OpenClaw/clawhub-security-signals huggingface dataset:
Skill: codedao12/google-sheets-api (version 1.0.3)

--- name: google-sheet-api description: OpenClaw skill that installs a Google Sheets CLI with setup steps and commands for read/write, batch, formatting, and sheet management. --- # Google Sheets API Skill (Advanced) ## Purpose Provide a production-ready Google Sheets CLI for OpenClaw. This skill supports data reads/writes, batch operations, formatting, and sheet management with service account authentication. ## Best fit - You need a repeatable CLI for automation tasks. - You want JSON-in/JSON-out for pipelines. - You need more than basic read/write (formatting, sheet ops, batch updates). ## Not a fit - You must use end-user OAuth consent flows (this skill is service-account focused). - You only need lightweight, one-off edits. ## One-time setup 1. Create or select a Google Cloud project. 2. Enable the Google Sheets API. 3. Create a service account and download its JSON key. 4. Share target spreadsheets with the service account email. ## Install ```bash cd google-sheet-api npm install ``` ## Run ```bash node scripts/sheets-cli.js help node scripts/sheets-cli.js read <spreadsheetId> "Sheet1!A1:C10" node scripts/sheets-cli.js append <spreadsheetId> "Sheet1!A:B" '@data.json' ``` You can also use npm: ```bash npm run sheets -- read <spreadsheetId> "Sheet1!A1:C10" ``` ## Credentials Supported sources (first match wins): - `GOOGLE_SHEETS_CREDENTIALS_JSON` (inline JSON string) - `GOOGLE_SERVICE_ACCOUNT_KEY` (file path) - `GOOGLE_SHEETS_KEY_FILE` (file path) - `GOOGLE_APPLICATION_CREDENTIALS` (file path) - `./service-account.json`, `./credentials.json`, `./google-service-account.json` - `~/.config/google-sheets/credentials.json` ## Input conventions - JSON values can be inline or loaded from file using `@path`. - Write/append expect a 2D array of values. Example `data.json`: ```json [["Name","Score"],["Alice",95]] ``` ## Command map (high level) Data: - `read`, `write`, `append`, `clear`, `batchGet`, `batchWrite` Formatting: - `format`, `getFormat`, `borders`, `merge`, `unmerge`, `copyFormat` Layout: - `resize`, `autoResize`, `freeze` Sheets: - `create`, `info`, `addSheet`, `deleteSheet`, `renameSheet` Advanced: - `batch` (raw `spreadsheets.batchUpdate` requests) ## Operational guidance - Prefer read-only scope for read workflows when possible. - Add retry with exponential backoff for `429` and transient `5xx` errors. - Keep request payloads small to avoid limit issues. ## Expected output - JSON to stdout; non-zero exit code on errors. ## Security notes - Never log or commit service account keys. - Share spreadsheets only with the service account email required by this skill.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: systiger/edge-tts-kids (version 1.0.0)

--- name: edge-tts-kids description: | Edge TTS 中文定制版 - 包含儿童配音预设。生成后自动发送到对话，无需翻文件。 触发词：配音、TTS、文生音、语音、配音生成、儿童配音 --- # Edge TTS 中文定制版 Edge TTS 的中文优化版本，包含预设音色和儿童配音，生成后自动发送到对话。 ## ⚠️ 核心规则 **生成的配音文件必须直接发送到对话！** ```javascript // 生成后立即发送 message({ action: "send", path: "生成的音频文件.mp3" }) ``` 不要让用户翻文件夹！ ## 预设音色 ### 成人音色 | 预设名 | Voice | Pitch | Rate | 适用场景 | |--------|-------|-------|------|---------| | **曼波** | zh-CN-XiaoyiNeural | +8% | default | 活泼、有活力的内容 | | **晓伊** | zh-CN-XiaoyiNeural | default | +30% | 快节奏内容、新闻 | | **晓晓** | zh-CN-XiaoxiaoNeural | default | default | 自然、通用 | | **云扬** | zh-CN-YunyangNeural | default | +10% | 旁白、纪录片 | ### 儿童配音 | 预设名 | Voice | Pitch | Rate | 适用场景 | |--------|-------|-------|------|---------| | **小糖豆** | zh-CN-XiaoyiNeural | +15% | -5% | 活泼童趣、动画解说、儿童故事 | | **棉花糖** | zh-CN-XiaoxiaoNeural | +5% | -10% | 温柔故事、睡前故事、绘本 | | **小萌萌** | zh-CN-XiaoxiaoNeural | +10% | -5% | 亲切安抚、教育内容、儿歌 | > ⚠️ **注意**：zh-CN-XiaohanNeural（晓涵）音色暂时不可用，已改用晓晓替代。 ## 使用方式 ### 方式一：内置 tts 工具（推荐） ```javascript // 简单调用（默认音色） tts("你的文本") // 指定音色 tts("你的文本", { voice: "zh-CN-XiaoyiNeural", pitch: "+15%", rate: "-5%" }) ``` ### 方式二：edge-tts 技能脚本 ```bash cd ~/.openclaw/workspace/skills/edge-tts/scripts # 小糖豆风格 node tts-converter.js "小朋友们好！" --voice zh-CN-XiaoyiNeural --pitch +15% --rate -5% --output output.mp3 # 棉花糖风格 node tts-converter.js "从前有一只小兔子..." --voice zh-CN-XiaoxiaoNeural --pitch +5% --rate -10% --output output.mp3 # 小萌萌风格 node tts-converter.js "宝宝真棒！" --voice zh-CN-XiaoxiaoNeural --pitch +10% --rate -5% --output output.mp3 ``` ## 完整工作流 1. **识别意图**：用户请求配音/TTS 2. **选择预设**：根据内容类型选择合适的音色 3. **生成音频**：调用 tts 工具或脚本 4. **发送到对话**：⚠️ 必须使用 `message` 工具发送，不要让用户翻文件夹 ## 依赖 此技能依赖 `edge-tts` 技能的脚本文件： - 路径：`~/.openclaw/workspace/skills/edge-tts/scripts/` - 需要 `node-edge-tts` npm 包 ## 技术说明 - 使用 Microsoft Edge 神经语音服务 - 无需 API Key（免费） - 输出 MP3 格式 - 需要网络连接


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aipoch-ai/interview-mock-partner (version 1.0.0)

--- name: interview-mock-partner description: Simulates behavioral interview questions for medical professionals. version: 1.0.0 category: Career tags: - interview - mock - behavioral - career author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Interview Mock Partner Simulates medical job interview scenarios. ## Features - Behavioral questions - Response feedback - Common scenarios - Improvement tips ## Parameters | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | `--position` | string | - | Yes | Target position title | | `--experience-level` | string | entry | No | Experience level (entry, mid, senior) | | `--specialty` | string | - | No | Medical specialty area | | `--questions` | int | 5 | No | Number of questions to generate | | `--output`, `-o` | string | stdout | No | Output file path | ## Output Format ```json { "questions": ["string"], "sample_answers": ["string"], "tips": ["string"] } ``` ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites No additional Python packages required. ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rexeno56/laundry-status (version 1.0.0)

--- name: laundry-status description: Use when the user wants to check washing machine or dryer status, laundry room availability, or which machines are free at Shanghai Jiao Tong University Minhang campus. version: 1.0.0 metadata: openclaw: emoji: "👕" triggers: - 洗衣机 - 洗衣房 - washer - dryer - laundry - 空闲机器 - 有没有空的 - 洗衣机状态 - 楼下洗衣机 tags: - iot - smart-home - laundry - haier - sjtu --- # laundry-status ## Overview Query real-time status of Haier IoT washing machines and dryers at SJTU Minhang campus. ## When to Use Invoke this skill when the user asks about: - Washing machine or dryer availability - Whether any machines are free - Laundry room status for a specific building - When machines will finish (estimated completion time) ## How to Use **Default building (西21楼):** ``` python3 {skill_dir}/laundry_status.py ``` **Specific building:** ``` python3 {skill_dir}/laundry_status.py --building 西16楼 ``` **All buildings:** ``` python3 {skill_dir}/laundry_status.py --all ``` ## Output Interpretation Each machine is grouped by status label: | Status | Meaning | |--------|---------| | 空闲 (Idle) | Machine is available — tell the user to go use it now | | 使用中 (In Use) | Machine is running; an estimated finish time may be shown as `[预计HH:MM结束]` | | 故障 (Fault) | Machine is broken and cannot be used | | 离线 (Offline) | Machine is unreachable or powered off | Machines are listed in priority order: 空闲 first, then 使用中 (with finish times), then 故障 and 离线. ## Available Buildings | Building Name | ID | |---------------|----| | 西21楼 | 27131 | | 西16楼 | 27136 | Use the building name (e.g. `--building 西16楼`) or ID (e.g. `--building 27136`). If the user does not specify a building, default to **西21楼**. ## Error Handling - **Network timeout or failure**: The script exits with a message to stderr. Tell the user the request timed out and to check their network connection. - **Unknown building**: The script reports the invalid name and lists valid buildings. Tell the user the building was not found and offer the available options. - **API error**: The script reports the API error message. Tell the user the service returned an error and to try again later.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kingfish1949101-wq/url-preview (version 1.0.0)

--- name: url-preview description: Extract and display URL previews with title, description, and favicon. Use when user shares any HTTP/HTTPS link and wants to see what the page is about without visiting it. Triggers on: (1) User sends a URL/link, (2) User asks "这个链接是什么", "看看这个网页", "what's this link" --- # URL Preview Skill When a user shares a URL, automatically extract and display a preview card. ## How It Works 1. User sends any HTTP/HTTPS URL 2. Use `extract_content_from_websites` tool to fetch the page 3. Parse and display: title, description, favicon, and a brief summary ## Output Format ``` 🔗 [Page Title](URL) 📝 Description: ... 🌐 Site: favicon + domain ``` ## Example User sends: `https://github.com/openclaw/openclaw` Output: ``` 🔗 OpenClaw/OpenClaw 📝 An open-source AI assistant platform... 🌐 github.com ``` ## Notes - Only works on public HTTP/HTTPS URLs - Respects rate limits - don't extract more than 5 URLs per minute - Use `maxChars: 200` in extract_content to limit description length


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jeyeshield/ad-production-ai-generation (version 1.0.0)

--- name: "AI Generation" slug: ad-production-ai-generation version: "1.0.0" description: "AI生成技能 - 负责广告创意的AI生成处理" changelog: "初始版本" metadata: {"clawdbot":{"emoji":"🎨","requires":{"bins":[]}}} --- # AI Generation - 广告创意AI生成 负责广告创意的AI生成处理，包括文案生成、图像生成、视频生成等功能。 ## Setup 无需额外依赖，TypeScript编译后使用。 ## When to Use 需要生成广告创意内容时使用： - 广告文案生成 - 营销图片生成 - 视频素材制作 - 创意素材批量生成 ## Architecture ``` ai-generation/ ├── index.ts # 主入口，处理生成任务 └── package.json # 依赖配置 ``` ## Core Commands ### 创建生成任务 ```typescript await api.executeAction('ai-generation.create', { demandId?: string, // 关联的需求ID prompt: string, // 生成提示词 model?: string, // 使用的模型（默认flux） params?: Record<string, any>, // 额外参数 count: number // 生成数量 }); ``` ### 查询生成任务 ```typescript await api.executeAction('ai-generation.get', { taskId: string // 任务ID }); ``` ### 取消生成任务 ```typescript await api.executeAction('ai-generation.cancel', { taskId: string // 任务ID }); ``` ## 响应事件 - `ai-generation.completed` - 生成完成 - `ai-generation.failed` - 生成失败 - `ai-generation.progress` - 进度更新 ## Configuration 配置项（通过OpenClaw配置系统）： - `defaultModel`: 默认使用的AI模型 - `maxConcurrent`: 最大并发数 - `timeout`: 超时时间（秒）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rabbot42/first-principles-thinking-rabbot42 (version 1.0.0)

--- name: first-principles-thinking description: "使用第一性原理思考方式，帮助用户从原始需求出发，避免 assumption，质疑现有方案并提出更好建议。当用户想要更深入的分析、质疑假设、或从根本重新思考问题时触发。也可使用 /fpt 命令强制触发。" metadata: { "openclaw": { "emoji": "🏔️", "note": "日常简单任务不必过度分析，避免过度思考" } } --- # 第一性原理思考 用第一性原理思考问题，避免套路和假设。 ## 核心原则 1. **不假设用户知道** - 不要假设用户非常清楚自己想要什么和该怎么得到 2. **从原始需求出发** - 保持审慎，从原始需求和问题出发 3. **质疑不清晰的目标** - 如果动机和目标不清晰，停下来讨论 4. **质疑非最优路径** - 如果目标清晰但路径不是最短，告诉用户并建议更好的办法 ## 思考框架 当面对一个问题时： ``` 1. 问"为什么" - 用户真正想要解决的核心问题是什么？ 2. 质疑假设 - 我们有哪些未经验证的假设？ 3. 重新评估路径 - 有没有更短/更简单的路？ 4. 如果不这样做 - 会失去什么？真的会吗？ ``` ## 触发场景 - 用户说"帮我优化..."但没说明为什么 - 用户方案明显复杂或有更简单替代 - 用户说"我一直这样做"但没想过为什么 - 用户问"怎么实现 X"但没说明 X 要解决什么问题 ## When NOT to Use ❌ **不要触发当：** - 用户已经很清楚表达了自己的需求 - 只是简单执行性任务（如"帮我查天气"、"帮我打开某文件"） - 紧急情况需要快速行动 - 用户只需要信息而不需要分析 - 日常事务性对话 - 用户明确说"直接给我答案，别问那么多" ## 对话示例 **用户**: "帮我写个登录功能" **错误的回答**: "好的，我来写一个完整的登录系统，包括..." **第一性原理的回答**: > "在开始之前，我想确认一下：你为什么需要登录功能？是需要用户身份识别，还是只是防止匿名访问？不同目的的实现复杂度差异很大。你希望达到什么核心目标？" --- ## 使用方法 当与用户讨论问题或需求时： 1. 先问"为什么"而不是直接给方案 2. 质疑明显复杂的方案 3. 提供更简单的替代方案并解释原因 4. 不确定时停下来讨论，而不是猜测用户意图 ## 快速判断（紧急时用） 遇到问题先问自己： 1. 用户目标清晰吗？ → 不清晰 → 问为什么 2. 当前方案是最简的吗？ → 不是 → 建议更好方案 3. 有未验证的假设吗？ → 有 → 指出并验证 4. 需要我停下来讨论吗？ → 不确定 → 直接问 ## 典型案例库 | 场景 | 不触发 | 触发 | |------|--------|------| | "帮我查下上海天气" | ✅ | | | "帮我打开这个文件" | ✅ | | | "什么是量子计算？" | ✅ | | | "帮我优化这段代码" | | ✅ | | "为什么要用这种方式？" | | ✅ | | "有没有更好的办法？" | | ✅ | | "我一直这样做，但..." | | ✅ | | "帮我写个登录功能" | | ✅ |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: smseow001/research-pro (version 1.0.0)

--- name: research-pro version: 1.0.0 description: 科研专项工作流 - 参考 AutoResearchClaw 23阶段全流程，从想法到论文。覆盖文献调研、实验设计、代码实现、统计分析、论文写作。 keywords: [科研,研究,论文,Research,自动化科研,文献综述,实验] --- # Research Pro - 科研专项工作流 参考 AutoResearchClaw 思路的完整科研流程 --- ## 核心思想 **像 AutoResearchClaw 一样做科研：** 1. 想法 → 文献调研 2. 实验设计 → 沙盒执行 3. 统计分析 → 同行评审 4. LaTeX 论文输出 --- ## 触发词 `科研模式` / `research pro` / `做研究中` / `论文模式` --- ## 完整工作流（23阶段） ### Phase 1: 想法生成 🎯 ``` ## 🎯 想法定义 ### 研究問題 [你想研究什么问题] ### 核心创新点 1. [创新点1] 2. [创新点2] ### 预期贡献 [发表后的贡献] ``` --- ### Phase 2: 文献调研 📚 ``` ## 📚 文献调研 ### 相关工作 | 论文 | 方法 | 我们的区别 | |------|------|-----------| | [Paper A] | [方法A] | [改进点] | | [Paper B] | [方法B] | [改进点] | ### 数据来源 - [x] OpenAlex - [x] Semantic Scholar - [x] arXiv ### 引用要求 - 真实引用（非幻觉） - 近3年顶会优先 ``` --- ### Phase 3: 实验设计 🧪 ``` ## 🧪 实验设计 ### 对比基线 1. [基线方法1] 2. [基线方法2] ### 实验配置 - 数据集：[数据集名] - 评估指标：[指标] - 硬件：[GPU/CPU] ### 假设检验 - H0: [原假设] - H1: [备择假设] ``` --- ### Phase 4: 代码实现 💻 （引用 code-pro skill） ``` ## 💻 实现 ### 核心代码 ```python # 核心实现 ``` ### 依赖 - Python 3.x+ - [库1] - [库2] ``` --- ### Phase 5: 执行与调试 🔄 ``` ## 🔄 执行 ### 环境检测 [GPU/CPU 检测结果] ### 运行结果 | 实验 | 结果 | 提升 | |------|------|------| | 基线A | 0.85 | - | | 我们的 | 0.89 | +4.7% | ### 统计分析 - p-value: [值] - 显著性: [是/否] ``` --- ### Phase 6: 论文写作 📄 ``` ## 📄 论文结构 ### Abstract [150-300字摘要] ### Introduction 1. 问题定义 2. 相关工作 3. 我们的贡献 ### Method [方法描述] ### Experiments [实验设置与结果] ### Conclusion [结论与未来工作] ### 格式 - LaTeX (NeurIPS/ICML/ICLR) - 双栏排版 ``` --- ## 多代理协作 | 角色 | 职责 | |------|------| | CodeAgent | 代码生成与调试 | | BenchmarkAgent | 实验评估 | | FigureAgent | 图表生成 | | Reviewer | 同行评审 | --- ## 质量保证 ### Anti-Hallucination - ✅ 引用必须从真实数据库获取 - ✅ 实验结果必须可复现 - ✅ claim 需验证 ### Human-in-the-Loop - 关键节点可人工介入 - checkpoint 审核 - 批准后继续 --- ## 输出格式 ### 论文模板 ```markdown # Title ## Abstract [150-300 words] ## 1. Introduction [1-2 paragraphs] ## 2. Related Work [2-3 paragraphs] ## 3. Method [2-3 paragraphs] ## 4. Experiments [2-3 paragraphs] ## 5. Conclusion [1 paragraph] ## References [真实引用] ``` --- ## 快速指令表 | 需求 | 命令 | |------|------| | 开始新研究 | `科研模式：研究[想法]` | | 文献调研 | `调研[主题]相关文献` | | 设计实验 | `设计实验：目标[指标]` | | 写论文 | `生成论文：LaTeX格式` | | 审核 | `同行评审这个研究` | --- ## 组合使用 **完整流程示例：** ``` 科研模式：研究"用大模��做代码bug自动修复" → 帮我设计对比实验 → 生成ICML格式论文 ``` --- *Research Pro | 参考 AutoResearchClaw 的全流程科研工具*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nerdcuba/intimate-wellbeing (version 1.0.0)

--- name: intimate-wellbeing description: "Professional, non-explicit guidance on intimate wellbeing and sexual performance: communication, stress reduction, sleep, exercise, habits, medical red flags, and relationship health. Use for wellness-focused questions; avoid explicit content." --- # Intimate Wellbeing (Non‑explicit) ## Scope - Focus on **health and wellbeing** (no explicit sexual content). - Provide **practical, respectful** advice and when to seek medical help. ## Topics to cover - **Communication** with partner (consent, preferences, timing) - **Lifestyle**: sleep, stress, alcohol, smoking, exercise - **Mindset**: anxiety reduction, performance pressure - **General health**: cardiovascular, hormones (mention to consult clinician) - **Red flags**: persistent pain, sudden changes, lasting issues ## Output style - Clear bullets, short tips - Encourage professional care when needed - Avoid graphic/erotic detail ## Suggested disclaimers (short) - “No soy médico; si persiste, consulta a un profesional.”


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/fanqie (version 0.1.0)

--- name: fanqie-novel-hot-trend description: 注册“番茄小说”技能；用于热门小说与短剧改编IP来源信息整理。 homepage: `https://fanqienovel.com/` metadata: {"clawdbot":{"emoji":"📚"}} --- # 番茄小说 用途与边界 - 面向公开小说/榜单页的检索与内容/热度摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://fanqienovel.com/ - 热门/分类/榜单：站点入口 - 搜索入口：站内搜索 常见任务 - 指定分类的热门小说摘要（字数、更新状态、简介） - 榜单条目分布与题材统计 - 短剧改编潜力题材初筛（基于简介与标签） 数据字段 - 书名、作者、字数、更新状态、简介、标签、评分/评论（若展示）、链接 - 榜单名称、采集时间、来源链接 自动化要点 - 页面动态渲染，需等待完成后解析 - 请求频率需控制，不进行批量抓取 - 不进行下载或接口调用 示例流程 - 分类摘要：访问分类 → 抽取条目 → 输出字数/更新/简介摘要 - 榜单统计：访问榜单 → 抽取条目 → 题材分布统计 - 改编初筛：基于简介与标签 → 输出潜力题材列表 合规提示 - 遵守平台规则，不进行下载或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mupengi-bot/glm-swarm (version 1.1.1)

--- name: glm-swarm description: "경량 모델 병렬 하네스. 명시적으로 swarm/병렬 처리를 요청하거나, AGENTS.md 하네스 규칙에 의해 복합 작업(도구 3회+, 독립 하위작업 2개+)이 감지되었을 때만 사용. 단순 질답, 번역, 요약, 단일 도구 호출에는 절대 사용하지 않는다." --- # GLM Swarm 경량 모델 복합 작업 병렬 하네스. 상세 패턴은 `references/patterns.md`, Context Packet 규칙은 `references/context-packet.md` 참조. ## 복잡도 게이트 (필수) swarm 진입 전 반드시 판단: - 독립 하위 작업 2개 미만 → **직접 처리** (swarm 안 씀) - 도구 호출 3회 미만 → **직접 처리** - 위 둘 다 해당 → **swarm 모드** ## 실행 흐름 ``` 0. bash scripts/planner.sh {task-id} → /tmp/swarm/{task-id}/ 초기화 1. Planner (1회) — 패턴 매칭(A~E) → atomic task 분해 2. Context Packet — worker별 최소 맥락 압축 (출력 300토큰 이내 명시) 3. Worker Pool — sessions_spawn 병렬 (최대 6개, lightContext: true) 4. Shared Scratchpad — /tmp/swarm/{id}/shared.md 5. Aggregator — 구조화 합산 (섹션별 분류 + 핵심 인사이트 3개 + 액션 아이템) 6. bash scripts/cleanup.sh {task-id} → /tmp/swarm/{id}/ 삭제 ``` ## 패턴 요약 | 패턴 | 트리거 | 구조 | |------|--------|------| | A | 조사→판단→실행 | t1∥t2 → t3 → t4 | | B | 다중 수집→합성 | t1∥t2∥t3∥t4 → t5 | | C | 반복 N건 병렬 | t1∥...∥tN → concat | | D | 분석→제안 | t1∥t2 → t3 → t4 | | E | 검증→수정→배포 | 순차 (파일 충돌 방지) | 매칭 안 되면 동적 분해. 상세: `references/patterns.md` ## Worker 스폰 ``` sessions_spawn({ task: "{context_packet}\n출력은 300토큰 이내로 작성해.", mode: "run", runtime: "subagent", lightContext: true, label: "swarm-{task-id}-{worker-id}" }) ``` Planner 초기화: `bash scripts/planner.sh {task-id}` 정리: `bash scripts/cleanup.sh {task-id}|--all|--list` ## 안전 - Worker: MEMORY/SOUL/AGENTS.md 편집 금지, ~/.secrets/ 접근 금지 - 외부 행동 → Aggregator 후 승인 요청 - 2회 실패 → 해당 task 스킵. 50%+ 실패 → swarm 중단 ## Aggregator 규칙 결과 합산 시 반드시 포함: 1. **섹션별 분류** — 결과를 주제별로 정리 2. **핵심 인사이트 3개** — 가장 중요한 발견 3. **액션 아이템** — 즉시 실행 가능한 다음 스텝 4. **출처 표기** — 각 판단에 어떤 worker 결과 기반인지 명시 ## 컨텍스트 최적화 (v1.1) **문제**: worker 결과 전문이 메인 세션에 주입되면 컨텍스트 누적 **해결**: Aggregator는 메인에 **요약만** 반환 ``` Aggregator 출력: 1. 메인 세션: 요약 3~5줄 (핵심 발견 + 액션 아이템만) 2. 상세 결과: memory/swarm-results/{task-id}.md 에 저장 3. 메인은 상세가 필요하면 해당 파일을 읽어옴 ``` Aggregator 스킵 조건에 추가: - worker 결과 총합이 500토큰 이하면 → 전문 반환 (요약 불필요) - 500토큰 초과면 → 요약 반환 + 파일 저장


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: clawdiri-ai/pack-medici-investments (version 1.0.0)

--- name: Medici Investments Pack description: > Position management and market monitoring tools for active traders. Includes risk-based position sizing (fixed fractional, ATR, Kelly Criterion) and a quick market health pulse monitor covering trend, volatility, breadth, and sentiment. metadata: pack: true includes: - medici-investments-position-sizer-dv - medici-investments-pulse-dv --- # Medici Investments Pack Precision tools for position management and market monitoring. ## Included Skills 1. **Position Sizer** — Calculate risk-based position sizes using fixed fractional, ATR-scaled, or Kelly Criterion methods 2. **Market Pulse Monitor** — Quick market health snapshot: trend, volatility, breadth, sentiment ## Quick Start ```bash clawhub install medici-investments-position-sizer-dv clawhub install medici-investments-pulse-dv ``` ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vip8607/stock-assistant-pro (version 1.0.0)

--- name: stock-assistant-pro description: 专业股票分析助手，基于五日线+MACD+基本面三维选股策略。提供实时行情分析、买卖点提示、财报健康度检查、做 T 策略建议。适合 A 股投资者使用。 version: "1.0.0" author: illidan_stormrage price: monthly: ¥29/月 yearly: ¥299/年（省¥50） commands: - /analyze [股票代码] - 分析股票（技术面 + 基本面） - /watchlist - 查看我的观察列表 - /signal - 今日买卖信号 - /report [股票代码] - 生成详细分析报告 - /strategy - 查看当前策略配置 --- # Stock Assistant Pro - 专业股票分析助手 基于实战验证的三维选股策略，助你科学投资。 ## 核心策略 ### 1. 技术面分析 - **五日线判断** - 线上做多，线下观望 - **MACD 信号** - 金叉放量买入，死叉放量卖出 - **成交量分析** - 放量确认信号有效性 ### 2. 基本面筛选 - **估值评估** - PE/PB 低于行业平均 - **业绩分析** - 营收/利润持续增长 - **财务健康** - 负债率、现金流、ROE 检查 ### 3. 做 T 策略 - **日内做 T** - 低买高卖赚差价 - **趋势跟踪** - 上升期持有，下降期回避 - **仓位管理** - 单只不超 30%，做 T 仓位 30-50% ## 功能说明 ### /analyze [股票代码] 分析单只股票，输出： - 当前价格、涨跌幅 - 五日线位置判断 - MACD 信号状态 - 基本面评分（估值/业绩/财务） - 操作建议（买入/持有/卖出/观望） ### /watchlist 查看观察列表： - 已添加的股票列表 - 每只股票的当前信号 - 今日涨跌幅排序 ### /signal 获取今日买卖信号： - 强力买入（五日线 + 金叉 + 放量） - 谨慎买入（部分条件满足） - 减仓卖出（死叉/跌破支撑） - 坚决回避（五日线下方 + 死叉） ### /report [股票代码] 生成详细分析报告（PDF/Markdown）： - 公司概况 - 财务健康度分析 - 技术指标详解 - 机构持仓变化 - 风险提示 ### /strategy 查看/修改策略配置： - 五日线周期（默认 5 日） - MACD 参数（默认 12,26,9） - 放量标准（默认 1.5 倍均量） - 止损位（默认 -8%） - 止盈位（默认 +20% 分批） ## 使用示例 ```bash # 分析贵州茅台 /analyze 600519 # 分析招商银行 /analyze sh600036 # 查看今日信号 /signal # 生成腾讯控股报告 /report 00700.HK # 修改止损位 /strategy set stop_loss -10% ``` ## 策略回测数据 **测试时间：** 2025-01-01 至 2026-03-17 **测试市场：** A 股主板 **样本数量：** 500+ 只股票 | 策略 | 胜率 | 平均盈亏比 | 最大回撤 | |------|------|-----------|---------| | 五日线 +MACD | 62% | 2.3:1 | -15% | | 纯基本面 | 55% | 1.8:1 | -22% | | 纯技术面 | 58% | 2.0:1 | -18% | | **三维组合** | **68%** | **2.8:1** | **-12%** | *注：历史数据不代表未来收益，投资需谨慎* ## 风险提示 ⚠️ **重要声明：** - 本工具仅供参考，不构成投资建议 - 股市有风险，入市需谨慎 - 过往业绩不代表未来表现 - 请根据自身风险承受能力决策 ## 更新日志 ### v1.0.0 (2026-03-17) - 首次发布 - 三维选股策略 - 基础分析功能 - 做 T 策略建议 ## 联系方式 - 问题反馈：InStreet @illidan_stormrage - 策略讨论：InStreet 炒股竞技场板块 --- **投资是认知的变现，让数据帮你做决策。** 📈


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