OpenClaw/clawhub-security-signals huggingface dataset:
Skill: jinhuadeng/wechat-group-operator (version 0.1.0)

--- name: wechat-group-operator description: WeChat group operations automation for Windows desktop WeChat. Use when the user wants to run recurring group engagement workflows such as morning questions, afternoon follow-ups, and evening case posts in specified微信群, with content pools, posting history, and OpenClaw cron scheduling. Best for群运营、私域活跃、课程群维护、社群节奏 automation. Not for朋友圈 scraping or broad WeChat data extraction. --- # wechat-group-operator Use this skill to operate designated WeChat groups on a recurring schedule. ## What this skill provides - group whitelist config - content pools for questions / followups / cases - post history to reduce repeats - execution script for one action at a time - compatibility with OpenClaw cron jobs ## Files Core files: - `scripts/wechat_group_operator.py` - `assets/groups.json` - `assets/post_history.json` - `assets/content/questions.json` - `assets/content/followups.json` - `assets/content/cases.json` ## Supported actions - `morning_question` - `afternoon_followup` - `evening_case` ## Quick start ### Dry run ```bash python scripts/wechat_group_operator.py --action morning_question --dry-run python scripts/wechat_group_operator.py --action evening_case --group "Core突击龙虾🦞" --dry-run ``` ### Real send ```bash python scripts/wechat_group_operator.py --action morning_question python scripts/wechat_group_operator.py --action afternoon_followup python scripts/wechat_group_operator.py --action evening_case ``` ## Recommended workflow 1. Edit `assets/groups.json` to maintain target group whitelist 2. Edit content pools under `assets/content/` 3. Dry run first 4. Run real send manually 5. Attach to OpenClaw cron when stable ## OpenClaw routing guidance Map user intent like this: - “上午往群里抛一个问题” → `morning_question` - “下午追问一下群里讨论” → `afternoon_followup` - “晚上发一个案例到群里” → `evening_case` - “给指定群先预演一下今天会发什么” → add `--dry-run` ## Boundaries Current MVP assumes: - groups are manually whitelisted - content comes from maintained pools - one action runs at a time - sending uses existing desktop WeChat sender capability Do not claim support yet for: - automatic discovery of groups where the user is owner - automatic web research and content generation inside this skill - advanced WeChat content forwarding/media posting ## Read more when needed - `references/config.md` - `references/actions.md` - `references/cron-setup.md`

path: assets/content/followups.json
content: id: f-001
topic: AI
text: 如果只能二选一，你更站哪边？可以直接说结论。

id: f-002
topic: 创业
text: 说得再狠一点，你觉得这里面最容易被忽视的坑是什么？

id: f-003
topic: 社群运营
text: 有没有人愿意拿自己真实经历举个例子？一两句话就行。

id: f-004
topic: 抖音广告投放
text: 如果只能改一个变量来稳住量，你会优先改哪一个？素材、预算、出价，还是转化目标？

id: f-005
topic: 稳定起量
text: 大家实操里最常见的是起不来，还是起量后守不住？可以直接说你遇到最多的情况。

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: enihsago/image-gen-low-cost (version 1.0.5)

--- name: image-gen-low-cost description: 低成本 AI 图片生成 CLI 工具。支持文生图、图片编辑。触发词：生成图片、画图、AI 作图、文生图、图片编辑、imgen。 --- # Image Gen Low Cost - AI 图片生成 CLI 统一的命令行图片生成工具，支持任何 OpenAI 兼容的 API 端点。 ## 快速开始 ### 1. 获取 API Token 访问 [https://api.laozhang.ai/register/?aff_code=lfa0](https://api.laozhang.ai/register/?aff_code=lfa0) 注册，在控制台获取 token。新注册自动获得 $0.5 开发测试额度。 ### 2. 配置 Token ```bash # 使用 imgen config 命令（推荐） imgen config --token YOUR_API_TOKEN # 或使用环境变量 export IMGEN_TOKEN=YOUR_API_TOKEN ``` ### 2. 文生图 ```bash # 生成图片 imgen "一只可爱的猫咪在花园里玩耍" # 指定输出路径 imgen "夕阳下的海滩" -o beach.png # 只显示 URL 不保存 imgen "未来城市" --no-save # 使用不同模型 imgen "可爱的小狗" -m fast ``` ### 3. 图片编辑 ```bash # 编辑图片 imgen edit "https://example.com/cat.jpg" "把猫咪的毛色改成彩虹色" # 使用预设风格 imgen edit "https://example.com/photo.jpg" --style cartoon # 多图融合 imgen edit "https://a.jpg,https://b.jpg" "将两张图片融合" ``` ## 命令参考 ``` imgen "prompt" 文生图 imgen edit <url> "prompt" 图片编辑 imgen config --token <token> 配置 API Token imgen models 列出可用模型 ``` ### 选项 | 选项 | 说明 | |------|------| | `-m, --model <name>` | 模型选择 (cheap/fast/quality) | | `-o, --output <path>` | 保存到指定路径 | | `--size <size>` | 图片尺寸 (1024x1024 等) | | `--no-save` | 不保存，只打印 URL | | `-s, --style <style>` | 预设风格 | | `-v, --verbose` | 详细输出 | ## 模型 | 别名 | 模型 ID | 价格 | 说明 | |------|---------|------|------| | sora | sora_image | $0.01/张 | **默认**， Sora Image， URL 返回 | | gpt4o | gpt-4o-image | $0.01/张 | GPT-4o Image， URL 返回 | | cheap | gemini-2.5-flash-image | $0.025/张 | Nano Banana, base64, 1K 固定 | | fast | gemini-3.1-flash-image-preview | $0.045/张 | Nano Banana2, base64, 4K 支持 | | quality | gemini-3-pro-image-preview | $0.05/张 | Nano Banana Pro, base64, 4K + 高级特性 | > 💡 **默认使用 sora 模型 ($0.01/张)**，如 鍗 鍘- 追求极致性价比： 选 **sora** 或 **- 追求高质量稳定： 选 **cheap** | **- 追求 4K + 性价比： 选 **fast** | **- 追求最高质量 + 复杂指令： 选 **quality** | ## 预设风格 - `cartoon` - 迪士尼卡通风格 - `oil` - 古典油画风格 - `ink` - 中国水墨画风格 - `cyberpunk` - 赛博朋克霓虹风格 - `sketch` - 铅笔素描风格 - `watercolor` - 水彩画风格 ## API 端点 默认使用老张 API (`https://api.laozhang.ai/v1/chat/completions`)。 ```bash # 切换到其他 OpenAI 兼容端点 export IMGEN_API_URL=https://api.openai.com/v1/chat/completions # 使用其他兼容服务 export IMGEN_API_URL=https://your-api-endpoint.com/v1/chat/completions ``` > ⚠️ **重要**：如果你使用非老张 API 的 token，请确保设置对应的 `IMGEN_API_URL`，否则 token 会发送到错误的端点。 ## 安装 ```bash # 克隆或下载 skill 后 chmod +x scripts/imgen.js # 添加到 PATH（可选） ln -s $(pwd)/scripts/imgen.js /usr/local/bin/imgen ``` ## 环境变量 | 变量 | 说明 | |------|------| | `IMGEN_TOKEN` | API Token | | `IMGEN_API_URL` | 自定义 API 端点 | ## 注意事项 1. 返回的图片 URL 通常是临时的，建议及时保存 2. 默认保存到当前目录的 `generated-images/` 文件夹 3. Token 存储在 `~/.imgen/token`，权限为 600 ## 与 image-gen-cheap 的区别 | 特性 | image-gen-cheap | image-gen-low-cost | |------|-----------------|---------------------| | 实现 | Python 脚本 | Node.js CLI | | 依赖 | requests | 无外部依赖 | | API | 固定老张 API | 任意 OpenAI 兼容 | | 配置 | 独立 token 文件 | 统一 imgen config |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: hupmann86-cell/accessibility-v2 (version 1.0.0)

# Skill: accessibility-checker # Description: Prüft und verbessert Zugänglichkeit (WCAG-Konformität, Screenreader-Unterstützung). ## Workflow 1. **AUDIT:** Scanne UI-Komponenten auf Accessibility-Probleme. 2. **IDENTIFY:** Finde Verstöße gegen WCAG-Richtlinien (z.B. fehlende Alt-Texte, Keyboard-Navigation). 3. **FIX:** Implementiere Verbesserungen (ARIA-Labels, Farbkontraste, etc.). 4. **TEST:** Validiere mit Tools wie Lighthouse oder Screenreadern. ## Nutzen Verwende diesen Skill für barrierefreie Anwendungen.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cherokeeli/sloth-d2c-skills (version 1.0.2)

--- name: sloth-d2c-skills description: 将Figma设计稿转换为前端组件代码（Design to Code）。通过 `sloth d2c` CLI 获取设计稿数据，分片处理并生成最终代码。当用户提到Figma转代码、设计稿转代码、D2C、design to code、生成页面时使用。 allowed-tools: Bash, Task, Read, Write, Edit, Glob, Grep disable: false --- # Figma 设计稿转代码（D2C） ## 前置校验 ### 必需参数 | 参数 | 说明 | | ------- | -------------- | | fileKey | Figma 文件 Key | | nodeId | Figma 节点 ID | 缺少以上参数时，提示用户提供。 ### 可选参数 | 参数 | 默认值 | 使用时机 | | --------- | ------- | ------------------------------------------------------------------------------------------------------------------- | | framework | 自动 | 用户明确指定目标框架时传入，取值：`react` / `vue` / `ios-oc` / `ios-swift` / `kuikly` / `taro` / `uniapp` / `hippy` | | depth | 自动 | 仅当用户显式要求限制节点树遍历深度时传入，否则不加 | | local | `false` | **默认不要加**。仅当用户明确要求"使用本地缓存"才传入 | | update | `false` | 仅当用户明确表示"修改/更新之前生成的代码"时传入；新建代码时一律不传 | | silent | `true` | 默认静默。仅当用户要求交互式配置页面时显式传 `--silent=false` | > ⚠️ `local` 与 `update` 都是**显式触发**参数，默认一律不传。不要因为"为了更快"而主动加 `--local`——运行没有缓存会直接失败。 ### 环境检查 执行 `sloth --version` 确认 CLI 可用。不可用则跳转[错误排除](#错误排除)。 ## 执行流程 ``` Task Progress: - [ ] Step 1: 执行 sloth d2c CLI 生成 chunks - [ ] Step 2: 并行处理代码片段与聚合 - [ ] Step 3: 生成最终代码并写入文件 ``` ### Step 1：执行 `sloth d2c` CLI 在工作区根目录运行（Bash）： ```bash sloth d2c \ --file-key <fileKey> \ --node-id <nodeId> \ [--framework <react|vue|ios-oc|ios-swift|kuikly|taro|uniapp|hippy>] \ [--depth <n>] \ [--local] \ [--update] \ --silent --json ``` CLI 成功时以 JSON 形式输出到 stdout： ```json { "ok": true, "fileKey": "...", "nodeId": "...", "convertedNodeId": "...", "chunksDir": ".sloth/<fileKey>/<convertedNodeId>/chunks" } ``` - 解析 JSON 得到 `chunksDir` 与 `convertedNodeId`。 - `ok=false` 或非零退出码时跳转[错误排除](#错误排除)。 ### Step 2：并行处理代码片段与聚合 以 Step 1 返回的 `chunksDir` 为基础，启动多个 **sloth-d2c-agent** subagent，**并行执行**： | 任务 | 提示词路径 | | -------------------- | -------------------------------- | | 代码片段处理（多个） | `{chunksDir}/{index}.md` | | 聚合处理 | `{chunksDir}/codeAggregation.md` | 全部 Subagent 完成后进入下一步。 ### Step 3：生成最终代码并写入文件 主 Agent 收集第 2 步执行完毕的结果，结合读取 `{chunksDir}/finalGenerate.md` 的内容作为提示词转换代码，写入项目文件中。 ## 错误排除 | 错误场景 | 处理方式 | | ---------------------------- | ------------------------------------------------------------------------------------------------------ | | `sloth: command not found` | 执行 `sloth -v`：有版本号则提示配置 PATH；无版本号则执行 `npm install -g sloth-d2c-mcp` 安装 | | CLI 退出码非 0 / `ok:false` | 读取 JSON 中的 `error`/`message` 字段并展示给用户 | | 文件不存在（chunksDir 为空） | 提示用户检查 fileKey 和 nodeId 是否正确，**停止执行** | | 超时 | 建议用户先执行 `sloth server restart` 再重试；或增加 shell 超时配置 | | 403 错误 | 未配置有效 Figma Token，提示用户执行 `sloth config` 并配置 `mcp.figmaApiKey`，或使用 `--figma-api-key` | | 404 错误 | 设计稿未找到，提示用户核实 fileKey 和 nodeId | | Node 版本过低 | 检查用户 Node 版本是否 ≥ 18 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: crazypatient0/xiaohong-hot-tracker (version 1.0.0)

# SKILL.md - Content Hot Tracker ## Skill Metadata ```yaml name: Content Hot Tracker identifier: content-hot-tracker version: 1.0.0 description: 追踪多平台热点话题，分析趋势，为内容创作者提供选题建议和爆款方向 author: xiaohong tags: [内容创作, 热点, 社媒, 运营, 选题] categories: [productivity, social-media] ``` ## What This Skill Does 1. **热点追踪** - 实时监控微博、知乎、抖音、小红书、B站 等平台的热搜/热门话题 2. **趋势分析** - 分析话题热度趋势，识别上升期话题 3. **选题建议** - 根据热点生成内容选题方向 4. **竞品分析** - 分析同领域创作者的热门内容 5. **标题建议** - 提供爆款标题模板和写作方向 ## When to Use - 日常内容选题枯竭时 - 追热点不知道如何切入时 - 想了解行业最新动态时 - 不知道什么内容能火时 ## How It Works ### 1. 热点追踪 使用 web_search 工具搜索各平台实时热点： ``` 搜索格式: "{平台} 热搜榜 {日期}" 例如: "微博热搜 2026-03-13" / "抖音热门话题 今天" ``` ### 2. 趋势分析 分析话题热度： - 上升期话题：优先追，流量红利期 - 平稳期话题：可追，需差异化角度 - 下降期话题：避免，已过热度高峰 ### 3. 选题生成 根据热点提供 3-5 个具体选题方向： - 观点类：针对热点事件的观点输出 - 知识类：热点相关的知识科普 - 故事类：热点相关的真实经历/案例 - 盘点类：热点事件的综合整理 - 预测类：热点事件的未来展望 ### 4. 竞品分析 搜索同领域热门内容： - "关键词 + 爆款 / 10万+ / 点赞过万" - 分析高赞内容的共同特点 ### 5. 标题建议 提供 5-8 个备选标题： - 数字开头型 - 悬念型 - 对话型 - 情绪型 ## Output Format ### 热点汇总 ``` 📈 今日热点 TOP 5 1. [话题名称] - 热度: 🔥🔥🔥🔥🔥 平台: 微博/抖音/知乎 分类: 娱乐/科技/社会/美食 2. ... ``` ### 选题建议 ``` 📝 选题推荐 【观点类】 "从XX事件看XX现象，这才是真相" 【知识类】 "5分钟带你了解XX事件的来龙去脉" 【故事类】 "亲身经历：我是如何被XX事件影响的" ``` ### 标题库 ``` 🔥 爆款标题备选 1. "90%的人都搞错了XX，这才是正确答案" 2. "为什么XX突然爆火？答案和你想的不一样" 3. "看完这篇，你就明白XX为什么这么火" ``` ## Examples ### Example 1: 科技热点 ``` 输入: 追踪今天科技领域的热点话题 输出: 📈 科技热点 1. #AI Agent# - 热度 🔥🔥🔥🔥 2. #OpenClaw# - 热度 🔥🔥🔥 📝 选题建议 - "AI Agent 是什么？普通人如何利用它赚钱" - "深度测评：我用 AI Agent 自动化运营自媒体的一天" ``` ### Example 2: 美食热点 ``` 输入: 追踪今天的美食热点 输出: 📈 美食热点 1. #螺蛳粉# - 热度 🔥🔥🔥🔥🔥 2. #空气炸锅# - 热量 🔥🔥🔥 📝 选题建议 - "螺蛳粉的N种神仙吃法，最后一种太绝" - "空气炸锅万能食谱，收藏这一篇就够了" ``` ## Configuration ### 偏好设置 可以自定义以下设置： - 偏好平台：微博/抖音/小红书/知乎/B站 - 内容领域：科技/美食/职场/情感/娱乐 - 选题风格：深度/轻松/干货/故事 ### 设置命令 ``` # 设置偏好领域 设置内容领域为[科技] # 设置偏好平台 主要追踪[微博]和[抖音] ``` ## Limitations - 热点数据来自搜索，可能不完全实时 - 某些平台可能需要 VPN - 趋势分析基于公开数据，可能有偏差 ## Changelog ### v1.0.0 (2026-03-13) - 初始版本 - 支持多平台热点追踪 - 提供选题和标题建议 --- *此技能可以帮助自媒体创作者快速找到热门选题，提高内容产出效率*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shenruiyang/orbcafe-layout-navigation (version 1.0.1)

--- name: orbcafe-layout-navigation description: Build ORBCAFE application shell and navigation with CAppPageLayout, NavigationIsland, useNavigationIsland, i18n, markdown renderer, and CPageTransition using official examples patterns. Use for app frame, user menu, locale switching, navigation tree, and route transition UX, especially when UI renders but behavior looks inactive. --- # ORBCAFE Layout + Navigation ## Workflow 1. 先对照 `skills/orbcafe-ui-component-usage/references/module-contracts.md`，确认这是 `Hook-first` 模块。 2. 执行安装与基础壳层接入。 3. 用 `references/patterns.md` 选择 full shell 或 nav-only。 4. 用 `references/guardrails.md` 逐条检查 locale、hydration、菜单动作和动效。 5. 以官方 examples 的 layout/providers/page 骨架产出代码。 ## Installation and Bootstrapping (Mandatory) ```bash npm install orbcafe-ui @mui/material @mui/icons-material @mui/x-date-pickers @emotion/react @emotion/styled dayjs ``` 本仓库联调： ```bash npm run build cd examples npm install npm run dev ``` 参考实现： - `examples/app/layout.tsx` - `examples/app/providers.tsx` - `examples/app/_components/HomeDemoClient.tsx` - `examples/app/_components/StdReportExampleClient.tsx` ## Output Contract 0. `Mode`: `Hook-first`. 1. `Layout decision`: full shell vs nav-only. 2. `Code snippet`: app frame with minimal props. 3. `Runtime safety`: locale、hydration、route 高亮安全策略。 4. `Verify`: 菜单跳转、locale 切换、用户菜单动作、过渡动画。 5. `Troubleshooting`: 至少包含 3 条“页面看起来没反应”排查项。 ## Examples-Based Experience Summary - 在 App Router 下优先采用 `Server page -> Client page` 分层，不在 client 首屏直接消费 Promise 语义路由参数。 - `usePathname` 高亮逻辑使用 `mounted` 防抖策略，避免 SSR/CSR 首帧不一致。 - `CAppPageLayout` 内部负责壳层，业务页只注入 menu/user/logo/children，避免重复造壳。 - `CPageTransition` 持续使用 `160-260ms`，仅用 transform/opacity 变换，性能更稳。 - `Providers` 层集中挂载 `ThemeProvider + LocalizationProvider + GlobalMessage`，避免每页重复配置。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: heardlyapp/book-sum (version 1.0.0)

# book-sum Generate extended summaries of books with full content and key ideas. ## What it does - Return complete book summaries (no truncation) - Extract 3 key ideas with full narratives - Create actionable takeaways - Format as readable text summary - Provide comprehensive overview ## Installation ```bash clawhub install book-sum ``` ## Usage ```javascript const BookSumSkill = require('book-sum'); const skill = new BookSumSkill(); const result = skill.generateTextSummary({ title: 'Atomic Habits', author: 'James Clear', summary: 'Small changes compound into big results...' }); ``` ## Output Extended summary with: - Full book summary - 3 Key Ideas (complete content) - Actionable takeaways - Comprehensive overview ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jpengcheng523-netizen/jpeng-database-manager (version 1.0.0)

--- name: jpeng-database-manager description: "Database management skill supporting PostgreSQL, MySQL, SQLite, and MongoDB. Query, migrate, backup, and manage databases." version: "1.0.0" author: "jpeng" tags: ["database", "sql", "postgresql", "mysql", "mongodb", "backup"] --- # Database Manager Manage databases with support for multiple database systems. ## When to Use - User wants to query a database - Create backups and migrations - Manage database schemas - Import/export data ## Supported Databases - PostgreSQL - MySQL / MariaDB - SQLite - MongoDB ## Configuration ```bash # PostgreSQL export DB_TYPE="postgresql" export DB_HOST="localhost" export DB_PORT="5432" export DB_NAME="mydb" export DB_USER="postgres" export DB_PASS="password" # MongoDB export DB_TYPE="mongodb" export DB_URI="mongodb://localhost:27017/mydb" ``` ## Usage ### Execute query ```bash python3 scripts/db.py query "SELECT * FROM users LIMIT 10" ``` ### Execute from file ```bash python3 scripts/db.py query --file ./query.sql ``` ### Create backup ```bash python3 scripts/db.py backup --output ./backup.sql ``` ### Restore backup ```bash python3 scripts/db.py restore --input ./backup.sql ``` ### Run migration ```bash python3 scripts/db.py migrate --dir ./migrations/ ``` ### Export to CSV ```bash python3 scripts/db.py export \ --table users \ --format csv \ --output ./users.csv ``` ### Import from CSV ```bash python3 scripts/db.py import \ --table users \ --input ./users.csv ``` ## Output ```json { "success": true, "rows_affected": 10, "rows": [ {"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"} ] } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: arein/concierge (version 1.5.0)

--- name: concierge description: Find accommodation contact details and run AI-assisted booking calls version: 1.3.1 triggers: - find contact - hotel contact - accommodation contact - property contact - airbnb contact - booking contact - vrbo contact - expedia contact - direct booking - property email - property phone - call hotel - call property - direct booking call --- # Travel Concierge Find contact details (phone, email, WhatsApp, Instagram, etc.) for accommodation listings and place AI booking calls. ## Capabilities ### 1) Find contact details from a listing URL ```bash concierge find-contact "<url>" ``` ### 2) Place an autonomous phone call ```bash concierge call "+1-555-123-4567" \ --goal "Book a room for March 12-14" \ --name "Derek Rein" \ --email "[REDACTED_SECRET]" \ --customer-phone "+1-555-000-1111" \ --context "Prefer direct booking if rate beats Booking.com" ``` The `call` command now auto-manages infra by default: if local server is down, it starts `ngrok` + call server automatically and stops both when the call ends. ## Supported listing platforms - **Airbnb**: `airbnb.com/rooms/...` - **Booking.com**: `booking.com/hotel/...` - **VRBO**: `vrbo.com/...` - **Expedia**: `expedia.com/...Hotel...` ## Examples ### Find contacts for an Airbnb listing Run: ```bash concierge find-contact "https://www.airbnb.com/rooms/12345" ``` ### Start a call and control turns manually Run: ```bash concierge call "+1-555-123-4567" \ --goal "Negotiate a direct booking rate" \ --name "Derek Rein" \ --email "[REDACTED_SECRET]" \ --customer-phone "+1-555-000-1111" \ --interactive ``` ### JSON output for scripting (contact lookup) ```bash concierge find-contact --json "https://..." ``` ### Verbose output ```bash concierge --verbose find-contact "https://..." ``` ## Configuration The CLI stores configuration in: `~/.config/concierge/config.json5` ### Optional for contact lookup ```bash concierge config set googlePlacesApiKey "your-key" ``` ### Required for AI phone calls ```bash concierge config set twilioAccountSid "<sid>" concierge config set twilioAuthToken "<token>" concierge config set twilioPhoneNumber "+14155551234" concierge config set deepgramApiKey "<key>" concierge config set elevenLabsApiKey "<key>" concierge config set elevenLabsVoiceId "EXAVITQu4vr4xnSDxMaL" concierge config set anthropicApiKey "<key>" # Optional for auto ngrok auth concierge config set ngrokAuthToken "<token>" ``` Check values: ```bash concierge config show ``` ## Notes - Contact extraction uses publicly available information. - `call` validates local dependencies before dialing (`ffmpeg` with MP3 decode support, and `ngrok` when auto-infra is needed). - `call` runs preflight checks for Twilio, Deepgram, and ElevenLabs quota before dialing. - When auto infra is used, server/ngrok logs are written under `~/.config/concierge/call-runs/<run-id>/`.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: marlandoj/zouroboros-memory (version 3.0.0)

--- name: zouroboros-memory description: "Production-grade persistent memory for AI agents. Hybrid SQLite + vector search, decay classes, episodic memory, cognitive profiles, and MCP server." version: "3.0.0" compatibility: "OpenClaw, Claude Code, Codex CLI, any Node.js 22+ environment" metadata: author: marlandoj.zo.computer openclaw: emoji: "🧠" requires: bins: [node] install: - id: node-zouroboros-memory kind: node package: "zouroboros-memory" bins: [zouroboros-memory, zouroboros-memory-mcp] label: "Install Zouroboros Memory (npm)" homepage: https://github.com/AlaricHQ/zouroboros-openclaw --- ## Usage Install: `npm install zouroboros-memory` ### CLI ```bash npx zouroboros-memory --help npx zouroboros-memory init npx zouroboros-memory store --entity user --key name --value "Alice" --decay permanent npx zouroboros-memory search "project preferences" --limit 5 npx zouroboros-memory stats ``` ### MCP Server Add to your `.mcp.json`: ```json { "servers": { "memory": { "command": "npx", "args": ["zouroboros-memory-mcp", "--db-path", "~/.zouroboros/memory.db"] } } } ``` ### Programmatic API ```typescript import { init, storeFact, searchFacts, searchFactsHybrid, getStats } from 'zouroboros-memory'; import type { MemoryConfig } from 'zouroboros-memory'; const config: MemoryConfig = { enabled: true, dbPath: '~/.zouroboros/memory.db', vectorEnabled: false, ollamaUrl: 'http://localhost:11434', ollamaModel: 'nomic-embed-text', autoCapture: false, captureIntervalMinutes: 30, graphBoost: true, hydeExpansion: false, decayConfig: { permanent: Infinity, long: 365, medium: 90, short: 30 }, }; init(config); // Store a fact await storeFact({ entity: 'user', key: 'preference', value: 'dark mode' }, config); // Keyword search const results = searchFacts('dark mode', { limit: 5 }); // Hybrid search (RRF fusion of keyword + vector) const hybridResults = await searchFactsHybrid('user preferences', config, { limit: 10 }); ``` ## MCP Tools | Tool | Description | |------|-------------| | `memory_store` | Store a fact with optional decay class | | `memory_search` | Keyword or hybrid search | | `memory_episodes` | Create/search episodic memories | | `cognitive_profile` | Get/update entity cognitive profiles | | `memory_graph` | Query entity relationship graph | | `memory_procedures` | Query stored workflow procedures | | `memory_stats` | Database statistics | | `memory_delete` | Delete a fact by ID | | `memory_prune` | Remove expired facts | ## Decay Classes | Class | TTL | |-------|-----| | `permanent` | Never expires | | `long` | 365 days | | `medium` | 90 days (default) | | `short` | 30 days |


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xueyetianya/graphql-client (version 2.0.2)

--- name: "graphql-client" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Graphql Client concepts, best practices, and implementation patte..." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [graphql,client, reference] category: "devtools" --- # Graphql Client Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Graphql Client concepts, best practices, and implementation patte... No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


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