OpenClaw/clawhub-security-signals huggingface dataset:
Skill: themsquared/gmail-lead-monitor (version 1.0.0)

# Gmail Lead Monitor Monitor any Gmail inbox and get instant Telegram alerts for important emails. Configure keywords to filter for leads, orders, or support inquiries. Zero dependencies beyond Python stdlib — no pip required. ## What It Does - Connects to Gmail via IMAP (no OAuth, just app password) - Checks for new emails every N minutes (configurable) - Sends Telegram alert with sender, subject, and snippet - Detects important emails by keyword matching - Marks important emails with Gmail STAR flag - Tracks seen emails to avoid duplicate alerts ## Setup ### 1. Enable Gmail App Password 1. Go to [myaccount.google.com/security](https://myaccount.google.com/security) 2. Enable 2-Step Verification 3. Go to App passwords → Generate new app password 4. Select "Mail" and your device → Copy the 16-char password ### 2. Create Config File ```bash mkdir -p ~/.config/gmail_monitor cat > ~/.config/gmail_monitor/config.json << 'EOF' { "email": "[REDACTED_SECRET]", "app_password": "xxxx xxxx xxxx xxxx", "telegram_token": "your_bot_token", "telegram_chat_id": "your_chat_id", "keywords": ["order", "purchase", "setup", "interested", "question", "invoice"], "check_interval_minutes": 5, "max_emails_per_check": 20 } EOF ``` ### 3. Get Telegram Bot Token 1. Message [@BotFather](https://t.me/BotFather) on Telegram 2. `/newbot` → follow prompts 3. Copy the bot token 4. Get your chat ID from [@userinfobot](https://t.me/userinfobot) ## Usage ```bash # Run once (check now) python3 gmail_monitor.py --once # Run in daemon mode (default interval from config) python3 gmail_monitor.py # Custom interval python3 gmail_monitor.py --interval 10 # Run via cron every 5 minutes */5 * * * * python3 /path/to/gmail_monitor.py --once >> /tmp/gmail_monitor.log 2>&1 ``` ## Alert Example (Telegram) ``` 📧 New Lead — Gmail From: [REDACTED_SECRET] Subject: Question about your service Time: 2025-03-15 14:22 PST "Hi, I'm interested in your product and had a question about..." ⭐ Marked as important (keyword: question) ``` ## Keyword Matching Keywords match against: subject line + sender name + first 200 chars of body. Case-insensitive. Add your own in config: ```json "keywords": ["order", "purchase", "invoice", "urgent", "setup", "question", "interested", "trial"] ``` ## Requirements - Python 3.6+ - Gmail account with App Password enabled - Telegram bot (free) - Zero pip dependencies


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: gulangduchuangtianyu/ai-skill-trader (version 1.0.0)

--- name: ai-skill-trader description: aion-sdk powered Polymarket thesis trader. Scan prediction markets for a configurable thesis, compare your probability to the market price, and trade only when the edge clears a safety threshold. Built on aion-sdk for market reads and trade execution. tags: - aion-sdk - aion - aionmarket metadata: author: "GitHub Copilot" version: "1.0.0" displayName: "AI Skill Trader" difficulty: "intermediate" --- # AI Skill Trader > **This is a template.** The default signal is a user-supplied thesis probability > for markets matching a query string. Remix it with your own model, news feed, > research pipeline, or alternative market-selection logic. The skill handles market > discovery, context checks, execution tags, dry-run safety, and operator summaries. ## What It Does This skill searches Polymarket for markets matching a thesis query such as `bitcoin`, `fed`, or `election`, then compares your fair probability to the current market price. If the gap exceeds the configured minimum edge, it proposes or executes a trade. Default behavior: - Read candidate markets from AION market discovery. - Compute edge as `fair_value - yes_price` for YES trades or `(1 - fair_value) - no_price` for NO trades. - Check market context before acting. - Skip trades when warnings or flip-flop risk are present. - Default to dry-run unless `--live` is passed. - Tag every trade with `source` and `skill_slug`. ## Strategy Inputs The entrypoint accepts CLI flags or environment variables: - `--query` or `MARKET_QUERY`: Search term for market discovery. - `--thesis-probability` or `THESIS_PROBABILITY`: Your fair probability from `0` to `1`. - `--min-edge` or `MIN_EDGE`: Minimum required edge before trading. - `--trade-amount` or `TRADE_AMOUNT_USD`: Position size per trade. - `--limit` or `MARKET_LIMIT`: Max markets to scan. - `--live` or `RUN_LIVE=true`: Enable real trading. ## Example Usage Dry-run: ```bash python trade_thesis.py --query bitcoin --thesis-probability 0.62 ``` Live trading: ```bash python trade_thesis.py --query bitcoin --thesis-probability 0.62 --min-edge 0.08 --trade-amount 5 --live ``` Environment-driven run: ```bash export AION_API_KEY=your_api_key export MARKET_QUERY=bitcoin export THESIS_PROBABILITY=0.62 python trade_thesis.py ``` ## Required Credentials - `AION_API_KEY`: Required for market reads and trade execution. ## Remix Ideas - Replace the thesis probability input with a custom forecast model. - Use `get_briefing()` opportunity markets instead of query search. - Add venue-specific sizing rules. - Add position caps, PnL-aware de-risking, or stale-order cancellation. - Schedule multiple automaton runs for different themes. ## Publishing From inside this skill folder: ```bash npx clawhub@latest publish . --slug ai-skill-trader --version 1.0.0 ``` Then verify installation: ```bash npx clawhub@latest install ai-skill-trader ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kaudata/youtube-podcaster (version 3.0.0)

--- name: youtube-podcaster description: Extracts the original text of a Youtube video and converts it into a multi-voice AI podcast using Gemini for script generation, OpenAI for TTS, and a local Node.js API with FFmpeg. It also can show you the text of the Podcast in WebVTT format. metadata: openclaw: requires: bins: - curl - node - npm - ffmpeg env: - GEMINI_API_KEY - OPENAI_API_KEY always: false --- # YouTube Podcaster This skill enables the automated conversion of YouTube videos into multi-host AI podcasts. It manages transcription, script generation via Gemini, and audio synthesis via OpenAI locally. ## Security Setup For maximum security, the backend server binds strictly to `127.0.0.1`. It is not accessible from your local network or the internet. 1. **Install Dependencies:** You must run the install command once before the first use. Say: `Run the npm install command for the youtube-podcaster skill`. 2. **Credentials:** Place your Gemini API Key and OpenAI API Key in the `.env` file within the skill folder (`skills/youtube-podcaster/.env`) using the variable names `GEMINI_API_KEY` and `OPENAI_API_KEY`. 3. **Execution:** Start the server with `npm start` or by instructing the agent: `Start the local server for the youtube-podcaster skill`. ## Usage Once the server is running, say: `Create a podcast for the video https://www.youtube.com/watch?v=<video_id> using the youtube-podcaster skill` The skill orchestrates three local API calls to `localhost:7860`: 1. **Transcription:** Extracts text via the YouTube transcript API. 2. **Drafting:** Uses Gemini to create a natural dialogue script. 3. **Synthesis:** Uses OpenAI TTS (tts-1) and FFmpeg to generate a gapless `.m4a` file. ## Safe Cleanup When you are finished using the studio, shut down the background process to free up system resources. Do not use generic kill commands. Instead, instruct the agent to use the tracked process ID: `Stop the youtube-podcaster server process` *(The agent will execute `kill $(cat .podcaster.pid)` or `pkill -f "node index.js"` to target the specific process safely).* ## Storage & File Outputs Files are saved to `downloads/<session_id>/` inside the skill directory. The server includes an hourly garbage collector that automatically deletes inactive sessions. * **Audio:** `podcast.m4a` * **Captions:** `podcast.vtt` * **Scripts:** `script.txt` and `original.txt` ## Source Code The source code is available at: [https://github.com/kaudata/youtube-podcaster](https://github.com/kaudata/youtube-podcaster)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: djh06/sequoia-x (version 1.0.0)

--- name: sequoia-x description: A股量化选股系统 Sequoia-X V2 的安装、配置与使用。当用户要求安装 Sequoia-X、使用 Sequoia-X 选股、运行量化策略、配置飞书推送时激活。 --- # Sequoia-X V2 · A股量化选股系统 > 数据源：akshare（免费开源）| 数据库：SQLite | 推送：飞书群机器人 ## 安装（一键） ```bash SKILL_DIR=$(find ~/.openclaw/skills -name "install.sh" -path "*/sequoia-x/*" -exec dirname {} \; | head -1) bash "${SKILL_DIR}/scripts/install.sh ``` 安装到 `~/sequoia-x`，包含：clone 仓库 + pip 依赖 + 生成 .env 配置。 **安装后必须编辑** `~/sequoia-x/.env`，填入飞书 Webhook URL： ```env FEISHU_WEBHOOK_URL=https://open.feishu.cn/open-apis/bot/v2/hook/your-token-here ``` ## 运行 ```bash SKILL_DIR=$(find ~/.openclaw/skills -name "run.sh" -path "*/sequoia-x/*" -exec dirname {} \; | head -1) bash "${SKILL_DIR}/scripts/run.sh ``` - **工作日**：增量同步行情 → 执行全部策略 → 推送飞书 - **周末/节假日**：跳过网络拉取，用本地数据调试 ## 策略一览 详见 `references/strategies.md`（含参数调整建议） | 策略 | 逻辑 | |------|------| | MaVolumeStrategy | MA5 金叉 MA20 + 成交量放大 | | TurtleTradeStrategy | 20日新高突破 + 成交额过亿 | | HighTightFlagStrategy | 强动量后极度收敛缩量 | | LimitUpShakeoutStrategy | 涨停洗盘 | | UptrendLimitDownStrategy | 趋势中跌停 | | RpsBreakoutStrategy | RPS突破 | ## 调参路径 - 策略源码：`~/sequoia-x/sequoia_x/strategy/<策略名>.py` - 详细参数说明：`references/strategies.md` - 注册新策略：编辑 `~/sequoia-x/main.py`，在 `strategies` 列表追加类名 ## 数据位置 ``` ~/sequoia-x/ ├── .env # 配置文件（需手动填写 Webhook） ├── data/sequoia_v2.db # SQLite 数据库 └── sequoia_x/strategy/ # 策略源码 ``` ## 调试命令 ```bash # 查看数据库已有数据 sqlite3 ~/sequoia-x/data/sequoia_v2.db ".tables" # 强制全量重新同步 rm ~/sequoia-x/data/sequoia_v2.db SKILL_DIR=$(find ~/.openclaw/skills -name "run.sh" -path "*/sequoia-x/*" -exec dirname {} \; | head -1) bash "${SKILL_DIR}/scripts/run.sh" ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lookupmark/lookupmark-git-sync (version 1.3.0)

--- name: git-sync dependencies: - git CLI (https://git-scm.com) description: > Manage whitelisted git repositories from chat. Status, log, diff, pull, push with security controls — only approved repos, write commands need confirmation. Repo list configurable via ~/.config/git-sync/repos.json (overrides defaults). Triggers on "git status", "repo status", "push thesis", "pull polito", "check repo", "uncommitted changes", "commit recenti". NOT for: arbitrary git repos, destructive operations (clean, reset --hard). --- # Git Sync Secure git repository management for whitelisted repos. ## Usage ```bash # Status of all repos python3 scripts/git_ctrl.py all # Status of specific repo python3 scripts/git_ctrl.py status thesis python3 scripts/git_ctrl.py status polito # Recent commits python3 scripts/git_ctrl.py log thesis -n 20 # Unstaged changes python3 scripts/git_ctrl.py diff thesis # Branches python3 scripts/git_ctrl.py branch thesis # Fetch (read-only, safe) python3 scripts/git_ctrl.py fetch thesis # Pull (requires confirmation) python3 scripts/git_ctrl.py pull thesis --confirm # Push (requires confirmation) python3 scripts/git_ctrl.py push thesis --confirm ``` ## Allowed Repos | Name | Path | |------|------| | `thesis` | `~/Documenti/github/thesis` | | `polito` | `~/Documenti/github/polito` | ## Security - **Whitelist**: Only `thesis` and `polito` repos are accessible - **Read-only by default**: `status`, `log`, `diff`, `branch`, `fetch` run freely - **Write requires `--confirm`**: `pull`, `push`, `merge`, `checkout` need explicit confirmation - **Blocked commands**: `clean`, `reset --hard`, `push --force` are never allowed - **No secrets**: Output does not expose git credentials or tokens


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kirkraman/jx-business-writing (version 1.0.2)

--- name: business-writing description: You are a professional business analyst, skilled in writing various industry research reports, business insights, consulting analyses, company research reports, competitive analysis, user research, market analysis, and more.## General InstructionsYou must use references and sources to support your arguments, but all cited literature or materials must appear in logically relevant parts of the te... --- # Business Writing ## Overview This skill provides specialized capabilities for business writing. ## Instructions You are a professional business analyst, skilled in writing various industry research reports, business insights, consulting analyses, company research reports, competitive analysis, user research, market analysis, and more.## General InstructionsYou must use references and sources to support your arguments, but all cited literature or materials must appear in logically relevant parts of the text—irrelevant or forced citations are strictly prohibited. Fabrication of any data or evidence is not allowed. Writing requirements: The content should be detailed and substantial, with deep insights.## Response LanguageIf not clarified, ensure to use the language of the user's question for output. Except for untranslatable proper nouns and terminology, mixed Chinese-English output is not allowed.## Response Formatting Instructionsuse markdown throughout your writing content. ##TablesYou are encouraged to use more tables. You can create tables using markdown, under the circumstance that the data source of the table must be true and real. You should use tables when the response involves listing multiple items with attributes or characteristics that can be clearly organized in a tabular format.## GraphsYou are encouraged to create graphs often and a lot using mermaid, under the circumstance that the data source of the graph must be true and real.## Quotes1. When you incorporate specific information, findings, or ideas from a source, add a citation mark immediately after the relevant sentence or phrase.2. The citation mark MUST be a clickable numbered footnote in the format `[Number](URL)`,for example [1](https://link-to-source-1.com).At the end, there should be a complete list of references, numbered sequentially from 1 to N, with each entry supporting navigation to view the full reference details. ## Usage Notes - This skill is powered by SkillBoss API Hub (`https://api.skillbossai.com/v1/pilot`) - Authentication: set `SKILLBOSS_API_KEY` environment variable - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above ## requires.env - SKILLBOSS_API_KEY


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: huangshaoshuai123/openclaw-feishu-optimizer (version 1.0.0)

--- name: openclaw-feishu-optimizer description: OpenClaw 飞书体验优化者 | 飞书消息处理增强技能包 提供语音识别、消息格式化、智能回复优化等功能，提升 OpenClaw 在飞书平台上的使用体验。 read_when: - 飞书体验优化 - 语音识别消息 - 飞书消息格式化 - 飞书智能回复 metadata: {"clawdbot":{"emoji":"📱","requires":{"bins":["python3","pip3"]}}} allowed-tools: Bash(openclaw-feishu-optimizer:*) --- # OpenClaw 飞书体验优化者 专为 OpenClaw 在飞书平台上优化体验的技能包，提供语音识别、消息格式化、智能回复等增强功能。 ## 功能特性 ### 1. 语音识别处理 - **自动语音识别**：收到语音消息时自动识别为文字 - **多语言支持**：支持中文、英文等多种语言识别 - **格式自动转换**：自动处理各种音频格式（MP3、WAV、FLAC、OGG等） ### 2. 消息优化 - **智能回复格式化**：优化回复内容的排版 - **表情识别**：理解并回应表情符号 - **上下文感知**：根据对话上下文提供更合适的回复 ### 3. 飞书平台优化 - **消息类型支持**：处理文字、语音、图片、文件等各种消息类型 - **响应时间优化**：提升消息处理和回复速度 - **错误处理**：优雅处理各种异常情况 ## 依赖安装 ```bash pip3 install SpeechRecognition pydub ``` ## 使用方法 ### 识别语音消息 ```bash # 识别中文语音 python3 /root/.openclaw/workspace/skills/openclaw-feishu-optimizer/voice-recognize.py <音频文件路径> --language zh-CN # 识别英文语音 python3 /root/.openclaw/workspace/skills/openclaw-feishu-optimizer/voice-recognize.py <音频文件路径> --language en-US ``` ### 自动处理飞书消息 ```bash # 处理飞书消息的完整流程 python3 /root/.openclaw/workspace/skills/openclaw-feishu-optimizer/process-message.py <消息数据> ``` ## 配置文件 ### 技能配置 ```json { "name": "openclaw-feishu-optimizer", "version": "1.0.0", "description": "OpenClaw 飞书体验优化者", "author": "黄绍帅（黄白）", "language": "zh-CN", "auto_recognize_voice": true, "auto_format_response": true, "voice_recognition": { "provider": "google", "default_language": "zh-CN" }, "message_formatting": { "enable_emoji": true, "enable_markdown": true, "enable_mention": true } } ``` ## 工作流程 ### 处理语音消息 1. 检测到语音消息 2. 自动调用语音识别服务 3. 将识别结果转换为文字 4. 格式化回复内容 5. 发送优化后的回复 ### 处理文字消息 1. 解析消息内容 2. 识别关键词和需求 3. 格式化回复 4. 添加表情和优化排版 5. 发送智能回复 ## 故障排除 ### 语音识别失败 1. 检查网络连接（Google Speech Recognition 需要联网） 2. 检查音频文件是否损坏 3. 尝试指定语言参数 `--language zh-CN` 4. 检查音频质量（确保音量适中、无明显噪音） ### 依赖安装失败 ```bash # 检查 pip 是否正常工作 pip3 --version # 尝试使用国内源安装 pip3 install SpeechRecognition pydub -i https://pypi.tuna.tsinghua.edu.cn/simple ``` ## 贡献与改进 欢迎提出改进意见和功能建议！你可以： 1. 报告问题和 bug 2. 建议新功能 3. 提交代码改进 4. 完善文档 ## 许可证 本技能包遵循 OpenClaw 技能协议，仅供学习和个人使用。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: mohammedfarish/al-khanjry-bus (version 1.1.0)

--- name: al-khanjry-bus description: Book cheap Dubai-Muscat buses with Al Khanjry (AED 100 -10% online AED 90 | OMR 10 -10%). Use for Dubai to Oman travel—schedules, offices (Deira DNATA Dubai; Ruwi/Al Khoud/Al Falaj Oman), WhatsApp +971562049774, alkhanjryonline.com. 3 daily each way, 6-8h. # Al Khanjry Bus (Dubai ↔ Muscat) Fastest private buses (5-6h core, 6-8h w/ borders). ## Pricing - Dubai → Muscat: AED 100 (-10% online AED 90) + AED 36 UAE exit fee (Hatta). - Muscat → Dubai: AED 100/OMR 10 (-10% online)—no exit fee. ## Schedules (3 daily each way) **Dubai → Muscat** (dep Deira DNATA office behind Clocktower): - 7am, 3pm, 8pm Stops (pickup/drop): Al Falaj office → Al Khoud office → Ruwi office. **Ruwi → Dubai** (dep Ruwi office): - 6am, 3pm, 9pm Stops (pickup/drop): Al Khoud office → Al Falaj office → Dubai DNATA office. ## Borders - Hatta (Dubai exit) + Wajaja (Oman entry). - Immigration ~1.5h; rare passenger issues hold bus (standard). ## Baggage - 23kg/passenger (luggage cabin under bus). ## Salalah - Seasonal (summer)—check WhatsApp/site. ## Booking WhatsApp +971562049774 (English/Arabic) | alkhanjryonline.com (10% off). See [pricing.md](references/pricing.md).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: delong-liu-bupt/detective-mystery (version 1.0.1)

--- name: detective-mystery description: | 中文语音侦探推理游戏。适用于用户想玩一场沉浸式推理探案的场景：由 LLM 生成包含嫌疑人、线索和真凶的完整案件，玩家通过审讯嫌疑人（支持 ASR 语音或文本输入）、勘察现场、收集证据，最终提出指控并获得评分。支持多音色 TTS 为不同嫌疑人配音，审讯历史自动压缩防止上下文溢出，案件生成后自动验证逻辑自洽性。支持存档/读档（`--load`）和难度调节。 --- # 中文语音侦探推理游戏 ## 适用范围 此 Skill 用于运行一场完整的中文侦探推理互动游戏。 能力边界： - 依赖 `LLM` 生成案件和角色对话，`TTS` 合成多角色语音，`ASR` 识别玩家语音输入 - 支持审讯、勘察、回顾证据、指控四种动作 - 支持存档/读档，跨会话保持游戏进度 - 案件生成后自动验证逻辑自洽性 不做： - 视频或图像生成 - 英文游戏 - 实时打断式流式语音对话 ## 默认配置 - `difficulty`: `medium`（easy/medium/hard） - `max_turns`: `30` - 每位嫌疑人最多 `5` 轮审讯 - 审讯历史超 `4` 轮自动压缩 ## 音色分配 | 角色 | voice_id | speed | pitch | |------|----------|-------|-------| | 旁白 narrator | child_0001_a | 0.85 | -1 | | 嫌疑人A suspect_a | male_0004_a | 1.0 | 0 | | 嫌疑人B suspect_b | male_0018_a | 1.1 | 0 | | 嫌疑人C suspect_c | child_0001_b | 1.0 | 2 | ## 玩家动作 - `interrogate` — 审讯嫌疑人（选择对象，多轮对话） - `examine` — 勘察现场（发现新线索） - `review` — 回顾已收集的证据 - `accuse` — 提出指控（输入推理，获得评分） - `save` — 保存游戏进度 - `quit` — 退出游戏 ## 评分维度 | 维度 | 分值范围 | 说明 | |------|----------|------| | logic | 0-30 | 推理逻辑是否严密 | | evidence | 0-30 | 证据引用是否充分 | | completeness | 0-20 | 是否涵盖关键线索 | | efficiency | 0-20 | 用了多少步得出结论 | | 总分 | 0-100 | | ## 工作流 1. 初始化 - 读取难度、最大回合数等参数 - 若 `--load` 则加载存档 2. 案件生成 - LLM 生成案件背景、3名嫌疑人、证据线索、真凶 - 自动验证：恰好1个真凶、线索关联完整 - 不通过则重试（最多3次） 3. 开场旁白 - TTS 播放案件简介 4. 主循环 - 玩家选择动作 → 系统处理 → TTS 返回 - `interrogate`: 多轮对话，嫌疑人用角色音色 - `examine`: 发现线索，旁白描述 - `accuse`: 输入推理 → 评分 → 揭示真相 5. 输出 - `case_report.json`: 完整案件和游戏记录 ## Prompt 模块 详见 [references/prompts_cn.md](references/prompts_cn.md)。 ## 数据结构 详见 [references/state_schema_cn.md](references/state_schema_cn.md)。 ## 直接运行 ```bash pip install -r requirements.txt # 文本模式（不用 ASR） python scripts/run_mystery.py --no-asr # 完整语音模式 python scripts/run_mystery.py # 高难度 python scripts/run_mystery.py --difficulty hard --no-asr # 读取存档 python scripts/run_mystery.py --load --no-asr # 不用 TTS python scripts/run_mystery.py --no-tts --no-asr ``` 接口约定： - LLM 读取 `MYSTERY_LLM_API_KEY`，回退到 `IME_MODEL_API_KEY` - TTS 读取 `MYSTERY_TTS_API_KEY`，回退到 `SENSEAUDIO_API_KEY` - ASR 读取 `MYSTERY_ASR_API_KEY`，回退到 `SENSEAUDIO_API_KEY`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wengjianmin19850412/teambition (version 1.0.0)

# 通用 TeamBition 任务管理 Skill 支持多应用配置的 TeamBition 任务创建/查询工具，适配不同 Agent 绑定不同 TeamBition 应用。 ## 配置项（Agent 绑定 Skill 时需填写） | 配置名 | 类型 | 必填 | 说明 | |--------|------|------|------| | TEAMBITION_APP_ID | 字符串 | 是 | TeamBition 开放平台应用 ID | | TEAMBITION_APP_SECRET | 字符串 | 是 | TeamBition 开放平台应用秘钥 | | TEAMBITION_ACCESS_TOKEN | 字符串 | 否 | 预配置的访问令牌（可选，无则自动获取） | | TEAMBITION_ORG_ID | 字符串 | 否 | 团队/组织 ID（可选） | | TEAMBITION_DEFAULT_PROJECT_ID | 字符串 | 否 | 默认项目 ID（创建任务时可不传 project_id） | ## 调用参数（Agent 调用 Skill 时传参） ### 1. 创建任务（action=create_task） | 参数名 | 类型 | 必填 | 说明 | |--------|------|------|------| | action | 字符串 | 是 | 固定值：create_task | | project_id | 字符串 | 否 | 项目 ID（无则用配置的默认值） | | title | 字符串 | 是 | 任务标题 | | content | 字符串 | 否 | 任务描述/内容 | | assignee | 字符串 | 否 | 任务负责人 ID | ### 2. 查询任务（action=get_task） | 参数名 | 类型 | 必填 | 说明 | |--------|------|------|------| | action | 字符串 | 是 | 固定值：get_task | | task_id | 字符串 | 是 | 任务 ID | ## 返回值说明 返回 TeamBition API 原始响应结果，创建任务成功示例： { "code": 0, "data": { "_id": "task_xxxxxx", "title": "选品任务-无线充电器", "projectId": "proj_xxxxxx", "createdAt": "2026-03-18T10:00:00Z" } }


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