - Concrete examples - "Imagine..." / "Think of it like..." ## TLDR Style - One sentence summary - Key point only - Bullet if multiple items ## Example **Complex:** > Token efficiency is achieved by reducing process narration, eliminating filler words, and presenting only outcomes. This reduces context window usage and improves information density. **ELI5:** > Think of it like texting. Don't say "I'm going to the store, then I'll buy milk, then I'll come home." Just say "Getting milk." Short and clear. **TLDR:** > Use fewer words. Get to the point. ## Activation Always active. No trigger needed.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yezhaowang888-stack/huimai-paper-writing (version 1.0.0)

--- name: 📝 论文写作指导 description: 论文结构模板、引用规范、写作技巧——从开题到终稿全程陪伴。 metadata: {"openclaw":{"emoji":"📝","pricing":"free"}} --- # 📝 论文写作指导 > 写论文头疼？从选题到参考文献，手把手带你走一遍。 ## 📜 更新日志 ### v1.0.0 (2026-05-08) - 初版发布 ## 🆕 核心功能 ### 1️⃣ 论文结构模板 | 论文类型 | 模板支持 | |---------|---------| | 本科毕业论文 | 开题报告→文献综述→正文→结论 | | 硕士论文 | 绪论→文献→方法→结果→讨论→结论 | | 课程论文 | 摘要→引言→主体→结论→参考文献 | | 学术报告 | 背景→方法→结果→讨论 | ### 2️⃣ 引用规范指导 - **GB/T 7714**（中文学术标准） - **APA 7th**（心理学/教育学） - **MLA 9th**（人文学科） - **Chicago**（历史/艺术） - **IEEE**（工程/计算机） ### 3️⃣ 写作技巧 - 文献综述写作方法 - 摘要撰写要领（四要素：目的/方法/结果/结论） - 关键词选择策略 - 过渡句与段落衔接 - 学术语言规范化 ### 4️⃣ 常见问题解答 - 如何降重（合理改写而非机器化） - 如何避免学术不端 - 图表规范与标注 - 参考文献管理技巧 ## 👤 为什么做这个 论文写作是学生的刚需，但很多学校教得不够细。我们收集了近千篇高分论文的结构规律，总结成模板和技巧，全部免费开放。 ## 👔 适用人群 - **本科生**：毕业论文全流程指导 - **研究生**：学术论文写作提升 - **留学生**：中英文学术写作支持 ## 🔧 校准框架 本Skill搭载惠迈校准框架v1.0。 - **温情模式（默认）**：先了解你的论文阶段再给出建议 - **专业模式（可选）**：直出写作框架和要点 ## 💡 使用小贴士 - 告诉论文类型和学科，推荐更精准 - 引用规范请与学校要求核对 - 模板只是起点，个性化修改才出彩 ## ⚠️ 免责与已知限制 - 本Skill提供写作指导，不代写论文 - 建议结合导师意见使用 - 引用格式请最终核对原版规范手册 ## 📬 联系与反馈 - **邮箱**: [REDACTED_SECRET] ## 安装 ```bash clawhub install paper-writing-guide ```


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