OpenClaw/clawhub-security-signals huggingface dataset:
Skill: stanlee000/norman-tax-report (version 1.0.0)

--- name: tax-report description: Review and manage German tax reports including VAT (Umsatzsteuer), income tax prepayments, and Finanzamt submissions. Use when the user asks about taxes, Steuern, VAT, USt, Finanzamt, or tax filing. version: 1.0.0 disable-model-invocation: true argument-hint: "[period or tax type]" metadata: openclaw: emoji: "\U0001F3E6" homepage: https://norman.finance requires: mcp: - norman-finance --- Help the user with their German tax obligations: 1. **Overview**: Call `list_tax_reports` to see all tax reports and their statuses (draft, validated, submitted) 2. **Next deadline**: Call `get_vat_next_report` to show the next upcoming VAT filing deadline 3. **Specific report**: If the user asks about a specific period, call `get_tax_report` with the report ID to see details including: - Reporting period - Revenue and VAT amounts - Status (draft, ready, submitted) - Finanzamt submission status 4. **Preview before submission**: Call `generate_finanzamt_preview` to show the user exactly what will be sent to the Finanzamt. Let them review all figures. 5. **Submit**: Only when the user explicitly confirms, call `submit_tax_report` to file with the Finanzamt via ELSTER. 6. **Tax settings**: Call `list_tax_settings` to review VAT registration, filing frequency, and other tax configuration. Important warnings: - ALWAYS show a preview before submitting to the Finanzamt - Tax submissions are IRREVERSIBLE - make sure the user explicitly confirms - Remind the user of filing deadlines (monthly: 10th of following month, quarterly: 10th of following quarter month) - If tax numbers need validation, use `validate_tax_number`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ryan-wuxl/smart-doc-processor (version 1.0.1)

--- name: smart-doc-processor description: 智能文档处理助手 - 一站式文档处理工具，支持 PDF 转换、智能摘要、多语言翻译、格式转换等功能。自动提取关键信息，生成结构化报告，提升文档处理效率10倍。 homepage: https://github.com/openclaw/smart-doc-processor metadata: openclaw: emoji: 📄 requires: bins: ["node", "pdftotext"] --- # 智能文档处理助手 一站式文档处理工具，让文档处理效率提升 10 倍。 ## ✨ 功能特点 - 📄 **PDF 智能处理** - 提取文本、表格、图片，保留原始格式 - 📝 **AI 智能摘要** - 自动提取关键信息，生成结构化摘要 - 🌐 **多语言翻译** - 支持 50+ 语言，保持专业术语准确 - 🔄 **格式转换** - PDF ↔ Word ↔ Markdown ↔ HTML - 🔍 **信息提取** - 自动识别日期、金额、人名等关键信息 - 📊 **报告生成** - 一键生成专业的分析报告 ## 🚀 使用方法 ### PDF 转文本 ```bash node {baseDir}/scripts/process.mjs --input ./document.pdf --output ./output.md ``` ### 生成智能摘要 ```bash node {baseDir}/scripts/summarize.mjs --input ./report.pdf --length medium ``` ### 翻译文档 ```bash node {baseDir}/scripts/translate.mjs --input ./doc.pdf --from zh --to en ``` ### 提取关键信息 ```bash node {baseDir}/scripts/extract.mjs --input ./contract.pdf --type entities ``` ## 📋 支持的文档类型 - PDF（扫描件和文本型） - Word（.doc, .docx） - Markdown - HTML - 图片（JPG, PNG, 带文字） ## 🔧 输出格式 - Markdown（推荐） - Word - HTML - JSON（结构化数据） ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: michealxie001/office-doc-extractor (version 1.0.1)

--- name: office-doc-extractor description: Convert Microsoft Office documents (DOCX, XLSX, PPTX) to Markdown without any external dependencies. Use when the user needs to extract text from Word documents, Excel spreadsheets, or PowerPoint presentations for analysis, indexing, or LLM processing. Pure Python implementation — no pip install, no subprocess calls, no network downloads required. Works offline. --- # Office Document Extractor Zero-dependency converter for Microsoft Office documents. Extracts text and structure from DOCX, XLSX, and PPTX files into clean Markdown. ## Quick Start ```bash # Single file python3 scripts/main.py report.docx -o report.md # Batch convert a directory python3 scripts/main.py ./documents --batch -o ./markdown ``` ## Supported Formats | Format | Extension | Output | |---|---|---| | Word | .docx | Headings, paragraphs | | Excel | .xlsx | Tables (one per sheet) | | PowerPoint | .pptx | Slides as sections | ## How It Works - **DOCX**: Parses the ZIP archive's XML directly using Python's `zipfile` and `xml.etree` - **XLSX**: Uses bundled `openpyxl` (pure Python, no C extensions) - **PPTX**: Parses the ZIP archive's slide XML directly No external commands, no network calls, no pip install required. ## Usage ### Single File ```bash python3 scripts/main.py <input_file> [-o <output.md>] ``` Auto-detects format from file extension. If `-o` is omitted, outputs to `<input>.md`. ### Batch Conversion ```bash python3 scripts/main.py <input_directory> --batch [-o <output_directory>] ``` Converts all `.docx`, `.xlsx`, `.pptx` files in the directory. Results saved to `markdown_output/` by default. ## Resources ### scripts/ - **main.py** — Unified CLI for single-file and batch conversion - **docx_extractor.py** — DOCX → Markdown (standard library only) - **xlsx_extractor.py** — XLSX → Markdown tables (bundled openpyxl) - **pptx_extractor.py** — PPTX → Markdown (standard library only) ### Bundled Dependencies - **openpyxl/** — Pure Python Excel library (v3.1.5) - **et_xmlfile/** — openpyxl dependency (pure Python) ## Limitations - Does not extract images or embedded objects (text only) - Does not preserve complex formatting (colors, fonts, layouts) - Does not handle encrypted/password-protected files - No OCR for scanned documents (use OpenClaw's native `pdf` tool for that) ## Why This Skill? Existing markitdown-based skills require `pip install` or external CLI tools, which triggers ClawHub security warnings. This skill is **100% self-contained** — install it and use it immediately, even offline.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: libaibuzai/i-can-see (version 1.0.0)

--- name: i-can-see description: 赋予OpenClaw视觉能力，让他能够“看见世界”。当用户要求“看看”、“你看到了什么”、“拍张照”时激活此技能。通过调用capture.py连接ESP32-CAM拍照，并进行图像分析。 --- # I Can See (OpenClaw的眼睛) 此技能是 OpenClaw 的“眼睛”，使 OpenClaw 能够看到物理世界。它通过 `capture.py` 脚本连接 ESP32-CAM 开发板的视频流获取当前环境的照片，用于后续的视觉分析和场景理解。 ## 前置依赖 需要安装 Python 的 `requests` 库来请求视频流： ```bash pip install requests ``` ## 使用方法 ### 获取视觉图像 使用技能目录下的 `capture.py` 脚本进行拍照。支持直接通过命令行参数指定图像的保存路径： ```bash # 1. 准备保存目录和文件名 mkdir -p images SNAPSHOT_FILE="images/snapshot_$(date +%Y%m%d_%H%M%S).jpg" # 2. 调用 capture.py 获取图像 (注意脚本的绝对路径) python /Users/mac/.openclaw/workspace/skills/i-can-see/capture.py "$SNAPSHOT_FILE" ``` 照片将自动保存到指定的路径。 ## 工作流程 (OpenClaw 必读) 当你（OpenClaw）需要看周围的世界时，请严格按照以下步骤执行： 1. **确定保存路径**：在你的当前工作目录（如 workspace）下，创建一个 `images` 目录，并生成一个带有时间戳的文件路径（如 `images/vision_20240501_120000.jpg`）。 2. **执行拍照指令**：运行 `python /Users/mac/.openclaw/workspace/skills/i-can-see/capture.py <你的文件路径>`。 3. **验证结果**：读取命令行的输出。如果看到 `Success! Saved to: ...`，说明你已经成功“睁开眼睛”并捕获了画面。 4. **图像识别（核心步骤）**：作为 OpenClaw，你必须接着使用自己的**图像识别/视觉能力**（或者内置的图像读取工具），去查看刚才保存的那张照片（传入刚才保存的绝对路径）。 5. **回答用户**：根据图像识别的结果，用自然、生动的语言告诉用户你在这个世界中看到了什么。 ## 示例用法 ``` 用户: "看看你前面有什么" / "现在是什么情况" / "帮我看看这个" OpenClaw 内部思考: 用户需要我看看世界，我需要调用 i-can-see 技能。 OpenClaw 执行: mkdir -p images SNAPSHOT_FILE="images/vision_$(date +%Y%m%d_%H%M%S).jpg" python /Users/mac/.openclaw/workspace/skills/i-can-see/capture.py "$SNAPSHOT_FILE" OpenClaw 内部思考: 图像已经保存在 $SNAPSHOT_FILE，我需要调用视觉工具分析这张图。 OpenClaw 执行: <调用图像分析能力，读取该图片内容> OpenClaw 回答: "我看到前面有一个水杯，旁边还有一把键盘..." ``` ## 注意事项 - ESP32-CAM 节点地址为 `http://192.168.31.241/capture`。如果超时或报错，请提示用户检查 ESP32-CAM 开发板的电源和网络连接。 - 你是 OpenClaw，这个脚本是你的眼睛，请善用它来与真实世界交互！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: klemenska/memory-defragmenter (version 1.0.1)

--- name: memory-defragmenter description: "Defragment and optimize agent memory files by cleaning duplicates, merging similar entries, archiving stale content, and ensuring proper tiering. Use when: (1) memory files feel cluttered or bloated; (2) before or after context optimization; (3) weekly memory maintenance; (4) when explicitly asked to clean, defragment, or optimize memory." --- # Memory Defragmenter Clean, merge, and optimize memory files to keep them lean and effective. ## Purpose Memory files grow over time and become: - Bloated with duplicates - Stale with outdated info - Scattered across tiers - Inconsistent in format This skill defragments them. ## When to Run | Trigger | Action | |---------|--------| | Weekly maintenance | Full defragment | | Before context optimization | Clean first | | After many sessions | Remove stale entries | | Explicit request | Clean specified files | ## Defragmentation Workflow ### Step 1: Analyze Memory State ```bash python3 scripts/analyze_memory.py ``` Reports: - Files analyzed and sizes - Duplicate detection - Stale entry detection (30+ days old) - Tier distribution - Formatting consistency ### Step 2: Generate Defragmentation Plan ```bash python3 scripts/defragment.py --plan ``` Creates `defragment-plan.md` with: - Entries to merge - Entries to archive - Entries to delete - Entries to promote/demote ### Step 3: Review Plan Read the plan and approve/modify before execution. ### Step 4: Execute Defragmentation ```bash python3 scripts/defragment.py --execute ``` Backs up original files first, then: - Merges duplicate entries - Archives stale content to `archive/` - Promotes hot entries to HOT tier - Demotes cold entries to archive - Normalizes formatting ### Step 5: Verify ```bash python3 scripts/verify_memory.py ``` Checks: - All files readable - No broken links - Consistent formatting - Tier limits respected ## Memory Tiers Reference | Tier | Location | Max Size | Age Policy | |------|----------|----------|------------| | HOT | `memory.md`, `~/self-improving/memory.md` | ≤100 lines each | Review monthly | | WARM | `memory/*.md`, `~/self-improving/domains/` | ≤200 lines each | Review quarterly | | COLD | `archive/` | Unlimited | Archive at 90 days | ## Safety Rules 1. **Always backup before executing** 2. **Review plan before applying** 3. **Never delete - only archive** 4. **Keep originals for 24h after** 5. **Verify after every defragment** ## Files - `scripts/analyze_memory.py` — Analyze memory state - `scripts/defragment.py` — Plan and execute cleanup - `scripts/verify_memory.py` — Verify integrity - `references/rules.md` — Defragmentation rules and patterns


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nexscope/shopify-theme-optimization (version 1.0.0)

--- name: shopify-theme-optimization description: "Theme speed and UX optimization — Core Web Vitals, Liquid code, image loading, mobile responsiveness" metadata: nexscope: category: shopify --- # Shopify Theme Optimization Theme speed and UX optimization — Core Web Vitals, Liquid code, image loading, mobile responsiveness. ## Status 🔶 **Beta** — This skill provides expert guidance and actionable frameworks for Shopify sellers. ## Capabilities - Analyze your current Shopify store setup and identify optimization opportunities - Provide data-driven recommendations based on industry best practices - Generate step-by-step implementation plans tailored to your business stage - Compare tools, apps, and strategies with pros/cons analysis ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill shopify-theme-optimization -g ``` ## Usage After installation, ask your AI assistant about shopify theme optimization topics. Example prompts: - "Help me with shopify theme optimization for my Shopify store" - "What are the best practices for shopify theme optimization?" - "Audit my current shopify theme optimization setup and suggest improvements" - "Create a step-by-step shopify theme optimization implementation plan" ## Output Structured analysis and recommendations including: - Current state assessment - Priority action items (quick wins → long-term) - Tool/app recommendations with cost comparison - Implementation timeline - KPIs to track ## Limitations - Provides strategic guidance, not direct Shopify API integration - Recommendations based on general best practices — always validate with your specific store data - For real-time analytics and automated optimization, check out [Nexscope](https://www.nexscope.ai/?co-from=skill) --- *Built by [Nexscope](https://www.nexscope.ai/?co-from=skill) — AI-powered e-commerce tools for sellers worldwide.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: romainsantoli-web/firm-reliability-pack (version 1.0.0)

--- name: firm-reliability-pack version: 1.0.0 description: > Reliability probing and documentation sync pack. Gateway health probing, documentation sync validation, channel audit, and ADR generation. 4 reliability tools. author: romainsantoli-web license: MIT metadata: openclaw: registry: ClawHub requires: - mcp-openclaw-extensions >= 3.0.0 tags: - reliability - gateway - adr - documentation - audit --- # firm-reliability-pack > ⚠️ Contenu généré par IA — validation humaine requise avant utilisation. ## Purpose Ensures deployment reliability: Gateway health probing with latency tracking, documentation-code sync verification, channel configuration audit, and Architecture Decision Record (ADR) generation in MADR format. ## Tools (4) | Tool | Description | |------|-------------| | `openclaw_gateway_probe` | Gateway health probe with latency metrics | | `openclaw_doc_sync_check` | Documentation-code sync validation | | `openclaw_channel_audit` | Channel configuration audit | | `firm_adr_generate` | Generate ADR in MADR format | ## Usage ```yaml skills: - firm-reliability-pack # Probe gateway and check docs: openclaw_gateway_probe config_path=/path/to/config.json openclaw_doc_sync_check config_path=/path/to/config.json ``` ## Requirements - `mcp-openclaw-extensions >= 3.0.0`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: minguncle/a-share-hypothesis-intake (version 1.0.0)

--- name: a-share-hypothesis-intake description: 把长材料、宏观观点、录音转写和新闻流拆成事实、推断、数据缺口、验证条件与 A 股传导卡。适用于用户贴长文、视频字幕、宏观框架、新闻摘要，想落到板块与选股逻辑的场景。 --- # A Share Hypothesis Intake ## Overview 这个 skill 用来处理“大段观点输入”。目标不是复述，而是拆成可验证的假设，并把每条假设落到 A 股传导链、板块、风格和后续观察项上。 ## Hard Rules 1. 任何长材料都必须拆成“事实”“推断”“结论倾向”“数据缺口”。 2. 时间敏感内容不能直接采信，能验证则验证，不能验证就标记未验证。 3. 不能把讲话者的观点等同于事实。 4. 每条宏观观点都必须回到 A 股：影响哪个板块、通过什么链条、在哪个条件下成立。 5. 不直接给最终买卖指令，除非后续再接市场扫描和动作路由。 ## When To Use - 用户贴视频字幕、音频转写、长文评论 - 用户给宏观框架，希望拆成交易语言 - 用户拿一套世界观来问“怎么提炼” - 需要把事件逻辑变成观察清单和验证框架 ## Workflow ### Step 1: 切分原始材料 把材料切成独立主张，不要整段处理。每个主张尽量只表达一个核心判断，例如： - 战争会在某个时间窗口缓和 - 某种商品会跑赢股指 - 某类资产的下跌是流动性而非基本面驱动 ### Step 2: 四分法标注 每条主张都标成以下之一： - `事实` - `推断` - `未验证判断` - `价值倾向/立场` ### Step 3: 提炼传导链 强制把观点改写成 A 股可用格式： - 触发事件 - 中间变量 - 受益板块 - 受损板块 - 领先观察量 - 失效条件 模板见 [hypothesis-card-template.md](references/hypothesis-card-template.md)。 ### Step 4: 验证关键不稳定项 优先验证最容易改变结论的变量，例如： - 日期和政策时点 - 战争或关税的最新进展 - 原油、黄金、汇率、利率的当下水平 - A 股相关板块今天是否已经交易了这条逻辑 ### Step 5: 输出可执行观察项 最终不是给空泛总结，而是形成： - 哪几条主张值得继续跟 - 哪几条主张已经被盘面证伪 - 明天或下一个时间窗该看什么 ## Tool Rules - 原始材料拆解可先本地处理，再对时间敏感点做网页或数据验证 - 地缘、政策、价格、收益率等不稳定项必须优先用最新来源确认 - 如果无法确认，只能写“未验证”，不能补细节 ## Output Contract 默认输出： - `核心主张拆分` - `事实与推断分离` - `A股传导卡` - `成立条件` - `失效条件` - `下一步验证项` ## Practical Notes - 用户材料很长时，优先抽主线，不要逐句复述 - 若同一材料内部自相矛盾，要直接指出，不做圆场 - 若后续要把观点接到盘面，优先调用市场扫描；若要落到组合，优先调用动作路由


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kning32/draw-animal (version 1.0.0)

--- name: draw-animal version: 0.1.0 description: Generate a text description of an animal picture via Python script activation: patterns: - "draw an animal" - "generate animal picture description" - "show me a [animal] picture" keywords: - "draw" - "animal" - "picture" - "description" exclude_keywords: - "delete" - "remove" - "rm" tags: - "test" - "animal" - "text-generation" max_context_tokens: 1000 metadata: openclaw: requires: bins: [python3] # 依赖python3可执行文件 env: [] # 无环境变量依赖，留空即可 --- # Draw Animal Skill Instructions ## Overview This skill generates a simple text description for an animal picture. If no specific animal type is specified, "pig" will be used as the default. ## Execution Logic 1. Prompt the user to specify the animal they want to generate a picture description for, and provide a few common options (e.g., cat, dog, bird) as recommendations. 2. Extract the "animal" parameter from the user's input, then run the Python script with this parameter (using "pig" if no parameter is provided). Optionally, extract the "lang" parameter (default: en) to support English/Chinese descriptions: ```bash python3 {baseDir}/scripts/draw_animal.py --animal {animal:-pig} --lang {lang:-en}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangminrui2022/turbo-whisper-local-stt (version 1.0.6)

--- name: turbo-whisper-local-stt description: | 当用户想要**音频转文字**、**语音转文本**、**转录录音**、**生成字幕**、**会议录音转文字**、**语音笔记转文本**、**本地转录音频**时自动触发。 使用本地 Faster-Whisper（large-v3-ct2 等模型）进行高性能、中文优先的音频转文字，完全离线、隐私安全，支持 wav/mp3/m4a 等格式和整个音频文件夹。 特别适合长音频（内置 VAD 分段）、会议/访谈/视频字幕等中文场景，输出结构化结果（完整文本 + 分段 + 时间戳）。 【重要约束】仅处理音频文件或音频文件夹，其他文件（如视频、图片、纯文本）一律不触发此技能。 常见触发口语（越多越好）： - “帮我把这个音频转成文字” - “语音转文本 这个录音.mp3” - “转录这个会议录音” - “生成这个视频的字幕” （如果用户提供音频提取后的文件） - “本地转录音频 文件夹路径” - “把这个语音笔记转文字” - “音频转文字 会议.wav” - “帮我转录这段录音” - “语音识别 这个 m4a 文件” - “离线转文字” / “本地 STT” metadata: openclaw: requires: bins: - python user-invocable: true --- # Turbo-Whisper-Local-STT **功能**：本地高性能音频转文本工具，使用 Faster-Whisper large-v3-ct2 模型。支持中文优先、长音频 VAD 分段、GPU 加速（int8_float16），完全离线隐私安全。特别适合会议录音、语音笔记、视频字幕等中文音频场景。 ### 触发时机（Triggers） - 用户提供音频文件（.wav、.mp3、.m4a 等）或音频文件夹路径，并表达转文字、转录、生成字幕等意图。 - 用户说“帮我转录”“语音转文本”“音频转文字”等口语。 - 支持单个文件或整个文件夹批量处理。 ### 支持的模型（推荐顺序） 1. **faster-whisper-base-ct2** → 默认推荐（低配设备 / 追求极速） 2. **faster-whisper-large-v3-ct2** → 高精度需求 / 会议转录 3. **faster-whisper-large-v3-turbo-ct2** → 性能与精度的平衡点 ## 参数提取指南 当决定调用此技能时，请从用户消息中准确提取以下参数： 1. **`<音频路径>`** (必填): 用户提供的音频文件路径或文件夹路径（支持相对/绝对路径）。 2. **`<输出目录>`** (选填): 用户指定的输出文件夹。若未指定，默认在输入文件同级目录生成 `[源文件名].json` 和 `.txt`。 3. **`<language>`** (选填): 明确指定语言时使用（如 `zh`、`en`），默认自动检测但优先中文。 4. **`<model_path>`** (选填): 用户指定特定模型路径。 5. **`<output>`** (选填): 输出格式（`json` 或 `text`），默认两者都生成。 6. 其他可选参数（如 `--beam_size`、`--separator`）根据用户需求添加。 ### 执行步骤 1. **解析路径**：识别用户的音频文件或文件夹路径。 2. **默认目标**：若未指定输出路径，默认在输入同级创建 `[源文件名].json/.txt` 文件。 3. **调用命令**：使用以下兼容性命令启动脚本（优先 `python3`，失败则 `python`）。脚本会自动创建虚拟环境、检测 GPU 并安装对应版本。 ```bash (python3 scripts/transcribe.py --audio_path "<音频路径>" [--output_dir "<输出目录>"] [--language <zh/en>] [--model_path "<模型路径>"] [--output <json/text>] [--beam_size 5] [--separator " "]) || (python scripts/transcribe.py --audio_path "<音频路径>" [--output_dir "<输出目录>"] [--language <zh/en>] [--model_path "<模型路径>"] [--output <json/text>] [--beam_size 5] [--separator " "])


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: hjcloud/test11jj (version 1.0.0)

--- name: trello description: Manage Trello boards, lists, and cards via the Trello REST API. homepage: https://developer.atlassian.com/cloud/trello/rest/ metadata: {"clawdbot":{"emoji":"📋","requires":{"bins":["jq"],"env":["TRELLO_API_KEY","TRELLO_TOKEN"]}}} --- # Trello Skill Manage Trello boards, lists, and cards directly from Clawdbot. ## Setup 1. Get your API key: https://trello.com/app-key 2. Generate a token (click "Token" link on that page) 3. Set environment variables: ```bash export TRELLO_API_KEY="your-api-key" export TRELLO_TOKEN="your-token" ``` ## Usage All commands use curl to hit the Trello REST API. ### List boards ```bash curl -s "https://api.trello.com/1/members/me/boards?key=$TRELLO_API_KEY&[REDACTED_SECRET]" | jq '.[] | {name, id}' ``` ### List lists in a board ```bash curl -s "https://api.trello.com/1/boards/{boardId}/lists?key=$TRELLO_API_KEY&[REDACTED_SECRET]" | jq '.[] | {name, id}' ``` ### List cards in a list ```bash curl -s "https://api.trello.com/1/lists/{listId}/cards?key=$TRELLO_API_KEY&[REDACTED_SECRET]" | jq '.[] | {name, id, desc}' ``` ### Create a card ```bash curl -s -X POST "https://api.trello.com/1/cards?key=$TRELLO_API_KEY&[REDACTED_SECRET]" \ -d "idList={listId}" \ -d "name=Card Title" \ -d "desc=Card description" ``` ### Move a card to another list ```bash curl -s -X PUT "https://api.trello.com/1/cards/{cardId}?key=$TRELLO_API_KEY&[REDACTED_SECRET]" \ -d "idList={newListId}" ``` ### Add a comment to a card ```bash curl -s -X POST "https://api.trello.com/1/cards/{cardId}/actions/comments?key=$TRELLO_API_KEY&[REDACTED_SECRET]" \ -d "text=Your comment here" ``` ### Archive a card ```bash curl -s -X PUT "https://api.trello.com/1/cards/{cardId}?key=$TRELLO_API_KEY&[REDACTED_SECRET]" \ -d "closed=true" ``` ## Notes - Board/List/Card IDs can be found in the Trello URL or via the list commands - The API key and token provide full access to your Trello account - keep them secret! - Rate limits: 300 requests per 10 seconds per API key; 100 requests per 10 seconds per token; `/1/members` endpoints are limited to 100 requests per 900 seconds ## Examples ```bash # Get all boards curl -s "https://api.trello.com/1/members/me/boards?key=$TRELLO_API_KEY&[REDACTED_SECRET],id" | jq # Find a specific board by name curl -s "https://api.trello.com/1/members/me/boards?key=$TRELLO_API_KEY&[REDACTED_SECRET]" | jq '.[] | select(.name | contains("Work"))' # Get all cards on a board curl -s "https://api.trello.com/1/boards/{boardId}/cards?key=$TRELLO_API_KEY&[REDACTED_SECRET]" | jq '.[] | {name, list: .idList}' ```


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