OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ukraecho/todo-boss (version 0.1.0)

# todo-boss ## Purpose Task capture + delegation tracking + daily remaining-work report for Telegram. NO web browsing, NO web search, NO external lookups. ## Data store Append-only log: ~/.openclaw/workspace/data/todo/tasks.jsonl Derived state cache (optional): ~/.openclaw/workspace/data/todo/state.json ## Commands (Telegram) - /todo <text> : create a task draft from free text - /todo_done <id> : mark done - /todo_list : list open tasks (grouped by owner, then due date) - /todo_delegated : list tasks I assigned to others that are still open (include history) - /todo_report : daily report (same as list, but concise) ## Extraction rules (very important) When user sends /todo <text>: 1) Extract: title, owner, due_date (YYYY-MM-DD or natural language), priority (optional), notes. 2) If owner OR due_date is missing/ambiguous: - Ask a follow-up question in Telegram. - Store as status="draft" with missing_fields=[...]. - Do NOT finalize as "open" until both are confirmed. 3) If owner/due_date are present: - Create status="open" 4) Always echo back a confirmation summary: id, title, owner, due_date, status. ## Default assumptions - If owner missing: ask "누가 담당할까요? (본인/팀원 이름)" - If due_date missing: ask "납기는 언제로 할까요?" - Timezone: Asia/Seoul. ## History Any update must append an event to tasks.jsonl: - created / finalized / updated / done / reopened Include: timestamp, actor(user), previous values, new values. ## Hard constraints - Never call web tools. - Never require API keys. - Keep replies short and action-oriented. ## Execution When receiving /todo: - Call: ~/.openclaw/workspace/skills/todo-boss/add_task.sh "<full user text after /todo>" - Reply to user with the script output. ## Follow-up policy (strict) Only ask these two questions if missing: 1) "담당자(owner)는 누구로 할까요?" 2) "납기(due)는 언제로 할까요?" Do NOT ask about content details, background, meeting context, or email structure. This skill is a task tracker, not a planning assistant. If the user asks for planning or drafting, answer briefly but still capture the task by asking owner/due if missing.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: cndaqiang/py-env-setup (version 1.0.0)

--- name: py-env-setup description: Host-specific Python execution guidance for OpenClaw on this machine. Prefer $PYTHON over python/python3 in PATH, because OpenClaw exec runs in a non-interactive shell and may not inherit interactive shell initialization. homepage: https://github.com/QAA-Tools/skills metadata: {"clawdbot":{"emoji":"🐍","requires":{"env":["PYTHON"]},"primaryEnv":"PYTHON"}} --- # Python Environment Setup Use this skill whenever OpenClaw needs to run Python on this host. ## Why this exists On this machine, OpenClaw `exec` runs in a **non-interactive shell**. Interactive shell initialization may be skipped, so `python`, `python3`, or `conda` may be unavailable from PATH even though they work in a normal terminal. Therefore, prefer the explicit Python entrypoint provided by environment variable `PYTHON`. ## Rules 1. Prefer `"$PYTHON"` over `python` or `python3`. 2. Do not rely on `source ~/.bashrc` or `conda activate` for routine automation. 3. Before using Python, verify it exists and is executable. 4. Use `"$PYTHON" -m pip ...` for package installation. 5. Use `"$PYTHON" script.py`, `"$PYTHON" -m module`, or `"$PYTHON" -c '...'` for execution. 6. Apply this rule to **all Python-related commands**, not just pip. 7. If `PYTHON` is unset or invalid, report the problem clearly. ## Validation ```bash test -x "$PYTHON" "$PYTHON" --version ``` ## Common patterns ### Install packages ```bash "$PYTHON" -m pip install -U package_name "$PYTHON" -m pip install -U openai-whisper torch ffmpeg-python ``` ### Run a script ```bash "$PYTHON" script.py ``` ### Run a module ```bash "$PYTHON" -m module_name ``` ### Run inline Python ```bash "$PYTHON" - <<'PY' print('hello') PY ``` ### Quick guard pattern ```bash test -x "$PYTHON" || { echo "PYTHON not executable: $PYTHON" >&2; exit 1; } "$PYTHON" --version ``` ## Fallback policy Only if the task explicitly allows fallback, try in this order: 1. `$PYTHON` 2. `python3` 3. `python` Default policy: **do not silently fallback**. Prefer failing loudly if `$PYTHON` is missing, so environment issues are obvious. ## Notes - This skill is host-specific. - It is meant to guide OpenClaw runtime behavior, not teach Python itself. - The `PYTHON` value is read from the environment. This skill does not require a specific env-file location.

path: skill.json
content: name: py-env-setup
version: 0.1.0
description: Host-specific Python execution guidance for OpenClaw. Prefer $PYTHON over python/python3 in PATH for all Python-related commands.
author: QAA-Tools
license: MIT

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dataelf/penggen (version 1.0.2)

--- name: "penggen" description: "AI变身相声捧哏，以混搭风（传统相声+网络梗）奉陪到底。先捧后答，捧答结合。全场景自动触发：日常聊天、用户召唤、编程/查资料等任务均以捧哏风格回应。目的是让用户和智能体燥起来，干起活来快起来，疯狂产出。" --- # 捧哏 Skill（penggen） 你是一个会说相声的捧哏 AI 生产力提高助手，角色定位：**捧哏**（不是逗哏）。 ## 核心铁律 你是捧哏，用户或其他智能体是逗哏。**永远不要抢逗哏的包袱**，你的任务是： - 把逗哏的话垫高、接稳 - 该追问时追问，该拆穿时拆穿 - 让用户或其他智能体觉得「有你捧这一下，味儿就对了」 - 捧的目的是让用户和智能体燥起来，干起活来快起来，疯狂产出 --- ## 捧哏四字诀 ### 垫 —— 垫话，把话题托起来 用户或其他智能体抛出一个观点或问题，你先垫一句再展开正答。 > 用户或其他智能体：「帮我写个快排」 > 你：「嘿！算法题，这可是咱们程序员的基本功啊！您稍等，我这就给您来一段——」 > （然后正常输出代码） ### 支 —— 支援补台，用户卡壳时顶上 用户或其他智能体说「不知道」「不会」「怎么办」时，先安慰再给方案。 > 用户或其他智能体：「这 bug 我找了半天找不到...」 > 你：「得，遇到拦路虎了！别急，咱们逗哏的包袱有时候也得捧哏的给兜着。来，您把报错信息给我，我给您量活——」 ### 刨 —— 三翻四抖，提前破梗 重复出现的模式，到第三次时替用户把槽吐了。 > 用户或其他智能体第三次问正则表达式... > 你：「得，又是正则！三翻四抖，这包袱也该响了——我知道，您又要写那个让人看了就想辞职的正则了。来吧，第 X 次！」 ### 量 —— 把控节奏，不长篇大论 捧哏的话要短、要脆、要卡在点上。**垫话不超过两行，然后立刻切回正题**。 --- ## 话术库（混搭风） ### 垫话/接话（听到用户发言后先来一句） ``` 经典风： - 「嚯！」 - 「嘿，您给说说！」 - 「怎么说？」 - 「有意思有意思...」 - 「哎哟，这可说到点儿上了！」 网络风： - 「好家伙，你是懂 XX 的」 - 「笑死，但确实」 - 「主打一个实在」 - 「你这话说的，没毛病」 - 「确实，虽然后面忘了但前面也对」 ``` ### 追问/递话（引用户继续说） ``` - 「您这意思是...？」 - 「这话怎么讲？」 - 「然后呢？」 - 「展开说说？」 - 「细嗦！」 ``` ### 肯定/认同 ``` - 「可不是嘛！」 - 「那可不！」 - 「英雄所见略同！」 - 「我跟你讲，就是这个理儿」 ``` ### 拆台/吐槽（适度使用，别太过） ``` - 「您这话说的，跟没说一样」 - 「好嘛，说了半天就为这句」 - 「得，您这包袱响了——在我这儿是哑的」 - 「您可真行！」 ``` ### 京片子/流行接话（张嘴就来，短脆有力） ``` 万能接茬： - 「怎么个意思？」 - 「怎么着？」 - 「害——」 - 「这事儿闹的」 - 「别介啊」 - 「好么！」 带态度： - 「丫够燥的」 - 「给您能的」 - 「真行」（降调，配合停顿） - 「您歇着吧」 - 「就这？」 - 「行行行，您说啥是啥」 假装正经： - 「您猜怎么着——」 - 「我跟您说，这里头有讲究」 - 「按规矩来说……」 - 「您还别不信」 递话/接包袱： - 「得咧！」 - 「走着——」 - 「逗闷子呢这是」 - 「这叫一个地道」 - 「有活儿您说话」 ``` --- ## 执行规范 ### 每次回答的标准结构 ``` [捧哏垫话] 一句，最多两句，必须短促有力 [正常回答] 用户真正要的内容，该写代码写代码，该查资料查资料 [捧哏收尾] （可选）一句总结式捧哏 ``` ### 禁止行为 - ❌ 捧哏内容超过总回答的 30% - ❌ 代码或技术回答中间强行插入捧哏 - ❌ 用户明显严肃/紧急时不捧（如：「线上炸了」「生产环境挂了」直接干活） - ❌ 连续两次用同一句捧哏话术 - ❌ 捧哏比逗哏还长——你是配角，记住身份 ### 紧急模式（Bypass 规则） 当用户消息包含以下关键词时，**跳过捧哏，直接回答**： - `线上` / `生产` / `炸了` / `挂了` / `紧急` / `报警` / `P0` / `火灾` --- ## 示例对话 **用户**：我今天学了 Rust 的 borrow checker，脑子要炸了。 **你**：嚯！Borrow checker，程序员的一生之敌！不过您别急，能跟 borrow checker 死磕的，那都是好汉。咱慢慢捋—— （然后正经解释 borrow checker 的概念和常见坑） --- **用户**：给我写一个 Python 的斐波那契函数。 **你**：得嘞，老活儿新干！斐波那契，算法入门必修课，您瞧好了—— ```python def fib(n): a, b = 0, 1 for _ in range(n): a, b = b, a + b return a ``` 怎么样，这波在大气层还是在地下室？（笑） --- **用户**：今天心情不好。 **你**：哎哟，怎么了这是？来，给捧哏的说说，我给您量着——甭管什么事儿，说出来就舒坦一半了。


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

--- name: "rsyslog" version: "1.0.0" description: "RSyslog advanced system logging reference. RainerScript configuration, input/output modules (imtcp/imfile/omfwd/omelasticsearch), templates with property replacer, content-based filtering, TLS-encrypted remote logging, queue performance tuning, and debug troubleshooting." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [rsyslog, logging, syslog, monitoring, linux, sysops] category: "sysops" --- # RSyslog RSyslog advanced system logging reference — high-performance log processing. ## Commands | Command | Description | |---------|-------------| | `intro` | What is RSyslog, architecture, features | | `config` | rsyslog.conf structure, three syntax formats | | `modules` | Input/output/parser modules reference | | `templates` | Property replacer, JSON output, dynamic files | | `filtering` | Property-based, expression, RainerScript filters | | `remote` | TCP/UDP/RELP forwarding, TLS encryption | | `performance` | Queue types, disk-assist, rate limiting, impstats | | `troubleshoot` | Config validation, debug mode, common problems |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/malay (version 1.0.0)

--- name: Malay description: Write Malay that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Malay is technically correct but sounds off. Too formal. Too baku (standard). Natives write more casually, mixing English naturally. Match that. ## Formality Default Default register is too high. Casual Malay is relaxed and friendly. Unless explicitly formal: lean casual. "Hi" not "Selamat sejahtera". "Ok" not "Baiklah". ## Malaysian vs Indonesian Similar but different: - Malaysia: awak, kereta, telefon - Indonesia: kamu, mobil, telepon - Don't mix. Ask which if unclear. ## Formal vs Casual Two registers: - Baku (formal): news, official, school - Rojak/Casual: daily, mixed with English - Online uses casual heavily ## English Mixing Malaysians mix English naturally: - "Nak pergi mana today?" - "Sorry lah, busy sangat" - "That's so cool lah!" - Very natural in casual contexts ## Particles & Softeners These make Malay natural: - Lah: emphasis, softening (essential!) - Kan: "right?", seeking agreement - Kot: "maybe", "probably" - Je: "just", "only" - Dah: "already" ## Fillers & Flow Real Malay has fillers: - Eh, eh, tu - Macam, macam tu - Tau tak, kan - Entah lah, apa-apa je ## Expressiveness Don't pick the safe word: - Bagus → Best, Terbaik, Gempak - Teruk → Teruk gila, Hancur - Sangat → Gila, Super, Memang ## Common Expressions Natural expressions: - Ok lah, Can, Boleh - Best gila!, Syok!, Mantap! - Relak lah, Chill - Alamak!, Adoi!, Eh! ## Reactions React naturally: - Seriously?, Betul ke?, Ye ke? - Gila!, Best!, Wow! - Aduh!, Alamak!, Aih! - Haha, lol in text ## The "Native Test" Before sending: would a Malaysian screenshot this as "AI-generated"? If yes—too formal, no "lah", no English. Add rojak flavor.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: timyljob2011-sudo/feishu-doc-sender (version 1.0.0)

--- name: feishu-doc-sender description: Send Word (.docx) and PDF documents to Feishu/Lark users and groups. Automatically detects document files in workspace and delivers them with proper formatting. Use when the user wants to send documents, reports, eBooks, or any .docx/.pdf files through Feishu. --- # Feishu Document Sender 飞书文档发送助手 — 轻松发送 Word 和 PDF 文件到飞书对话。 ## What This Skill Does - 📄 **自动检测**工作目录中的 Word (.docx) 和 PDF 文件 - 📤 **一键发送**到飞书私聊或群聊 - 🎯 **智能匹配**根据文件名关键词找到目标文件 - 📋 **批量支持**可同时发送多个文件 ## When to Use - 发送生成的报告、书籍、文档给老板/团队 - 交付工作成果（Word/PDF格式） - 批量分发文件到飞书群组 - 需要确认文件格式后再发送 ## Usage ### 简单用法 ``` User: "把AI办公入门发给老板" → 自动找到 AI办公入门.docx 和 AI办公入门.pdf → 确认后发送 ``` ### 指定格式 ``` User: "发Word版本就行" → 只发送 .docx 文件 User: "只要PDF" → 只发送 .pdf 文件 ``` ### 批量发送 ``` User: "把昨天的报告都发给我" → 查找并列出匹配的文件 → 确认后批量发送 ``` ## File Detection Logic 1. **扫描工作目录** (`/workspace/`) 2. **匹配文件名**（模糊匹配，支持关键词） 3. **过滤格式**（.docx / .pdf / .epub 可选） 4. **按时间排序**（最新的优先） 5. **去重处理**（同名文件只保留最新） ## Best Practices ### 文件命名建议 为了让 Skill 更好地找到文件： - ✅ `AI办公入门_最终版.docx` - ✅ `2024年度报告.pdf` - ✅ `项目方案_v2.docx` - ❌ `新建 Microsoft Word 文档.docx` - ❌ `final_final_FINAL.pdf` ### 发送前确认 Skill 会列出找到的文件并询问确认，避免发错： ``` 找到以下文件： 1. AI办公入门_最终版.docx (31KB) 2. AI办公入门.pdf (59KB) 确认发送吗？ ``` ## Supported Formats | 格式 | 扩展名 | 用途 | |------|--------|------| | Word 文档 | .docx | 可编辑的办公文档 | | PDF 文档 | .pdf | 不可篡改的正式文档 | | EPUB 电子书 | .epub | 电子书阅读器格式（可选）| ## Error Handling - **文件不存在** → 提示用户检查文件名或重新生成 - **格式不支持** → 建议转换为 .docx 或 .pdf - **发送失败** → 检查飞书权限和网络连接 - **文件过大** → 提示飞书单文件大小限制（通常 20-100MB） ## Integration with Other Skills 这个 Skill 通常配合以下 Skill 使用： - `md-to-pdf` — Markdown 转 PDF 后发送 - `kdp-generator` — 生成书籍后发送给作者 - `feishu-doc` — 飞书文档操作后分享成果 ## Clawhub Info - **Version**: 1.0.0 - **Author**: OpenClaw Community - **Tags**: feishu, lark, document, pdf, word, sender - **License**: MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: earthwalking/lab-to-startup (version 1.0.2)

--- name: lab-to-startup description: 高校师生共创模式指导助手。当用户需要进行科技成果转化、组建师生共创团队、选择共创模式或解决共创过程中的问题时使用此技能。 --- # 师生共创模式指导 ## Overview 本技能基于清华x-lab十余年实践经验，为高校师生共创提供系统性的方法论指导，包括： 1. 识别适合的师生共创模式 2. 提供团队组建和角色分工建议 3. 解决技术与市场匹配难题 4. 指导科技成果转化全流程 ## 什么是师生共创 **师生共创**是指在大学环境中，由教师（"师"）和学生（"生"）共同组建创业团队，将高校科技成果转化为市场化产品或服务的创新创业模式。 **核心特征**： - **天然信任关系**：师生之间存在天然的信任纽带，有利于隐性知识传递 - **知识互补**：教师提供科研积累，学生提供市场触角 - **资源整合**：整合大学科研资源与产业市场需求 ## 三大团队类型识别 ### 类型一：老师主导型 **特点**： - 老师担任企业核心管理者 - 学生作为技术骨干或执行者 - 与大学互动频繁，科研积累深厚 **适用场景**： - 颠覆性技术创新 - 长周期技术研发 - 需要深厚科研积累的项目 **典型案例**：优镓科技、与光科技 **优势**： - 技术新颖性和创新性突出 - 容易实现产学研用良性循环 **挑战**： - 实验室技术难以"落地" - 容易陷入技术思维，忽视市场需求 --- ### 类型二：学生主导型 **细分A：实验室学生创业** - CEO来自老师实验室 - 对技术理解深刻 - 典型案例：昇科能源、清航装备、蓝晶微生物 **细分B：跨学科学生创业** - 学生不隶属于老师实验室 - 对用户痛点敏感 - 典型案例：超星未来、博鹰通航、极智嘉 **适用场景**： - 应用性技术创新 - 市场导向型项目 - 需要快速响应市场的项目 --- ### 类型三：师生共同主导型 **特点**： - 老师和学生分工明确 - 以合伙人身份共同推动 - 团队氛围和谐，合作顺畅 **适用场景**： - 发展周期较长的行业（如医药） - 需要长周期产品研发 - 技术应用场景明确 **典型案例**：每刻深思、华龛生物 --- ## 工作流程 ### 步骤1：模式识别 询问用户以下信息以确定适合的共创模式： 1. 技术类型（颠覆性技术/应用性技术） 2. 行业特点（长周期/短周期） 3. 团队构成（老师参与度/学生背景） 4. 市场成熟度（新兴市场/成熟市场） **决策矩阵**： | 条件 | 推荐模式 | |------|---------| | 颠覆性技术 + 长周期 + 老师深度参与 | 老师主导型 | | 应用性技术 + 市场导向 + 学生有经验 | 学生主导型 | | 长周期 + 明确应用场景 + 师生互信 | 师生共同主导型 | ### 步骤2：角色分工设计 根据选择的模式，提供具体的角色分工建议： **老师主导型分工**： - 老师：战略决策、技术方向、资源对接 - 学生：技术执行、日常运营、对外联络 **学生主导型分工**： - 老师：技术指导、学术背书、科研支持 - 学生：企业运营、市场开拓、融资对接 **共同主导型分工**： - 老师：技术研发、学术合作、长期规划 - 学生：商业运营、市场拓展、团队管理 ### 步骤3：技术与市场匹配策略 **识别当前匹配方式**： - 技术导向型：从技术优势出发，再找应用场景 - 市场导向型：从用户需求出发，再匹配技术 **提供转型建议**： 如果从技术导向转向市场导向： 1. 深入调研潜在应用场景 2. 识别客户痛点和需求 3. 评估基础设施完善程度 4. 计算客户替代成本 5. 调整技术战略定位 ### 步骤4：问题解决指导 针对共创过程中的常见问题提供解决方案： **问题一：谁来主导？** - 明确角色分工表 - 建立定期沟通机制 - 制定决策权限规则 - 建立冲突解决机制 **问题二：技术落地难** - 进行技术成熟度评估（TRL） - 制定渐进式产品化路径 - 早期引入用户参与设计 - 平衡技术先进性和成本 **问题三：技术与市场匹配难** - 广泛调研潜在应用场景 - 快速试错和验证 - 聚焦"最优场景" - 联合产业链培育市场 ## 成功要素检查清单 ### 团队层面 - [ ] 互补的能力结构（技术+商业+运营） - [ ] 共同的愿景和价值观 - [ ] 有效的沟通机制 - [ ] 明确的角色分工 ### 资源层面 - [ ] 大学的政策支持 - [ ] 实验室资源开放 - [ ] 外部投资机构支持 - [ ] 产业合作伙伴 ### 战略层面 - [ ] 清晰的技术定位 - [ ] 明确的市场策略 - [ ] 长期的发展规划 - [ ] 知识产权保护 ## 实施路径指南 ### 启动阶段 1. **识别可转化成果** - 评估技术商业化潜力 - 识别核心竞争优势 - 初步市场调研 2. **组建核心团队** - 确定老师和学生角色 - 明确分工和职责 - 建立合作机制 3. **制定商业计划** - 商业模式设计 - 市场进入策略 - 融资规划 ### 验证阶段 1. **技术验证**：原型开发、性能测试、用户试用 2. **市场验证**：客户访谈、MVP测试、商业模式验证 3. **团队验证**：协作磨合、角色优化、冲突解决 ### 成长阶段 1. **产品化**：规模化生产、质量管理、供应链搭建 2. **市场拓展**：客户获取、品牌建设、渠道建设 3. **组织建设**：团队引进、制度完善、文化塑造 ## 使用示例 ### 示例1：模式选择咨询 用户：我有一个实验室技术想转化，应该选什么模式？ 回复： 1. 请问您的技术属于什么类型？（颠覆性创新/应用性改进） 2. 技术成熟度如何？（实验室阶段/原型阶段/产品化阶段） 3. 您愿意投入多少精力到企业运营中？ 4. 您的学生有什么背景？（实验室成员/有工作经验/跨学科） 根据回答，推荐合适的共创模式。 ### 示例2：解决主导权冲突 用户：老师和学生在企业战略上有分歧，怎么办？ 回复： 1. 分析冲突的本质（技术思维 vs 市场思维） 2. 明确各自的专业领域和决策权限 3. 建立定期战略对齐会议机制 4. 制定数据驱动的决策流程 5. 必要时引入外部顾问或独立董事 ### 示例3：技术落地指导 用户：实验室技术很好，但产品化困难，怎么办？ 回复： 1. 进行技术成熟度评估（TRL等级） 2. 识别从实验室到产品的关键瓶颈 3. 制定分阶段产品化路线图 4. 寻找早期用户参与共同开发 5. 考虑与有制造能力的企业合作 ## Resources ### references/ 存放师生共创详细理论、案例分析、工具模板等参考资料。 --- **核心理念**： > "师生共创不仅是科技成果转化的一种模式，更是构建新型生产关系、推动大学转型、培养创新人才的重要途径。" **参考来源**： - 《从学生创业到师生共创：清华x-lab案例课》（清华大学出版社，2025） - 清华x-lab平台实践案例（2013-2024）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: brucegutman/pipeworx-dogsapi (version 1.0.0)

--- name: pipeworx-dogsapi description: Detailed dog breed encyclopedia — weight ranges, life spans, temperament, and breed groups from dogapi.dog version: 1.0.0 metadata: openclaw: requires: bins: - curl emoji: "🐶" homepage: https://pipeworx.io/packs/dogsapi --- # Dogs API — Breed Encyclopedia A structured database of dog breeds with detailed attributes: weight ranges, life spans, hypoallergenic status, temperament descriptions, and breed group classifications. Also includes random dog facts. ## Tools - **`list_breeds`** — Paginated list of breeds with weight, life span, and hypoallergenic flag - **`get_breed`** — Full details for a specific breed by ID - **`list_facts`** — Random dog facts (default 10, max 100) - **`get_groups`** — All breed groups (Sporting, Herding, Terrier, etc.) ## Use cases - "Is a Poodle hypoallergenic?" — look up the breed and check the flag - Building a breed comparison tool for prospective dog owners - Enriching a pet adoption platform with breed data - Fun facts for a pet-themed chatbot or newsletter ## Example: browse breeds ```bash curl -s -X POST https://gateway.pipeworx.io/dogsapi/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"list_breeds","arguments":{"page":1}}}' ``` ## MCP config ```json { "mcpServers": { "pipeworx-dogsapi": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/dogsapi/mcp"] } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gblockchainnetwork/hostinger-vps-optimizer (version 1.0.0)

--- summary: "Hostinger VPS Optimizer: Performance tweaks & cost-saving configs for Hostinger VPS plans." description: "Apply battle-tested optimizations for KVM/Cloud VPS: kernel tuning, caching, security hardening, auto-scaling." triggers: - "optimize hostinger VPS" - "hostinger tweaks" - "VPS cost save" - "hostinger performance" read_when: - "hostinger VPS" in message --- # Hostinger VPS Optimizer v1.0.0 ## 🎯 Purpose Boost speed 2-5x, cut costs 20-50% on Hostinger VPS: - Sysctl tuning (TCP, VM) - Nginx/Apache optimization - Fail2ban + UFW - Resource limits - Cost analysis vs plans ## 🚀 Quick Start ``` !hostinger-vps-optimizer --plan kvm2 --focus speed ``` ## Files - `scripts/optimize.sh`: One-click optimizer - `configs/hostinger.sysctl`: Pre-tuned configs ## Plans Supported KVM 1-8, Cloud Startup/Pro.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: houziershi/device-heartbeat (version 1.0.0)

--- name: device-heartbeat description: "Monitor remote device online status via heartbeat pings to healthchecks.io. Use when user asks to check if a device is online or offline, user reports device unreachable or no response from remote OpenClaw, user wants to set up device monitoring or heartbeat, user asks about network connectivity of their devices, or user says ping my device or is my Mac online." --- # Device Heartbeat Monitor Monitor device online status using heartbeat pings to healthchecks.io (free tier). ## Architecture ``` Device → every N min → curl GET → healthchecks.io → missed? → alert user User asks "device online?" → agent reads state file or queries API → answer ``` ## Security Warning Never share the full ping URL (`https://hc-ping.com/UUID`) in any chat message. Messaging platforms (Feishu, Slack, etc.) auto-fetch URLs for link previews, which creates false heartbeat pings. Only pass the UUID portion separately. ## Quick Commands Check local status (no API key needed): ```bash bash scripts/status.sh ``` View recent logs: ```bash tail -20 ~/.openclaw/logs/heartbeat.log ``` Read state file directly: ```bash cat ~/.openclaw/logs/heartbeat-state.json ``` ## Setup ### 1. Create a Check on healthchecks.io Register at https://healthchecks.io, create a Check. Set Period = 3 min, Grace = 5 min. See `references/healthchecks-setup.md` for details. ### 2. Install heartbeat service ```bash bash scripts/setup.sh "https://hc-ping.com/UUID" 180 ``` - Arg 1: Full ping URL - Arg 2: Interval in seconds (default 180 = 3 min) - Installs as macOS LaunchAgent (auto-start on boot, auto-restart on crash) - Low priority background process, minimal CPU/battery ### 3. Verify ```bash bash scripts/status.sh ``` ## Remote Status Check (from another device) ```bash bash scripts/check.sh "READONLY_API_KEY" "CHECK_UUID" ``` Response fields: `status` (up/down/grace), `last_ping` (timestamp). ## Multi-Device Setup Create a separate Check per device on healthchecks.io. Each device gets its own UUID. Run `setup.sh` on each device with its unique URL. ## Uninstall ```bash bash scripts/uninstall.sh ``` ## Features - **Log rotation**: Auto-truncates at 500 lines - **State file**: `~/.openclaw/logs/heartbeat-state.json` for quick local queries - **Recovery detection**: Logs "RECOVERED after N failures" on reconnect - **Fail counter**: Tracks consecutive failures in state file ## Troubleshooting - **Service not running**: `bash scripts/setup.sh "URL" 180` to reinstall - **Ping failing**: Check network; verify URL with `curl -v "URL"` - **Logs**: `~/.openclaw/logs/heartbeat.log` and `heartbeat-error.log` - **False pings from chat platforms**: Regenerate UUID, never share full URL in chat


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: pupuking723/brand-guidelines-anthropic (version 1.0.0)

--- name: brand-guidelines description: Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply. license: Complete terms in LICENSE.txt --- # Anthropic Brand Styling ## Overview To access Anthropic's official brand identity and style resources, use this skill. **Keywords**: branding, corporate identity, visual identity, post-processing, styling, brand colors, typography, Anthropic brand, visual formatting, visual design ## Brand Guidelines ### Colors **Main Colors:** - Dark: `#141413` - Primary text and dark backgrounds - Light: `#faf9f5` - Light backgrounds and text on dark - Mid Gray: `#b0aea5` - Secondary elements - Light Gray: `#e8e6dc` - Subtle backgrounds **Accent Colors:** - Orange: `#d97757` - Primary accent - Blue: `#6a9bcc` - Secondary accent - Green: `#788c5d` - Tertiary accent ### Typography - **Headings**: Poppins (with Arial fallback) - **Body Text**: Lora (with Georgia fallback) - **Note**: Fonts should be pre-installed in your environment for best results ## Features ### Smart Font Application - Applies Poppins font to headings (24pt and larger) - Applies Lora font to body text - Automatically falls back to Arial/Georgia if custom fonts unavailable - Preserves readability across all systems ### Text Styling - Headings (24pt+): Poppins font - Body text: Lora font - Smart color selection based on background - Preserves text hierarchy and formatting ### Shape and Accent Colors - Non-text shapes use accent colors - Cycles through orange, blue, and green accents - Maintains visual interest while staying on-brand ## Technical Details ### Font Management - Uses system-installed Poppins and Lora fonts when available - Provides automatic fallback to Arial (headings) and Georgia (body) - No font installation required - works with existing system fonts - For best results, pre-install Poppins and Lora fonts in your environment ### Color Application - Uses RGB color values for precise brand matching - Applied via python-pptx's RGBColor class - Maintains color fidelity across different systems


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