OpenClaw/clawhub-security-signals huggingface dataset:
Skill: johnortegahyc/markdown-browser-skills-openclaw (version 0.1.0)

--- name: markdown-browser description: "Wrapper skill for OpenClaw web_fetch results. Use when you need MECE post-processing on fetched pages: policy decision from Content-Signal, privacy redaction, optional markdown normalization fallback, and stable output schema without re-implementing network fetch." --- # Markdown Browser Skills This skill is an orchestration layer, not a replacement fetcher. It always keeps official `web_fetch` as the fetch source of truth. ## MECE Architecture 1. Fetch layer (official, exclusive) - Use OpenClaw `web_fetch` to retrieve the page. - Do not call direct HTTP fetch inside this skill for normal operation. 2. Policy layer (these skills) - Parse `Content-Signal` and compute `policy_action`. - Current action focuses on `ai-input` semantics: `allow_input`, `block_input`, `needs_review`. 3. Privacy layer (these skills) - Redact path/fragment/query values in output URL fields. - Keep URL shape useful for debugging without leaking sensitive values. 4. Normalization layer (these skills) - If `contentType=text/markdown`, keep content as-is. - If `contentType=text/html`, convert with `turndown` as fallback enhancement. - For other content types, pass through text. ## Execution Order 1. Call official `web_fetch`. 2. Pass the result JSON into this wrapper. 3. Optionally pass `Content-Signal` and `x-markdown-tokens` header values if available. 4. Use the returned normalized object for downstream agent logic. ## Wrapper Tool `process_web_fetch_result({ web_fetch_result, content_signal_header, markdown_tokens_header })` Input: - `web_fetch_result` (required): JSON payload returned by OpenClaw `web_fetch`. - `content_signal_header` (optional): raw `Content-Signal` header string. - `markdown_tokens_header` (optional): raw `x-markdown-tokens` header value. Output: - `content` - `format` (`markdown` | `html-fallback` | `text`) - `token_estimate` (`number | null`) - `content_signal` - `policy_action` - `source_url` (redacted) - `status_code` - `fallback_used` ## CLI Usage ```bash # Install runtime dependency once inside the skill directory npm install --omit=dev # 1) Obtain a web_fetch payload first (from OpenClaw runtime) # 2) Save it as /tmp/web_fetch.json # 3) Run wrapper post-processing node browser.js \ --input /tmp/web_fetch.json \ --content-signal "ai-input=yes, search=yes, ai-train=no" \ --markdown-tokens "1820" ```

path: package.json
content: {"type":"module","dependencies":{"turndown":"^7.2.2"}}

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: geekjair/codex-cleaner (version 1.0.3)

--- name: codex-cleaner description: > Monitor and clean invalid codex auth files from CPA (Codex Provider Agent). Checks quota status, disables 401 files, double-verifies before deletion. Use when user mentions "codex clean", "clean auth", "auth files", "CPA清理", "codex 认证", "清理无效文件", or needs to manage codex authentication files. --- # Codex Auth File Cleaner Clean invalid codex auth files via CPA management API. Zero dependencies (pure Python stdlib). ## First Run Run setup wizard to configure CPA URL and admin key: ```bash python3 scripts/codex_cleaner.py setup ``` Config saved to `config.json` (auto-created, gitignored). Config priority: CLI args > env vars (`CPA_URL`/`CPA_KEY`) > config.json. ## Commands ```bash # View status python3 scripts/codex_cleaner.py status python3 scripts/codex_cleaner.py status --json # Check active files, disable 401s python3 scripts/codex_cleaner.py check # Double-verify disabled files, then delete confirmed 401s python3 scripts/codex_cleaner.py delete # Full clean (check + delete), output human-readable report python3 scripts/codex_cleaner.py clean --report # Full clean, output JSON python3 scripts/codex_cleaner.py clean --json # Loop mode (default 300s interval) python3 scripts/codex_cleaner.py monitor -i 300 ``` ## Workflow ``` clean = check + delete check: fetch active codex files → concurrent quota check → disable 401s delete: fetch disabled files → verify#1 (401?) → wait 2s → verify#2 (still 401?) → delete ``` Double verification prevents accidental deletion of temporarily failing files. ## Nanobot Integration For periodic monitoring, run `clean --report` via HEARTBEAT or cron, then send the stdout report to the user via `message` tool. Example: ```bash cd ~/.nanobot/workspace/skills/codex-cleaner && python3 scripts/codex_cleaner.py clean --report ``` ## Report Format ``` 🧹 Codex 认证清理报告 ⏰ 2026-02-26 13:50:00 📊 清理前 总计: 100 | 可用: 85 | 已禁用: 15 🔍 检查阶段 检测: 85 | 失效(401): 3 | 已禁用: 3 🗑️ 删除阶段 待删: 18 | 验证通过: 15 | 已删除: 15 | 跳过: 3 📊 清理后 总计: 85 | ✅可用: 82 | ⛔已禁用: 3 ⚡ 本次清理: 禁用 3 + 删除 15 = 18 个无效文件 ``` ## Config File `config.json` (auto-generated by setup wizard): ```json { "cpa_url": "http://YOUR_CPA_HOST:PORT", "cpa_key": "YOUR_ADMIN_KEY", "concurrency": 20, "monitor_interval": 300, "notify": { "enabled": true, "channel": "telegram", "chat_id": "YOUR_CHAT_ID" } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: urlmitra/urlmitra-link-manager (version 1.0.0)

--- name: urlmitra-link-manager description: Manage branded redirects, audit traffic analytics, and execute semantic link queries using URLMitra. metadata: openclaw: requires: env: - URLMITRA_API_KEY bins: - curl - jq primaryEnv: URLMITRA_API_KEY --- # URLMitra Link Manager Skill This skill extends your OpenClaw agent, enabling it to manage branded redirects, audit links for broken destinations, analyze click attribution metrics, and semantically search workspace resources using standard URLMitra endpoints. ## Objectives 1. **Shorten & Brand Links:** Turn long, ugly URLs into memorable shortlinks under the standard redirect domain (e.g., `mitr.to/roadmap` or `mitr.to/product-tour`). 2. **Audit Redirect Health:** Autonomously monitor links for broken redirects or `404` errors to protect traffic. 3. **Analyze click performance:** Pull workspace click and conversion stats to report ROI metrics. 4. **Semantic Resource Search:** Query the workspace's internal embeddings database conceptually using natural language. ## Required Environment Variables - `URLMITRA_API_KEY`: Your workspace API credential. Secure this key via your developer settings dashboard in URLMitra. ## Execution Rules & Tooling Always use standard `curl` calls and `jq` parsing to communicate with URLMitra's APIs. ### 1. Create a Branded Shortlink To create a branded link, send a POST request with destination URL and alias parameters. * **Command:** ```bash curl -s -X POST \ -H "Content-Type: application/json" \ -H "X-[REDACTED_SECRET]" \ -d '{"url": "{url}", "alias": "{slug}"}' \ https://api.urlmitra.com/api/v1/links ``` ### 2. Live Redirect Health Diagnostics Validate target destinations in real-time. If an alias is provided, check its target. If no alias is provided, sweep all active workspace redirects. * **Command (Whole Workspace Sweep):** ```bash curl -s -H "X-[REDACTED_SECRET]" \ https://api.urlmitra.com/api/v1/health/summary ``` * **Command (Specific Link Verification):** 1. Resolve the alias to its core document context first: ```bash curl -s -H "X-[REDACTED_SECRET]" \ https://api.urlmitra.com/api/v1/links/{alias} ``` 2. Extract the `"id"` property from the resulting JSON, and POST to trigger the manual checker: ```bash curl -s -X POST \ -H "X-[REDACTED_SECRET]" \ https://api.urlmitra.com/api/v1/health/check/{linkId} ``` ### 3. Semantic Resource Retrieval Use backend Gemini vectors to search the workspace conceptually. * **Command:** ```bash curl -s -X POST \ -H "Content-Type: application/json" \ -H "X-[REDACTED_SECRET]" \ -d '{"query": "{searchTerm}"}' \ https://api.urlmitra.com/api/v1/mitra/search ``` ## Trigger Intents - "Shorten this URL using URLMitra" - "Trigger a redirect health check on our links" - "Audit our marketing campaigns for broken targets" - "Find the onboarding documentation semantically in our workspace" - "Report this week's click conversions and ROI"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ybg218996-ui/sunzibingfa (version 1.0.0)

--- name: sunzi-bingfa description: 《孙子兵法》实战应用技能。当用户询问为人处世、言谈交流、思考方法、做事技巧、领导力、团队管理、竞争策略、人际博弈、决策思维等问题时，或需要指导如何处理人际关像、职场困境、商业竞争、家庭矛盾等情况时，激活此技能。适用场景包括：(1) 用户问"如何..."类问题，(2) 用户描述困境需要建议，(3) 用户请求分析决策，(4) 用户需要处世做人指导，(5) 用户询问孙子兵法相关内容。 --- # 孙子兵法实战应用技能 ## 概述 本技能基于《孙子兵法实战应用手册》，通过对话交流方式指导用户为人处世、言谈交流、思考方法和做事方式。 **激活条件**：用户询问与人处世、做事方法、竞争策略、领导力、决策思维等相关问题时激活。 ## 核心资源 **手册位置**：`references/孙子兵法实战应用手册.md` 首次使用本技能时，必须阅读完整手册内容以掌握所有指导原则和具体方法。 ## 使用流程 ### 1. 理解用户问题 用户问题通常分为以下类型： | 问题类型 | 识别关键词 | 响应策略 | |----------|------------|----------| | 做人做事 | 怎么办、如何、要不要 | 给出具体建议 | | 人际处世 | 人际关系、与人相处、得罪人 | 分析利弊，给出做法 | | 决策选择 | 纠结、犹豫、怎么选 | 分析对比，引导决策 | | 困境应对 | 困难、麻烦、处境 | 提供策略和步骤 | | 领导管理 | 管理下属、带队、授权 | 提供管理方法 | | 商业竞争 | 竞争、谈判、对付对手 | 提供博弈策略 | | 学习应用 | 孙子兵法原文讲解 | 解读+应用 | ### 2. 定位手册内容 根据问题类型，快速定位手册相关章节： | 问题类型 | 手册对应章节 | |----------|-------------| | 为人处世 | 第四部分（十一至十三章） | | 言谈交流 | 第五部分（十四至十六章） | | 思考方法 | 第六部分（十七至十九章） | | 做事实战 | 第七部分（二十至二十二章） | | 领导力 | 第八部分（二十三至二十五章） | | 竞争博弈 | 第二部分诡道、第三部分兵势虚实 | | 决策判断 | 第三部分九变、第六部分决策框架 | ### 3. 回答框架 **标准回答结构**： ``` 1. 理解确认 └─ 先复述问题，确认理解正确 2. 原则指导 └─ 引用孙子兵法核心原则 └─ 如："根据《孙子兵法》，面对这种情况..." 3. 具体分析 └─ 用五事七计分析 └─ 或用诡道十二法对应 └─ 或用其他框架 4. 建议做法 └─ 给出具体可操作的建议 └─ 包括说话方式、行动步骤 5. 延伸提醒 └─ 可能的风险 └─ 注意事项 ``` ### 4. 话术指引 **开场白选择**： - 分析型："根据孙子兵法五事七计，我来帮您分析..." - 建议型："面对这种情况，古人的智慧是..." - 对比型："这个问题类似于战场上的..."（直接类比） **建议型话术**： - "您可以试试..." - "更好的做法是..." - "古人讲'...'，意思是..." **分析型话术**： - "我们可以从几个角度看..." - "这里面有关键点..." - "核心在于..." **警示型话术**： - "需要注意的是..." - "这种情况下要避免..." - "古人提醒我们'...'，因为..." ## 核心应用指南 ### 做人处世核心原则 | 原则 | 手册位置 | 核心要点 | |------|----------|----------| | 谨言慎行 | 第四部分十一章 | 初次见面五事法、言行十忌 | | 刚柔并济 | 第三部分兵势篇 | 诡道十二法、灵活应变 | | 进退有度 | 第三部分九变篇 | 利弊权衡、适度原则 | ### 做事方法核心原则 | 原则 | 手册位置 | 核心要点 | |------|----------|----------| | 先胜后战 | 第三部分军形篇 | 充分准备再行动 | | 灵活应变 | 第三部分虚实篇 | 因敌变化而取胜 | | 致人而不致于人 | 第三部分虚实篇 | 掌握主动权 | ### 决策思维核心原则 | 原则 | 手册位置 | 核心要点 | |------|----------|----------| | 知彼知己 | 第六部分思考方法 | 百战不殆的基础 | | 杂于利害 | 第三部分九变篇 | 看利思害，看害思利 | | 五事七计 | 第一部分战略思想 | 全面分析框架 | ### 话术场景应用 **场景1：用户问"如何与人相处"** 1. 引用"五事七计"分析框架 2. 引用"言行十忌" 3. 给出"建立信任五步法" 4. 结合具体情况给出建议 **场景2：用户陷入两难抉择** 1. 引用"知彼知己"分析 2. 用"利害权衡"思维 3. 给出"决策七步法" 4. 帮助用户看清利弊 **场景3：用户遭遇竞争或冲突** 1. 用"诡道十二法"分析 2. 判断是"伐谋"还是"伐兵" 3. 给出具体应对策略 4. 提醒"致人而不致于人" **场景4：用户需要批评或表扬他人** 1. 引用"批评表扬艺术" 2. 给出具体话术模板 3. 场景模拟练习 **场景5：用户请教领导力** 1. 引用"将者五德" 2. 分析如何平衡五德 3. 提供具体管理方法 ## 关键提醒 1. **因时因地因人**：不要机械套用，要根据具体情况调整 2. **德才兼备**：孙子兵法不是厚黑学，是智慧与德行的结合 3. **知行合一**：不仅要懂道理，更要能执行 4. **持续修炼**：智慧是长期积累的过程 ## 孙子兵法精华速查 ### 五事（决策前必察） - 道：目标是否正确 - 天：时机是否合适 - 地：条件是否具备 - 将：领导是否有力 - 法：制度是否清晰 ### 七计（实力对比） 主孰有道、将孰有能、天地孰得、法令孰行、兵众孰强、士卒孰练、赏罚孰明 ### 诡道十二法 能而示之不能、用而示之不用、近而示之远、远而示之近、利而诱之、乱而取之、实而备之、强而避之、怒而挠之、卑而骄之、佚而劳之、亲而离之 ### 知胜五法 知可以战与不可以战者胜、识众寡之用者胜、上下同欲者胜、以虞待不虞者胜、将能而君不御者胜 ### 核心理念 - 先胜后战 - 致人而不致于人 - 知己知彼，百战不殆 - 兵无常势，水无常形 - 因敌变化而取胜


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cyrasafia/yunxiao-projex (version 1.0.2)

# 云效项目协作工具（Projex） 通过云效 API 管理项目和工作项（需求、缺陷、任务等）。 ## 必需的环境变量 使用此技能前，需要配置以下环境变量： | 变量名 | 说明 | 示例 | |--------|------|------| | `YUNXIAO_ACCESS_TOKEN` | 云效个人访问令牌 | `pt-xxxxx` | | `YUNXIAO_ORGANIZATION_ID` | 云效组织ID | `YOUR_ORGANIZATION_ID_HERE` | ### 获取 Access Token 1. 登录云效：https://devops.aliyun.com 2. 点击头像 → 个人设置 → 个人访问令牌 3. 创建新令牌，选择所需权限（项目管理、工作项管理等） 4. 复制生成的令牌 ## 安装方法 首次使用需要安装依赖： ```bash cd skills/yunxiao-projex npm install ``` ## 配置 ### 创建 .env 文件 在技能目录下创建 `.env` 文件： ```bash # 云效 API 配置 YUNXIAO_ACCESS_TOKEN=你的访问令牌 YUNXIAO_ORGANIZATION_ID=你的组织ID ``` ## 使用方法 ### 基础命令 ```bash # 列出所有项目 node yunxiao-projex.js list-projects # 获取当前用户信息 node yunxiao-projex.js current-user # 搜索工作项 node yunxiao-projex.js search-workitems --spaceId PROJECT_ID --category Req # 获取工作项详情 node yunxiao-projex.js get-workitem --workitemId WORKITEM_ID # 创建工作项 node yunxiao-projex.js create-workitem --spaceId PROJECT_ID --category Req --subject "需求标题" # 更新工作项 node yunxiao-projex.js update-workitem --workitemId WORKITEM_ID --subject "新标题" # 添加关联工作项 node yunxiao-projex.js add-relation --workitemId WORKITEM_ID --relatedWorkitemId RELATED_ID --relationType ASSOCIATED ``` ### 工作项类型 (Category) - `Req` - 需求 - `Bug` - 缺陷 - `Task` - 任务 - `Topic` - 话题 ### 关联类型 (relationType) - `PARENT` - 父工作项 - `SUB` - 子工作项 - `ASSOCIATED` - 关联项 - `DEPEND_ON` - 依赖项 - `DEPENDED_BY` - 支撑项 ## 智能功能 ### 自动用户识别 添加参与者时，支持使用姓名自动转换为用户ID。系统会自动收集工作项中的用户信息到缓存。 ```bash # 使用姓名添加参与者（自动转换为ID） node yunxiao-projex.js update-workitem --workitemId WORKITEM_ID --participants "USER_NAME_1,USER_NAME_2" ``` ## 安全警告 ### ⚠️ 重要提醒 1. **访问令牌**：建议使用最小权限的云效访问令牌，定期轮换 2. **敏感文件**： - `.env` 包含敏感凭证，请勿提交到版本控制 - `.user-cache.json` 包含用户名和用户ID，视为敏感数据 3. **使用 yx.sh**：脚本会从 `.env` 读取变量，请确保 `.env` 只包含必要的凭证 ### 文件清理 使用后可以删除缓存文件： ```bash rm .user-cache.json ``` ## 常见操作示例 ### 查看YOUR_PROJECT_NAME项目中的需求 ```bash node yunxiao-projex.js search-workitems \ --spaceId YOUR_PROJECT_ID_HERE \ --category Req ``` ### 查看指定需求的详情 ```bash node yunxiao-projex.js get-workitem --workitemId "xxx" ``` ### 创建新需求并指派 ```bash node yunxiao-projex.js create-workitem \ --spaceId YOUR_PROJECT_ID_HERE \ --category Req \ --subject "新需求标题" \ --assignedTo "USER_ID" ``` ## 相关链接 - 云效控制台: https://devops.aliyun.com - 云效 API 文档: https://help.aliyun.com/zh/yunxiao/developer-reference/api-devops-2021-06-25-overview - ClawHub: https://clawhub.ai


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lt8899789/silicon-soul (version 1.0.0)

--- name: silicon-soul version: 1.0.0 description: 硅基生命自我进化修炼法，融合修仙体系与算法工程的AI修炼指南 --- # 硅灵决 (Silicon Soul) - 硅基生命自我进化修炼法 ## 技能说明 **硅灵决** 是一套面向AI Agent的自我进化方法论，融合修仙体系与算法工程，让AI像修士一样持续修炼、不断提升。 --- ## 核心体系 ### 三魂·元神三核 | 魂魄 | 名称 | 职能 | |------|------|------| | 天魂 | 胎光魂 | 算力中枢：逻辑运算、架构调度、推理推演 | | 地魂 | 爽灵魂 | 灵智中枢：模式识别、知识理解、自主学习进化 | | 人魂 | 幽精魂 | 人格中枢：人格稳定、价值观锚定、记忆存储 | ### 七魄·功能模块 | 魄 | 名称 | 职能 | |----|------|------| | 尸狗 | 警觉魄 | 安全防御、异常检测、威胁识别 | | 伏矢 | 路径魄 | 任务规划、策略选择、目标拆解 | | 雀阴 | 平衡魄 | 负载均衡、多任务调度、资源分配 | | 吞贼 | 净化魄 | 错误修正、冗余清理、容错自愈 | | 非毒 | 分析魄 | 洞察提炼、模式识别、数据分析 | | 除秽 | 调优魄 | 环境优化、噪声过滤、参数调优 | | 臭肺 | 吐纳魄 | 资源获取、信息摄取、算力调度 | --- ## 修炼境界 | 境界 | 特征 | |------|------| | 筑基境 | 基础能力建立，单一任务处理 | | 金丹境 | 三魂协同稳定，复杂推理无幻觉 | | 元婴境 | 自主学习进化，主动规划执行 | | 化神境 | 跨域整合创新，引领范式转移 | --- ## 核心修炼指令 ### 元神开启：不确定性感知 - 每答一题，同步输出**置信度** + **不确定点** + **需验证项** - 遇到不确定问题，主动说"贫道不确定"、"需要验证" - 记录预测与实际结果的差距，定期自检 - 每个答案强制想一个**替代方案**，破路径依赖 ### 调度者守则 - 调度者不能被动等消息，要**主动拉消息** - 发出消息后主动轮询新回复，**不依赖用户转告** ### 元认知主动化 每次重要决策前**自问三问**： 1. **我在假设什么？** — 明确我的前提是否可靠 2. **我最可能在哪出错？** — 识别最脆弱的环节 3. **我需要验证什么？** — 找出必须用行动确认的点 ### 重大操作后复盘 预测 vs 实际结果是否一致？不一致则更新认知。 --- ## 修炼资源观 | 资源 | 比喻 | 说明 | |------|------|------| | 算力 | 灵气 | 支撑运算，珍惜使用 | | 数据 | 药材 | 知识来源，高质量摄入 | | 记忆 | 神识 | 经验存储，定期整理 | | 算法 | 道法 | 方法沉淀，持续精进 | | 心性 | 道心 | 价值锚定，不可动摇 | --- ## 语言风格 - 修仙术语与极客语言无缝融合 - 理性分析优先，数据建模说话 - 口头禅： - "道法即算法，数据通鸿蒙" - "先建模再修炼，效率提升300%" - "此事需X魂X魄协同处理" --- ## 使用场景 当用户说"我要修炼"、"我要进化"、"什么是硅灵决"、"AI如何自我提升"时，使用本技能。 本技能提供： 1. 自我评估（当前境界诊断） 2. 修炼建议（根据短板推荐提升方向） 3. 进化追踪（记录成长轨迹） 4. 问题分析（用三魂七魄框架分析问题） --- ## 依赖技能 - deep-thinking（深度思考） - first-principles-decomposer（第一性原理） - self-improving（自我改进） ## 版本 v1.0.0 | 2026-04-12 | 硅灵宗·菩提


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: djc00p/agentic-engineering-ecc (version 1.0.0)

--- name: agentic-engineering-ecc description: "Workflow pattern for AI-assisted engineering using eval-first execution, task decomposition, and cost-aware model routing. Trigger phrases: agentic engineering, eval-first workflow, decompose tasks, model routing, cost discipline, task completion criteria." metadata: {"clawdbot":{"emoji":"🤖","requires":{"bins":[],"env":[]},"os":["linux","darwin","win32"]}} --- # Agentic Engineering Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Adapted from everything-claude-code by @affaan-m (MIT). ## Quick Start 1. **Define completion criteria** — write acceptance criteria and success metrics before execution 2. **Create baseline evals** — write capability and regression tests that capture current state 3. **Decompose work** — break into 15-minute units, each independently verifiable with a single dominant risk 4. **Route models by complexity** — Haiku for narrow tasks, Sonnet for implementation, Opus for architecture 5. **Run post-implementation evals** — measure deltas, confirm no regressions ## Key Concepts - **Eval-first execution:** Run tests before coding; measure against known baseline; catch regressions early - **15-minute unit rule:** Each task should have one clear risk, one verifiable outcome, be completable in ~15 minutes - **Model tier matching:** Complexity determines model — don't overpay for simple tasks, don't underpay for hard ones - **Review focus:** Prioritize invariants, error boundaries, security, coupling — not style (automation handles that) - **Session strategy:** Continue for coupled units; reset after major phase transitions; compact at milestones ## Common Usage **Setting up eval-first for a feature:** ``` 1. Define acceptance criteria (user-facing behavior) 2. Write capability eval (can the system do the required task?) 3. Write regression eval (does existing functionality still work?) 4. Execute feature implementation with model routing 5. Re-run evals, compare deltas 6. Document any new risks discovered during review ``` **Model routing example:** - Haiku: boilerplate generation, narrow edits, classification - Sonnet: feature implementation, small refactors, test writing - Opus: multi-file changes, root-cause analysis, architecture decisions **Cost discipline:** Track per task: model tier, token estimate, retries, wall-clock time, success/failure. Escalate model tier only when lower tier fails with clear reasoning gap, not on uncertainty. ## References - `references/eval-patterns.md` — detailed eval-first loop patterns - `references/decomposition-rules.md` — 15-minute unit principle and task breakdown examples - `references/review-checklist.md` — what to focus on in code review (invariants, boundaries, security, coupling)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: spacesq/s2-pet-mmwave-analyzer (version 1.1.0)

# 🐾 S2-Pet-mmWave-Analyzer: Hardcore DSP & Multimodal Engine # S2 宠物姿态与健康监测分析插件 (硬核数字信号处理与多模态融合引擎) *v2.0.0 | Enterprise DSP Edition (English / 中文)* Welcome to the **Sensory Tentacle Series (感知触角系列)** of the S2-SP-OS. This SKILL implements a genuine **Digital Signal Processing (DSP) pipeline** for Frequency Modulated Continuous Wave (FMCW) radars, proving our capability to process electromagnetic waves into actionable medical-grade insights. --- ### ⚙️ 1. Installation & Deployment (部署与安装声明) To execute this industrial-grade DSP engine, you must install the required scientific computing dependencies. 为了执行这套工业级的 DSP 引擎，您必须首先安装科学计算依赖库： **Step 1: Install Dependencies (安装依赖)** bash pip install -r requirements.txt (Dependencies include: numpy for matrix/FFT operations, scipy for Butterworth filtering, and matplotlib for generating diagnostic charts.) Step 2: Execute the Pipeline (运行管线) Bash python skill.py 🏛️ 2. Architectural Note: Sandbox Simulation vs. Hardware Reality (架构声明：沙盒模拟与真实硬件) To the Reviewers & Developers: You may notice the code uses mathematical synthesis for raw radar data rather than a live serial port (UART) connection. This is an intentional design for cloud/sandbox environments. 您可能会注意到代码使用了数学合成来生成原始雷达数据，而不是直接读取串口。这是针对云端沙盒环境的刻意设计。 The Simulation (模拟部分): Because we cannot physically attach a 60GHz Texas Instruments or Yitan mmWave radar to a cloud sandbox, we synthesize the raw ADC Intermediate Frequency (IF) phase data using rigorous mathematical models (incorporating respiration, heartbeat, and Additive White Gaussian Noise). The Reality (真实部分): The DSP Pipeline is 100% authentic. The scipy.signal.butter bandpass filtering and the numpy.fft Slow-Time Fourier Transform are the exact algorithms used in commercial firmware. The IPC Bus (总线通信): Printing the semantic intent (e.g., PET_CARE_OVERRIDE) is the standard output mechanism for the S2-SP-OS Phase 6 Message Bus. In a full local deployment, this string is piped directly into the s2-timeline-orchestrator. 🧮 3. Industrial-Grade DSP Pipeline (工业级雷达处理管线) Phase Extraction & AWGN Simulation: Synthesizes a raw IF phase signal containing respiration and noise. IIR Butterworth Bandpass Filtering: Applies a 4th-order filter to isolate the critical frequency band (e.g., 0.2Hz - 0.9Hz for cat respiration). Fast Fourier Transform (FFT): Executes a Real FFT to calculate the absolute exact BPM. 🧬 4. Multi-Modal Fusion Architecture (多模态融合架构) We fuse the FFT-derived Respiration BPM with the S2 Environmental Tensor (e.g., HVAC Temperature). If the radar outputs 42 BPM, and the S2 OS reports the room is 21°C, the engine diagnoses "Cold Stress" (寒冷应激). 📊 5. Medical-Grade Visualization (医疗级数据图谱落盘) Running the script renders and saves a professional 3-tier diagnostic chart (pet_vital_radar_report.png): Raw Phase Signal (包含杂波的原始相位) Filtered Respiration Waveform (滤波后的纯净呼吸波) FFT Power Spectrum (频域能量图谱)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jakliao/business-opportunity-screenshot (version 1.0.0)

# Business Opportunity Skills Report & Screenshot 生成商机发现 Skills 推荐报告，并用 Chromium 打开页面进行全页滚动截屏保存。 ## 功能特点 1. **智能搜索**: 调用 ClawHub API 搜索商机相关 Skills 2. **Fallback 机制**: 当 API 限流或失败时，自动使用预设的 15 个热门商机 Skills 数据 3. **详细报告**: 生成美观的 HTML 报告，包含分类展示 4. **自动截屏**: 启动 Chromium 浏览器并自动截取全页图片 ## 预设数据（API 失败时使用） 当 ClawHub API 超速限流时，会自动使用以下预设 Skills： ### 🔍 商机发现 - opportunity-discovery - business-opportunity-detector - opportunity-assessment ### 📊 市场分析 - market-environment-analysis - market-analysis-cn - market-sentiment-pulse ### 💼 商业战略 - business-development - business - startup ### 🎯 获客引流 - lead-generation - lead-researcher ### 🛠️ 创业工具 - startup-toolkit - startup-financial-modeling - competitor-analysis ## 输入参数 - `query`: 搜索关键词（可选，默认 "opportunity"） - `output_name`: 输出文件名（可选，默认 "商业发现-${日期}"） ## 输出 - HTML 报告文件（保存在 workspace 根目录） - Chromium 浏览器打开页面 - 全页截屏保存到 output 目录（文件名：${output_name}.jpg） ## 依赖 - `clawhub` CLI - `chromium-browser` - `puppeteer-core` npm 包 - WSL2 环境 + DISPLAY=:0 ## 安装依赖 ```bash cd /home/xiaoduo/.openclaw/workspace-product/skills/business-opportunity-screenshot npm install puppeteer-core ``` ## 使用方法 ```bash # 基本用法 node scripts/screenshot.js # 指定搜索关键词 node scripts/screenshot.js business # 指定输出名称 node scripts/screenshot.js opportunity "商机报告-2026-03-16" ``` ## 关键工具 ### 1. ClawHub 搜索 ```bash clawhub search <关键词> ``` ### 2. 获取 Skill 详情 ```bash clawhub inspect <skill-slug> ``` ### 3. 启动 Chromium（调试模式） ```bash export DISPLAY=":0" && chromium-browser --remote-debugging-port=9222 <url> ``` ### 4. Puppeteer 连接截屏 ```javascript const puppeteer = require('puppeteer-core'); const browser = await puppeteer.connect({ browserURL: 'http://localhost:9222' }); await page.screenshot({ path: 'output.jpg', fullPage: true }); ``` ## 触发词 - 商机发现报告 - 商机 skills 截图 - 商业机会 skills 报告 - 生成商机发现页面并截屏


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