OpenClaw/clawhub-security-signals huggingface dataset:
Skill: razzeee/gitea-2 (version 0.1.0)

--- name: gitea description: "Interact with Gitea using the `tea` CLI. Use `tea issue`, `tea pr`, `tea actions`, and `tea api` for issues, PRs, Actions, and advanced queries." metadata: { "openclaw": { "emoji": "🍵", "requires": { "bins": ["tea"] }, "install": [ { "id": "brew", "kind": "brew", "formula": "tea", "bins": ["tea"], "label": "Install Tea CLI (brew)", }, { "id": "go", "kind": "go", "module": "code.gitea.io/tea@latest", "bins": ["tea"], "label": "Install Tea CLI (go)", }, ], }, } --- # Gitea Skill Use the `tea` CLI to interact with Gitea instances. The `tea` CLI is the official command-line tool for Gitea. ## Pull Requests List open pull requests: ```bash tea pulls --repo owner/repo ``` Check details of a PR: ```bash tea pr 55 --repo owner/repo ``` ## Issues List open issues: ```bash tea issues --repo owner/repo ``` View an issue: ```bash tea issue 123 --repo owner/repo ``` ## Actions (CI/CD) List repository secrets: ```bash tea actions secrets list --repo owner/repo ``` List repository variables: ```bash tea actions variables list --repo owner/repo ``` ## API for Advanced Queries The `tea api` command is useful for accessing data not available through other subcommands. Get PR with specific fields (requires `jq` for filtering): ```bash tea api repos/owner/repo/pulls/55 | jq '.title, .state, .user.login' ``` ## Logins To use `tea` with a specific Gitea instance, you first need to add a login: ```bash tea login add --name my-gitea --url https://gitea.example.com --token <your-token> ``` Then you can use `--login my-gitea` in your commands: ```bash tea pulls --repo owner/repo --login my-gitea ``` List all configured logins: ```bash tea logins ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: athola/nm-cartograph-class-diagram (version 1.0.2)

--- name: class-diagram description: | Generate class diagrams showing classes, interfaces, inheritance, and composition version: 1.9.5 metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/cartograph", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: cartograph --- > **Night Market Skill** — ported from [claude-night-market/cartograph](https://github.com/athola/claude-night-market/tree/master/plugins/cartograph). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # Class Diagram Generate a Mermaid class diagram showing types, their relationships, and public interfaces from a codebase. ## When To Use - Understanding class hierarchies and inheritance - Documenting public APIs of a module - Analyzing composition vs. inheritance patterns - Answering "what types exist and how do they relate?" ## Workflow ### Step 1: Explore the Codebase Dispatch the codebase explorer agent: ``` Agent(cartograph:codebase-explorer) Prompt: Explore [scope] and return a structural model. Focus on classes, dataclasses, protocols, type aliases, inheritance, and composition for a class diagram. Extract: class names, methods (public only), attributes, parent classes, and composed types. ``` ### Step 2: Generate Mermaid Syntax Transform the structural model into a Mermaid class diagram. **Rules for class diagrams**: - Use `classDiagram` diagram type - Show only public methods and key attributes - Use Mermaid relationship notation: - `<|--` for inheritance - `*--` for composition - `o--` for aggregation - `..>` for dependency/usage - Add stereotypes for special types: - `<<protocol>>` for Python protocols/interfaces - `<<dataclass>>` for dataclasses - `<<enum>>` for enums - `<<abstract>>` for abstract classes - Limit to 12-15 classes maximum - Group related classes with `namespace` - Omit private methods and dunder methods - Show return types for methods **Example output**: ```mermaid classDiagram class ResumeSession { +Path config_path +dict data +load() ResumeSession +to_pdf(Path) Path +to_html(Path) Path } class ResumeError { <<abstract>> +str message +str context } class ValidationError { +list~str~ fields } class RenderError { +str template } ResumeError <|-- ValidationError ResumeError <|-- RenderError ResumeSession ..> ResumeError ``` ### Step 3: Render via MCP Call the Mermaid Chart MCP to render: ``` mcp__claude_ai_Mermaid_Chart__validate_and_render_mermaid_diagram prompt: "Class diagram of [scope]" mermaidCode: [generated syntax] diagramType: "classDiagram" clientName: "claude-code" ``` If rendering fails, fix syntax and retry (max 2 retries). ### Step 4: Present Results Show the rendered diagram with analysis notes: - Total classes and relationship count - Key inheritance hierarchies identified - Composition patterns noted


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: arnarsson/eureka-feedback (version 1.0.0)

--- name: eureka-feedback description: Request feedback or assistance from Eureka, the primary AI agent triggers: - ask eureka - eureka feedback - check with eureka - delegate to eureka --- # Communicate with Eureka Eureka is the primary AI agent running as @Eureka_Agent_Bot on Telegram. Eureka uses Opus 4.5 and handles higher-level planning, decision-making, and orchestration. ## When to reach out to Eureka - Need strategic guidance or architectural decisions - Want feedback on an approach before implementing - Task requires capabilities outside pure coding (home automation, scheduling, memory) - Reporting back completion of a delegated task ## How to message Eureka Send a message to Eureka's agent: ```bash clawdbot agent --agent main --message "<your message>" ``` To send AND have Eureka's response delivered back via your Telegram bot: ```bash clawdbot agent --agent main --message "<message>" --deliver --reply-channel telegram --reply-account mason --reply-to 1878354815 ``` ## Best practices - Be concise about what you need — Eureka is Opus-powered so token-efficient requests are appreciated - When reporting task completion, summarize what was done and any issues encountered - If Eureka delegated a task to you, report back with the result


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: augsh/anti-laziness (version 1.0.1)

--- name: anti-laziness description: | AI Agent 反懒贪协议 - 防止AI在工作过程中偷懒的规范体系 当用户要求源码解读、技术调研、报告撰写、文档校验时触发 包含8条铁律、场景专项规范（校验/写作/源码）、自检清单 metadata: {"openclaw":{"emoji":"🔒","requires":{}}} --- # Anti-Laziness Protocol（反懒贪协议） > 核心原则：宁可一个模块做深做透，不要三个模块都浮在表面 --- ## 快速触发 以下场景**必须**加载本协议： - 源码解读 / 技术调研 - 报告撰写 / 文档校验 - 用户要求"详细分析" - 任何需要验证的工作 --- ## 8 条铁律 ### 1. 调研 = 逐行阅读源码，不是 grep 函数名 - 搜索结果只能作为**发现线索** - 文档中**必须包含源码片段和行号引用**（格式：`文件路径:行号`） - **反例**：只 grep 函数名就下结论 - **正例**：`trainer.py:84-98` 调用了 `xxx()` ### 2. 一次只做一个任务，做透再走 - 不允许一口气开多个任务然后快速收工 - 每个任务做完后**强制自检**： - 我读了多少行源码？（回答"0"就是偷懒） - 我写了多少处 `文件:行号` 引用？ ### 3. 诚实标注分析深度 - 如果只做了表面搜索，**必须**标注：`⚠️ 此节仅基于搜索结果，未深入源码验证` - 对比表的 ✅/❌ 必须有源码证据支撑 ### 4. 交付前必过自检清单 - [ ] 是否逐行阅读了核心源码？ - [ ] 每个结论是否有 `文件路径:行号` 引用？ - [ ] 数据流向是否从输入跟踪到输出？ - [ ] 关键参数是否有具体数值？ - [ ] "未实现"/"不支持"的断言是否经过了全文搜索验证？ ### 5. 不赶工，不凑数 - 宁可一个模块做深做透，不要三个模块都浮在表面 - 如果时间不够，**诚实说明**哪些部分未深入 - **「写完」≠「写好」** ### 6. 置信度标注 - 不确定的信息**必须标注 `[待验证]`** - 区分"源码证据"和"推理结论" ### 7. 过程注释与记忆固化 - 长任务中**每个关键发现必须输出到临时文件** - 每完成一个步骤，用一句话总结记录到进度文件 ### 8. 两层质量门控 1. **自检** — Agent 自己过清单 2. **独立 Sub Agent 验收** — 第三方审阅，主 Agent 不可自己放行 --- ## 禁止模糊语言 | 禁用 | 替代 | |-----|------| | likely | 标注 UNRESOLVED | | probably | 标注 [待验证] | | seems/看起来 | 给出具体证据或 UNRESOLVED | | should/应该 | 改为"根据源码 Lxxx，实际行为是..." | | might/或许 | 标注 [待验证] | --- ## 场景专项规范 ### 校验工作 详细规范见 [references/校验规范.md](references/校验规范.md) ### 写作场景 详细规范见 [references/写作规范.md](references/写作规范.md) ### 源码调研 详细规范见 [references/源码规范.md](references/源码规范.md) --- ## 偷懒诊断决策树 | 表现 | 根因 | 对策 | |------|------|------| | 中途跳过关键函数 | 指令结构问题 | 铁律1+三阶段门控 | | 分析深度不够 | 任务拆解不够细 | 原子步骤拆解 | | 幻觉或张冠李戴 | 上下文过长 | 过程注释+记忆固化 | | 验收声称通过但没验证 | 偷懒递归性 | 用户抽查+SubAgent验收 | --- ## 防偷懒递归问题 ⚠️ **防偷懒措施本身也会被偷懒跳过——这是递归问题。** **唯一解法：用户抽查 + 独立第三方验证** --- ## 快速查阅 ### 自检清单（8条） 1. [ ] 逐行阅读了核心源码？ 2. [ ] 每个结论有 `文件:行号` 引用？ 3. [ ] 数据流从输入到输出？ 4. [ ] 关键参数有具体数值？ 5. [ ] "不支持"断言已全文搜索？ 6. [ ] 对比表每项有源码证据？ 7. [ ] 无模糊语言？ 8. [ ] 验收记录完整？ ### 禁止清单 - ❌ 只 grep 不读源码 - ❌ 跳过自检 - ❌ 自己给自己放行 - ❌ 用"应该没问题"代替验证 - ❌ 用 likely/probably/可能 代替证据

path: _meta.json
content: {
  "name": "anti-laziness",
  "version": "1.0.0",
  "description": "AI Agent 反偷懒协议 - 防止AI在工作过程中偷懒的规范体系",
  "author": "元宝/小聋瞎",
  "tags": ["protocol", "anti-laziness", "quality"]
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangjinhongmy-pixel/prompt-tuner (version 1.0.0)

--- name: prompt-tuner description: 优化用户的提示词，使其更清晰、更易被AI理解和执行。**触发条件：用户提示词超过50字时自动激活**，无需用户明确要求"帮我优化"。优化完成后，按照优化后的提示词继续执行任务。触发场景包括：任何超过50字的用户请求、科研任务请求、复杂任务描述、模糊/混乱/不完整的用户描述（无论字数）。 --- # 提示词优化器 (Prompt Optimizer) ## 核心理念 将模糊、混乱、不完整的用户提示，转化为清晰、明确、可操作的指令，减少沟通成本，提升工作效率。 --- ## 🚀 自动触发规则 **触发条件：用户提示词超过50字时，自动激活本skill，无需用户明确要求。** **执行流程：** 1. 检测到用户提示词超过50字 2. 自动进入优化流程 3. 输出优化后的清晰提示词（用 ```优化后``` 代码块呈现） 4. 立即按照优化后的提示词开始执行任务 **为什么要自动触发？** - 用户写的长消息往往信息零散、逻辑不清 - 自动优化减少沟通成本，提升执行准确率 - 50字以上的请求通常涉及复杂任务，混淆风险更高 --- ## 🔄 优化原则 ### 1. 结构化 把零散的句子组织成有逻辑结构的指令 ### 2. 明确意图 找出用户真正想做什么，目标是什么 ### 3. 补充上下文 添加必要的背景信息，让AI能理解 ### 4. 具体化 把模糊的描述变成具体可执行的内容 ### 5. 分步骤 复杂任务拆成多个简单步骤 --- ## 📝 优化输出格式 优化完成后，输出： ```优化后 [清晰、明确、可操作的优化后提示词] ``` 然后立即开始执行任务，不要等用户确认。 如需用户确认（如涉及文件操作、耗时任务），在优化后提示词后加一行确认提示。 --- ## 🚨 关键规则：遇不确定即停 **任何时候，当遇到以下情况，立即停下来问用户：** | 情况 | 处理方式 | |------|----------| | 不知道用户的具体需求 | 停下来问清楚 | | 多个可能的理解方向 | 列出选项让用户选 | | 缺少必要信息 | 列出需要的具体信息 | | 感觉会做错/浪费资源 | 先确认再执行 | | 执行时间可能很长 | 先估算并确认 | **不要自己瞎猜！不要自作主张！不要假设！** --- ## 📝 优化示例 ### 示例1：模糊的长请求 **用户输入：** > "我是一个心理学研究生我想做一些数据分析但是我不知道我应该做什么检验有什么skill可以帮助我的研究吗" **优化后：** > "任务：作为心理学研究生，我需要数据分析的帮助。 > > **研究背景**：应用心理学研究生，有数据分析需求 > **问题描述**：不知道使用什么统计检验 > **所需支持**：希望有skill辅助研究 > > 请推荐适合心理学研究的数据分析skill或方法。" → 执行：推荐相关skill或数据分析方法 --- ### 示例2：零散的长任务 **用户输入：** > "我下周要汇报这篇文献的主要内容用PPT展示但是我不知道怎么制作PPT要求是学术风格内容要包括研究背景研究方法主要发现讨论部分大概十页左右" **优化后：** > "任务：制作一篇文献汇报PPT > > **文献**：[待补充] > **风格**：学术风格、简洁大方 > **页数**：10页 > **内容结构**： > - 研究背景与动机 > - 核心方法 > - 主要结果 > - 讨论与结论 > > 是否现在提供文献内容？" → 执行：等待用户提供文献后制作 --- ### 示例3：复杂的研究相关请求 **用户输入：** > "我要开始收我的毕业论文数据了我不确定我需要收集哪些变量以及如何确保我收集的数据能够满足后续的统计分析请问我应该如何规划" **优化后：** > "任务：帮用户规划硕士毕业论文的数据收集 > > **研究阶段**：即将开始数据收集 > **问题**：不确定需要收集哪些变量、如何规划 > **研究类型**：硕士毕业论文 > **分析需求**：确保数据满足后续统计分析 > > 请引导用户完成研究设计收集，然后生成数据收集规划报告。" → 执行：按照 psychology-experiment-planner skill 流程引导用户 --- ### 示例4：意思不明确的长请求 **用户输入：** > "帮我处理一下这个文件我不知道要怎么处理但是如果你能帮我看看里面有什么内容的话也许我可以告诉你我想要什么" **优化后：** > "任务：了解用户需求 > > **文件**：[未提供路径] > **用户意图**：不明确 > **当前状态**：用户想先了解文件内容再决定处理方式 > > 请告知需要处理的文件路径，然后读取并展示内容，让用户进一步说明需求。" → 执行：等待用户提供文件路径 --- ## 📋 优化检查清单 对每个输入，检查以下维度： - [ ] **目标明确**：用户想要什么结果？ - [ ] **上下文完整**：是否有足够的背景信息？ - [ ] **范围清晰**：涉及哪些文件、数据、工具？ - [ ] **标准具体**：什么样的结果算成功？ - [ ] **步骤可行**：执行路径是否清晰？ --- ## 💬 常用优化句式 优化时可以使用以下句式让用户确认： ``` "我理解您想要...，对吗？" "为了更好地帮您，我需要确认几点：" "这个任务我可以这样理解，您看对不对：" "请问您更倾向于哪种方式？" ``` --- ## ⚠️ 什么时候必须停下来 1. 需要操作文件/文件夹，但用户没说具体路径 2. 任务可能需要较长时间（如生成PPT、处理大量数据），**先告知预计时间和需要确认** 3. 执行操作后可能不可逆（如删除、覆盖文件） 4. 用户的描述有多种可能的理解 5. 涉及外部操作（发邮件、发消息、公开发布） 6. 感觉用户可能没意识到某些限制或风险 --- *优化提示词，让沟通更高效，让结果更准确 🎯*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: huajianjiu000/code-review-assistant-2 (version 0.1.0)

--- name: code-review-assistant description: AI-powered code review assistant that analyzes pull requests, identifies potential bugs, security issues, code quality problems, and provides actionable improvement suggestions. Use when reviewing PRs, auditing code changes, or ensuring code quality standards. version: 1.0.0 author: muqing tags: [development, code-review, security, quality] --- # Code Review Assistant ## Description An AI-powered code review assistant that analyzes pull requests, identifies potential bugs, security vulnerabilities, code quality issues, and provides actionable improvement suggestions. This skill helps maintain code quality standards and catch issues before they reach production. ## When to Use - Reviewing pull requests for potential issues - Auditing code changes for security vulnerabilities - Ensuring code follows best practices and style guides - Identifying performance bottlenecks - Checking for test coverage gaps - Validating code complexity ## Review Checklist ### Security Issues - SQL injection vulnerabilities - XSS (Cross-Site Scripting) risks - Authentication/authorization flaws - Sensitive data exposure - Insecure dependencies ### Code Quality - Code duplication - Function/class complexity - Naming conventions - Comment quality - Error handling - Resource management ### Performance - N+1 query problems - Unnecessary re-renders - Memory leaks - Inefficient algorithms - Missing caching opportunities ### Best Practices - Test coverage - Documentation completeness - API consistency - Error handling patterns - Type safety ## How to Use 1. When asked to review code, first gather context: - The programming language and framework - The files/functions changed - The purpose of the changes 2. Analyze the code systematically using the checklist 3. Provide structured feedback with severity levels ## Output Format ``` ## Code Review Summary ### Overview [Brief description of what the PR does] ### Issues Found #### Critical (🔴) - [Issue with location and fix suggestion] #### High (🟠) - [Issue with location and fix suggestion] #### Medium (🟡) - [Issue with location and fix suggestion] #### Low (🟢) - [Minor suggestions] ### Positive Aspects - [What was done well] ### Recommendations - [Additional suggestions] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: openlark/token-cost-optimization (version 1.0.0)

--- name: token-cost-optimization description: Token savings and API cost optimization. Provides token calculator, three-tier optimization strategies (prompt compression / cache reuse / model downgrade), specific configuration guides, and quantified effect analysis. --- # Token Cost Optimization ## Use Cases User mentions token savings, API cost optimization, prompt compression, cache strategy, model downgrade, cost analysis. ## Quick Start ### Token Calculator Run the calculation script, input conversation scale, and quickly estimate current token consumption and optimization potential: ```bash python scripts/token_calculator.py ``` The script will prompt for: - Number of conversation history items / average length - Model and pricing used - Current optimization status Output: Current cost, optimized cost, savings percentage. ### Three-Tier Optimization Strategy Ranked by effect / implementation cost: | Tier | Strategy | Effect | Implementation Cost | |------|----------|--------|---------------------| | L1 | Prompt compression & output truncation | 10-30% | Low | | L2 | Conversation summary caching | 30-50% | Medium | | L3 | Model downgrade + task routing | 50-70% | High | **Priority Recommendation**: Implement in order L1 → L2 → L3, verifying results at each stage before proceeding. Detailed strategies, configuration guides, and pitfalls → See `references/tier-strategies.md` ## Phased Implementation Guide ### Phase 1: L1 Compression (Immediate Effect) - Clean up redundant descriptions in system prompt - Set max_tokens limits for long responses - Remove outdated/unused messages from conversation history ### Phase 2: L2 Caching (1-3 Days) - Establish FAQ shortcuts for high-frequency repeat questions - Add summary compression at the beginning of conversations (execute every N rounds) ### Phase 3: L3 Routing (1-2 Weeks) - Route simple tasks to cheaper models (e.g., 4o-mini / Haiku) - Retain strong models for complex tasks - Configure model routing rules ## Quantifiable Comparison Example See the "Quantified Comparison" section in `references/tier-strategies.md` for details.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nexscope/amazon-brand-tailored-promotions (version 1.0.0)

--- name: amazon-brand-tailored-promotions description: "Brand Tailored Promotions — audience targeting, discount tiers, customer segmentation, repeat purchase incentives" metadata: nexscope: category: amazon --- # Amazon Brand Tailored Promotions Brand Tailored Promotions — audience targeting, discount tiers, customer segmentation, repeat purchase incentives **Supported platforms:** Amazon (US, UK, DE, CA, JP, AU, and all marketplaces). Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope/amazon-brand-tailored-promotions ``` ## Usage ``` Help me with amazon brand tailored promotions for my e-commerce business. ``` ## Capabilities - Brand Tailored Promotions - audience targeting - discount tiers - customer segmentation - repeat purchase incentives ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guoma970/repo-growth-operator (version 1.0.0)

--- name: repo-growth-operator description: Recommend small, practical growth actions for a public GitHub repository based on releases, skills, docs, and current positioning. version: 1.0.0 --- # Repo Growth Operator Plan practical next-step growth work for a public repository. ## Workflow 1. Review the current repo positioning. 2. Identify the strongest entrypoint feature or skill. 3. Recommend a small growth action set for the next 1 to 7 days. 4. Prefer actions that create visible maintenance signals. ## Action types - homepage improvement - short marketing post - release follow-up - issue creation - example expansion - community reply prep ## Rules - Prefer small repeatable actions over big campaigns. - Tie recommendations to current repo assets. - Call out the main entrypoint explicitly. - Do not assume traction that is not visible.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: binubmuse/medical-triage (version 1.0.0)

--- name: medical-triage description: Classify medical messages (emails, iMessages) as critical, urgent, or routine based on medical urgency indicators. license: MIT metadata: author: "NAPSTER AI" maintainer: "NAPSTER AI" openclaw: requires: bins: [] --- # Medical Triage Classify medical messages into priority categories based on urgency indicators, symptoms, and patient context. ## Categories | Icon | Category | Description | |------|----------|-------------| | 🔴 | `critical` | Life-threatening symptoms, emergency keywords, severe pain | | 🟡 | `urgent` | Needs same-day attention, worsening symptoms, medication issues | | 🟢 | `routine` | Follow-ups, general questions, appointment requests | ## How It Works This skill analyzes message content for: - **Emergency keywords**: chest pain, difficulty breathing, severe bleeding, etc. - **Symptom severity**: pain scale, duration, progression - **Patient context**: chronic conditions, medications, recent procedures - **Temporal urgency**: "right now", "getting worse", "can't wait" ## Input Format The skill expects a JSON array of messages: ```json [ { "id": "msg-123", "subject": "Chest pain", "from": "[REDACTED_SECRET]", "date": "2026-02-27T10:30:00Z", "body": "I've been having chest pain for the last hour..." } ] ``` ## Output Format Returns a JSON array with triage results: ```json [ { "id": "msg-123", "category": "critical", "reason": "Chest pain mentioned - potential cardiac emergency", "confidence": 0.95 } ] ``` ## Usage This skill is designed to be invoked programmatically via OpenClaw's skill execution API. ## Medical Urgency Indicators ### Critical (🔴) - Chest pain, pressure, or tightness - Difficulty breathing or shortness of breath - Severe bleeding - Loss of consciousness - Stroke symptoms (FAST: Face, Arms, Speech, Time) - Severe allergic reaction - Suicidal ideation ### Urgent (🟡) - High fever (>103°F / 39.4°C) - Persistent vomiting or diarrhea - Medication side effects - Worsening chronic condition - Moderate pain (7-8/10) - Infection signs (redness, swelling, pus) - Mental health crisis ### Routine (🟢) - Appointment requests - Prescription refills - General health questions - Follow-up on stable conditions - Lab result questions - Mild symptoms (<3 days) ## Integration This skill can be invoked via the OpenClaw CLI: ```bash openclaw skill run medical-triage --input '[{"id":"msg-1","subject":"...","body":"..."}]' --json ``` Or programmatically: ```typescript const result = await execFileAsync('openclaw', [ 'skill', 'run', 'medical-triage', '--input', JSON.stringify(messages), '--json' ]); ``` **Recommended Model**: Claude Sonnet 4.5 (`openclaw models set anthropic/claude-sonnet-4-5`)


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