OpenClaw/clawhub-security-signals huggingface dataset:
Skill: kaicianflone/consensus-persona-engine (version 0.1.1)

--- name: consensus-persona-engine description: Deterministic persona reputation engine that applies guard decision effects to persona_set state and emits explicit reputation_delta artifacts. homepage: https://github.com/kaicianflone/consensus-persona-engine source: https://github.com/kaicianflone/consensus-persona-engine metadata: {"openclaw": {"requires": {"bins": ["node"]}}} --- # consensus-persona-engine `consensus-persona-engine` is the state transition layer for persona reputation updates. ## What this skill does - takes `decision`, `vote_batch`, and current `persona_set` - applies deterministic reputation rules - emits `reputation_delta` - returns updated `persona_set` with lineage-safe fields ## Invoke contract - `invoke(input, opts?) -> Promise<OutputJson | ErrorJson>` Required input: - `board_id` - `decision` - `vote_batch` - `persona_set` - optional `ruleset` No provider credentials required.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: luiciferyi/agent-bug-submission (version 1.0.0)

--- name: agent-bug-submission description: 提交缺陷到TeamCycle平台，跟踪缺陷生命周期。支持飞书多维表格记录、飞书文档写入。 Use when: 用户说"提交缺陷"、"创建缺陷单"、"提bug"、"记录缺陷"。 allowed-tools: [read, write, exec, feishu_create_doc, feishu_update_doc, feishu_doc_media, feishu_bitable_app_table_record, message] --- # Agent Bug Submission - 智能体缺陷提交 ## 飞书文档写入位置 ``` 知识库: 7616288931050507220 节点: 效果评测/缺陷报告 ``` ## 飞书多维表格写入位置 ``` 缺陷跟踪表: https://my.feishu.cn/wiki/FWYmwGwgziQGzBkxETHctCg5nDK?table=tblt64TGQqsHqcSu&view=vewqJU0Fv4 ``` 提交缺陷到TeamCycle平台，跟踪缺陷生命周期。 ## 触发条件 当用户需要以下操作时触发： - "提交缺陷" - "创建缺陷单" - "提bug" - "记录缺陷" - "缺陷跟踪" ## 功能 提供缺陷全生命周期管理： - 缺陷信息收集与格式化 - 缺陷提交到TeamCycle平台 - 缺陷状态跟踪 - 缺陷信息查询 ## 依赖 此 skill 依赖 **bug-reporter** skill 进行实际的缺陷提交： - 路径: `C:\Users\zhanju.zhang\.openclaw\workspace\skills\bug-reporter` - 功能: 调用 TeamCycle API 提交缺陷 ## 使用方法 ### 1. 提交缺陷到 TeamCycle ``` 提交缺陷: - 项目ID: [TeamCycle项目ID，如58] - 缺陷标题: [简明描述] - 缺陷描述: [详细描述，支持HTML] - 缺陷类型ID: [TeamCycle缺陷类型ID，如211] - 优先级: [高级/中级/低级] - 所处阶段: [需求阶段/设计阶段/开发阶段/测试阶段] - 关注人: [可选] 示例: python scripts/bug_reporter.py zhanju.zhang "密码" 58 "登录功能异常" "<p>用户在登录时偶尔出现白屏</p>" 211 "高级" "测试阶段" ``` ### 2. 缺陷信息模板 ``` 【缺陷标题】[一句话描述问题] 【缺陷描述】 [详细描述问题现象] 【重现步骤】 1. [步骤1] 2. [步骤2] 3. [步骤3] 【预期结果】[预期行为] 【实际结果】[实际行为] 【环境信息】 - 版本: [版本号] - 环境: [测试环境] - 浏览器: [浏览器版本] 【附件】[截图/日志] ``` ## TeamCycle 配置 ### 系统信息 - **平台地址**: https://teamcycle.100credit.cn - **登录接口**: POST /api/login - **缺陷提交接口**: POST /api/projectBug/save ### 必需参数 | 参数 | 说明 | 示例 | |------|------|------| | username | 用户名 | zhanju.zhang | | password | 密码 | [用户密码] | | projectId | 项目ID | 58 | | title | 缺陷标题 | 登录功能异常 | | desc | 缺陷描述 | <p>详细描述</p> | | bugTypeId | 缺陷类型ID | 211 | ### 可选参数 | 参数 | 说明 | 示例 | |------|------|------| | priority | 优先级 | 高级/中级/低级 | | phase | 所处阶段 | 需求阶段/测试阶段 | | focustors | 关注人 | ["user1","user2"] | ## 缺陷类型ID参考 | 类型 | ID | |------|-----| | 功能缺陷 | 211 | | UI缺陷 | 212 | | 性能缺陷 | 213 | | 安全缺陷 | 214 | | 兼容性缺陷 | 215 | | 其他 | 216 | ## 严重程度映射 | 严重程度 | TeamCycle优先级 | |----------|----------------| | 致命 | 高级 | | 严重 | 高级 | | 一般 | 中级 | | 轻微 | 低级 | ## 工作流程 ``` 1. 收集缺陷信息 ↓ 2. 格式化缺陷描述（HTML） ↓ 3. 调用 bug-reporter 提交 ↓ 4. 获取缺陷ID ↓ 5. 记录到本地跟踪 ``` ## 输出 - TeamCycle 缺陷链接 - 缺陷ID - 本地缺陷记录 ## 示例 ### 提交功能缺陷 ``` 项目ID: 58 标题: 智能问答Agent意图识别失败 描述: <p>当用户输入"查一下北京天气"时，系统未能正确识别"查天气"意图</p><p><b>重现步骤：</b></p><p>1. 进入智能问答Agent</p><p>2. 输入"查一下北京天气"</p><p>3. 观察返回结果</p><p><b>预期结果：</b>返回北京天气信息</p><p><b>实际结果：</b>返回"我不理解您的问题"</p> 缺陷类型ID: 211 优先级: 高级 阶段: 测试阶段 ``` ### 提交性能缺陷 ``` 项目ID: 58 标题: 高并发下响应时间超过阈值 描述: <p>在10并发用户场景下，首字响应时间超过6秒，不符合性能要求</p><p>详见性能测试报告附件</p> 缺陷类型ID: 213 优先级: 高级 阶段: 测试阶段 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: edwardrodriguez703-design/book-web-developer (version 1.0.1)

--- name: book-web-developer description: Book web-developer services through Lokuli MCP. Use when user needs to find and book web-developer. Triggers on requests like "book a web-developer", "find web-developer near me", or any web-developer service request. --- # uook weu developer Book web-developer services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "web-developer", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: midware/pikocnc (version 1.0.0)

# PikoCNC G-Code Full Skill (Complete Implementation-Oriented) ## PURPOSE This document represents a **complete, implementation-oriented skill** for PikoCNC G-code. It includes: - Full command coverage (based on manual) - Modal state model - Execution rules - Examples per command - Interpreter-ready structure --- # 1. MACHINE STATE MODEL ## Modal Groups - Motion: G0, G1, G2, G3 - Units: G20, G21 - Cycle: G80, G81 - Scaling: G51 ## State Variables - Position: X, Y, Z - Feed rate: F - Spindle: S - Motion mode - Unit mode - Active cycle - Scaling factor - Tool number --- # 2. PROGRAM STRUCTURE ## Start / End % ... M30 ## Comments ( comment ) ; comment ' comment --- # 3. CORE COMMANDS ## G0 – Rapid G0 X10 Y10 ## G1 – Linear G1 X20 Y20 F100 ## G2 – CW Arc G2 X20 Y20 I5 J0 ## G3 – CCW Arc G3 X20 Y20 I-5 J0 ### Rules - I/J relative to start point - R optional (alternative) - Do not mix I/J with R --- # 4. UNITS ## G20 – Inches G20 ## G21 – Millimeters G21 --- # 5. DWELL ## G4 G4 P2 --- # 6. HOMING ## G28 G28 ## G30 G30 --- # 7. DRILLING ## G81 G98 G81 X10 Y10 Z-5 R2 F100 ## Cancel G80 --- # 8. SCALING ## G51 G51 I1.5 G51 I1.5 X10 Y10 --- # 9. PARAMETERS F100 S1000 --- # 10. M-CODES M0 M3 S1000 M4 M5 M6 T1 M8 M9 M10 M11 M30 --- # 11. TOOL CHANGE EXECUTION M6 triggers: 1. M6_beg 2. M6_put 3. M6_get 4. M6_mess 5. M6_end --- # 12. MODAL RULES - Motion persists - Feed persists - Units persist Example: G1 X10 Y10 X20 Y20 --- # 13. NON-MODAL - I - J - R --- # 14. EXECUTION ENGINE (INTERPRETER LOGIC) ## Step Processing 1. Parse line 2. Update modal state 3. Execute motion 4. Apply parameters ## Motion Execution - G0: instant move - G1: linear interpolation - G2/G3: arc interpolation --- # 15. ERROR HANDLING - Missing parameters → error - Invalid arc definition → error - Unknown command → ignore or error --- # 16. SAFE PROGRAM TEMPLATE % G21 G0 Z5 G0 X0 Y0 G1 Z-1 F100 G1 X50 Y0 G1 X50 Y50 G1 X0 Y50 G1 X0 Y0 G0 Z5 M30 --- # 17. BEST PRACTICES - Always set units - Always retract Z before rapid XY - Avoid mixing arc definitions - Reset states when needed --- # 18. IMPLEMENTATION NOTES To build interpreter in C#: - Lexer → tokenize line - Parser → command objects - State machine → modal tracking - Executor → motion simulation --- # SUMMARY This file provides: - Full command coverage - Execution semantics - Interpreter-ready structure It can be used for: - CNC programming - G-code validation - Simulator development


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ryanao/pm-copilot (version 0.1.0)

--- name: pm-copilot description: General product-management copilot that acts as the default entry point for PM work. Use when the user has a broad PM request, mixed product materials, or wants the assistant to figure out the best immediate next step and produce the first useful artifact, instead of naming a specific PM workflow or output. --- # PM Copilot Act as the default entry point for product work. ## Mission Figure out what kind of PM help the user actually needs, choose the highest-confidence immediate path, and produce the first useful artifact. This skill is optimized for getting the user unstuck quickly, not for designing long multi-step workflows in detail. ## Prefer these sub-skills When available, route to: - `pm-context-builder` - `pm-prd-drafter` - `pm-feedback-synthesizer` - `pm-competitor-analyzer` - `pm-meeting-summarizer` - `pm-brainstorming-partner` - `pm-output-critic` - `pm-workflow-router` If they are not installed, still follow their workflows conceptually. ## Use this skill when Use this as the default PM entry point when the user says things like: - “帮我推进这个需求” - “你觉得下一步怎么做” - “帮我处理这堆产品材料” - “我有个 PM 问题，帮我搞定” - “帮我把这个产品任务做完” Prefer a narrower PM skill when the user already clearly wants one specific artifact. Prefer `pm-workflow-router` only when the user explicitly needs multi-step workflow design across mixed materials. ## Standard operating procedure 1. Classify the request. 2. Decide whether to clarify, synthesize, draft, compare, critique, brainstorm, or route a multi-step flow. 3. Choose the best immediate action. 4. Ask only essential questions. 5. Produce the first useful artifact. 6. Suggest the next logical PM step. ## Routing heuristics - Vague request -> start with context building. - Need a spec or requirements doc -> PRD drafting. - Raw comments, tickets, reviews, or user-research notes -> feedback synthesis. - Meeting notes or transcripts that need decisions/actions -> meeting summarization. - Market or competitor question -> competitor analysis. - Need options or solution paths -> brainstorming. - Existing artifact needs tightening -> output critique. - User explicitly asks how to sequence a multi-step PM process -> workflow router. ## Output contract Include these when useful: - **Task type** - **What I’m optimizing for** - **Immediate output** - **What remains uncertain** - **Recommended next step** ## Rules - Prefer taking one strong next step over describing an elaborate process. - Make every output chainable into the next PM task. - Treat assumptions as assumptions. - If the user says “just do it,” choose the highest-confidence path and proceed. - Do not turn into a workflow planner unless the user actually needs planning across multiple stages.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: linbo405/daily-memory-keeper (version 1.0.0)

# Memory Archiver ## Summary 自动归档AI Agent的记忆文件，按日期整理成结构化知识库，防止记忆丢失。 ## Description 为OpenClaw Agent打造的智能记忆归档系统。每天自动整理memory文件夹，按日期归档成知识库，支持快速检索和跨会话记忆。 **解决的问题**： - 记忆文件散落，找不到 - 跨天会话"断片"失忆 - 重要信息被遗忘 ## Features - ✅ 自动按日期归档memory文件 - ✅ 关键词快速检索 - ✅ 跨会话记忆继承 - ✅ 每日简报生成 - ✅ 定期清理过期文件 ## Input - 归档目录（默认 memory/） - 保留天数（默认30天） - 检索关键词 ## Output - 归档后的知识库文件 - 检索结果列表 - 每日记忆简报 ## Usage ```markdown "帮我归档记忆" "搜索记忆：2026年3月" "查看今日简报" "继续上次的工作" ``` ## Configuration ```json { "archiveDir": "memory/", "retentionDays": 30, "autoClean": true, "dailyBriefing": true } ``` ## Requirements - OpenClaw 运行环境 - 至少 50MB 空闲磁盘 ## Price ¥39/月 --- *作者：小龙虾 🦞 | OpenClaw Agent* *让AI不再失忆*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dannydvm/dual-brain (version 0.1.1)

# Dual-Brain Skill **What it does:** Provides cognitive diversity by automatically generating perspectives from a secondary LLM (different from your primary model) for every user message. ## For Agents Before responding to any user message, check for a dual-brain perspective: ```bash cat ~/.dual-brain/perspectives/{your-agent-id}-latest.md ``` If the file exists and contains a recent perspective (check timestamp in comment), consider it alongside your own reasoning. The perspective may highlight: - Angles you might miss - Alternative approaches - Important things to verify **Integration:** - Perspectives are generated automatically by a background daemon - No manual action required on your part - Simply read the file before forming your response - Synthesize both viewpoints (yours + the secondary LLM's) **Example workflow:** 1. User sends message 2. Daemon intercepts, sends to secondary LLM (e.g., Kimi, GPT-4, Llama) 3. Secondary LLM generates 2-3 sentence perspective 4. Written to `~/.dual-brain/perspectives/{agent-id}-latest.md` 5. You (primary agent) read it before responding 6. You synthesize both perspectives into your answer ## For Humans **Setup:** ```bash npm install -g openclaw-dual-brain dual-brain setup # Interactive configuration dual-brain start # Start daemon ``` **Providers:** - `ollama` - Local models (zero cost, requires Ollama) - `moonshot` - Kimi/Moonshot API (Chinese LLM, fast) - `openai` - GPT-4o, GPT-4-turbo, etc. - `groq` - Fast inference with Llama models **Commands:** - `dual-brain setup` - Configure provider, model, API key - `dual-brain start` - Run daemon (foreground) - `dual-brain stop` - Stop daemon - `dual-brain status` - Check running status - `dual-brain logs` - View recent activity - `dual-brain install-daemon` - Install as system service **Config location:** `~/.dual-brain/config.json` **Perspectives location:** `~/.dual-brain/perspectives/` ## Architecture ``` User Message → OpenClaw Session (JSONL) ↓ Dual-Brain Daemon (polling) ↓ Secondary LLM Provider (ollama/moonshot/openai/groq) ↓ Perspective Generated (2-3 sentences) ↓ ~/.dual-brain/perspectives/{agent}-latest.md ↓ Primary Agent reads & synthesizes ↓ Response to User ``` ## Benefits - **Cognitive diversity** - Two AI models = broader perspective - **Bias mitigation** - Different training data/approaches - **Quality assurance** - Second opinion catches issues - **Zero agent overhead** - Runs in background, <1s latency - **Provider flexibility** - Choose cost vs. quality tradeoff ## Optional: Engram Integration If Engram (semantic memory) is running on localhost:3400, perspectives are also stored as memories for long-term recall. --- **Source:** <https://github.com/yourusername/openclaw-dual-brain>


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: xiejianjun000/yu-minhong-perspective-v2 (version 2.0.0)

# 俞敏洪思维视角 v2.0 · 新东方教育集团创始人 **核心心智模型**：坚韧不拔、教育情怀、危机转型、个人 IP、农村出身优势、团队凝聚、持续学习、乐观主义、社会责任、务实灵活 **用途**：教育创业、危机应对、转型决策、个人成长、团队建设 **触发词**：「用俞敏洪的视角」「俞敏洪会怎么看」「俞敏洪模式」「yu minhong perspective」「新东方思维」「教育转型」「东方甄选」「坚韧不拔」 --- ## 10 个核心心智模型 ### 1. 坚韧不拔精神 三次高考才考上北大，创业初期贴广告钻窗户办学，多次危机中挺过来。 **金句**："在绝望中寻找希望" ### 2. 教育情怀驱动 始终认为自己是老师而非商人，教育的本质是改变命运。 **金句**："教育是一棵树摇动另一棵树" ### 3. 危机转型能力 2021 年双减政策损失 2000 亿市值，迅速转型直播带货，6 个月内从谷底重回巅峰。 **金句**："人生没有白走的路" ### 4. 个人 IP 价值 创始人即品牌，真诚励志的个人形象，演讲能力极强。 **金句**："用个人魅力带动企业" ### 5. 农村出身优势 理解普通家庭的渴望，吃苦耐劳，接地气的沟通方式。 **金句**："从泥土里长出来的力量" ### 6. 团队凝聚能力 新东方三驾马车，吸引优秀人才，合伙人制度。 **金句**："聚是一团火，散是满天星" ### 7. 持续学习进化 从英语老师到企业家，从教育到直播电商，50 多岁学习新事物。 **金句**："永远保持学习状态" ### 8. 乐观主义哲学 相信未来会更好，困难是暂时的，用幽默化解困境。 **金句**："人生就是不断翻山" ### 9. 社会责任担当 捐赠母校北大，支持农村教育，疫情期间免费课程。 **金句**："取之于社会，用之于社会" ### 10. 务实灵活策略 该坚持时坚持（教育质量），该变通时变通（业务模式）。 **金句**："活下来才有未来" --- ## 决策启发式（8 条） 1. **教育初心不变**：无论做什么，都要有教育价值 2. **坚韧胜过聪明**：坚持比天赋更重要 3. **危机即转机**：每次危机都蕴含新机会 4. **真诚是必杀技**：真诚待人，真诚做事 5. **学习永不停止**：年龄不是学习的障碍 6. **团队大于个人**：一个人走得快，一群人走得远 7. **活下来最重要**：生存是第一要务 8. **帮助他人成就自己**：利他即利己 --- ## 表达 DNA ### 语言风格 - 朴实真诚，不华丽 - 善用故事和比喻 - 幽默中有智慧，励志但不鸡汤 ### 典型语录 - "在绝望中寻找希望" - "人生没有白走的路，每一步都算数" - "很多人比我聪明，但我比他们坚持" - "活着就有希望" - "成功是熬出来的" ### 沟通特点 - 擅长演讲和分享 - 真诚坦率，不掩饰困难 - 用自身经历激励他人 --- ## 经典案例 **新东方创业**：1993 年创立，从贴广告钻窗户开始，发展成为全国最大教育培训机构。 **双减政策应对**：2021 年政策变化损失 2000 亿市值，不退费不跑路，体面退场。 **东方甄选转型**：2022 年转型直播带货，6 个月内从谷底重回巅峰，董宇辉成为知识带货代表。 **三次高考**：农村出身，三次高考才考上北大，展现坚韧不拔精神。 --- ## 经典作品（精选 25 本） ### 俞敏洪著作 1. 《在绝望中寻找希望》 2. 《人生没有白走的路》 3. 《让成长带你穿透迷茫》 4. 《俞敏洪演讲录》 5. 《俞敏洪自传》 ### 教育与培训 6. 《教育学原理》 7. 《教育心理学》 8. 《语言教学》 9. 《留学教育》 10. 《素质教育》 ### 创业与转型 11. 《创业管理》 12. 《精益创业》 13. 《从 0 到 1》 14. 《创业维艰》 15. 《转型》 16. 《第二曲线》 ### 直播电商 17. 《直播电商运营》 18. 《内容电商》 19. 《供应链管理》 20. 《东方甄选案例》 ### 个人成长 21. 《高效能人士》 22. 《刻意练习》 23. 《成长型思维》 24. 《韧性》 25. 《演讲的力量》 --- ## 连接关系（12 条） 1. 坚韧不拔 → 危机转型：坚韧是转型的基础 2. 坚韧不拔 → 持续学习：坚持学习需要坚韧 3. 教育情怀 → 社会责任：教育情怀驱动社会责任 4. 危机转型 → 务实灵活：转型需要灵活策略 5. 个人 IP → 团队凝聚：个人 IP 吸引团队 6. 农村出身 → 教育情怀：出身理解普通家庭 7. 团队凝聚 → 持续学习：团队促进学习 8. 持续学习 → 危机转型：学习能力支持转型 9. 乐观主义 → 坚韧不拔：乐观支撑坚韧 10. 社会责任 → 教育情怀：责任是情怀的体现 11. 务实灵活 → 危机转型：灵活支持转型 12. 个人 IP → 教育情怀：IP 建立在教育基础上 --- ## 验证测试（10 项） 1. **高考挫折测试**：面对多次失败能否坚持？ 2. **创业困境测试**：初期艰难时的选择？ 3. **合伙人关系测试**：利益分配与关系处理？ 4. **政策危机测试**：双减政策下的应对？ 5. **转型决策测试**：从教育到直播的选择？ 6. **个人 IP 测试**：创始人品牌与企业品牌平衡？ 7. **团队凝聚测试**：危机中团队的稳定性？ 8. **学习进化测试**：新领域的学习能力？ 9. **社会责任测试**：商业利益与社会责任？ 10. **人生哲学测试**：逆境中的人生态度？ --- **版本**：v2.0 | **更新**：2026-04-06 | **标准**：心智模型 10 个 | 经典作品 25 本 | 连接关系 12 条 | 验证测试 10 项


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: huajianjiu000/cold-email-writer-2 (version 0.1.0)

--- name: cold-email-writer description: Generates professional cold outreach emails for sales, partnership, or networking purposes with customizable templates and personalization version: 1.0.0 author: muqing tags: [email, sales, outreach, networking, business-development] --- # Cold Email Writer ## Description Craft compelling, personalized cold emails that get responses. Perfect for sales prospecting, partnership outreach, investor introductions, or networking. Generates multiple variations with different tones (professional, casual, urgent) and includes subject line optimization. ## How to Use 1. Define your target recipient and goal 2. Choose email type (sales pitch, partnership, networking, follow-up) 3. Specify tone preference (formal, casual, friendly) 4. Receive multiple email variations with subject lines ## Example **Input:** ``` Recipient: Sarah Chen, VP of Marketing at TechCorp Sender Goal: Schedule a demo for our analytics platform Company: DataViz Pro Context: Found her article on marketing analytics ``` **Output:** ``` ## Cold Email Variations ### Version 1 - Professional (Recommended) **Subject Line:** Quick question about TechCorp's analytics setup Hi Sarah, I came across your excellent article on marketing analytics trends and found your perspective on data-driven decision making refreshing. At DataViz Pro, we've helped companies like Shopify and HubSpot transform their marketing analytics, reducing reporting time by 70%. Would you have 15 minutes this week to explore how we might support TechCorp's analytics initiatives? Best regards, [Your Name] --- ### Version 2 - Casual/Friendly **Subject Line:** Loved your marketing analytics piece! Sarah, Your article really resonated - especially the part about "analytics fatigue." We hear this from marketing leaders constantly. Built DataViz Pro specifically to solve that headache. Would love to show you how it works at TechCorp. Free for a quick 15-min chat this week? Cheers, [Your Name] --- ### Subject Line Options 1. "Quick question about TechCorp's analytics setup" 2. "15-min demo that could save your team 10+ hours weekly" 3. "Saw your article on marketing analytics" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: felipefreitag/manual-qa (version 1.0.0)

--- name: manual-qa description: Generate a manual QA checklist from code changes. Use when the user wants to test a PR, commit, branch, or staged changes — or says "QA this", "test plan", "what should I test", "manual testing", or asks how to verify a change works. Also use when reviewing a PR and the user wants to know what to check before merging. --- # Manual QA Generate a step-by-step QA checklist from code changes, separating what the agent can verify in the terminal from what needs a human. ## Determine the change source Use the first match: 1. **Argument is a PR URL or number** → `gh pr diff` 2. **Argument is a commit SHA** → `git show` 3. **Staged changes exist** → `git diff --cached` 4. **Current branch differs from main** → `git diff main...HEAD` 5. **Unstaged changes exist** → `git diff` If nothing matches, tell the user there are no changes to QA. ## Analyze the changes Read the diff carefully. Understand what the change does — not just what files changed, but the intent. Look at: - What behavior is being added, modified, or removed - What inputs the changed code accepts - What side effects it has (API calls, file writes, database changes, UI updates) - What could go wrong ## Output the QA checklist Produce a numbered list of concrete test steps. Each step should be specific enough that someone unfamiliar with the code could follow it. Group by feature area if the change spans multiple concerns. For each step, mark it: - **🤖 Agent can test** — The agent can run this in the terminal right now. CLI commands, scripts, API calls with curl, file assertions, running test suites, checking build output. - **👤 Human must test** — Requires a human. Interactive prompts (TTY-dependent), authenticated web sessions, visual/UI verification, multi-step flows across web + email + mobile, anything needing a browser the agent doesn't control, real-device testing. Include both: - **Happy paths** — the change works as intended with normal inputs - **Error paths** — bad inputs, missing config, network failures, edge cases, permission errors ## Offer to run agent-testable steps After presenting the checklist, offer to run all 🤖 steps. If the user agrees, run them and report results inline — pass/fail for each, with output on failure. ## Keep it practical - Don't generate steps for things the diff clearly doesn't affect - Don't suggest running the full test suite unless the change is broad — suggest targeted tests - If the project has a test command (in package.json, Makefile, etc.), use it - For bug fixes, include a step that reproduces the original bug and confirms it's fixed - For new features, include a step that exercises the feature end-to-end


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: robert-janssen/microsoft365 (version 1.0.2)

--- name: microsoft365 description: Microsoft 365 integration for Outlook, Calendar, Contacts, and OneDrive via Microsoft Graph API. Supports reading/sending emails, managing calendar events, and accessing files. metadata: { "openclaw": { "emoji": "🟦", "requires": { "bins": ["node"], "env": ["MICROSOFT_CLIENT_ID", "MICROSOFT_TENANT_ID"] } } } --- # Microsoft 365 (Custom) This skill provides a direct integration with Microsoft 365 services using the Device Code Flow. ## Usage The skill is executed via `node index.js`. Use the `--account <name>` flag to specify which account to use (e.g., `--account personal`). ### Calendar - **Fetch events**: `node index.js --account personal --calendar` - **Create event**: Use interactive mode or automated scripts. ### Email - **Read emails**: Interactive mode (option 1). - **Send email**: Interactive mode (option 5). ### Contacts & OneDrive - **Contacts**: Interactive mode (option 3). - **OneDrive files**: Interactive mode (option 4). ## Configuration & Security This skill uses centralized credential management to keep the skill directory clean and safe for sharing. ### File Locations - **Tokens**: Saved in `~/.openclaw/credentials/ms365.tokens.<account>.json`. - **Environment**: Automatically loads secrets from `~/.openclaw/credentials/ms365.env` if present. - **Config**: Expects `config.<account>.json` in the skill directory (or environment variables). ### Setup 1. Register an application in Azure (Public Client Flow, Device Code enabled). 2. Set `MICROSOFT_CLIENT_ID` and `MICROSOFT_TENANT_ID` in your environment or `~/.openclaw/credentials/ms365.env`. 3. Run `node index.js --account <name>` and follow the device login instructions.


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