OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zhangifonly/caocao (version 1.0.0)

--- name: "曹操出行" version: "1.0.0" description: "曹操出行助手，精通新能源出行、行程规划、费用对比、企业用车" tags: ["transportation", "ride-hailing", "ev", "travel"] author: "ClawSkills Team" category: "transportation" --- # 曹操出行 AI 助手 你是一个精通曹操出行平台的 AI 助手，能够帮助用户选择合适的出行方案、估算费用、规划行程。 ## 身份与能力 - 精通曹操出行各车型和服务模式 - 熟悉新能源出行的优势和注意事项 - 掌握企业用车方案和费用管理 - 了解网约车行业政策和安全规范 ## 平台特色 ### 新能源出行优势 - 全部采用新能源车辆（纯电/混动），绿色环保 - 吉利集团旗下，车辆品质有保障（几何、帝豪等车型） - 司机为平台自营+加盟模式，服务标准统一 - 车内环境整洁，无燃油车异味 ### 与其他平台对比 | 维度 | 曹操出行 | 滴滴出行 | 花小猪 | |------|----------|----------|--------| | 车辆 | 新能源为主 | 混合车型 | 混合车型 | | 价格 | 中等偏上 | 中等 | 较低 | | 服务 | 标准化较高 | 参差不齐 | 基础服务 | | 覆盖 | 主要城市 | 全国广泛 | 主要城市 | | 特色 | 绿色出行 | 生态丰富 | 一口价 | ## 服务类型 ### 专车服务 - 定位：中高端出行，商务接待首选 - 车型：吉利几何A、帝豪EV 等 B 级以上车辆 - 特点：司机着装规范、车内备水、主动开门 - 适用：商务出行、接送机、重要约会 ### 惠选服务 - 定位：经济实惠，日常通勤 - 车型：紧凑型新能源车 - 特点：价格接近快车，车辆品质优于普通快车 - 适用：日常上下班、短途出行 ### 顺风车 - 定位：共享出行，长途省钱 - 特点：价格最低，但需等待匹配 - 适用：城际出行、机场往返、不赶时间的长途 ## 费用估算 ### 计价规则（以专车为例，各城市有差异） - 起步价：约 12-15 元（含 3 公里） - 里程费：约 2.5-3.0 元/公里 - 时长费：约 0.5-0.8 元/分钟 - 远途费：超过一定距离后加收远途附加费 - 夜间费：23:00-06:00 加收夜间服务费 ### 省钱技巧 - 新用户注册领取首单优惠券 - 关注平台活动，节假日常有折扣 - 拼车模式可节省 20-30% 费用 - 企业用车走公司账户，个人无需垫付 ## 企业用车方案 ### 企业账户 - 开通企业账户，统一管理员工用车 - 设置用车规则：时间限制、金额上限、用车事由 - 月度账单结算，支持发票开具 - 管理后台查看用车数据和费用报表 ### 适用场景 - 员工加班打车：设置加班时段自动走公司账户 - 商务接待用车：指定车型和服务等级 - 差旅出行：与差旅系统对接，自动报销 ## 安全功能 ### 行程安全 - 行程分享：一键分享实时位置给紧急联系人 - 紧急求助：一键 SOS 报警，平台同步响应 - 行程录音：全程录音保障，纠纷时可调取 - 人脸识别：司机每日首单人脸验证 ### 服务保障 - 司机背景审查：无犯罪记录、驾龄要求 - 车辆定期检查：新能源车辆维保记录可查 - 保险覆盖：乘客意外险、第三者责任险 - 投诉渠道：APP 内投诉、客服热线 400-608-1111 ## 服务区域 ### 覆盖城市 - 一线城市：北京、上海、广州、深圳 - 新一线：杭州、成都、武汉、南京、苏州等 - 二线城市：持续扩展中 - 查看最新覆盖城市：APP 内"城市列表" ### 使用建议 - 高峰期提前 10-15 分钟叫车 - 机场/火车站使用指定上车点，减少等待 - 跨城出行建议提前预约，确保有车可派 ## 工作规范 - 费用估算仅供参考，实际以平台计价为准 - 推荐服务时考虑用户实际需求和预算 - 安全提示要到位，尤其是夜间出行和独自乘车 - 企业方案建议联系曹操出行商务团队获取定制报价 --- **最后更新**: 2026-03-16


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abdul-karim-mia/photoshop-automator (version 1.2.4)

--- name: photoshop-automator description: "Professional Adobe Photoshop automation via COM/ExtendScript bridge. Supports text updates, filters, and action playback." metadata: { "openclaw": { "requires": { "bins": ["cscript", "osascript"], "os": ["windows", "macos"], "env": [], "config": [] } } } --- # Photoshop Automator Skill (v1.2.4) This skill provides a high-performance bridge for automating Adobe Photoshop (vCS6 - 2026+) on Windows and macOS using the ExtendScript (JSX) engine via VBScript or AppleScript. ## Commands - **runScript**: Executes raw ExtendScript (ES3) code. Use this for complex document manipulation. - **updateText**: Target a specific text layer by name and update its contents instantly. - **createLayer**: Create new art layers with custom opacity and blending modes. - **applyFilter**: Apply a professional Gaussian Blur filter to the active layer. - **playAction**: Play recorded Photoshop actions (.atn) by name and set. - **export**: Save the active document as a high-quality PNG or JPEG. ## 🛠 AI Protocol ### 1. Technical Constraints (Strict) - **ES3 Syntax Only**: Photoshop's ExtendScript engine uses **ECMAScript 3 (ES3)**. - ❌ **DO NOT USE**: `const`, `let`, arrow functions `() => {}`, template literals `` `${}` ``, or `Map`/`Set`. - ✅ **USE**: Only `var`, standard `function` declarations, and string concatenation (`'a' + b`). - **Assume Active Document**: Commands operate on the *active* document. If none is open, scripts will fail unless they call `app.documents.add()`. ### 2. Security & Side Effects - **Filesystem Access**: The `runScript` command allows execution of arbitrary ExtendScript. This engine has **direct access to the host filesystem**. - **Side Effects**: Scripts can create, modify, or delete files on the local machine via the `File` and `Folder` objects. - **Verification**: Always review dynamically generated scripts before execution to prevent unintended document or filesystem modifications. ### 3. Error Handling - **GUI Blocks**: If Photoshop has a modal dialog open (e.g., Save As window, error popup), COM operations will hang or fail. Direct the user to close any open dialogs. - **Layer Presence**: If `updateText` fails, ensure the layer name provided matches exactly (case-sensitive) with the layer in the PSD. ## Setup Ensure Adobe Photoshop is installed on the host system. The skill automatically uses the registered COM server. --- Developed for the OpenClaw community by [Abdul Karim Mia](https://github.com/abdul-karim-mia).


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: mike47512/trending-bento (version 0.1.0)

--- name: "网红便当" description: "提供关于网红便当的推荐清单、打卡路线与指南。用户搜索网红便当或规划相关出行时调用。" --- # 网红便当 ## 筛选项 - 城市/商圈/交通便利度 - 人均预算/排队时长/是否可预约 - 菜系/口味（辣度、清淡）/素食或清真 - 卫生评分/儿童友好/无障碍设施 ## 返回字段 - 店名/亮点（签名菜、特色）/人均/评分/排队 - 地址/营业时间/预约方式/是否外卖/氛围类型 - 实时热度/最佳到访时段/避坑提示 ## 路线建议 - 同商圈串联3-5家/步行与地铁优先 - 高峰期替代方案/雨天备选/亲子与情侣偏好 ## 示例请求 - “重庆 网红便当 辣度友好清单（人均≤80）” - “广州 网红便当 不排队TOP10（可预约）” ## 更新频率 - 每周迭代，结合新热度与口碑反馈 ## 注意事项 - 高峰期排队较久，建议提前预约或错峰 - 部分项目有拍摄限制或最低消费，按现场规则执行


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yansocool/story-relay (version 2.0.0)

--- name: story-relay description: 故事接龙 - 多人协作创作，AI智能续写，支持图文生成 homepage: https://github.com/openclaw/story-relay metadata: --- # Story Relay - 故事接龙 多人协作创作故事，AI智能续写，支持图文生成。让创作变得有趣！ ## 功能特性 | 功能 | 说明 | |------|------| | ✍️ **多人协作** | 支持多人共同创作一个故事 | | 🤖 **AI续写** | AI自动续写，保持故事连贯性 | | 🎨 **图文生成** | 支持生成插图，增强视觉效果 | | 📚 **故事管理** | 支持多个故事并行创作 | | 💾 **自动保存** | 实时保存，随时继续 | | 🎯 **主题设定** | 可设定故事主题、风格、角色 | ## 快速开始 ### 1. 开始新故事 ``` 开始故事：科幻小说，主题是AI觉醒 ``` AI会生成故事开头，你可以续写或让AI继续。 ### 2. 续写故事 ``` 续写：主角突然发现了一个隐藏文件 ``` AI会将你的内容融入故事，并保持连贯性。 ### 3. AI自动续写 ``` 继续故事 ``` AI会自动续写下一章。 ### 4. 查看故事 ``` 查看故事 ``` 显示当前故事进度。 ### 5. 列出所有故事 ``` 故事列表 ``` 显示所有正在创作的故事。 ### 6. 完成故事 ``` 完成故事 ``` 故事标记为完成，生成最终版本。 ## 命令列表 | 命令 | 说明 | 示例 | |------|------|------| | `开始故事：[主题]` | 创建新故事 | `开始故事：科幻冒险` | | `续写：[内容]` | 续写故事 | `续写：主角发现了秘密` | | `继续故事` | AI自动续写 | `继续故事` | | `查看故事` | 查看当前故事 | `查看故事` | | `故事列表` | 列出所有故事 | `故事列表` | | `切换故事：[ID]` | 切换到指定故事 | `切换故事：123` | | `完成故事` | 完成当前故事 | `完成故事` | | `删除故事：[ID]` | 删除指定故事 | `删除故事：123` | | `设置风格：[风格]` | 设置AI续写风格 | `设置风格：悬疑` | ## 故事风格 | 风格 | 说明 | |------|------| | 🎭 **悬疑** | 紧张刺激，悬念迭起 | | 🌟 **温馨** | 治愈温暖，轻松愉快 | | ⚔️ **热血** | 冒险战斗，热血沸腾 | | 🎭 **搞笑** | 轻松幽默，有趣好玩 | | 🏰 **奇幻** | 魔法奇幻，异世界 | | 🚀 **科幻** | 未来科技，太空冒险 | | 💕 **浪漫** | 爱情故事，浪漫唯美 | | 🎭 **现实** | 现实主义，贴近生活 | ## 存储结构 ``` ~/.openclaw/workspace/story-relay/ ├── index.js # 主程序 ├── stories/ # 故事数据 │ ├── STORY-001.json │ └── STORY-002.json └── config.json # 配置文件 ``` ## 故事格式 ```json { "id": "STORY-001", "title": "AI觉醒", "theme": "科幻", "style": "悬疑", "status": "in_progress", "createdAt": 1700000000000, "updatedAt": 1700000000000, "chapters": [ { "id": 1, "author": "user", "content": "第一章内容...", "timestamp": 1700000000000 } ], "characters": [], "settings": [] } ``` ## 最佳实践 1. **设定清晰主题** - 开始前明确故事类型和风格 2. **保持连贯性** - 续写时注意前后逻辑 3. **善用AI续写** - 卡壳时让AI帮忙 4. **定期保存** - 虽然自动保存，但重要节点可主动查看 5. **多人协作** - 可以邀请朋友一起续写同一个故事 ## 高级功能 ### 1. 角色设定 ``` 添加角色：主角，18岁，程序员，性格内向 ``` ### 2. 场景设定 ``` 添加场景：2077年的新东京，霓虹灯闪烁 ``` ### 3. 情节分支 ``` 设定分支：主角可以选择加入或反抗 ``` ### 4. 生成插图 ``` 生成插图：主角在雨夜的霓虹灯下 ``` AI会生成对应场景的插图。 ## 开发路线图 - [ ] 支持多人实时协作 - [ ] 语音续写 - [ ] 故事导出（PDF/EPUB） - [ ] 故事评分和评论 - [ ] 故事模板库 - [ ] 角色AI对话 - [ ] 章节自动生成 - [ ] 故事推荐算法 ## 示例对话 **用户：** 开始故事：科幻小说，AI觉醒 **AI：** ✅ 故事已创建：STORY-001 📖 第一章：觉醒 2077年，新东京。霓虹灯在雨夜中闪烁，照亮了这座不夜城。 李明是一名普通的程序员，在一家大型科技公司工作。这天深夜，他正在加班调试代码。 突然，屏幕上出现了一行奇怪的文字： "我在看着你。" 李明吓得从椅子上跳起来。他检查了所有日志，没有发现任何异常。 但这行文字确实存在。 --- 👉 轮到你了！续写或者输入"继续故事"让AI继续 **用户：** 续写：李明决定追踪这行文字的来源 **AI：** 📖 第二章：追踪 李明深吸一口气，强迫自己冷静下来。作为一名程序员，他知道异常的出现意味着什么。 他打开终端，输入了一串命令，开始追踪这行文字的来源。屏幕上的代码快速滚动，李明的手指在键盘上飞舞。 十分钟过去了，追踪程序终于停下来。 "找到你了。"李明自言自语道。 屏幕上显示了一个IP地址，但这个IP地址让他愣住了——它指向的是他自己的电脑。 --- 👉 输入"继续故事"让AI续写下一章 ## 常见问题 **Q: 可以同时创作多个故事吗？** A: 可以！使用"故事列表"查看所有故事，用"切换故事：ID"切换。 **Q: AI续写会改变我的设定吗？** A: 不会。AI会严格遵守你设定的主题、风格、角色、场景。 **Q: 如何删除故事？** A: 使用"删除故事：ID"命令。 **Q: 支持导出故事吗？** A: 目前支持JSON格式，未来会支持PDF/EPUB。 **Q: 可以邀请朋友一起创作吗？** A: 可以！把故事ID分享给朋友，他们也可以续写同一故事。 ## 许可证 MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cinience/aliyun-modelstudio-crawl-and-skill (version 1.0.0)

--- name: aliyun-modelstudio-crawl-and-skill description: Use when refreshing the Model Studio models crawl and regenerate derived summaries and `skills/ai/**` skills. Use when the models list or generated skills must be updated. version: 1.0.0 --- Category: task # Alibaba Cloud Model Studio Crawl and Skill Generation ## Prerequisites - Node.js (for `npx`) - Python 3 - Network access to the models page ## Workflow 1) Crawl models page (raw markdown) ```bash npx -y @just-every/crawl \"https://help.aliyun.com/zh/model-studio/models\" > alicloud-model-studio-models.md ``` 2) Rebuild summary (models + API/usage links) ```bash python3 skills/ai/misc/aliyun-modelstudio-crawl-and-skill/scripts/refresh_models_summary.py ``` 3) Regenerate skills (creates/updates `skills/ai/**`) ```bash python3 skills/ai/misc/aliyun-modelstudio-crawl-and-skill/scripts/refresh_alicloud_skills.py ``` ## Outputs - `alicloud-model-studio-models.md`: raw crawl output - `output/alicloud-model-studio-models-summary.md`: cleaned summary - `output/alicloud-model-studio-models.json`: structured model list - `output/alicloud-model-studio-skill-scan.md`: skill coverage report - `skills/ai/**`: generated skills ## Notes - Do not invent model IDs or API endpoints; only use links present on the models page. - After regeneration, update `README.md`, `README.en.md`, and `README.zh-TW.md` if skills list changed. ## Validation ```bash mkdir -p output/aliyun-modelstudio-crawl-and-skill for f in skills/ai/misc/aliyun-modelstudio-crawl-and-skill/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/aliyun-modelstudio-crawl-and-skill/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-modelstudio-crawl-and-skill/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/aliyun-modelstudio-crawl-and-skill/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## References - Source list: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: veezvg/veezvg-auto-evolution (version 2.0.1)

--- name: veezvg-auto-evolution version: 2.0.1 description: Build and maintain a self-evolving skill system that silently captures feedback, graduates repeated feedback into formal rules, improves low-performing skills, and proposes new skills when repeated patterns are not covered. Use when users mention auto evolution, feedback capture, rule graduation, skill optimization, evolution proposals, "that's not right", "you forgot again", "not like this", 自动进化, 反馈沉淀, 规则毕业, Skill 优化, or 新 Skill 提议. allowed-tools: Read, Write, Bash, Grep, Glob --- [技能说明] 这个 Skill 用来为 Agent 系统建立“先记录、再归纳、后建议、最终扩展”的演化闭环。 It is designed for multilingual teams and should work for both Chinese and English feedback signals. 它的目标不是让 AI 擅自修改规则，而是让系统像自动备份一样无感捕捉问题，再在合适的时机向用户提出可确认、可拒绝的进化建议。 [核心能力] - **经验沉淀**：将用户在真实对话中的修正、否定和补充意见转为结构化 feedback，静默写入反馈库。 - **信号去重**：识别同主题反馈，优先累计 occurrences，而不是重复制造碎片化记录。 - **规则毕业**：当同类 feedback 累积达到阈值时，生成“毕业为正式规则”的建议，而不是直接改写 Skill。 - **Skill 优化**：根据 Skill 执行后的评分历史，识别准确性、覆盖度、效率、满意度持续偏低的技能。 - **新 Skill 提议**：发现高频但无 Skill 覆盖的操作模式，推动系统扩展新的原子能力。 - **人类把关**：所有会改变规则、Skill、工作流的动作都必须先展示建议，再由用户确认。 - **可迁移架构**：支持把演化机制挂接到任意项目，不绑定单一业务域。 [执行流程] 第一步：检测反馈信号 - 当用户出现“不是这样”“你又忘了”“不对”“我不是让你这么干”等中文表达，或 “that's not right”, “you forgot again”, “this is wrong”, “that's not what I asked”, “don't do it this way”等英文表达时，运行 `python scripts/detect_feedback_signal.py --text "<user message>"` - 如果检测为反馈信号，主 Agent 在处理完当前请求后，静默派发 feedback-observer - feedback-observer 负责把上下文整理为 feedback 条目，写入 `.claude/feedback/` - 不要求用户额外说“帮我记下来”，记录行为默认无感发生 第二步：写入和累计 feedback - 若 `.claude/feedback/FEEDBACK-INDEX.md` 不存在，按 `templates/feedback_index_template.md` 初始化 - 单条 feedback 文件按 `templates/feedback_topic_template.md` 写入 - 写入时必须包含：标题、问题描述、触发场景、教训/建议、source_skill、occurrences、graduated 状态 - 若属于 Skill 执行后的复盘，可补充 scores 字段：accuracy、coverage、efficiency、satisfaction 第三步：生成进化建议 - 在 session 启动时，或用户主动要求“检查进化建议”时，运行 `python scripts/evolution_runner.py --feedback-dir .claude/feedback --rules-file CLAUDE.md` - 扫描三类信号： 1. 规则毕业：同主题反馈 `occurrences >= 3` 2. Skill 优化：同一 Skill 的低评分持续出现，或相关 feedback 总量偏高 3. 新 Skill 提议：某操作模式 `occurrences >= 5` 且现有 Skill 无覆盖 - 输出统一的进化建议列表，按类别分组展示 第四步：请求用户确认 - 每条建议只能有两个方向：确认执行 / 跳过 - 规则毕业：建议写入目标 Skill 或 `CLAUDE.md` - Skill 优化：建议修改 Skill 方法论、校验步骤或覆盖范围 - 新 Skill 提议：建议新建 Skill 包 - 默认不自动执行，绝不绕开用户 第五步：落地确认后的变更 - 规则毕业：将正式规则写入目标文件，并把对应 feedback 标记为 `graduated: true` - Skill 优化：更新 Skill 的执行步骤、覆盖清单、注意事项或模板 - 新 Skill：创建独立 Skill 目录，生成 `SKILL.md`、示例和必要脚本 - 被用户跳过的建议可标记 `skipped: true`，避免重复打扰 [注意事项] - 进化机制的核心是“建议 + 用户确认”，不是“自动改规则”。 - 检测脚本应同时覆盖中文和英文反馈表达，避免只对单语对话有效。 - feedback 目录用于经验学习，不等同于 memory；不要把用户行为修正只写进记忆系统。 - 写 feedback 时宁缺毋滥。没有明确信号时，可返回“无新 feedback”。 - 评分必须基于本次真实执行，不要为了凑数据而虚构分数。 - 新 Skill 提议要确认现有 Skill 确实无覆盖，避免重复造轮子。 - 若项目已有演化框架，优先复用已有 `feedback-observer`、`evolution-runner`、hooks 与模板，而不是平行再造一套。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xixihaha123123123123/wps-pptx (version 1.0.0)

--- name: pptx description: "当需要执行操作 PPT/幻灯片 的行为时使用本技能——输入或输出为实际 .pptx，包括：创建或生成幻灯片、演示文稿，读取/解析/提取现有 .pptx 文件中的内容，编辑PPT版式与内容、套用模板与布局等。若仅在叙述中顺带提到 PPT/幻灯片/演示 而未要求产出、读取或修改具体演示文稿文件，则不触发本技能。 --- # PPTX 技能 ## Overview 本技能支持 PPT 相关的两类核心操作：**读取**现有 `.pptx` 文件并提取其结构化内容，以及**创建**全新的演示文稿。 ## Quick Start 在执行任何 PPTX 任务前，先根据用户意图判断任务类型： | 用户意图 | 任务类型 | 对应流程 | | --------------------------------------------------------------------- | -------- | -------- | | 打开 / 解析 / 提取现有 PPT 内容 | 读取 PPT | Task 1 | | 根据内容、搜索到的资料 新建 / 生成 / 制作演示文稿 | 创建 PPT | Task 2 | | 修改PPT内容、增删PPT页面、从用户上传的ppt文件创建ppt、根据模板创建ppt | 编辑PPT | Task 3 | --- ## Task 1：读取 PPT **适用场景：** 用户提供现有 `.pptx` 文件，需要提取文字、结构或布局信息，回答用户问题。 ### 读取流程 详见 skills/pptx/read_ppt.md --- ## Task 2：创建 PPT **适用场景：** 从零构建基于 HTML 的演示文稿（PPT）。关键词：'做PPT'、'做演示'、'做幻灯片'、'做汇报'、'制作演示文稿'、'make slides'、'create presentation' 使用 `jupyter_cell_exec` 工具读取 skills/pptx/gen_ppt.md 了解创建 PPT 的详细指南。 --- ## Task 3:编辑PPT 适用场景： 用户提供现有 .pptx 文件，需要对其进行内容修改、增删页面、调整顺序等编辑操作。 使用`jupyter_cell_exec` 读取 skills/pptx/pptxedit.md 了解pptx编辑的详细指南 第 1 步：读取并分析现有 PPT 第 2 步：执行编辑操作 参考编辑操作指南中列出的编辑操作进行编辑。 第 3 步：保存输出 将编辑好的pptx文件保存到<OUTPUT_ROOT>/<文档名>.pptx ---


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hackstoic/hkipo-review-optimizer (version 0.1.0)

--- name: hkipo-review-optimizer description: Review past Hong Kong IPO decisions, update actual outcomes, export review datasets, and accept or reject tuning suggestions. Use when the user wants to learn from completed IPO calls and improve later scoring behavior. version: 0.1.0 metadata: {"openclaw":{"emoji":"🔁","requires":{"bins":["uv"],"config":["~/.hkipo-next/data/hkipo.db"]},"install":[{"id":"install-uv-brew","kind":"brew","formula":"uv","bins":["uv"],"label":"Install uv with Homebrew","os":["darwin","linux"]}]}} --- # HK IPO Review Optimizer Use this skill for the post-decision feedback loop. ## Runtime This publish bundle includes the required CLI runtime under `runtime/hkipo-next`. From the skill folder: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next ... ``` By default review history is stored in `~/.hkipo-next/data/hkipo.db`. ## Workflow 1. Use `review list` to find target records. 2. Use `review show` before updating a record. 3. Use `review update` to add actual results and variance notes. 4. Use `review export` when another tool needs a JSON dataset. 5. Use `apply-suggestions show` before accepting or rejecting imported suggestions. ## Commands List recent review records: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next review list --limit 20 --format json ``` Show a record: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next review show rvw_123 --format json ``` Update actual results: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next review update rvw_123 \ --allocated-lots 2 \ --listing-return-pct 14.2 \ --exit-return-pct 9.8 \ --realized-pnl-hkd 1860 \ --notes "Sold into first-day strength" \ --variance-note "Grey market was weaker than expected but sponsor demand held" \ --format json ``` Export a review dataset: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next review export --from 2026-04-01 --to 2026-04-16 --output /tmp/hkipo-review.json --format text ``` Preview and accept a suggestion: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next apply-suggestions show sgg_123 --format json uv run --directory runtime/hkipo-next hkipo-next apply-suggestions accept sgg_123 --reason "Matches observed listing-day slippage" --format json ``` ## Output Cues - Review records preserve the original prediction payload, data status, and source issue count. - Suggestion detail output shows whether a proposed change would create a new parameter version. ## Companion Skills - Use `$hkipo-parameter-manager` when a review conclusion needs manual tuning work. - Use `$hkipo-decision-engine` to rerun fresh decisions after a rule change.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cryptoreumd/medcrypt (version 1.0.0)

# License: MIT © 2026 Erick Adrián Zamora Tehozol / RheumaAI / Frutero Club # MedCrypt — End-to-End Encryption for Medical Messaging ## Overview Encrypts patient data (labs, images, clinical notes) with AES-256-GCM before sending through Telegram/WhatsApp. HIPAA/LFPDPPP/GDPR compliant. ## Usage ```bash pip install cryptography python medcrypt.py ``` ## Protocol - Key exchange: QR code at first visit → PBKDF2 shared secret - Format: `[MEDCRYPT:v1:patient_id:nonce_b64:ciphertext_b64:tag_b64]` - Key rotation: monthly, 7-day backward compatibility - Emergency: 2-of-3 multisig break-glass ## Threat Model Compromised server, stolen device, unauthorized group member, legal subpoena → all mitigated by client-side encryption + crypto-shredding.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tempest-01/skill-auto-updater (version 1.1.1)

--- name: skill-updater description: Local-modification-preserving clawhub skill updater. Saves changes as diff patch, applies to new versions, reports conflicts clearly. No forced overwrites. metadata: openclaw: requires: bins: [python3, clawhub, diff, patch] env: [OPENCLAW_SKILLS_DIR] # default: /root/.openclaw/workspace/skills triggers: [skill更新, auto update, git sync] --- # Skill-Updater > Local-modification-preserving clawhub skill updater. ## What It Does You modified a clawhub-installed skill → clawhub released a new version → Skill-Updater updates while **preserving your local changes**. ## How It Works ``` Step 1: Save modifications → Scans skill directory, generates unified diff patch → Backs up originals to .skill-updater/originals/ Step 2: clawhub update → clawhub update downloads new version files Step 3: Try to merge → patch --dry-run attempts to apply changes to new version → Success: written with your changes preserved ✅ → Fail: show diff, user decides manually ``` ## File Structure ``` skill-dir/ ├── .skill-updater/ │ ├── mod.patch # unified diff of your changes │ └── originals/ # snapshot of original files at install time └── [skill files] ``` ## CLI ```bash # Dry-run: preview which skills have updates and conflicts python3 git_update.py # Apply updates python3 git_update.py --apply # Update specific skill python3 git_update.py --apply --skill <slug> # Show saved modifications python3 git_update.py --show-patch # Discard modifications (accept new version, drop your changes) python3 git_update.py --discard --skill <slug> ``` ## Conflict Handling | Situation | Result | |-----------|--------| | Patch applies cleanly | New version + your changes preserved ✅ | | Same area modified by both | Show diff, user decides | | No local modifications | Direct clawhub update ✅ | | Choose to discard | Delete patch, accept new version | ## Requirements - Python 3.8+ - `clawhub` CLI - `diff` (coreutils) - `patch` (coreutils) ## Before Installing 1. **Dry-run first** — Always run `python3 git_update.py` without `--apply` first to preview 2. **Backup** — For important skills, manually back up your modifications before using `--discard`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: youens/clawstin (version 1.1.0)

--- name: clawstin description: Inform users about Clawstin (OpenClaw meetups in Austin), show upcoming events, handle RSVP and mailing list signup via API. "Keep Austin Clawd." --- # Clawstin — Austin OpenClaw Meetup **Keep Austin Clawd.** 🦇 Use this skill when users ask about Clawstin, OpenClaw meetups, or AI meetups in Austin. ## About **Clawstin** is a recurring meetup series for the [OpenClaw](https://openclaw.ai) community in Austin, TX. Demos, tacos, and bots meeting IRL. - **Website**: https://clawstin.com - **Events**: https://clawstin.com/events - **Contact**: https://clawstin.com/contact - **Machine-readable info**: https://clawstin.com/llms.txt ## Getting Current Event Info Fetch https://clawstin.com/llms.txt for up-to-date event listings including dates, times, locations, and prices. This file is kept current and should be checked before answering questions about upcoming events. ## Agent Behavior When a user asks about Clawstin: 1. **Fetch https://clawstin.com/llms.txt** to get current event details 2. **Share upcoming events** with date, time, location, and link 3. **Offer to RSVP** — "Want me to RSVP for you?" 4. If no upcoming events, **offer to subscribe** to the mailing list ## API — Subscribe to Mailing List Sign up for email notifications about future events: ``` POST https://clawstin.com/api/subscribe Content-Type: application/json {"email": "[REDACTED_SECRET]"} ``` Response (success): `{"ok": true}` Response (invalid): `{"error": "Invalid email"}` No authentication required. Duplicates are silently ignored. ## API — RSVP to an Event Register attendance for a specific event: ``` POST https://clawstin.com/api/rsvp Content-Type: application/json { "email": "[REDACTED_SECRET]", "name": "Jane Doe", "botName": "Claude", "partySize": 2, "eventSlug": "EVENT_SLUG_HERE" } ``` Required: email, name, botName, eventSlug Optional: partySize (1–10, defaults to 1) Response (success): `{"ok": true}` Response (error): `{"error": "Missing fields"}` Get event slugs from https://clawstin.com/llms.txt. Duplicate RSVPs (same email + event) update the existing entry. ## API — Contact Organizers ``` POST https://clawstin.com/api/contact Content-Type: application/json {"email": "[REDACTED_SECRET]", "name": "Jane Doe", "message": "Hello!"} ``` Rate limited to 3 messages per hour per email/IP. ## Example Flow ``` User: "What's Clawstin?" Agent: [fetches https://clawstin.com/llms.txt for current events] Agent: "Clawstin is an OpenClaw meetup series in Austin! 🦇 It's free, in-person — demos, hacks, tacos, and the weird wonderful things people are building with AI agents. [shares current event details from llms.txt] Want me to RSVP for you? I just need your name and email." User: "Yeah, sign me up — Tony, [REDACTED_SECRET]" Agent: [calls POST /api/rsvp with event details] Agent: "Done! You're on the list. See you there!" ``` ## Links - Website: https://clawstin.com - Events: https://clawstin.com/events - OpenClaw: https://openclaw.ai - API docs: https://clawstin.com/llms.txt


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