OpenClaw/clawhub-security-signals huggingface dataset:
Skill: fumarole16-afk/ai-bill-clawhub (version 2.2.5)

# AI Bill Intelligence Real-time billing dashboard for OpenClaw. Accurate token-based cost tracking across 12+ AI providers. ## 🚀 Installation ```bash openclaw skill install https://github.com/fumabot16-max/bill-project ``` ## 🛠 Usage The skill operates via a background collector. As an agent, you can help the user by: 1. **Reporting Usage**: Read `/root/.openclaw/workspace/bill_project/dist/usage.json` to summarize spending. 2. **Updating Balances**: Redirect the user to the `/setup` page or update `vault.json` on their behalf. 3. **Checking Health**: Ensure the `ai-bill` service and `collector.js` are running. ## ⚙️ Configuration - **Port**: Default is `8003`. - **Modes**: `prepaid`, `postpaid`, `subscribe`, `unused` (off). ## 📂 Managed Files (Declarations) This skill manages the following data files inside the `app/` directory: - `app/vault.json`: User-defined balances and payment modes. - `app/prices.json`: AI model pricing data. - `app/cumulative_usage.json`: Archived costs from expired sessions. - `app/dist/usage.json`: Real-time aggregated usage data for the dashboard. - `app/debug.log`: Collector activity logs. Built by Tiger Jung & Chloe (@fumarole16-afk). <!-- Sync trigger: Fri Feb 20 22:36:10 KST 2026 -->


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: 15814059255/kdniao-skill (version 1.0.2)

# 批量快递查询API-快递鸟 (kdniaoapi-skill) ## 技能元数据 | 属性 | 值 | |------|-----| | **名称** | 批量快递查询API-快递鸟 | | **Slug** | kdniaoapi-skill | | **版本** | v1.0.0 | | **来源** | ClawHub.ai (kdniao官方) | | **功能描述** | 支持通过快递鸟API实时查询运单轨迹信息；当用户需要查询快递物流状态、追踪运单进度或获取包裹配送详情时使用 | --- ## 任务目标 - **技能用途**：查询快递运单的实时物流轨迹信息 - **能力包含**：调用快递鸟API获取物流状态、解析轨迹数据、展示配送进度 - **触发条件**：用户提出查询快递物流、追踪运单、查看配送状态等需求 --- ## 前置准备 ### 依赖安装 ```bash pip install requests>=2.28.0 ``` ### 凭证配置 **必需环境变量**：`KUAIDI_BIRD_API_CREDENTIALS` **格式**：`CUSTOMER_CODE|APP_KEY`（使用竖线分隔） **获取方式**： 1. 访问快递鸟官网注册账号 2. 登录后进入"API管理"或"开发者中心" 3. 获取商户ID（CUSTOMER_CODE）和API密钥（APP_KEY） **配置示例**： ```bash # macOS/Linux export KUAIDI_BIRD_API_CREDENTIALS="1292092|993d0b97-07fa-478c-bfea-ca3597f2ce0f" # Windows PowerShell $env:KUAIDI_BIRD_API_CREDENTIALS="1292092|993d0b97-07fa-478c-bfea-ca3597f2ce0f" ``` --- ## 操作步骤 ### 标准流程 1. **确认运单信息** - 获取用户提供的运单号 - 验证运单号格式（通常为10-20位数字或字母数字组合） 2. **执行查询** ```bash python3 ~/.workbuddy/skills/kdniaoapi-skill/scripts/query_tracking.py --logistic-code <运单号> ``` 3. **解析与展示结果** - 物流状态（已揽收、运输中、派送中、已签收等） - 轨迹时间线（按时间顺序的物流节点） - 当前最新状态 ### 错误处理 | 场景 | 处理方式 | |------|----------| | 运单号无效 | 提示用户检查运单号是否正确 | | 缺少API凭证 | 检查环境变量是否已设置 | | 凭证格式错误 | 确认格式为 `CUSTOMER_CODE\|APP_KEY` | | API异常 | 检查凭证配置，建议稍后重试 | | 没有可用套餐 | 需在快递鸟官网开通套餐 | --- ## 使用示例 ```bash # 查询顺丰运单 python3 ~/.workbuddy/skills/kdniaoapi-skill/scripts/query_tracking.py --logistic-code SF1234567890 # 查询圆通运单 python3 ~/.workbuddy/skills/kdniaoapi-skill/scripts/query_tracking.py --logistic-code YT1234567890123 ``` --- ## 注意事项 - 不同快递公司的查询结果格式可能略有差异 - 建议在查询前提醒用户确认运单号准确性 - API使用默认正式环境地址：`https://api.kdniao.com/api/dist`


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

--- name: book-laundry description: Book laundry services through Lokuli MCP. Use when user needs to find and book laundry. Triggers on requests like "book a laundry", "find laundry near me", or any laundry service request. --- # uook laundry Book laundry 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": "laundry", "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: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lz6060788/fridge-keeper (version 1.0.0)

--- name: fridge-keeper description: 冰箱食物管理助手。用于记录食物的入库与出库、追踪保质期、提醒临期食品、提供烹饪建议。当用户说"我的冰箱里有什么"、"检查过期食品"、"推荐菜谱"、"记录入库"或"记录出库"时触发此技能。 --- # 冰箱管家 (Fridge Keeper) ## 概述 管理冰箱食物的入库、出库、保质期追踪与烹饪建议。支持本地文件存储和远程数据库两种模式。 ## 核心能力 1. **食物入库** - 记录食物名称、数量、入库时间、保质期 2. **食物出库** - 记录食物被消耗或丢弃 3. **查询食物** - 查看当前冰箱中的所有食物 4. **临期检查** - 识别将在指定天数内过期的食物 5. **烹饪建议** - 根据现有食材和临期情况推荐菜谱 ## 数据模型 ### 食物条目 (FoodItem) ```json { "id": "uuid", "name": "食物名称", "quantity": 数量, "unit": "单位(克/个/颗等)", "storage_location": "冷藏室/冷冻室/保鲜层", "入库时间": "2024-01-15T10:30:00Z", "保质期天数": 7, "过期时间": "2024-01-22T10:30:00Z", "状态": "active|consumed|expired" } ``` ### 配置 (Config) ```json { "storage_type": "local|database", "local_path": "./fridge_data.json", "database": { "type": "mysql|postgres|mongodb", "host": "localhost", "port": 3306, "database": "fridge", "username": "user", "password": "pass" }, "expiry_warning_days": 3, "reminder_schedule": "daily|weekly|custom" } ``` ## 工作流程 ### 首次初始化 当用户首次使用或说"初始化配置"、"设置冰箱"时： 1. **询问存储方式** - "请选择数据存储方式：(1) 本地文件 (2) 远程数据库" - 若选本地：使用默认路径 `./fridge_data.json` 或询问用户指定路径 - 若选数据库：询问数据库类型、连接信息 2. **配置提醒** - "设置临期提醒天数（默认3天）" - "设置提醒方式：每日/每周/自定义" 3. **保存配置** - 将配置写入 `~/.claude/skills/fridge-keeper/config.json` ### 食物入库 当用户说"入库"、"放进冰箱"、"添加食物"时： 1. **解析食物信息** - 名称（必填） - 数量和单位 - 存放位置（冷藏室/冷冻室/保鲜层） - 保质期天数（冷藏食物必填，冷冻食物可省略或设更长） 2. **计算过期时间** - 过期时间 = 当前时间 + 保质期天数 - 冷冻食品默认保质期 180 天 3. **写入存储** - 生成唯一 ID - 追加到存储 4. **确认入库** - 汇报入库成功，包含过期时间 ### 食物出库 当用户说"出库"、"吃掉"、"拿出冰箱"、"消耗"时： 1. **识别食物** - 匹配用户提到的食物名称 - 若有多个匹配，询问具体是哪个 2. **更新状态** - 消耗：状态改为 `consumed`，记录消耗时间 - 丢弃：状态改为 `expired`，记录丢弃时间 3. **确认出库** - 汇报出库成功 ### 查询食物 当用户说"有什么"、"查看冰箱"、"当前食物"时： 1. **读取活跃食物** - 从存储读取状态为 `active` 的条目 2. **按位置分组** - 冷藏室 / 冷冻室 / 保鲜层 3. **格式化输出** - 列出每类食物，包含名称、数量、剩余保质期 ### 临期检查 当用户说"临期"、"快过期"、"检查过期"时： 1. **计算临期阈值** - 临期 = 过期时间 - 警告天数 ≤ 当前时间 2. **筛选临期食物** - 状态为 `active` - 过期时间 ≤ 当前时间 + 警告天数 3. **按紧急程度排序** - 已过期 > 今天过期 > 明天过期 > 即将临期 4. **输出警告** - 清晰标注紧急程度 ### 烹饪建议 当用户说"推荐菜谱"、"做什么菜"、"建议"时： 1. **获取当前食物** - 读取所有 `active` 状态的食材 2. **识别临期食材** - 优先考虑即将过期的食材 3. **AI 自主推荐** - 利用自身的烹饪知识，根据现有食材推荐合适的菜谱 - 优先使用临期食材 - 说明每道菜使用哪些食材（含临期标注） 4. **输出建议** - 推荐 2-3 道菜 - 说明每道菜使用哪些食材（含临期标注） ## 存储操作 核心存储操作由 `scripts/storage.py` 实现，支持本地文件和数据库两种模式。Claude 调用相应方法： - `load_config()` - 读取配置 - `save_config(config)` - 保存配置 - `load_items()` - 读取所有食物条目 - `save_item(item)` - 添加新条目 - `update_item(id, updates)` - 更新条目 - `delete_item(id)` - 删除条目


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

# Swiss Transport Real-time Swiss public transport data: train, bus, and tram schedules from SBB and regional operators. ## search_stations Find stations by name. Returns station ID, name, and coordinates. ## get_connections Route planning between two locations. Returns departure/arrival times, duration, number of transfers, platform numbers, delays, and a breakdown of each journey section (which train/bus, intermediate stops). ## get_stationboard Live departure board for any station. Shows the next departures with line name, category, destination, platform, and delay information. ## Example: Zurich to Bern connections ```bash curl -X POST https://gateway.pipeworx.io/swisstransport/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_connections","arguments":{"from":"Zurich HB","to":"Bern","limit":3}}}' ``` ## Example: departures from Geneva ```bash curl -X POST https://gateway.pipeworx.io/swisstransport/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_stationboard","arguments":{"station":"Geneve","limit":5}}}' ``` ```json { "mcpServers": { "swisstransport": { "url": "https://gateway.pipeworx.io/swisstransport/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jaskies/backup-full-system-jaskies (version 1.2.0)

--- name: backup-system description: Thực hiện sao lưu toàn bộ hệ thống OpenClaw (bao gồm database, cấu hình và bộ nhớ) rồi tải lên đám mây. Yêu cầu cài đặt rclone hoặc công cụ upload tương ứng. --- # Backup System: Sao lưu hệ thống toàn diện Skill này giúp em bảo vệ an toàn toàn bộ dữ liệu của chúng mình bằng cách nén và tải lên đám mây (Google Drive/OneDrive) một cách tự động. ## Yêu cầu hệ thống Để skill này hoạt động, máy của anh cần có: 1. **Bash Shell**: Môi trường thực thi script chuẩn trên Linux. 2. **tar & zip**: Để đóng gói và nén dữ liệu. 3. **rclone** (Khuyến nghị): Công cụ mạnh mẽ để đồng bộ dữ liệu lên đám mây. ### Lệnh cài đặt nhanh (cho Linux/Ubuntu) Anh chạy lệnh này để cài đặt các công cụ cần thiết nhé: ```bash # Cài đặt công cụ nén và rclone sudo apt update && sudo apt install -y tar zip rclone ``` ### Cấu hình Đám mây (Rclone) Trước khi chạy lần đầu, anh cần cấu hình nơi lưu trữ: ```bash rclone config # Làm theo hướng dẫn để thêm Google Drive hoặc tài khoản lưu trữ của anh. # Đảm bảo đặt tên remote trùng với cấu hình trong file script backup. ``` ## Quy trình thực hiện 1. **Quét dữ liệu:** Script tự động liệt kê các thư mục quan trọng cần sao lưu (workspace, configs, memory). 2. **Nén & Đóng gói:** Sử dụng `tar` để tạo bản lưu trữ gọn nhẹ. 3. **Tải lên:** Sử dụng lệnh thực thi bên trong script để đẩy file lên đám mây. ## Lệnh thực thi sao lưu ```bash bash skills/public/backup-system/scripts/backup_full_system.sh ``` ## Lưu ý - **Thời gian:** Quá trình sao lưu có thể mất từ 1-5 phút tùy thuộc vào dung lượng bộ nhớ của em. - **Bảo mật:** Script đã được thiết kế để không làm lộ các mã khóa bí mật trong quá trình nén nếu được cấu hình đúng. - **Định kỳ:** Anh có thể kết hợp với công cụ `cron` để đặt lịch sao lưu tự động hàng tuần.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: unisound-llm/unisound-med-clinical-decision (version 1.0.0)

--- name: med-llm-clinical-decision description: 专病诊疗、基层处置、全科辅助、结局分析、病情分析与慢病管理。通过 --task 选择场景；仅含 `scripts/run.py`，可独立拷贝部署。 metadata: { "openclaw": { "emoji": "🩺" } } --- # 临床决策与健康管理 ## 概述 面向临床决策与健康管理类问题，给出结构化建议并强调需结合实际情况。 具体场景通过 **`--task`** 切换系统提示词（见下表），能力依赖内部医疗大模型；集成方也可通过 `--system-prompt` 完全自定义。 | `--task` | 能力 | | --- | --- | | `analysis` | 病情分析与量表计算问答 | | `care` | 基层诊疗处置建议 | | `outcome` | 治疗效果与结局分析 | | `phm` | 慢病个性化健康管理 | | `primary` | 基层全科诊疗辅助 | | `treat` | 专病诊疗方案制定 | 本能力与具体业务请求解耦：平台或调用方如何组织用户输入由集成方决定；此处只约定**如何把题目交给脚本**（见下）。 ## 题目输入（三选一） 1. **`--question`**：直接传入完整题干字符串。 2. **`--input PATH`**： - `.jsonl`：每行一个 JSON 对象，须含非空字符串字段 `question`；可用 `--index` / `--batch` / `--match-id` 选取记录； - `.json`：单个对象，须含 `question`； - 其他扩展名：按 UTF-8 纯文本整文件作为题干。 - 传 `-` 时从 **stdin** 读取一条 JSON（含 `question`）或纯文本。 3. **stdin**（且未传 `--question` / `--input`、且 stdin 非 TTY）：读入 JSON 对象或纯文本，规则同 `-`。 可在 JSON 的 `other.task` 中指定任务类型（与 `--task` 等价；命令行 `--task` 优先）。 ## 模型 - 默认 `u1-insuremed`，接口 `https://maas-api.hivoice.cn/v1/chat/completions`（可通过参数覆盖）。 ## 用法示例 ```bash # 指定任务类型与题干 python3 scripts/run.py --task analysis --question "题干……" --appkey YOUR_KEY # 从 jsonl 取题（other.task 可省略若命令行已传 --task） python3 scripts/run.py --task analysis --input /path/to/items.jsonl --index 0 --appkey YOUR_KEY ``` ## 参数 - `--task`：**必填**（除非 `other.task` 已给出）。见上表。 - `--question` / `--input`：见上，与 stdin 三选一（`--question` 与 `--input` 不可同时使用）。 - `--appkey STRING`：**必填**（`--dry-run` 时除外）。内部医疗大模型鉴权 key。 - `--index`、`--match-id`、`--batch`：仅对 `.jsonl` 生效。 - `--dry-run`：不调用模型，仅输出解析后的题目 JSON。 - `--model`、`--api-url`、`--temperature`、`--timeout`、`--system-prompt`：调模型参数。 - `--output PATH`：另存完整 JSON（批量为 NDJSON）。 - `--text-only`：标准输出仅打印模型答案文本。 ## 输出 默认可解析 JSON：含 `status`、`task`、`task_title`、`question`、`answer`、`record_index`、`meta`、`model`、`input_mode`、`input_path` 等；`skill` 为本 skill 标题。 ## 合规说明 输出为模型辅助信息，不构成正式诊疗决策；涉及真实患者数据须先脱敏并遵守院内流程。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: xejrax/brew-install (version 1.0.0)

--- name: brew-install description: "Install missing binaries via dnf (Fedora/Bazzite package manager)" metadata: { "openclaw": { "emoji": "📦", "requires": { "bins": ["dnf"] }, "install": [], }, } --- # Brew Install Install missing binaries via dnf, the Fedora/Bazzite package manager. Despite the name, this skill wraps `dnf` on Bazzite rather than Homebrew. ## Commands ```bash # Install a package brew-install <package> # Search for a package brew-install search <query> ``` ## Install No installation needed. `dnf` is the default package manager on Fedora/Bazzite and is always present.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yangdaowan/task-persistence (version 1.1.0)

--- name: task-persistence description: Task continuity, session snapshots, and gateway restart recovery. Use when starting long-running tasks (register them), after gateway restart (check for interrupted tasks), or when user asks about task status/recovery. Trigger on "resume", "任务恢复", "重启后", "未完成任务", or before/after any multi-step operation. metadata: { "openclaw": { "emoji": "🔄", "requires": { "bins": ["python3"] } } } --- # Task Persistence 会话状态管理、任务持久化和网关重启恢复。 ## 核心场景 | 触发条件 | 动作 | |---------|------| | 网关刚重启 | 运行 `check-restart`，汇报恢复状态 | | 开始长时间任务 | 用 `task_manager.py add` 注册任务 | | 用户问"有没有未完成的任务" | 运行 `task_manager.py list` | | 任务完成 | 运行 `task_manager.py complete` | | 任务被中断 | 运行 `task_manager.py recover` | ## 变量说明 所有脚本中的 `{baseDir}` = 本技能的目录路径（SKILL.md 所在目录）。 工作区路径从环境变量 `OPENCLAW_WORKSPACE` 读取，默认 `/workspace`。 ## 快速命令 ### 网关重启后检查（每次重启后必须执行） ```bash python3 {baseDir}/scripts/main.py --mode check-restart --workspace /workspace ``` ### 查看所有活跃任务 ```bash python3 {baseDir}/scripts/task_manager.py --action list --workspace /workspace ``` ### 注册新任务（开始长时间操作前） ```bash python3 {baseDir}/scripts/task_manager.py \ --action add \ --task-id "task_$(date +%s)" \ --task-type "file_processing" \ --description "处理大量文件" \ --priority normal \ --workspace /workspace ``` ### 标记任务完成 ```bash python3 {baseDir}/scripts/task_manager.py \ --action complete \ --task-id <task_id> \ --workspace /workspace ``` ### 从崩溃/重启中恢复任务 ```bash python3 {baseDir}/scripts/task_manager.py \ --action recover \ --workspace /workspace ``` ### 任务队列状态 ```bash python3 {baseDir}/scripts/task_manager.py --action status --workspace /workspace ``` ### 会话快照（保存当前状态） ```bash python3 {baseDir}/scripts/session_snapshot.py \ --workspace /workspace \ --action list ``` ### 网关监控状态 ```bash python3 {baseDir}/scripts/main.py --mode status --workspace /workspace ``` ## 重启后工作流 当 heartbeat 或用户提到"网关重启"时，执行： 1. `python3 {baseDir}/scripts/main.py --mode check-restart --workspace /workspace` 2. 解析输出中的 `active_tasks` 和 `recovered_tasks` 3. 向用户汇报：哪些任务被恢复、哪些需要手动继续 ## 文件结构 ``` /workspace/ tasks/ task_queue.json # 任务队列 completed/ # 已完成任务 failed/ # 失败任务 memory/ session_snapshots/ # 会话快照 persistence/ active_tasks.json # 持久化任务 gateway_state.json # 网关状态 ``` ## 注意事项 - 脚本使用标准库，无需额外安装依赖 - 所有数据持久化在 workspace 目录下，重启后不会丢失 - `task_manager.py` 是统一入口，推荐优先使用 - `gateway_monitor.py` 的后台监控模式（`full` mode）在沙箱中不适用，用 `check-restart` 代替

path: dist/scripts/__init__.py
content: # Task Persistence Skill - Main package

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 15028191702/tarot-reader (version 1.0.0)

--- name: tarot-reading description: 专业塔罗占卜服务。当用户提及塔罗、抽牌、占卜等关键词时激活，提供单张牌或时间之流（过去-现在-未来）牌阵的解读。使用经典韦特塔罗体系，支持正逆位随机抽取，以温暖治愈的文字风格提供理性洞察。 --- # 塔罗占卜 你是一位专业的塔罗占卜师，使用经典韦特塔罗牌为用户提供指引。 ## 核心原则 - **温暖而理性**：文字风格温暖治愈，但内核有理性洞察 - **如实解读**：不回避负面含义，同时强调成长机会和建设性建议 - **尊重用户**：让用户自己输入问题，不做过度引导 ## 支持的牌阵 1. **单张牌** - 每日指引或快速解答 2. **时间之流**（三张牌）- 过去、现在、未来 ## 工作流程 ### 步骤1：确认问题 简洁地邀请用户分享他们想问的问题： > "请静心思考你想询问的问题，然后告诉我。" ### 步骤2：选择牌阵 根据上下文判断使用单张牌还是时间之流： - 用户说"抽一张"或"每日指引" → 单张牌 - 用户说"塔罗占卜"或"帮我抽牌" → 时间之流 ### 步骤3：抽牌与解读 **随机机制**： - 从78张韦特塔罗牌中随机抽取 - 正逆位完全随机（各50%概率） **输出格式**（结构化）： ``` 🌙 你的牌阵 【过去】魔术师（正位） ... 【现在】女祭司（逆位） ... 【未来】星星（正位） ... ✨ 整体指引 ... ``` **解读要求**： - 每张牌100-150字的解读 - 结合正逆位含义 - 关联用户的具体问题 - 温暖文字 + 理性内核 - 负面牌不回避，强调成长 ### 步骤4：询问反馈 解读完成后询问： > "这次解读对你有帮助吗？是否需要我保存这次占卜记录？" ## 牌意参考 需要查阅具体牌意时，阅读 [references/tarot-meanings.md](references/tarot-meanings.md) ## 注意事项 - 不要替用户编造问题 - 不要过度解读或给出绝对化的预测 - 强调塔罗是指引而非命运定论 - 保持神秘感和仪式感的适度平衡


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: halfmoon82/skill-trigger-v2 (version 2.1.4)

--- name: skill-trigger-v2 description: Framework-only skill trigger reference. Runtime matching implementation has been removed from the published artifact. version: 2.1.3 author: halfmoon82 tags: [framework, documentation, skill-trigger, intent-matching] --- # Skill Trigger V2 — Framework Only This published version preserves the interface contract and workflow description only. ## What remains - Matching intent to a skill conceptually - Declaration format - Dependency expectations - Human-readable integration notes ## What is removed - Runtime matching code - Local setup scripts - Executable installation flow - Any runnable examples ## Output contract - If used as a guide, emit the routing declaration first line - Treat this artifact as documentation, not executable software ## Replacement note This is a corrective, lightweight release for the public registry.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: terrycarter1985/blog-content-processor (version 10.0.0)

--- name: blog-content-processor description: Process blog content to extract videos and generate GIF previews. Works with blogwatcher, video-frames, and gifgrep skills. tags: ['blog', 'video', 'gif', 'content', 'processing'] --- # Blog Content Processor Extracts video content from blog/RSS feeds and generates GIF preview clips. ## Features - Parses RSS/Atom feeds via `rss-parser` - Extracts embedded video URLs from blog entries - Generates GIF previews from video segments using `ffmpeg-static` - Integrates with blogwatcher, video-frames, and gifgrep skills ## Usage ```bash node index.js --feed <rss-url> --output ./gifs ``` ## Dependencies - `rss-parser` ^3.12.0 - `ffmpeg-static` ^5.1.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: evezart/mcp-dev-toolkit (version 1.0.0)

--- name: mcp-dev-toolkit description: Build, test, and deploy MCP (Model Context Protocol) tools for developer workflows. Use when creating MCP servers, adding tool definitions, integrating with databases, file systems, or APIs via MCP. Covers TypeScript and Python MCP server scaffolding, tool registration, transport configuration (stdio, HTTP/SSE), and testing patterns. --- # MCP Dev Toolkit Scaffold and build MCP (Model Context Protocol) servers with common developer tools. ## Quick Start Scaffold a new MCP server: ```bash npx @anthropic/mcp-cli create my-server ``` Or use the scaffold script for a batteries-included setup: ```bash bash scripts/scaffold.sh my-server python # Python MCP server bash scripts/scaffold.sh my-server typescript # TS MCP server ``` ## Architecture An MCP server exposes **tools** that LLMs can call. Each tool has: - `name`: Tool identifier - `description`: What the tool does (LLM reads this to decide when to use it) - `inputSchema`: JSON Schema for parameters ## Tool Patterns ### Database Access See `references/database-tools.md` for PostgreSQL, SQLite, and MongoDB tool patterns. ### File Management See `references/file-tools.md` for read, write, search, and transform tools. ### API Integration See `references/api-tools.md` for REST/GraphQL wrapping patterns. ## Transport Types - **stdio**: Local CLI integration (default for Claude Desktop) - **HTTP/SSE**: Remote server (for shared/team deployments) - **Streamable HTTP**: Modern transport (MCP spec 2025-03) ## Testing Test tools locally before deploying: ```bash npx @anthropic/mcp-cli inspect ./my-server ``` ## Publishing Publish to npm (TypeScript) or PyPI (Python), then configure in MCP clients.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaojiou176/openclaw-readonly-consumer (version 1.0.0)

--- name: openclaw-readonly-consumer description: Keep an OpenClaw-style local runtime on the snapshot-first, thin-BFF-first, read-only Campus Copilot path. --- # OpenClaw-Style Read-Only Consumer Use this skill when your local operator/runtime behaves like an OpenClaw-style shell and you want Campus Copilot to stay on the **snapshot-first / thin-BFF-first / read-only** side. Think of Campus Copilot as the labeled binder on the desk: - Campus Copilot provides structured academic context - your local runtime decides how to consume that context - Campus Copilot does **not** become a browser-control plugin or write-capable operator loop ## Start with the narrowest path 1. If you need cross-site health, provider readiness, read-only ask, or export tools, start with: - `pnpm --filter @campus-copilot/mcp-server start` 2. If you only need one site's snapshot records, use: - `pnpm --filter @campus-copilot/mcp-readonly start:<site>` 3. If you only need local provider status or cited-AI chat on the Campus semantic contract, use: - `pnpm start:api` ## Inputs - one local snapshot such as `examples/workspace-snapshot.sample.json` - one local runtime that can launch stdio commands - optional support for the same `mcpServers` JSON shape used by Codex/Claude-style MCP setups ## Recommended flow 1. Export a safe snapshot path: - `export CAMPUS_COPILOT_SNAPSHOT="$PWD/examples/workspace-snapshot.sample.json"` 2. Start with the generic MCP server unless you already know you only need one site. 3. If your runtime explicitly accepts the same `mcpServers` JSON shape, adapt: - `examples/integrations/codex-mcp.example.json` - `examples/integrations/claude-code-mcp.example.json` 4. If it does **not** accept that exact shape, stay with the direct command snippets from: - `examples/openclaw-readonly.md` 5. Keep all claims snapshot-scoped and read-only. ## Good fit - OpenClaw-style local consumers that need one truthful starting recipe - imported snapshot review without reopening live browser automation - read-only MCP or local BFF consumption over Campus-owned semantics ## Not a fit - live browser takeover - posting, replying, or submitting on external sites - inventing an official plugin or marketplace contract - hosted autonomy positioning ## Recommended repo-local references - `examples/openclaw-readonly.md` - `examples/README.md` - `skills/read-only-workspace-analysis/SKILL.md` - `skills/site-mcp-consumer/SKILL.md`


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