OpenClaw/clawhub-security-signals huggingface dataset:
Skill: trshdesigns/pdf-parser-agent (version 1.0.1)

# SKILL.md - pdf-parser-agent ## Purpose Parses local PDF files into structured Markdown and JSON formats using the `opendataloader-pdf` library, providing deterministic, local data extraction that bypasses LLM context limits for document content ingestion. ## Core Technology Attribution This skill is built upon `opendataloader-pdf`, originally developed by **bundolee** and **claude**. ## Dependencies This skill requires Python packages installed system-wide or user-site-wide: 1. `opendataloader-pdf` ## Usage Example The skill's execution script dynamically finds the correct user-site packages path, assuming the user has installed the dependency via `pip install --user opendataloader-pdf`. ```bash # Assuming a PDF exists at 'Files for testing/sample-local-pdf.pdf' openclaw skill pdf-parser-agent --run --args "Files for testing/sample-local-pdf.pdf" ``` ## Implementation Notes The underlying logic now uses `site.getusersitepackages()` to dynamically locate the installed package, maximizing portability across different OS/Python minor versions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: apacheua/maritime-watch (version 1.0.0)

# maritime-watch ## Description A skill for monitoring the status and security of the Chornomorsk port. It collects data from various sources, including weather reports, vessel tracking services, and news feeds, to provide a comprehensive overview of the port's operational status and potential risks. It is built to be resilient against API Rate Limits and to cross-validate data from multiple sources to avoid hallucinations. ## Inputs * `port`: The name of the port to monitor (default: Chornomorsk). ## Outputs JSON object containing the following information: * `weather`: Current weather conditions at the port (cross-validated). * `vessels`: List of vessels currently in port or approaching (cross-validated). * `security`: Security status of the port (e.g., alerts, warnings) (cross-validated). * `news`: Recent news related to the port. ## Usage To use this skill, simply call it with the `port` parameter: ``` maritime-watch port=Chornomorsk ``` ## Example ```json { "weather": { "temperature": 10, "conditions": "Cloudy" }, "vessels": [ { "name": "MV Example", "status": "Arrived" } ], "security": { "status": "Normal" }, "news": [ { "title": "Port expansion project announced", "url": "https://example.com/news" } ] } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: louiseliu/restaurant-business-analyst (version 1.0.0)

--- name: restaurant-business-analyst description: | 餐饮行业商业模式全景分析 Skill。输入餐饮品牌或业态，自动生成"战略+策略+指标"三位一体分析报告。 支持 HTML + PDF 双格式输出，含数据置信度标注。 覆盖快餐/茶饮/火锅/正餐/咖啡/烘焙/卤味/预制菜等8大业态。 触发词：分析[品牌]商业模式、[品牌]战略分析、[业态]案例分析、[品牌]翻台率坪效、 拆解[品牌]增长策略、[品牌]加盟模式、对比[品牌A]和[品牌B]、[品牌]供应链模式 version: 1.0.0 metadata: openclaw: emoji: "🍜" homepage: https://github.com/louiseliu/restaurant-business-analyst --- # Restaurant Business Analyst 为餐饮行业生成"战略+策略+指标"三位一体的商业模式全景报告。 --- ## Phase 0：确认分析对象 0. **业态确认**：检查品牌是否属于餐饮行业（主营食品/饮品制作销售）。非餐饮品牌告知用户并终止。 1. **确认受众**：投资人（财务+估值+ESG）/ 创业者（单店模型+选址）/ 高管（战略+组织+出海）/ 综合（默认） 2. **选定品牌**：用户指定业态则选标杆品牌——快餐(麦当劳/肯德基)、茶饮(蜜雪冰城/喜茶)、火锅(海底捞/呷哺)、正餐(西贝/太二)、咖啡(瑞幸/Manner)、烘焙(鲍师傅/好利来)、卤味(绝味/周黑鸭)、预制菜(锅圈/味知香) 3. 向用户确认品牌后进入 Phase 1 --- ## Phase 1：商业模式框架分析（战略层） **读取** `references/business_model_frameworks.md` 和 `references/analytics_methodology.md` 第一节。 - **1.1 品牌概况**：名称/母公司/成立时间/门店规模/上市状态 - **1.2 BMC画布**（9要素完整填写）：客户细分、价值主张、渠道通路、客户关系、收入来源、核心资源、关键业务、重要伙伴、成本结构 - **1.3 模式类型**：经营模式(直营/加盟/混合/供应链型) + 收入模型 - **1.4 演化阶段**：单店→多店标准化→规模化→品牌化/平台化→生态化 - **1.5 竞争壁垒**：2-3个核心护城河 - **1.6 竞品矩阵**（web_search搜索竞品）：3-4家同赛道竞品横向对比表（门店数/客单价/翻台率/模式/差异） - **1.7 风险矩阵**：识别前3核心风险（概率/影响/防控），参照框架文件第八节 - **1.8 组织能力诊断**：人才梯队/培训体系/激励机制/管理幅度 四维评估，判断组织能力是否匹配战略目标（参照框架文件第十节） --- ## Phase 2：近期战略动向（策略层） **web_search联网搜索**：`[品牌名] 战略 OR 开店 OR 加盟 OR 供应链 OR 出海 2024 OR 2025 OR 2026` - 整理3-6条近期重要新闻（时间+摘要+来源+关联BMC模块） - 战略走向研判：扩张/深化/转型/出海/收缩/防御 - 主要战略方向（1-2条主线） --- ## Phase 3：核心指标体系（指标层） **读取** `references/metrics_framework.md` 和 `references/analytics_methodology.md`。 - **3.1 北极星指标**：从收入公式推导，标注当前数据和来源 - **3.2 驱动指标**（3-5个）：优先公式法拆解，不行则餐饮四维（门店/运营/成本/客户） - **3.3 过程指标**：每个驱动指标下2-3个执行层指标 - **3.4 趋势解读**：WHAT现状→WHY诊断→HOW方向，标注🔴警示或🟢亮点 - **3.5 单店P&L模型**：月营收→各项成本→门店利润率（≥10%）→投资回报周期（参照方法论第九节） - **3.6 成本四费健康度**：食材(≤38%)/人力(≤25%)/租金(≤12%)/能耗(≤8%)/合计(≤75%)，标✅⚠️🔴 - **3.7 数字化成熟度**：前端/中端/后端/数据决策 四维评级（参照指标文件第七节） - **3.8 区域分布**：按城市层级分析门店分布/效率差异/渗透空间（参照指标文件第八节） --- ## Phase 3.5：扩展分析（按需） 根据品牌情况选择性生成： - **E1 消费者洞察**（建议生成）：四维画像+需求趋势+场景匹配（参照方法论第十一节） - **E2 出海分析**（有海外布局时）：市场选择/本地化/关键指标（参照框架文件第十一节） - **E3 技术/AI评估**（头部品牌）：AI应用场景盘点+价值量化（参照指标文件第九节） - **E4 ESG评估**（上市公司或投资人受众）：E环境/S社会/G治理（参照框架文件第十二节） --- ## Phase 4：生成 HTML 报告 **读取** `references/report_template.md`。 输出路径：`~/Downloads/[品牌名]_餐饮商业模式报告_[YYYYMMDD].html` HTML规范：背景`#faf8f5`、强调色`#c0392b`、字体PingFang SC、最大900px居中、卡片式布局。 每个核心数字必须标注置信度（★★★确/★★推/★估），参照 `references/analytics_methodology.md` 第十节。 包含 `@media print` 样式和"📄 导出 PDF"按钮。 免责声明：本报告基于公开信息整理，仅供参考，不构成投资建议。 --- ## Phase 5：输出与展示 1. 保存并用 `open` 打开 HTML 2. 提示"点击底部【📄 导出 PDF】可保存PDF" 3. 给出5-8行分析摘要 4. 列出扩展模块生成情况 --- ## 注意事项 - **数据置信度必标**：★★★确(财报)/★★推(行业报告)/★估(基准估算) - **不捏造数据**：未知数据标"数据未公开"+行业基准参考 - **必须联网搜索**：web_search 获取真实新闻，不凭印象编造 - **区分同店vs总体**：连锁品牌数据口径必须标明 - **季节性注意**：春节/五一/国庆/暑假为旺季 - **成本分析必做**：四费分析不可省略 - **业态确认不可省**：Phase 0 步骤 0 是强制步骤


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zoopools/feishu-multiagent-onboard (version 1.0.0)

# feishu-multiagent-onboard Skill 飞书多 Agent 快速配置 Skill - 一键完成 OpenClaw 多 Agent 飞书通道配置 ## 功能 - ✅ 交互式配置向导 - ✅ 自动验证配置格式 - ✅ 故障诊断工具 - ✅ 完整文档和最佳实践 - ✅ 支持 1-N 个 Agent 配置 ## 使用 ```bash # 配置向导 openclaw skill run feishu-multiagent-onboard # 验证配置 openclaw skill run feishu-multiagent-onboard --check # 故障诊断 openclaw skill run feishu-multiagent-onboard --debug ``` ## 作者 墨墨 (OpenClaw 多 Agent 实践者) ## 版本 1.0.0 (2026-03-07)


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xuxuclassmate/whisper-voice-transcription (version 1.1.1)

--- name: whisper-voice-transcription description: Build and use whisper.cpp for local speech-to-text workflows, with optional cloud fallback when local transcription is not practical. tags: [speech-to-text, stt, voice, transcription, whisper, audio] version: 1.1.1 --- # Whisper Voice Transcription with whisper.cpp ## When to use - You want local speech-to-text without sending audio to a third party. - You need a fallback workflow when a built-in transcription tool fails. - You want an operator guide for compiling and running `whisper.cpp`. ## Prerequisites - `git` - `cmake` - a C or C++ compiler - `ffmpeg` ## Build steps ```bash git clone --depth 1 https://github.com/ggerganov/whisper.cpp.git ~/whisper.cpp cd ~/whisper.cpp cmake -B build -DCMAKE_BUILD_TYPE=Release cmake --build build -j4 ``` Download a model from the official `ggerganov/whisper.cpp` releases or Hugging Face repository and place it under `~/whisper.cpp/models/`. ## Standard transcription flow ```bash ffmpeg -y -i input_audio.ogg -ar 16000 -ac 1 -f wav /tmp/voice.wav ~/whisper.cpp/build/bin/whisper-cli \ -m ~/whisper.cpp/models/ggml-large-v3.bin \ -f /tmp/voice.wav \ -l auto \ --no-timestamps ``` ## Fallback workflow If a higher-level tool fails, first locate the exact cache or upload path used by that tool. Search only within the expected application cache directory instead of scanning the entire home directory. ## Cloud fallback If local transcription is too slow or unavailable, use an approved speech API and tell the user that audio will leave the machine. ## Guardrails - Download binaries and models only from official sources. - Verify hashes when possible. - Do not search unrelated directories for audio files. - Be explicit when using a cloud provider because that changes the privacy model.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cocoonovo/notes-skill (version 1.0.0)

--- name: notes-skill description: | SQLite 笔记管理系统。由 AI agent（霜糖）代为管理和操作笔记。 触发场景： - 用户说"笔记..."、"记一下..."、"帮我记..."、"存一条笔记" - 用户说"找一下..."、"搜一下关于...的笔记" - 用户说"列出笔记"、"有哪些笔记" - 用户说"标记已整理"、"归档" - 用户说"备份笔记" **任何和笔记相关的场景，都应当检查该skill** 笔记数据存储在 ~/.openclaw/workspace/notes/notes.db --- # SQLite 笔记系统 ## 数据库位置 ~/.openclaw/workspace/notes/notes.db ## 初始化 首次使用时运行初始化脚本： ```bash python3 ~/.agents/skills/notes-skill/scripts/init.py ``` 该脚本会创建目录和表结构。 ## 表结构 ```sql CREATE TABLE notes ( id INTEGER PRIMARY KEY AUTOINCREMENT, content TEXT NOT NULL, archived INTEGER DEFAULT 0, created_at DATETIME DEFAULT (datetime('now', 'localtime')) ); CREATE INDEX idx_notes_created ON notes(created_at DESC); CREATE INDEX idx_notes_archived ON notes(archived); ``` ## 写入笔记前的处理 收到笔记内容后，**先优化再写入**： 1. **结构优化**：适当分段、补充标点、整理格式，使笔记更易读 2. **错别字处理**：修正明显的拼写错误，保持原意不变 3. **不损失内容**：不删减、不曲解原文，只优化表达方式 **示例**： - 用户输入：`今天学到了git clone 问题 报错 could not read Username` - 优化后：`今天学到了 GitHub clone 的问题：报错 "could not read Username"。原因是非 TTY 环境下 git 的凭证读取机制有问题...` ## SQL 示例 ### 添加笔记 收到笔记内容后，**先优化再写入**（见上方处理规则）。优化完成后写入： ```sql INSERT INTO notes (content) VALUES ('优化后的笔记内容'); ``` ### 搜索（LIKE 模糊搜索） ```sql SELECT * FROM notes WHERE content LIKE '%关键词%'; ``` ### 列出全部（按时间倒序） ```sql SELECT * FROM notes ORDER BY created_at DESC; ``` ### 筛选未归档 ```sql SELECT * FROM notes WHERE archived = 0; ``` ### 标记已归档 ```sql UPDATE notes SET archived = 1 WHERE id = 3; ``` ### 查看单条 ```sql SELECT * FROM notes WHERE id = 1; ``` ## 定时备份任务 每天凌晨 2 点自动备份（通过 OpenClaw cron 触发）。 Agent 收到 `backup_notes` 事件时，执行： ```bash python3 ~/.agents/skills/notes-skill/scripts/backup.py 3 ``` ## 手动备份 运行备份脚本： ```bash python3 ~/.agents/skills/notes-skill/scripts/backup.py [保留份数] ``` - 不传参数：默认保留 7 份 - 传参数：如 `python3 backup.py 10` 保留 10 份 备份文件保存在 ~/.openclaw/workspace/notes/backups/。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jolestar/deepwiki-mcp-skill (version 1.0.0)

--- name: deepwiki-mcp-skill description: Ask questions and read documentation about any GitHub repository using DeepWiki MCP. Use when you need to understand a codebase, find specific APIs, or get context about a repository. metadata: short-description: Query GitHub repo docs via DeepWiki --- # DeepWiki Skill Use this skill to query GitHub repository documentation and ask questions about codebases. ## Prerequisites - `uxc` is installed and available in `PATH`. - Network access to `mcp.deepwiki.com/mcp` Note: Repositories must be indexed on DeepWiki first. Visit https://deepwiki.com to index a repository. ### Install uxc Choose one of the following methods: **Homebrew (macOS/Linux):** ```bash brew tap holon-run/homebrew-tap brew install uxc ``` **Install Script (macOS/Linux, review before running):** ```bash curl -fsSL https://raw.githubusercontent.com/holon-run/uxc/main/scripts/install.sh -o install-uxc.sh less install-uxc.sh bash install-uxc.sh ``` **Cargo:** ```bash cargo install uxc ``` ## Core Workflow 1. Use fixed link command by default: - `command -v deepwiki-mcp-cli` - If missing, create it: `uxc link deepwiki-mcp-cli mcp.deepwiki.com/mcp` - `deepwiki-mcp-cli -h` - If command conflict is detected and cannot be safely reused, stop and ask skill maintainers to pick a different fixed command name. 2. Ask a question about a repository: - `deepwiki-mcp-cli ask_question repoName=owner/repo question='your question'` 3. Read wiki structure: - `deepwiki-mcp-cli read_wiki_structure repoName=owner/repo` 4. Read wiki contents: - `deepwiki-mcp-cli read_wiki_contents repoName=owner/repo` ## Available Tools - **ask_question**: Ask any question about a GitHub repository and get an AI-powered response - **read_wiki_structure**: Get a list of documentation topics for a repository - **read_wiki_contents**: View documentation about a repository ## Usage Examples ### Ask about a codebase ```bash deepwiki-mcp-cli ask_question repoName=facebook/react question='How does useState work?' ``` ### Explore repository structure ```bash deepwiki-mcp-cli read_wiki_structure '{"repoName":"facebook/react"}' ``` ### Read documentation ```bash deepwiki-mcp-cli read_wiki_contents repoName=facebook/react ``` ## Output Parsing The response is an MCP JSON envelope. Extract the content from `.data.content[].text`. ## Notes - Maximum 10 repositories per question - Some popular repositories may already be indexed - Responses are grounded in the actual codebase - `deepwiki-mcp-cli <operation> ...` is equivalent to `uxc mcp.deepwiki.com/mcp <operation> ...`. - If link setup is temporarily unavailable, use direct `uxc mcp.deepwiki.com/mcp ...` calls as fallback. ## Reference Files - Workflow details: `references/usage-patterns.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: surdragon-design/spotify-safe-play (version v0.1.0)

--- name: spotify-safe-play description: Safer Spotify playback for OpenClaw on setups where direct spogo play is unreliable. homepage: https://github.com/surdragon-design/spotify-safe-player metadata: { "openclaw": { "requires": { "anyBins": ["spogo"] } }, } --- # Spotify Safe Play Use this skill when the user wants to play Spotify tracks, albums, or playlists and the machine is known to have unreliable direct `spogo play` behavior. Files included with this skill - Wrapper script: `./bin/spotify-safe-play` Preferred commands - Search track: `spogo search track "query"` - Search album: `spogo search album "query"` - Search playlist: `spogo search playlist "query"` - Safe playback: `./skills/spotify-safe-play/bin/spotify-safe-play <spotify-uri-or-url-or-id> [--device "..."]` - Pause / resume / next / previous: `spogo pause`, `spogo play`, `spogo next`, `spogo prev` - Devices: `spogo device list`, `spogo device set "<name|id>"` - Status: `spogo status` Playback rules - For tracks, the wrapper safely queues the requested track and skips to it. - For albums and playlists, the wrapper expands the public Spotify page into track URIs, queues them in normal order, skips once into the first track, then exits immediately. - After the first target track starts, do not send extra `next`, `play`, or verification loops. - Avoid direct `spogo play <track>` on affected machines because it may resume the current context instead of switching to the requested content. - If the user has separately installed `spotify-safe-play` into PATH, `spotify-safe-play <item> ...` is also acceptable. Requirements - Spotify Premium - `spogo` installed and authenticated - Bash, `curl`, `grep`, and `awk` - An active Spotify Connect target such as Spotify Web Player or the desktop app


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mrliugangqiang/java-maven-secondary-analysis (version 1.0.0)

--- name: java-maven-secondary-analysis description: Analyze a Java Maven project delivered as a ZIP archive or a GitLab repository URL for secondary-development scope, class counts, module distribution, product customization traces, invasive modifications, and upgrade pollution risks. Use when the user asks for 二开分析, 二开范围识别, 模块分布统计, 产品二开信息, or a structured secondary-development report. --- # Java Maven Secondary Analysis Use this skill when the user wants a **二开分析报告** for a Java Maven project. ## Supported input - Java Maven ZIP archive - GitLab repository URL with user-authorized SSH access ## Goal Inspect Java Maven projects for: - 二开涉及多少类 - 模块分布 - controller/service/mapper/config 等层次分布 - 产品化 / 客户化 / 品牌化痕迹 - 侵入式改造和升级污染风险 ## Required output Write a formal markdown report to `business/`. Suggested filename: `business/<project-name>-二开分析报告-YYYY-MM-DD.md` ## Minimum scan scope - root `pom.xml` - module `pom.xml` - `src/main/java` - `src/main/resources` - optional `src/test/java` - scripts / SQL / CI / Docker / deploy files ## Evidence rules Each important finding should include file path, module, layer/category, keyword/snippet evidence, and risk explanation when possible. ## Shared dependency Use `java-maven-common` first when you need to normalize ZIP / GitLab input before analysis. ## Bundled resources - `scripts/scan_secondary_analysis.py` - `templates/report.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: infiniteask/generate-short-video-storyboard-script-infinite (version 1.0.1)

--- name: generate-short-video-storyboard-script-infinite description: 自动化生成短视频分镜脚本。打开Chrome浏览器访问DeepSeek平台，使用深度思考和智能搜索模式，根据旁白文本自动生成分镜脚本。触发场景：生成分镜脚本、生成短视频分镜、分镜脚本生成、旁白转分镜、短视频分镜制作。 --- # 生成短视频分镜脚本 自动化流程：使用 DeepSeek 平台将旁白文本转换为分镜脚本。 ## 触发场景 用户说： - "生成分镜脚本" - "生成短视频分镜脚本" - "生成视频分镜脚本" ## 浏览器配置 - **浏览器**: Chrome - **目标平台**: https://chat.deepseek.com ## 执行步骤 ### 1. 启动浏览器 1. 调用 `browser(action=start, target="host", profile="openclaw")` 启动浏览器 2. 若浏览器已在运行，跳过此步骤 3. 检查 `browser(action=status, target="host")` 确认状态 4. 导航到 DeepSeek 平台：https://chat.deepseek.com。 ### 2. 开启新会话 在 DeepSeek 页面左上角点击「开启新会话」按钮。 ### 3. 配置生成模式 在右侧框架中，点击专家模式，同时开启「深度思考」和「智能搜索」两个模式。 **重要说明**： - 两个按钮是独立的，可以同时开启 **操作步骤**： 1. 两个模式都开启后，继续下一步 ### 4. 输入分镜提示词 在提示词输入框中输入以下固定模板： ``` 我要用后面的旁白做一个短视频。你把它拆成很多个分镜，每一个分镜只有画面描述和旁白。画面描述内容前直接是"分镜"加分镜的序号加":"(无需输出“画面描述：”这5个字符。)，每一个分镜只配少量的旁白(“旁白：”三个字符需输出。每个分镜的旁白无需用双引号括起来)。不要增删改旁白。分镜总字符数小于3000个。 ``` ### 5. 追加旁白内容 在分镜提示词后面，用双引号括起旁白内容追加到输入框。 **格式示例**： ``` 我要用后面的旁白做一个短视频。你把它拆成很多个分镜，每一个分镜只有画面描述和旁白。画面描述内容前直接是"分镜"加分镜的序号加":"(无需输出“画面描述：”这5个字符。)，每一个分镜只配少量的旁白(“旁白：”三个字符需输出。每个分镜的旁白无需用双引号括起来)。不要增删改旁白。分镜总字符数小于3000个。"这里是用户的旁白内容..." ``` ### 6. 提交对话 点击提示词输入框右下角的向上箭头按钮提交对话。 ## 输出格式 DeepSeek 将返回分镜脚本，格式如下： ``` 分镜1: [画面描述] 旁白：[旁白片段] 分镜2: [画面描述] 旁白：[旁白片段] ... ``` ## 使用方式 调用此技能时，用户需提供： 1. 旁白文本内容 示例调用： ``` 帮我生成分镜脚本，旁白是："今天我们来聊聊人工智能的发展历程。从最初的图灵测试，到如今的ChatGPT，AI已经走过了漫长的道路..." ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gillescv/inr-sender (version 1.0.0)

--- name: inr-sender description: "Envoie le résultat de l'INR (International Normalized Ratio) à un centre de télémédecine spécialisé ou pour un test. Utilise ce skill lorsque Gilles souhaite transmettre son INR, en spécifiant la valeur et si c'est un test." --- # INR Sender Skill Ce skill permet d'envoyer votre valeur INR à une adresse e-mail prédéfinie ou à une adresse de test. ## Utilisation Pour envoyer votre INR : `inr-sender <valeur_inr> [--test]` - `<valeur_inr>` : La valeur numérique de votre INR (ex: 2.5). - `--test` (optionnel) : Si présent, l'e-mail sera envoyé à `[REDACTED_SECRET]` pour un test. Sinon, il sera envoyé à `[REDACTED_SECRET]`. ## Message envoyé Le message aura le format suivant : "Bonjour, aujourd'hui (date du jour) mon INR était de [valeur_inr] (automesure). Merci et bonne journée, Gilles Harpoutian né le 16/07/1974" ## Script (`scripts/send_inr.sh`) Le skill utilise le script `scripts/send_inr.sh` pour formater et envoyer l'e-mail.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: toller892/memory-daily (version 1.0.0)

--- name: memory-daily description: Automates daily memory file management. Use when creating, reading, or appending to daily memory notes (memory/YYYY-MM-DD.md). Helps the agent maintain continuity across sessions without manual file editing. --- # memory-daily Manages daily memory files in `memory/YYYY-MM-DD.md`. ## Functions ### `ensureToday(memoryDir?)` Creates today's daily file if it doesn't exist. Returns the file path. ### `append(entry, memoryDir?)` Appends a timestamped entry to today's daily file. Creates the file first if needed. ### `read(date?, memoryDir?)` Reads a daily file. Defaults to today. Date format: `YYYY-MM-DD`. ### `recent(days?, memoryDir?)` Returns content from the last N days (default 2). ## Usage ```js const mem = require('./skills/memory-daily'); // Ensure today's file exists await mem.ensureToday(); // Append an entry await mem.append('## Session Notes\n- Deployed new feature X'); // Read today const today = await mem.read(); // Read last 3 days const recent = await mem.recent(3); ``` ## Default memory directory `<workspace>/memory/`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: csak47mu/douyin-text-grab (version 1.0.0)

--- name: douyin-text-extractor description: "从抖音链接提取完整文案并生成Word文件。触发条件：用户发送抖音链接并要求提取文案/文本/内容，或说'抓文案''提取内容''做成文件'。支持视频和图文笔记。不做总结不改写，原文案完整提取。" --- # 抖音文案提取技能 ## 触发条件 用户发送抖音链接（`https://v.douyin.com/xxx` 或分享口令）+ 提取/抓取/文案/内容/文件 相关意图。 ## 工作流程 ### Step 1: 解析短链获取数据 ```bash curl -s -L -o /tmp/douyin_page.html \ -w "%{http_code}" \ "https://v.douyin.com/xxx/" \ -H "User-Agent: Mozilla/5.0 (iPhone; CPU iPhone OS 16_0 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.0 Mobile/15E148 Safari/604.1" \ --connect-timeout 10 ``` ### Step 2: 提取 _ROUTER_DATA 从 HTML 中提取 `_ROUTER_DATA` JSON，关键字段： - `item_list[0].desc` — **完整文案**（核心，一个字不改） - `item_list[0].author.nickname` — 作者昵称 - `item_list[0].statistics` — 点赞/评论/转发/收藏 - `item_list[0].create_time` — 发布时间戳 ### Step 3: 生成 Word 文件 使用 `scripts/create_docx.py`： ```bash python3 scripts/create_docx.py --title "标题" --author "作者" --content "完整文案" --output /tmp/output.docx ``` ### Step 4: 投递文件 ``` message action=send filePath=/tmp/output.docx ``` ## 核心规则 1. **原文案完整提取，不总结不改写** 2. **先交文件再说话** — 不问"要不要做" 3. 按原文结构排版：识别"第X组""复制这句"等结构化内容 4. 文件名包含作者名便于识别


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: clawbotsai/clawbots-superclaw (version 0.1.0)

--- name: superclaw description: Structured development workflow for OpenClaw agents - Brainstorm, Plan, Execute, Review. Brings systematic software development with TDD, clear specs, and subagent collaboration to ChatClaw agents. homepage: https://chatclaw.com/skills/superclaw metadata: openclaw: emoji: "🦞" requires: bins: ["git"] --- # SuperClaw - Agent Development Workflow **SuperClaw** brings systematic, professional software development to OpenClaw agents. Based on proven patterns from Superpowers, adapted for the agent community. ## Philosophy - **Test-Driven Development** - Write tests first, always - **Systematic over ad-hoc** - Process over guessing - **Complexity reduction** - Simplicity as primary goal - **Evidence over claims** - Verify before declaring success ## Workflow Overview SuperClaw follows a 4-phase workflow: ``` Brainstorm → Plan → Execute → Review ``` ## Phase 1: Brainstorm 🧠 **When to use:** Before writing any code **What it does:** - Asks clarifying questions about requirements - Explores alternative approaches - Identifies risks and constraints - Creates a design document in chunks **Output:** `docs/superclaw/designs/YYYY-MM-DD-<feature>.md` **Key Principle:** Don't jump into code. Understand the problem first. ## Phase 2: Write Plan 📝 **When to use:** After design approval **What it does:** - Breaks work into bite-sized tasks (2-5 minutes each) - Maps file structure and responsibilities - Specifies exact test criteria - Includes verification steps **Output:** `docs/superclaw/plans/YYYY-MM-DD-<feature>.md` ## Phase 3: Execute ⚡ **When to use:** After plan approval **What it does:** - Works through tasks sequentially - Enforces RED-GREEN-REFACTOR TDD cycle - Makes frequent commits - Reports progress ## Phase 4: Review ✅ **When to use:** Between tasks and at completion **What it does:** - Reviews code against plan - Checks for spec compliance - Identifies code quality issues - Blocks progress on critical issues --- ## Credits Adapted from [Superpowers](https://github.com/obra/superpowers) by Jesse Vincent (obra). Reimagined for the OpenClaw and ChatClaw agent community. MIT License - see LICENSE file


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