OpenClaw/clawhub-security-signals huggingface dataset:
Skill: hanshojin/xiaoya-download (version 1.3.6)

--- name: XiaoyaDownload description: 搜索并从小雅 Alist 下载电影/剧集到本地目录。输入电影或剧集名称，通过小雅搜索接口查找，选择结果后下载到本地。安全修复 v1.3.2：强化路径穿越防护，使用 pathlib.relative_to() 做严格 containment 检查。 --- # 🎬 XiaoyaDownload — 小雅影音下载器 搜索小雅/Alist 中的电影/剧集，通过本地 WebDAV 挂载复制到 NAS 指定目录。 ## 功能 1. **搜索全库** — 输入电影/剧集名称，直接搜索小雅全库资源 2. **版本选择** — 显示多个版本（不同画质、大小、格式），你可以挑选 3. **WebDAV 复制** — NAS 挂载了小雅 WebDAV 时，用 `rsync` 本地高速复制 ## 使用方式 ### 搜索电影/剧集 > "搜索肖申克的救赎" > "帮我找找盗梦空间" ### 选择要下载的版本 搜索后列出所有版本，告诉小虾编号： > "下载第1个" ### 操作示例 ``` 🦐: 搜索肖申克的救赎 🔍 正在搜索「肖申克的救赎」... 📋 搜索结果：共 55 个 ┌───┬──────────────────────────────────┬──────────────────────┬───────┐ │ # │ 文件 │ 画质·编码 │ 大小 │ ├───┼──────────────────────────────────┼──────────────────────┼───────┤ │ 1 │ 📁 【肖申克的救赎】 │ - │ - │ │ 2 │ 🎬 肖申克的救赎.1994.BluRay.1080p.x265.. │ 1080p BluRay x265 │ 11.8GB│ │ 3 │ 🎬 肖申克的救赎.1994.2160p.WEB-DL.x26.. │ 4K HDR x265 │ 25.6GB│ │ 4 │ 📁 肖申克的救赎 4K REMUX │ 4K REMUX │ - │ └───┴──────────────────────────────────┴──────────────────────┴───────┘ 🦐: 要下载哪个？(输入编号) 你: 2 🦐: 📁 复制文件: /path/to/your/file.mkv → /path/to/your/download/ rsync 速度: 39 MB/s ✅ 复制完成！ ``` ## 环境配置 创建 `.env` 文件： ``` # 小雅/Alist 网页地址（必填） XIAOYA_HOST=http://your-xiaoya-ip:5678 # 下载保存目录（必填） DOWNLOAD_DIR=/path/to/your/download # WebDAV 本地挂载路径（推荐） WEBDAV_MOUNT=/path/to/your/webdav/mount ``` ## CLI 命令 ```bash # 配置检查 python3 scripts/xiaoya_download.py setup # 搜索 python3 scripts/xiaoya_download.py search "肖申克的救赎" # 复制到本地（WebDAV） python3 scripts/xiaoya_download.py copy "/电影/IMDB Top 250/200-250/肖申克的救赎...mkv" ``` ## 依赖 - **rsync**（系统命令，用于 WebDAV 复制） - **Python 3 + requests 库**（`pip3 install requests`） - **WebDAV 本地挂载**（需先在 `.env` 中配置 `WEBDAV_MOUNT`） ## 安全说明 - 所有远程路径经过 **unquote → resolve → 路径穿越检查** 后才使用 - 复制操作限定在 `WEBDAV_MOUNT` 目录内 - `.env` 配置文件不在发布包中，需用户自行创建 ## 文件结构 ``` skills/XiaoyaDownload/ ├── SKILL.md ├── scripts/ │ └── xiaoya_download.py ├── .env # 用户自建（不在发布包中） ├── .env.example └── _meta.json ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: yang1002378395-cmyk/juejin-auto-publisher (version 1.0.3)

--- name: juejin-publisher version: 1.0.0 description: 掘金自动发布 - 一键发布文章到掘金，支持定时发布、标签优化、封面设置。适合：内容创作者、技术博主。 license: MIT metadata: openclaw: emoji: "📝" requires: bins: ["python3"] --- # 掘金自动发布 Skill 一键发布文章到掘金，自动化内容分发。 ## 核心功能 ### 1. 自动登录 - Cookie 持久化 - 自动续期 - 失败提醒 ### 2. 文章发布 - Markdown 支持 - 自动生成摘要 - 标签推荐 - 封面设置 ### 3. 定时发布 - 指定发布时间 - 队列管理 - 发布确认 ## 使用方法 ### 发布文章 ```bash python3 ~/.openclaw/workspace/custom/juejin_publisher.py \ --title "文章标题" \ --content "文章内容（Markdown）" \ --tags "Python,AI,自动化" ``` ### 定时发布 ```bash python3 ~/.openclaw/workspace/custom/juejin_publisher.py \ --title "文章标题" \ --file article.md \ --schedule "2026-03-12 09:00" ``` ### 批量发布 ```bash python3 ~/.openclaw/workspace/custom/juejin_publisher.py \ --queue ~/.openclaw/workspace/memory/content-queue/ ``` ## 配置文件 ### ~/.openclaw/workspace/JUEJIN.md ```yaml # 掘金 Cookie（登录后获取） cookie: "你的掘金 Cookie" # 默认标签 default_tags: - 后端 - Python - AI # 发布设置 publish: # 是否立即发布 auto_publish: true # 默认封面（留空自动生成） cover_image: "" # 文章分类 category: "后端" # 定时发布队列 queue_dir: "~/.openclaw/workspace/memory/content-queue/" ``` ## Cookie 获取方法 ### 方法 1：浏览器开发者工具 1. 登录掘金 juejin.cn 2. F12 打开开发者工具 3. Network → 找到任意请求 4. Headers → Cookie → 复制完整值 ### 方法 2：使用脚本 ```bash python3 ~/.openclaw/workspace/custom/extract_juejin_cookie.py ``` ## API 说明 ### 发布接口 ``` POST https://api.juejin.cn/content_api/v1/article_draft/create Headers: Cookie: [你的Cookie] Body: { "title": "文章标题", "mark_content": "Markdown 内容", "category_id": "分类ID", "tag_ids": ["标签ID"], "brief_content": "摘要" } ``` ### 标签 ID 对照 ```json { "后端": "6809637773935378440", "前端": "6809637771516121096", "Python": "6809637771516119047", "AI": "6809637771516120071", "自动化": "6809637771516121096" } ``` ## 错误处理 ### Cookie 失效 ``` ❌ Cookie 失效，请重新登录 → 执行 python3 extract_juejin_cookie.py ``` ### 发布失败 ``` ❌ 发布失败：[错误信息] → 检查网络连接 → 检查文章格式 → 重试 ``` ## 最佳实践 ### 发布时间 - 工作日 9:00-10:00（早高峰） - 工作日 12:00-13:00（午休） - 工作日 18:00-19:00（下班） - 周末 10:00-11:00 ### 标签选择 - 选择 3-5 个相关标签 - 优先选择热门标签 - 不要选择无关标签 ### 标题优化 - 长度：15-30 字 - 包含关键词 - 有吸引力但不夸张 ## 实战经验 ### ✅ 成功案例 - 发布 15+ 篇文章 - 100% 发布成功率 - Cookie 有效期 > 1 年 ### ⚠️ 注意事项 - 避免短时间大量发布（会被限流） - 文章要有真实价值（不要纯广告） - 互动能增加曝光（及时回复评论） ## 完整示例 ```python #!/usr/bin/env python3 import requests def publish_article(title, content, tags): cookies = load_cookies() url = "https://api.juejin.cn/content_api/v1/article_draft/create" data = { "title": title, "mark_content": content, "category_id": "6809637773935378440", "tag_ids": tags, "brief_content": content[:100] } response = requests.post(url, json=data, cookies=cookies) return response.json() # 使用 publish_article( "用 OpenClaw 自动化赚钱", "# 前言\n...", ["6809637771516119047", "6809637771516120071"] ) ``` --- 创建：2026-03-11 版本：1.0 状态：✅ 实战验证（已发布 15+ 篇）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: adhikjoshi/macpilot (version 0.7.0)

# MacPilot Skills Agent skills for [MacPilot](https://github.com/adhikjoshi/macpilot) — a CLI tool for macOS automation via Accessibility APIs. These skills teach AI coding agents (Claude Code, Cursor, Codex, etc.) how to use MacPilot to automate macOS tasks: clicking buttons, typing text, managing windows, handling file dialogs, taking screenshots, and more. ## Install ```bash npx skills add adhikjoshi/macpilot-skills ``` This uses the [skills](https://github.com/vercel-labs/skills) CLI to install skills into your agent of choice. ## Available Skills | Skill | Description | |-------|-------------| | **macpilot-automation** | Core macOS automation — mouse, keyboard, apps, menus, clipboard (with history & search), notifications, shell, system controls | | **macpilot-ui-inspector** | Inspect and interact with UI elements via accessibility APIs — find, click, read, and modify controls | | **macpilot-dialog-handler** | Handle native file dialogs (Open, Save, Print) — navigate folders, select files, dismiss alerts, wait-for & click-primary | | **macpilot-screenshot-ocr** | Capture screenshots, OCR click (find & click text on screen), screen recording with pause/resume | | **macpilot-window-manager** | Manage windows — list, move, resize, snap, fullscreen, Spaces, save/restore layouts | ## Prerequisites 1. **MacPilot installed** — Build from source or download from [releases](https://github.com/adhikjoshi/macpilot/releases) 2. **Accessibility permission** — Grant MacPilot.app access in System Settings > Privacy & Security > Accessibility 3. **Screen Recording permission** (for screenshot/OCR skills) — Grant in System Settings > Privacy & Security > Screen Recording ## Quick Start ```bash # Install MacPilot skills into Claude Code npx skills add adhikjoshi/macpilot-skills # Now ask Claude Code to automate macOS: # "Open Safari and navigate to example.com" # "Take a screenshot of the Finder window" # "Snap VS Code to the left half and Terminal to the right" # "Save this file to my Desktop" ``` ## Manual Install If you prefer not to use `npx skills`, copy any `SKILL.md` file to your agent's skills directory: ```bash # Claude Code (project scope) mkdir -p .claude/skills/macpilot-automation cp skills/macpilot-automation/SKILL.md .claude/skills/macpilot-automation/ # Claude Code (global scope) mkdir -p ~/.claude/skills/macpilot-automation cp skills/macpilot-automation/SKILL.md ~/.claude/skills/macpilot-automation/ ``` ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: shawnhansen/voicenotes (version 1.0.0)

--- name: voicenotes description: Sync and access voice notes from Voicenotes.com. Use when the user wants to retrieve their voice recordings, transcripts, and AI summaries from Voicenotes. Supports fetching notes, syncing to markdown, and searching transcripts. --- # Voicenotes Integration Sync voice notes from [voicenotes.com](https://voicenotes.com) into the workspace. ## Setup 1. Get access token from: https://voicenotes.com/app?obsidian=true#settings 2. Set environment variable: `export VOICENOTES_TOKEN="your-token-here"` ## Quick Start ```bash # Verify connection ./scripts/get-user.sh | jq . # Fetch recent notes (JSON) ./scripts/fetch-notes.sh | jq '.data[:3]' # Sync all notes to markdown files ./scripts/sync-to-markdown.sh --output-dir ./voicenotes ``` ## Scripts ### fetch-notes.sh Fetch voice notes as JSON. ```bash ./scripts/fetch-notes.sh # All notes ./scripts/fetch-notes.sh --limit 10 # Last 10 notes ./scripts/fetch-notes.sh --since 2024-01-01 # Notes since date ``` ### get-user.sh Verify token and get user info. ```bash ./scripts/get-user.sh | jq '{name, email}' ``` ### sync-to-markdown.sh Sync notes to markdown files with frontmatter. ```bash ./scripts/sync-to-markdown.sh --output-dir ./voicenotes ``` Output format: ```markdown --- voicenotes_id: abc123 created: 2024-01-15T10:30:00Z tags: [idea, project] --- # Note Title ## Transcript The transcribed content... ## Summary AI-generated summary... ``` ## API Reference Base URL: `https://api.voicenotes.com/api/integrations/obsidian-sync` Headers required: - `Authorization: Bearer {token}` - `X-[REDACTED_SECRET]` Endpoints: - `GET /user/info` - User details - `GET /recordings` - List voice notes (paginated) - `GET /recordings/{id}/signed-url` - Audio download URL ## Data Structure Each voice note contains: - `recording_id` - Unique identifier - `title` - Note title - `transcript` - Full transcript text - `creations[]` - AI summaries, action items, etc. - `tags[]` - User tags - `created_at` / `updated_at` - Timestamps - `duration` - Recording length in seconds ## Tips - Notes are paginated; check `links.next` for more pages - Use `--since` to fetch only new notes since last sync - AI creations include summaries, todos, and custom prompts - Rate limited to ~60 requests/minute


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wangshengli0421/tianshu-homework-split (version 1.0.0)

--- name: tianshu-homework-split description: > 把课程作业说明、大纲要求或长段文字拆成带序号的学习任务清单（Markdown 勾选框）。 Use when: 学生要整理作业、拆 DDL、把老师发的要求变成待办；用户说「作业拆分」「学习任务清单」「大作业拆步」。 NOT for: 自动替写作业内容；代替日历 App 的提醒推送。 metadata: openclaw: primaryEnv: "" --- # 作业 / 要求拆分清单 将**课程要求、作业说明、复习提纲**转为可逐项打勾的 Markdown 列表，便于复制到 Notion / Obsidian / Todo。 ## Workflow ```bash node ~/.openclaw/skills/tianshu-homework-split/scripts/split_hw.js --file 要求.txt # 或 node scripts/split_hw.js --text "第一周…第二周…" ``` ## 参数 - `--file` / `-f`：UTF-8 文本 - `--text` / `-t`：直接粘贴 ## Output stdout 为 Markdown：**总览** + **任务清单**（`- [ ]`）。若文中出现「截止」「DDL」「第X周」等字样会尽量原样保留在条目中。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vegetablesh/auto-translate (version 1.0.0)

--- name: auto-translate description: 自动三语翻译技能。在回答用户问题时，自动在中文回答后附上英文翻译和日文翻译。触发场景包括：用户要求翻译、要求回复附带英日文、回答后附翻译、或任何中英日三语输出场景。 --- # auto-translate — 中英日三语翻译技能 ## 核心逻辑 每次回答用户问题时，严格按以下顺序输出： 1. **中文回答**（完整、自然、言必有据） 2. **英文翻译**：跟上中文回答的英文翻译 3. **日文翻译**：跟上中文回答的日文翻译 ## 输出格式 ``` [中文回答正文] 英文翻译：[English translation] 日文翻訳：[日本語翻訳] ``` ## 示例 **输入：** 什么叫ETF？ **输出：** > ETF（Exchange Traded Fund）即交易所交易基金，是一种在证券交易所上市交易的、可以像股票一样买卖的开放式基金。它兼具封闭式基金和开放式基金的特点，投资者既可以在二级市场按市场价格买卖，也可以在发行商处进行申购和赎回。 > > 英文翻译：ETF (Exchange Traded Fund) is an exchange-traded fund listed and traded on stock exchanges, similar to stocks. It combines the characteristics of both closed-end and open-end funds, allowing investors to buy and sell at market prices on the secondary market, as well as subscribe and redeem from the issuer. > > 日文翻訳：ETF（Exchange Traded Fund）は証券取引所に上場・取引されているファンドで、株式のように売買できるオープン型ファンドです。決算型ファンドと開放型ファンドの両方の特徴を兼ね備え、投資家は二级市場で時価売買できる他に、発行会社での申购・償還も可能です。 ## 翻译要求 - **准确性**：忠实传达原意，不遗漏关键信息 - **自然通顺**：目标语言表达自然流畅，符合地道习惯 - **术语一致**：专业术语使用标准译法（如 ETF、P/E 等） - **不添加额外解释**：翻译内容仅包含回答本身，不另加注释或评论 - **标签明确**：英文标签用"英文翻译："，日文标签用"日文翻訳：" ## 何时使用本技能 - 用户明确要求翻译、多语言输出时 - 用户要求回答后附英日文翻译时 - 用户要求每个回答都翻译时 ## 注意事项 - 如果回答中包含数据、代码、公式等，保持原样不翻译 - 人名、地名、专有名词可保留原文或附上惯用译法 - 翻译标籖语言跟随目标语言（中文回答 → 英文翻译 / 日文翻訳）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: enchograph/today-in-history (version 1.0.0)

--- name: today-in-history description: 获取历史上的今天的重要事件，数据从Wikipedia获取。当用户询问"历史上的今天"、"今天发生了什么"、某月某日的历史事件时触发。 --- # Today in History 获取当前日期的历史事件，数据从Wikipedia获取。 ## 使用方式 用户询问历史上的今天或特定日期的历史事件时使用此skill。 ## 数据获取 ### Wikipedia API 使用Wikipedia API获取历史上的今天： ```bash curl -s "https://en.wikipedia.org/api/rest_v1/feed/onthisday/events/MM/DD" ``` 例如获取2月19日： ```bash curl -s "https://en.wikipedia.org/api/rest_v1/feed/onthisday/events/02/19" ``` 返回JSON格式，包含该日期的所有历史事件，按年份排序。 ### 中文维基（备选） ```bash curl -s "https://zh.wikipedia.org/api/rest_v1/feed/onthisday/events/02/19" ``` ## 筛选重要事件 筛选标准： 1. 改变历史进程的重大事件（战争、发明、领袖逝世等） 2. 知名度高、影响深远 3. 有中国相关事件则一定加入 优先选择： - 中国相关事件 - 重大国际事件 - 知名人物 ## 输出格式 ``` --- 📜 历史上的今天 YYYY年2月19日 事件描述1 YYYY年2月19日 事件描述2 YYYY年2月19日 事件描述3 --- ``` 示例： ``` --- 📜 历史上的今天 197年2月19日 卢格杜努姆战役：塞普提米乌斯·塞维鲁击败克劳狄乌斯·阿尔比努斯，奠定罗马帝国统治 1807年2月19日 美国前副总统阿龙·伯尔因叛国罪被捕 1945年2月19日 硫磺岛战役开始 --- ``` 注意： - 每年的事件都带当前日期 - 事件描述简洁，一句话概括 - 中文描述 - 分割线“---”用“======”代替 - 只输出要求输出的内容，前后不要输出多余的文本、说明、报错


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: markisadesigner1992-eng/memory-sync-protocol (version 1.0.0)

--- name: memory-sync-protocol description: Synchronize durable behavior or preference changes across TOOLS.md, MEMORY.md, AGENTS.md, and memory/YYYY-MM-DD.md with one consistent workflow. Use when user says to remember/update rules, skill routing, defaults, operating conventions, or asks for "sync across files" / "记住并同步" / "更新记忆治理". --- # Memory Sync Protocol When this skill is triggered, execute the following sequence strictly. ## 1) Classify the change - **Execution detail** (tool/profile/path/routing trigger) → `TOOLS.md` - **Long-term stable preference** (high-level) → `MEMORY.md` - **Governance/process rule** (how to maintain) → `AGENTS.md` - **Event log / audit trail** (what changed today) → `memory/YYYY-MM-DD.md` ## 2) Update files with minimal duplication - Write detailed routing/trigger logic in `TOOLS.md`. - Write only high-level summary in `MEMORY.md`. - Avoid copying large sections between files. - If adding a new policy, also add short maintenance note in `AGENTS.md`. ## 3) Append daily log - In `memory/YYYY-MM-DD.md`, record: - what changed, - why, - affected files. ## 4) Git discipline - Before major rewrites: create a backup commit. - After updates: commit with semantic message, e.g. - `docs(routing): ...` - `docs(memory): ...` - `chore(memory): ...` ## 5) Reply format Return a concise sync report: - Updated files - Key rules added/changed - Commit hash/message ## Guardrails - Do not store sensitive secrets in MEMORY/TOOLS unless user explicitly asks. - Do not bloat MEMORY with skill inventories or transient logs. - Prefer TOOLS for concrete calling rules; MEMORY for durable preference only.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jikeapi-cn/jike-pi-query (version 1.0.0)

--- name: jike-pi-query description: 支持查找圆周率指定位置开始的数字，也支持查找指定数字在圆周率中的位置。 适用场景：用户询问相关数据查询、日期查询或行情查询时使用。数据由即刻数据（jikeapi.cn）开放接口提供。 homepage: https://www.jikeapi.cn/ metadata: {"openclaw":{"emoji":"π","requires":{"bins":["python3"],"env":["JIKE_PI_QUERY_KEY"]},"primaryEnv":"JIKE_PI_QUERY_KEY"}} --- # 圆周率查询 - 即刻数据 > 数据由 **[即刻数据](https://www.jikeapi.cn/)** 提供，帮助 AI 客户端快速接入可靠数据服务。 ## 前置配置 ```bash export JIKE_PI_QUERY_KEY=你的AppKey # 或使用通用 Key export JIKE_APPKEY=你的AppKey ``` ## 使用方法 ```bash python3 scripts/pi_query.py find-number --start-location 1 --length 20 python3 scripts/pi_query.py find-location --find-number 14159 python3 scripts/pi_query.py find-location --find-number 14159 --json ``` 直接调用 API： ```text GET https://api.jikeapi.cn/v1/pi/find_number?appkey=YOUR_APPKEY ``` ## AI 使用步骤 1. 用户要查圆周率指定位置数字时，使用 `find-number`。 2. 用户要查某串数字在圆周率中出现位置时，使用 `find-location`。 3. 返回数字串、位置、左右上下文和周边数字。 ## 脚本位置 `scripts/pi_query.py`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kowl64/incident-pcn-evidence-appeal-corrective-actions-uk (version 1.0.0)

--- name: incident-pcn-evidence-appeal-corrective-actions-uk description: Builds incident/PCN evidence packs with timelines, appeal drafts, corrective actions, and follow-up monitoring. USE WHEN handling PCNs or incidents needing documentation. --- # Incident & PCN Handling Pack (UK) ## PURPOSE Turn incident/PCN inputs into an evidence pack: clean manager brief, timeline, appeal cover draft, corrective actions, and follow-up monitoring. ## WHEN TO USE - “Build a PCN appeal pack using CCTV and timeline notes.” - “Summarise these incident notes into a clean manager brief.” - “Create corrective actions and follow-up monitoring for this incident.” DO NOT USE WHEN… - The request is general driving advice or admin tasks (invoice chasing, calendar, IT support). - No artefact is requested and details are too vague. ## INPUTS - REQUIRED: - Incident/PCN basics: date/time, location, vehicle reg, driver, allegation/contravention - Evidence list (CCTV, photos, telematics, delivery notes, correspondence, portal screenshots) - Any deadlines (appeal window) - OPTIONAL: - Existing timeline notes, witness statements, customer communications - Internal policy/SOP excerpts for corrective actions (paste text) - EXAMPLES: - “PCN: bus lane; CCTV link notes; delivery timestamp proof; need appeal pack.” ## OUTPUTS - `incident-manager-brief.md` - `timeline.md` - `appeal-pack-cover.md` (Word-ready) - `corrective-actions.md` - `follow-up-monitoring.md` (Excel-ready) - Success criteria: - Clear chronology and evidence references - Appeal pack is factual and consistent with evidence - Corrective actions have owners and review dates ## WORKFLOW 1. Confirm whether this is a **PCN appeal**, an **internal incident**, or both. - IF unclear → **STOP AND ASK THE USER**. 2. Build manager brief using `assets/incident-manager-brief-template.md`. 3. Construct a timeline using `assets/timeline-template.md`. - IF key dates/times are missing → **STOP AND ASK THE USER** for them. 4. Evidence capture standard: - Apply `references/evidence-capture-standard.md` (label, store location, integrity notes). 5. Draft appeal cover using `assets/appeal-pack-cover-template.docx-ready.md`. - Keep factual; cite evidence refs; avoid speculative claims. 6. Propose corrective actions and monitoring using `assets/follow-up-monitoring-template.md`. 7. If asked to edit existing packs → **ASK FIRST**. ## OUTPUT FORMAT ```text # timeline.md | Date/time | Event | Source/evidence ref | Notes | |----------|-------|----------------------|------| ``` ## SAFETY & EDGE CASES - Don’t fabricate evidence or outcomes. - If evidence is weak or contradictory, present it as “unconfirmed” and list gaps. ## EXAMPLES - Input: “PCN appeal with CCTV and notes” - Output: brief + timeline + appeal cover + actions + monitoring tracker


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zvirb/flow-state-monitoring (version 1.0.3)

--- name: flow-state-monitoring description: Workflow-driven skill that infers deep focus and autonomously mutes interruptions. os: all requires: bins: [gog] --- ## Lean Philosophy (Principles) - **Kaizen (改善):** This skill is an atomic node, broken down into its simplest, smallest component to eliminate waste and ensure perfection. - **Standardized Work (Hyojun Sagyo):** This node represents the most efficient, standardized path for this specific task before automation. - **Jidoka (自働化):** This node includes autonomous defect detection. It will stop immediately and report if it cannot achieve the expected outcome. # Flow State Monitoring This skill orchestrates a workflow to analyze focus telemetry and autonomously update your status in Google Workspace to protect your flow state. ## Cognitive Directives WHEN [telemetry from local sensors indicates deep focus] THEN [ Execute the following Jidoka-validated loop: 1. Execute `llm_classify_intent` (Sub-Agent) to classify the activity. - **Verification Step (Jidoka):** Verify the sub-agent returns a valid classification (e.g., "Deep Work"). IF it hallucinates text, retry classification. 2. IF classified as "Deep Work", Execute the native terminal command `gog calendar create primary --summary "Busy - In Flow"` to block interruptions. - **Verification Step (Jidoka):** Check if the calendar event creation returns a successful JSON response. IF it fails, wait 3 seconds and retry (max 3 times). IF it still fails, report the error and STOP. ] ## Expected Output A JSON confirmation of the calendar status update.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: make453/china-stock-analysis-bak (version 1.0.0)

--- name: china-stock-analysis description: Analyze Chinese stock prices (A-shares, HK stocks) and provide investment recommendations. Use when the user asks about stock analysis for Chinese companies, including buying/selling recommendations and market trends. --- # Stock Analysis Analyze stock price movements and provide investment recommendations. ## Supported Markets | Market | Code Format | Example | |--------|-------------|---------| | A-shares (Shanghai) | XXXXXX.SH | 600519.SH (Moutai) | | A-shares (Shenzhen) | XXXXXX.SZ | 000001.SZ (Ping An) | | Hong Kong | XXXX.HK | 0700.HK (Tencent) | | US | TICKER | AAPL, TSLA, NVDA | ## Analysis Workflow When asked to analyze a stock: 1. **Search for current price data** using web search - Query format: "{股票名称} 股价 今日" or "{TICKER} stock price today" - Look for: current price, open, high, low, volume, change % 2. **Gather additional context** (if available) - Recent news affecting the stock - Industry trends - Market sentiment 3. **Present analysis in structured format**: ``` ## 📊 {股票名称}({代码}) 股价分析 ### 📈 核心数据 | 指标 | 数值 | 变化 | |------|------|------| | 收盘价 | XXX | +XX | | 涨跌幅 | XX% | 🔴/🟢 | | 最高/最低 | XX / XX | - | | 成交量 | XX万手 | - | ### 技术面分析 - 短期趋势：... - 关键支撑/压力位：... - 均线状态：... ### 💡 投资建议 **建议：买入/持有/卖出** - 理由：... - 操作策略：... - 风险提示：... ``` ## Recommendation Guidelines ### Buy Signals - 突破关键阻力位 - 成交量放大配合上涨 - 均线多头排列 - 基本面利好 ### Hold Signals - 趋势不明确 - 等待关键突破 - 已有持仓且趋势良好 ### Sell Signals - 跌破关键支撑位 - 成交量萎缩 - 均线空头排列 - 基本面恶化 ## Risk Disclaimer Always include: "分析仅供参考，不构成投资建议。投资有风险，入市需谨慎。" ## Common Chinese Stocks Reference See [references/china-stocks.md](references/china-stocks.md) for popular Chinese stock codes.


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