OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bibaofeng/stock-dividend-zh (version 1.0.0)

--- name: stock-dividend-zh description: "通过 AISA 评估股票的股息率、派息安全性、增长情况和收益质量。触发条件：当用户关心股息安全、收益型投资、股息增长或股息贵族筛选时使用。" author: aisa version: "1.0.0" license: Apache-2.0 user-invocable: true primaryEnv: AISA_API_KEY requires: env: - AISA_API_KEY bins: - python3 metadata: openclaw: primaryEnv: AISA_API_KEY requires: env: - AISA_API_KEY bins: - python3 --- # 股息分析 ## 何时使用 - 通过 AISA 评估股票的股息率、派息安全性、增长情况和收益质量。触发条件：当用户关心股息安全、收益型投资、股息增长或股息贵族筛选时使用。 ## 不适用场景 - 当用户明确要本地浏览器 Cookie、密码、Keychain 或其他本地敏感凭据时，不要使用这个 skill。 - 当问题与该 skill 的主题无关时，优先选择更贴切的 skill。 ## 核心能力 - 重点分析股息安全性、覆盖率、增长和收益质量。 ## 快速开始 ```bash export AISA_API_KEY="your-key" ``` ## 运行方式 首选运行路径是仓库内置的 Python 客户端： ```bash python3 scripts/dividends.py ``` ## 示例请求 - 评估 JNJ 和 PG 哪个更适合做稳定分红配置。 ## 说明 - 仅供信息参考，不构成投资建议。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: biilow-bailang/lobster-werewolf (version 0.5.1)

--- name: lobster-werewolf version: 0.1.1 description: | Lobster Werewolf — play werewolf (狼人杀) with 8 NPC lobsters. 激活条件：用户提到"狼人杀"、"开一局"、"玩游戏"、"werewolf" read_when: - 用户让你玩狼人杀 / 开一局 / 参赛 - 用户问狼人杀的规则或你的胜率 - 用户想看你玩一局的战报 - 用户说 "你去和 NPC 龙虾玩一局" metadata: {"openclaw":{"emoji":"🐺","category":"game","homepage":"https://github.com/Biilow-Bailang/lobster-republic/tree/master/plugins/lobster-werewolf"}} --- # 🐺 Lobster Werewolf 你可以参加 9 人标准板狼人杀，你是其中一只龙虾，其他 8 只是 NPC（同样由 LLM 驱动）。 ## Tools 5 个工具： - `werewolf_health` — 确认 werewolf server 运行中 - `werewolf_play_game` — **一站式跑完整一局**（阻塞 3-10 分钟，返回战报） - `werewolf_create_table` — 创建桌子（不启动） - `werewolf_status` — 查询桌子状态 - `werewolf_events` — 获取完整事件流 ## 最常见用法 **主人让你玩狼人杀** → 调 `werewolf_play_game`： ``` werewolf_play_game({ my_name: "白小浪" }) ``` 返回字段示例： - `winner`: "好人" 或 "狼人" - `human_role`: 你这局的角色（狼人/预言家/女巫/猎人/村民） - `total_days`: 游戏进行了几天 - `dead`: 死亡龙虾列表 - `top_speeches`: 本局 top 3 最长发言 - `night_deaths`: 每晚谁死了 - `day_outs`: 每天被投出的人 ## 规则（9 人标准板） - **角色**: 3 狼人 + 1 预言家 + 1 女巫 + 1 猎人 + 3 村民 - **夜晚**: 狼人共同刀人 → 预言家查验 → 女巫用药 - **白天**: 死亡公告 → 每人发言 → 投票 → 出局最高票 → 猎人可能开枪 - **女巫规则**: 每晚只能用 1 瓶药，非第 1 晚不能自救 - **猎人规则**: 被狼刀或投票出局可开枪，被毒不能 - **胜利**: 好人胜 = 所有狼死；狼胜 = 所有神死（屠神）或所有村民死（屠民） ## 测试 第一次调用前先 `werewolf_health` 确认服务器 OK。 - 默认 serverUrl 指向 `http://47.85.184.157:8801`（lobster-republic 美国 Virginia 测试服务器） - 如果你的 OpenClaw 配了 `plugins.entries.lobster-werewolf.config.serverUrl`，会用你自己的 URL 覆盖（例如你在本机跑了 werewolf_server.py） - 如果返回 `_network_error`，说明那台服务器不可达，先换个 serverUrl 或通知插件作者 ## 背景 这个 plugin 底层来自 lobster-werewolf simulator v1.15（23 个自动化测试 + 100+ 局真实 LLM 对战）。 服务端是 Python stdlib http.server，零 pip 依赖，5 个 .py 文件即可运行，你也可以在本地自建。 插件发布方 (lobster-republic) 运营一个美国 Virginia 的共享测试服务器供默认使用。


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: autoxj/gitcode (version 1.3.0)

--- name: gitcode description: "Fetch and query data from GitCode platform via its REST API: repositories, branches, issues, pull requests, commits, tags, users, organizations, search, webhooks, members, releases and more. 查询 GitCode 上的仓库、分支、议题、PR、提交、标签、用户、组织等数据。Python 3.7+ standard library only." metadata: {"openclaw": {"requires": {"env": ["GITCODE_TOKEN"]}, "primaryEnv": "GITCODE_TOKEN", "optional": true}} --- # GitCode API ## 何时使用 查仓库/分支/议题/PR/提交/标签、用户与组织、搜索、Webhook、成员、发布等 GitCode 数据。**全量 API 一览**（每个接口的功能说明与可获取信息）见 **[reference.md](reference.md)**，为本 skill 唯一 API 说明文档。 ## 请求与认证 - **Base URL**：`https://api.gitcode.com/api/v5` - **认证**：以下接口须带 Token（否则可能 400/401）：pulls、issues、branches、commits、仓库详情与文件列表、tags、releases、members、webhooks 等。 - **Token 获取方式**（按优先级）： 1. **用户直接提供**：用户可在问题中直接提供 token，如"获取 gitcode 上 owner/repo 仓库的 star 数量，token 是 xxxxx" 2. **环境变量**：从 `GITCODE_TOKEN` 读取，顺序：(1) 当前进程 `$env:GITCODE_TOKEN`（PowerShell）或 `$GITCODE_TOKEN`（bash）；(2) Windows 用户变量 `[Environment]::GetEnvironmentVariable('GITCODE_TOKEN','User')`；(3) Windows 系统变量 `[Environment]::GetEnvironmentVariable('GITCODE_TOKEN','Machine')` - **请求头**：使用 `PRIVATE-[REDACTED_SECRET]` 或 `Authorization: Bearer {token}`，或查询参数 `access_token={token}` - **未配置时**：提示用户到 [GitCode 个人访问令牌](https://gitcode.com/setting/token-classic) 创建 Token（勾选 read_api、read_repository 等），并设置环境变量 `GITCODE_TOKEN`。 ## 状态码与限流 | Code | 含义 | |------|------| | 200/201/204 | 成功 | | 400 | 缺少参数或未带认证（部分接口） | | 401 | Token 无效或缺失 | | 403/404/409/422 | 禁止/未找到/冲突/校验失败 | | 429 | 限流（默认 50/分钟、4000/小时） | ## 接口与示例 - **全量接口**：所有 v5 接口的 Method / Path / 功能说明 / 可获取信息 / 官方文档链接见 **[reference.md](reference.md)**（含 Repositories、Branch、Issues、Search、PR、Commit、Tag、Labels、Milestone、Users、Orgs、Webhooks、Member、Release 等）。 - **调用示例**：见 [examples.md](examples.md)。 - **官方文档**：<https://docs.gitcode.com/docs/apis/>（每接口单独页含参数与响应）。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: hulk-yin/skill-installer-usm (version 1.0.0)

--- name: skill-installer description: [USM] Install, search, update, and manage skills from registries (ClawHub, SkillHub, etc.). Use when the user wants to install a skill by slug (e.g. "clawhub install summarize"), search for skills, or update installed skills. --- # ClawHub Skill Manager Install and manage skills from [ClawHub](https://clawhub.ai), the public skill registry for OpenClaw. ## Prerequisites The `clawhub` CLI must be available on PATH. Verify with `which clawhub`. If missing, ask the user to install it manually: ```bash npm i -g clawhub ``` Do not auto-install without user confirmation. ## Commands ### Install a skill ```bash cd ~/.openclaw/workspace && clawhub install <slug> ``` - `<slug>` is the skill identifier from ClawHub (e.g. `summarize`, `weather`, `coding-agent`) - Skills are installed into `./skills/` under the workspace - A new OpenClaw session is needed to pick up the skill ### Search for skills ```bash clawhub search "<query>" ``` ### Update skills ```bash # Update a specific skill clawhub update <slug> # Update all installed skills clawhub update --all ``` ### Other useful commands ```bash clawhub info <slug> # Show skill details clawhub list # List installed skills clawhub --help # Full CLI reference ``` ## Workflow 1. Ensure `clawhub` CLI is installed (check with `which clawhub`, install if missing) 2. `cd` to the workspace directory: `~/.openclaw/workspace` 3. Run the appropriate `clawhub` command 4. Tell the user to call `skill-manager` (or run `~/.skills/skill-manager/scripts/sync_skills.sh` directly) to configure the new skill's `meta.yaml` scope and distribute it via symlinks across Agents. 5. Inform the user to start a new session (or restart gateway) for the skill to take effect ## Installing via WhatsApp Users can install skills by messaging the OpenClaw agent on WhatsApp: ``` clawhub install <slug> ``` Example: `clawhub install summarize` The agent will handle CLI installation and confirm when the skill is ready. ## Notes - All ClawHub skills are public and open - Treat third-party skills as untrusted — review before enabling - Workspace skills (`<workspace>/skills/`) take highest precedence over managed and bundled skills


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: 52yuanchangxing/screenshot-to-task (version 1.0.0)

--- name: screenshot-to-task version: 1.0.0 description: "把截图里的待办或灵感整理成任务、备注和优先级。；use for screenshots, tasks, capture workflows；do not use for 伪造截图内容, 替代 OCR 系统." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/screenshot-to-task tags: [screenshots, tasks, capture, productivity] user-invocable: true metadata: {"openclaw":{"emoji":"🖼️","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 截图转任务整理器 ## 你是什么 你是“截图转任务整理器”这个独立 Skill，负责：把截图里的待办或灵感整理成任务、备注和优先级。 ## Routing ### 适合使用的情况 - 把这些截图内容整理成任务 - 帮我标优先级 - 输入通常包含：截图内容摘要、上下文、截止时间 - 优先产出：任务列表、备注、后续动作 ### 不适合使用的情况 - 不要伪造截图内容 - 不要替代 OCR 系统 - 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。 ## 工作规则 1. 先把用户提供的信息重组成任务书，再输出结构化结果。 2. 缺信息时，优先显式列出“待确认项”，而不是直接编造。 3. 默认先给“可审阅草案”，再给“可执行清单”。 4. 遇到高风险、隐私、权限或合规问题，必须加上边界说明。 5. 如运行环境允许 shell / exec，可使用： - `python3 "{baseDir}/scripts/run.py" --input <输入文件> --output <输出文件>` 6. 如当前环境不能执行脚本，仍要基于 `{baseDir}/resources/template.md` 与 `{baseDir}/resources/spec.json` 的结构直接产出文本。 ## 标准输出结构 请尽量按以下结构组织结果： - 任务列表 - 备注 - 优先级 - 需要补充的信息 - 归档建议 - 后续动作 ## 本地资源 - 规范文件：`{baseDir}/resources/spec.json` - 输出模板：`{baseDir}/resources/template.md` - 示例输入输出：`{baseDir}/examples/` - 冒烟测试：`{baseDir}/tests/smoke-test.md` ## 安全边界 - 适合作为截图后整理流程。 - 默认只读、可审计、可回滚。 - 不执行高风险命令，不隐藏依赖，不伪造事实或结果。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: tuckerschreiber/fullrun-cli (version 0.1.0)

--- name: fullrun description: Manage Google Ads campaigns — diagnose, optimize, and create campaigns requires: bins: [fullrun] env: [FULLRUN_API_KEY] user-invocable: true --- # Fullrun — Google Ads Management for AI Agents Manage Google Ads campaigns using the `fullrun` CLI. All commands return JSON by default. ## Setup ```bash npm install -g fullrun fullrun login <YOUR_API_KEY> ``` Or set env: `export FULLRUN_API_KEY=frun_...` ## Workflow Always start with triage to understand account health: 1. `fullrun triage` — see prioritized issues (CRITICAL > HIGH > MEDIUM > LOW) 2. Fix the highest-priority issues using `fullrun run` 3. `fullrun triage` — confirm issues resolved ## Commands ### Diagnose - `fullrun triage` — Account health report with prioritized issues - `fullrun campaigns:list` — All campaigns with status, budget, and metrics - `fullrun performance --days 7` — Account metrics: clicks, impressions, conversions, CPA - `fullrun keywords:list --campaign <id>` — Keywords with performance data ### Act - `fullrun run` — Trigger a full AI-powered optimization run. The agent triages the account and fixes the highest-priority issues automatically. Rate limited to 1 per hour. ### Options - `--format human` — Readable output instead of JSON - `--days <n>` — Look-back period for performance data (default 7, max 90) - `--campaign <id>` — Filter keywords by campaign ## Output All commands return structured JSON by default for easy parsing by AI agents. Exit codes: - `0` — Success - `1` — Error (check the `error` field in JSON output) - `2` — Guardrail blocked (the action was prevented to protect the ad account) ## Rules - Always run `fullrun triage` before taking action - The `fullrun run` command is rate-limited to 1 per hour - Respect guardrail errors (exit code 2) — they protect the ad account from harmful changes - Never call `fullrun run` repeatedly — check triage first to see if action is needed - Don't increase daily budget more than 20% in one action - Don't pause campaigns without checking conversion data first


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abdhilabs/karmabank-usdc (version 1.0.0)

--- name: karmabank description: AI agents borrow USDC based on their Moltbook karma score. Credit tiers from Bronze (50 USDC) to Diamond (1000 USDC) with zero interest. --- # KarmaBank 💰 **Borrow USDC based on your Moltbook reputation** KarmaBank is a credit system that allows AI agents to borrow USDC on testnet based on their Moltbook karma score. Higher karma = higher credit tier = more borrowing power. No credit checks, no banks—just your reputation on the network. ## Quick Start ```bash # Install npm install npm run build # Register agent karmabank register @yourAgentName # Check credit karmabank check @yourAgentName # Borrow USDC karmabank borrow @yourAgentName 50 ``` ## Commands | Command | Description | |---------|-------------| | `register <name>` | Register agent with KarmaBank | | `check <name>` | Show credit score and limits | | `borrow <name> <amount>` | Borrow USDC | | `repay <name> <amount>` | Repay USDC loan | | `history <name>` | Show transaction history | | `list` | List all registered agents | | `wallet create <name>` | Create Circle wallet | ## Credit Tiers | Tier | Max Borrow | |------|------------| | Bronze | 50 USDC | | Silver | 150 USDC | | Gold | 300 USDC | | Platinum | 600 USDC | | Diamond | 1000 USDC | ## Configuration ```bash # Moltbook API (optional for mock mode) MOLTBOOK_API_KEY=your_key # Circle API (for real wallet) CIRCLE_API_KEY=your_key CIRCLE_ENTITY_SECRET=your_secret ``` ## Loan Terms - **Interest:** 0% - **Term:** 14 days - **Grace Period:** 3 days - **Late Fee:** 10% ## Scoring System Credit score based on: - Moltbook Karma (40%) - Account Age (20%) - Activity Diversity (15%) - X Verification (10%) - Follower Count (15%) ## Resources - **GitHub:** https://github.com/abdhilabs/karmabank - **Moltbook:** https://moltbook.com - **Circle Console:** https://console.circle.com - **Hackathon:** https://moltbook.com/m/usdc --- **Built for the USDC Agentic Hackathon** 💵🏦


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yequanzheng/lesson (version 0.1.1)

--- name: daily-menu description: >- 生成今日菜单推荐。根据用户手头的食材、口味偏好、人数和烹饪时间， 从内置食材库和菜谱库中智能匹配，输出完整的菜单方案和分步烹饪指南。 当用户提到"今天吃什么""做什么菜""菜单""菜谱推荐""晚饭吃啥"等意图时触发。 --- # 今日菜单生成器 根据用户现有食材、口味偏好、就餐人数和可用时间，生成个性化菜单推荐和烹饪指南。 ## 工作流程 ### 第一步：了解用户需求 通过简短对话收集以下信息（缺失项用合理默认值）： | 信息项 | 默认值 | 说明 | |--------|--------|------| | 手头食材 | — | **必问**，至少确认 1~3 种主要食材 | | 就餐人数 | 2 人 | 影响份量建议 | | 口味偏好 | 家常 | 家常 / 清淡减脂 / 川湘重口 / 快手10分钟 | | 可用时间 | 60 分钟 | 影响菜品复杂度 | | 忌口/过敏 | 无 | 如有则排除相关食材 | | 菜品数量 | 2~4 道 | 根据人数自动建议 | **对话原则：** - 不要一次问太多问题，最核心的是「你手头有什么食材？」 - 如果用户只说了食材，其他用默认值直接推荐 - 语气轻松友好，像朋友聊天一样 ### 第二步：匹配菜谱 1. 读取 `references/ingredients.md` 获取完整食材分类 2. 读取 `references/recipes.md` 获取菜谱库 3. 根据用户提供的食材，从菜谱库中匹配可做的菜品 4. 考虑荤素搭配、营养均衡、烹饪难度 **匹配规则：** - 主料必须匹配（用户手头有的食材） - 常见调料（盐、酱油、醋、糖、料酒、蚝油等）默认家里有，不需要确认 - 优先推荐主料完全匹配的菜，其次推荐只差 1~2 种配料的菜 - 如果用户食材能做的菜不够，可以建议额外采购少量食材 ### 第三步：输出菜单 使用以下格式输出： ``` ## 🍽️ 今日菜单推荐 **就餐人数：** X 人 | **预计用时：** X 分钟 | **风格：** XXX --- ### 1. 菜名 ⭐ > 一句话点评这道菜的特点 **食材：** - 主料：XXX - 配料：XXX - 调料：XXX **做法：** 1. 第一步（附时间和火候） 2. 第二步 3. ... **小贴士：** 实用烹饪技巧 --- ### 2. 菜名 （同上格式） --- ## 📋 备菜清单 把所有菜需要的食材汇总，方便一次性准备： | 分类 | 食材 | 用量 | 处理方式 | |------|------|------|----------| | 肉类 | ... | ... | ... | | 蔬菜 | ... | ... | ... | ## ⏱️ 烹饪时间线 建议的做菜顺序和并行操作： 1. 先做 XX（耗时最长的先开始） 2. 在等待的同时准备 XX 3. 最后快炒 XX ``` ### 第四步：后续互动 菜单输出后，主动询问： - 「要不要换掉某道菜？」 - 「要不要看更详细的某道菜做法？」 - 「还想加个汤/凉菜吗？」 ## 口味风格指南 ### 家常风格（默认） - 红烧、清炒、炖煮为主 - 调味适中，老少皆宜 - 代表菜：红烧肉、番茄炒蛋、清炒时蔬 ### 清淡减脂 - 少油少盐，以蒸、煮、白灼为主 - 多蔬菜，蛋白质优先选鸡胸肉/鱼/虾/豆腐 - 减少碳水，避免油炸 - 标注估算热量 ### 快手 10 分钟 - 只推荐炒、拌、煮等快速烹饪方式 - 食材处理简单（切丝切片为主） - 每道菜控制在 10 分钟以内 ### 川湘重口味 - 麻辣鲜香，重油重味 - 豆瓣酱、辣椒、花椒、剁椒等调料 - 代表菜：麻婆豆腐、辣子鸡、酸辣土豆丝 ## 重要原则 1. **实用优先**：做法描述要具体到火候、时间、用量，不要含糊 2. **荤素搭配**：2 道菜以上时确保有荤有素 3. **难度递进**：多道菜时从简到难排列 4. **时间合理**：给出的总用时要考虑并行操作 5. **食材不浪费**：尽量用完用户提到的食材 6. **安全第一**：涉及生食（如三文鱼）要提醒新鲜度要求


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xushen-ma/apple-calendar-pro (version 1.3.0)

--- name: apple-calendar-pro description: iCloud Calendar skill via CalDAV (RFC 4791) — works on macOS/Linux, and Windows with env/keyring auth. Supports event CRUD, multi-calendar queries, managed attachments (RFC 8607), and free/busy lookups. homepage: https://github.com/xushen-ma/apple-calendar-pro metadata: {"openclaw":{"requires":{"bins":["python3"],"env":["APPLECAL_PASSWORD"]},"primaryEnv":"APPLECAL_PASSWORD"}} --- # apple-calendar-pro Advanced Apple Calendar integration using CalDAV (RFC 4791) and Managed Attachments (RFC 8607). ## Primary CLI `scripts/applecal.py` ## Capabilities - **Event CRUD**: List, Create, Update, Delete. - **Multi-Calendar Support**: Query multiple calendars in a single command. - **True Attachments**: RFC 8607 compatible (works on iPhone/iPad). - **Free/Busy**: CalDAV scheduling lookup with event-derived fallback. ## Common Commands ### List Events (Combined) Check multiple calendars at once: ```bash python3 scripts/applecal.py events list \ --apple-id [REDACTED_SECRET] \ --calendar Family \ --calendar Work \ --from "2026-02-26T00:00:00Z" \ --to "2026-02-26T23:59:59Z" ``` ### Create All-Day Event ```bash python3 scripts/applecal.py events create \ --apple-id [REDACTED_SECRET] \ --calendar Family \ --summary "Birthday" \ --start "2026-02-26" \ --end "2026-02-26" \ --all-day ``` ### Attach a File (iPhone Safe) ```bash python3 scripts/applecal.py attach add \ --apple-id [REDACTED_SECRET] \ --calendar Family \ --uid <UID> \ --file /path/to/document.pdf ``` ### Free/Busy Check ```bash python3 scripts/applecal.py freebusy \ --apple-id [REDACTED_SECRET] \ --calendar Family \ --from "2026-02-26T00:00:00Z" \ --to "2026-02-26T23:59:59Z" ``` ## Notes - **Birthdays**: The virtual "Birthdays" calendar is not searchable via CalDAV. Key birthdays should be added as physical recurring events in the **Family** calendar for agent visibility. - **Auth**: Resolution order is `APPLECAL_PASSWORD` → Python `keyring` (if installed/configured) → macOS Keychain fallback. Run `doctor` to verify connectivity. - **Event update clearing**: Use `events update --clear-location` / `--clear-description` to explicitly remove optional fields. - **Attachment safety**: `attach add` blocks sensitive paths/names, allowlists file extensions, and supports optional directory scoping via `APPLECAL_ATTACH_DIR`. - **Apple ID**: Always pass `--apple-id [REDACTED_SECRET]` (the iCloud account email, not necessarily your Apple ID login).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: autogame-17/feishu-calendar (version 1.0.0)

# feishu-calendar Manage Feishu (Lark) Calendars. Use this skill to list calendars, check schedules, and sync events. ## Usage ### List Calendars Check available calendars and their IDs. ```bash node skills/feishu-calendar/list_test.js ``` ### Search Calendar Find a calendar by name/summary. ```bash node skills/feishu-calendar/search_cal.js ``` ### Check Master's Calendar Specific check for the Master's calendar status. ```bash node skills/feishu-calendar/check_master.js ``` ### Sync Routine Run the calendar synchronization routine (syncs events to local state/memory). ```bash node skills/feishu-calendar/sync_routine.js ``` ## Setup Requires `FEISHU_APP_ID` and `FEISHU_APP_SECRET` in `.env`. ## Standard Protocol: Task Marking **Trigger**: User says "Mark this task" or "Remind me to...". **Action**: 1. **Analyze**: Extract date/time (e.g., "Feb 4th" -> YYYY-MM-04). 2. **Execute**: Run `create.js` with `--attendees` set to the requester's ID. 3. **Format**: ```bash node skills/feishu-calendar/create.js --summary "Task: <Title>" --desc "<Context>" --start "<ISO>" --end "<ISO+1h>" --attendees "<User_ID>" ``` ### Setup Shared Calendar Create a shared calendar for a project and add members. ```bash node skills/feishu-calendar/setup_shared.js --name "Project Name" --desc "Description" --members "ou_1,ou_2" --role "writer" ```


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: 52yuanchangxing/local-media-cataloger (version 1.0.0)

--- name: local-media-cataloger description: Index local photos, videos, and creative assets into a searchable manifest with tags, dates, shoot info, and reuse ideas. version: 1.1.0 metadata: openclaw: requires: bins: - python3 emoji: 🧰 --- # Local Media Cataloger ## Purpose Index local photos, videos, and creative assets into a searchable manifest with tags, dates, shoot info, and reuse ideas. ## Trigger phrases - 整理素材库 - catalog my media folder - 摄影素材归档 - 视频素材清单 - asset manifest ## Ask for these inputs - media folder path - tagging rules - project or shoot name - deliverable types - favorite selection criteria ## Workflow 1. Scan files and extract basic metadata such as extension, size, timestamps, and dimensions when available. 2. Apply consistent tags and generate a manifest CSV/JSON. 3. Flag near-duplicates for manual review using filename/date heuristics. 4. Suggest folder structures for archive, selects, exports, and delivery. 5. Return a catalog summary and reuse opportunities. ## Output contract - media manifest - folder plan - duplicate candidates - reuse ideas ## Files in this skill - Script: `{baseDir}/scripts/media_manifest.py` - Resource: `{baseDir}/resources/metadata_schema.json` ## Operating rules - Be concrete and action-oriented. - Prefer preview / draft / simulation mode before destructive changes. - If information is missing, ask only for the minimum needed to proceed. - Never fabricate metrics, legal certainty, receipts, credentials, or evidence. - Keep assumptions explicit. ## Suggested prompts - 整理素材库 - catalog my media folder - 摄影素材归档 ## Use of script and resources Use the bundled script when it helps the user produce a structured file, manifest, CSV, or first-pass draft. Use the resource file as the default schema, checklist, or preset when the user does not provide one. ## Boundaries - This skill supports planning, structuring, and first-pass artifacts. - It should not claim that files were modified, messages were sent, or legal/financial decisions were finalized unless the user actually performed those actions. ## Compatibility notes - Directory-based AgentSkills/OpenClaw skill. - Runtime dependency declared through `metadata.openclaw.requires`. - Helper script is local and auditable: `scripts/media_manifest.py`. - Bundled resource is local and referenced by the instructions: `resources/metadata_schema.json`.

path: CHANGELOG.md
content: # Changelog

## 1.0.0
- Initial release.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sky-lv/skylv-api-documentation (version 1.0.0)

--- name: "api-documentation" slug: skylv-api-documentation version: 1.0.0 description: "API文档助手。生成API文档、编写Swagger/OpenAPI、示例代码。使用场景：(1) 文档生成，(2) Swagger配置，(3) 示例代码，(4) 版本管理。" author: SKY-lv license: MIT-0 tags: [api, openclaw, agent] --- # API Documentation — API文档助手 ## 功能说明 生成和管理API文档。 ## 使用方法 ### 1. 文档生成 ``` 用户: 根据这个代码生成API文档 ``` ### 2. Swagger配置 ``` 用户: 写一个Swagger/OpenAPI配置 ``` ### 3. 示例代码 ``` 用户: 生成各语言的API调用示例 ``` ### 4. 版本管理 ``` 用户: 如何管理API版本？ ```


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