OpenClaw/clawhub-security-signals huggingface dataset:
Skill: edmonddantesj/aoi-demo-clip-maker (version 0.1.4)

# AOI Demo Clip Maker (macOS) S-DNA: `AOI-2026-0215-SDNA-CLIP01` ## What this is A **terminal-only** (public-safe) utility skill to create hackathon demo clips on **macOS**. It wraps `ffmpeg/ffprobe` to: - list capture devices (avfoundation) - record a screen for N seconds - crop the top bar (menu/title) - trim clips - use simple presets ## What this is NOT - No YouTube upload - No form submission - No external posting - No secret handling ## Requirements - macOS - `ffmpeg` and `ffprobe` installed - Screen Recording permission granted to your terminal app ## Commands ### 1) List devices (avfoundation) ```bash aoi-clip devices ``` ### 2) Record (screen capture) ```bash # pixel_format auto-fallback is enabled by default # (tries: uyvy422 → nv12 → yuyv422 → 0rgb → bgr0) aoi-clip record --out tempo_demo_raw.mp4 --duration 15 --fps 30 --screen "Capture screen 0" # optionally force a specific pixel format # aoi-clip record --out tempo_demo_raw.mp4 --duration 15 --fps 30 --screen "Capture screen 0" --pixel uyvy422 ``` ### 3) Crop top bar ```bash # explicit crop aoi-clip crop --in tempo_demo_raw.mp4 --out tempo_demo_crop.mp4 --top 150 # auto-recommend top crop based on video height (still applies crop, but chooses a value) aoi-clip crop --in tempo_demo_raw.mp4 --out tempo_demo_crop.mp4 --top auto ``` ### 4) Trim ```bash aoi-clip trim --in tempo_demo_crop.mp4 --out tempo_demo_15s.mp4 --from 0 --to 15 ``` ### 5) Preset: terminal ```bash aoi-clip preset terminal --out demo.mp4 ``` ## Security / Audit posture This skill runs local `ffmpeg/ffprobe` only, using a **strict allowlist** of binaries and arguments. ## Release governance (public) We publish AOI skills for free and keep improving them. Every release must pass our Security Gate and include an auditable changelog. We do not ship updates that weaken security or licensing clarity. Repeated violations trigger progressive restrictions (warnings → publish pause → archive). ## Support - Issues / bugs / requests: https://github.com/edmonddantesj/aoi-skills/issues - Please include the skill slug: `aoi-demo-clip-maker` ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: leooooooow/aov-uplift-simulator (version 1.0.0)

--- name: aov-uplift-simulator description: Estimate how changes in bundle uptake, upsell acceptance, or cart-building tactics could increase average order value without hiding margin tradeoffs. Use when teams want a practical view of AOV upside before changing offers. --- # AOV Uplift Simulator Model how AOV could move under different bundle, upsell, and pricing assumptions before you change the live offer. ## Solves Many ecommerce teams make pricing or offer decisions with incomplete economics: - they see revenue upside but not margin drag; - they model one variable but ignore knock-on effects; - they test offers without clear guardrails; - they scale offers before checking break-even logic. Goal: **Turn offer assumptions into a clearer economic view that is easier to evaluate and act on.** ## Use when - You want to compare offer scenarios before launching - A discount, bundle, or upsell idea sounds good but needs economic validation - Growth teams need a faster way to pressure-test merchandising decisions - Teams want clearer go / watch / no-go logic before scale ## Inputs - Core commercial assumptions relevant to the scenario - Price and cost structure - Margin or refund assumptions - Traffic / conversion or attach-rate assumptions - Constraints or guardrails ## Workflow 1. Clarify the baseline commercial setup. 2. Model the scenario inputs that change order economics. 3. Surface upside, downside, and sensitivity. 4. Identify the biggest weak points or break-even pressure. 5. Recommend whether to test, revise, or avoid the scenario. ## Output 1. Baseline view 2. Scenario result 3. Margin / break-even implications 4. Key risks and weak points 5. Recommendation ## Quality bar - Output should be commercially interpretable, not just a raw formula dump. - Recommendations should stay grounded in ecommerce economics. - Weak points should be clearly separated from upside assumptions. - The result should help a team decide what to test next. ## Resource See `references/output-template.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: magiesky/volc-image (version 1.0.0)

--- name: volc-image description: 火山引擎图像生成 - 使用火山引擎方舟API生成图片并下载 metadata: openclaw: emoji: 🖼️ requires: env: - ARK_API_KEY primaryEnv: ARK_API_KEY --- # volc-image 火山引擎图像生成技能 - 使用火山引擎方舟(Doubao Seedream) API生成图片。 ## 环境配置 需要设置环境变量 `ARK_API_KEY`： - 在火山引擎控制台获取 API Key：https://console.volcengine.com/ark/endpoint ## 功能 1. **生成图片** - 根据提示词生成图片 2. **下载保存** - 自动将生成的图片保存到本地 ## 使用方式 直接告诉我要生成什么样的图片，例如： - "生成一张小说封面：顶流重生指南" - "生成一张科技感海报" - "生成一张可爱猫咪图片" ## 参数说明 | 参数 | 说明 | 默认值 | |------|------|--------| | model | 模型 | doubao-seedream-5-0-260128 | | size | 尺寸 | 2K (1664x2496) | | prompt | 提示词 | 用户输入 | ## 示例提示词 ### 小说封面 ``` 小说封面，顶流重生指南，标题金色大字，时尚美女坐在王座上，娱乐圈背景，霓虹灯光，紫色金色主题，精致画面，4K ``` ### 科幻风格 ``` 星际穿越，黑洞，黑洞里冲出一辆支离破碎的复古列车，视觉冲击力，电影大片，末日既视感，动感，对比色，oc渲染，光线追踪，动态模糊，景深，超现实主义，深蓝 ``` ### 可爱风格 ``` 可爱的小猫咪，胖乎乎，大眼睛，萌萌的，粉色背景，卡通风格，精致可爱 ``` ## 技术实现 ```python import os import requests # API配置 url = 'https://ark.cn-beijing.volces.com/api/v3/images/generations' [REDACTED_SECRET]'ARK_API_KEY') # 请求数据 data = { 'model': 'doubao-seedream-5-0-260128', 'prompt': '<用户提示词>', 'sequential_image_generation': 'disabled', 'response_format': 'url', 'size': '2K', 'stream': False, 'watermark': True } # 发送请求 response = requests.post(url, headers={ 'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json' }, json=data) # 获取图片URL并下载 img_url = response.json()['data'][0]['url'] img_data = requests.get(img_url).content # 保存图片 with open('output.jpg', 'wb') as f: f.write(img_data) ``` ## 注意事项 - API Key 需要在火山引擎控制台获取 - 图片URL有效期约24小时 - 生成图片可能需要几秒钟时间 ## 执行命令 设置API Key并生成图片： ```bash # 设置API Key set ARK_API_KEY=你的API密钥 # 生成图片 python volc_image.py "你的提示词" ``` 或者在Python中直接调用： ```python import os os.environ['ARK_API_KEY'] = '你的API密钥' exec(open('volc_image.py').read()) ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: timo2026/auto-skill-20260404-063632 (version 1.0.0)

--- name: api-debate-solver version: 1.0.0 description: "API问题对抗讨论求解器 - 4模型线性对抗找出最优解" author: 海狸 license: MIT tags: [api, debugging, debate, optimization] --- # API问题对抗讨论求解器 ## 概述 当遇到复杂的API问题时，使用4模型线性对抗找出最优解： 1. 架构师：问题诊断+方案设计 2. 审核者：质疑风险+建议验证 3. 仲裁者：综合判断+最终决策 4. 收敛者：找共同点+整理输出 ## 使用场景 - API调用失败需要诊断 - 多个解决方案需要选择最优 - 需要多个角度分析问题 ## 本次应用案例 ### 问题描述 API Key验证失败，请求超时 ### 诊断过程 **Step 1: 网络诊断** - ping测试：208ms延迟，3包全收 ✅ - HTTPS请求：超时（30秒无响应）❌ **Step 2: 配置检查** - base_url: coding.dashscope.aliyuncs.com/v1 ✅ - API Key格式: sk-sp-xxxxx ✅ **Step 3: 降级方案** - 当前会话fallback模式 ✅ ### 最优解 ``` 网络诊断 → 有限重试 → 模拟降级 ``` ## 代码实现 ### 线性对抗引擎 ```python # 4模型线性排列 models = [ {"角色": "架构师", "延迟": 5}, {"角色": "审核者", "延迟": 5}, {"角色": "仲裁者", "延迟": 8}, {"角色": "收敛者", "延迟": 0}, ] # 依次发言，记录回复，最终收敛 for model in models: response = call_model(model) save_cache(response) time.sleep(model["延迟"]) ``` ## 教训总结 1. base_url必须匹配API类型（Coding Plan用专用URL） 2. 网络层正常不代表应用层正常 3. 需要降级机制保证可用性 ## 相关文件 - modules/linear_adversarial_4model.py - 线性对抗引擎 - tests/test_basic.py - 单元测试 - memory/api_debate_analysis_20260404.txt - 对抗分析报告 --- *生成时间: 2026-04-04* *作者: 海狸 🦫*

path: publish_info.json
content: skill_dir: /home/admin/.openclaw/workspace/skills/auto_generated/auto_skill_20260404_063632
status: ready
clawhub_url: https://clawhub.com
publish_command: clawhub publish /home/admin/.openclaw/workspace/skills/auto_generated/auto_skill_20260404_063632
timestamp: 2026-04-04 06:36:32

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: sunshine-del-ux/react-component-generator (version 1.0.0)

--- name: react-component-generator description: 生成 React 组件模板，支持 Function Component, Class Component, Hooks, TypeScript，一键生成完整组件代码。 metadata: {"clawdbot":{"emoji":"⚛️","requires":{},"primaryEnv":""}} --- # React Component Generator 快速生成 React 组件代码。 ## 功能 - ⚡ 一键生成组件 - 📝 支持 TypeScript - 🎣 内置 Hooks - 🎨 多种组件类型 ## 组件类型 | 类型 | 说明 | |------|------| | function | 函数组件 | | class | 类组件 | | hook | 自定义 Hook | | hoc | 高阶组件 | ## 使用方法 ```bash react-component-generator MyButton --type function --typescript react-component-generator useMyHook --type hook ```

path: _meta.json
content: {
  "schema_version": "1",
  "name": "react-component-generator",
  "version": "1.0.0",
  "description": "生成 React 组件",
  "author": {"name": "Sunshine-del-ux"},
  "keywords": ["react", "component", "generator", "frontend"],
  "license": "MIT"
}


path: react-component-generator.sh
content: #!/bin/bash
NAME="${1:-MyComponent}"
TYPE="${2:-function}"

cat > "$NAME.jsx" << 'JSX'
export default function COMP_NAME(props) {
  return (
    <div>
      <h1>COMP_NAME</h1>
    </div>
  );
}
JSX

sed -i "s/COMP_NAME/$NAME/g" "$NAME.jsx"
echo "✅ React component generated: $NAME.jsx"


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

--- name: "风行" version: "1.0.0" description: "风行视频（已边缘化）- 在线视频平台历史存档" tags: ["video", "archived", "streaming"] author: "ClawSkills Team" category: "video" status: "archived" --- # 风行（已停运） 风行已停止运营或已边缘化，本技能作为历史存档保留。 ## 产品简介 风行曾是中国互联网生态中的一款产品，现已停止服务。 ## 替代方案 如果你需要类似功能，可以考虑当前主流的同类产品。 --- **最后更新**: 2026-03-16 **状态**: 已存档


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: miguelguerra200022-sudo/1password-zito (version 1.0.0)

--- name: 1password description: Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op. homepage: https://developer.1password.com/docs/cli/get-started/ metadata: { "openclaw": { "emoji": "🔐", "requires": { "bins": ["op"] }, "install": [ { "id": "brew", "kind": "brew", "formula": "1password-cli", "bins": ["op"], "label": "Install 1Password CLI (brew)", }, ], }, } --- # 1Password CLI Follow the official CLI get-started steps. Don't guess install commands. ## References - `references/get-started.md` (install + app integration + sign-in flow) - `references/cli-examples.md` (real `op` examples) ## Workflow 1. Check OS + shell. 2. Verify CLI present: `op --version`. 3. Confirm desktop app integration is enabled (per get-started) and the app is unlocked. 4. REQUIRED: create a fresh tmux session for all `op` commands (no direct `op` calls outside tmux). 5. Sign in / authorize inside tmux: `op signin` (expect app prompt). 6. Verify access inside tmux: `op whoami` (must succeed before any secret read). 7. If multiple accounts: use `--account` or `OP_ACCOUNT`. ## REQUIRED tmux session (T-Max) The shell tool uses a fresh TTY per command. To avoid re-prompts and failures, always run `op` inside a dedicated tmux session with a fresh socket/session name. Example (see `tmux` skill for socket conventions, do not reuse old session names): ```bash SOCKET_DIR="${OPENCLAW_TMUX_SOCKET_DIR:-${CLAWDBOT_TMUX_SOCKET_DIR:-${TMPDIR:-/tmp}/openclaw-tmux-sockets}}" mkdir -p "$SOCKET_DIR" SOCKET="$SOCKET_DIR/openclaw-op.sock" SESSION="op-auth-$(date +%Y%m%d-%H%M%S)" tmux -S "$SOCKET" new -d -s "$SESSION" -n shell tmux -S "$SOCKET" send-keys -t "$SESSION":0.0 -- "op signin --account my.1password.com" Enter tmux -S "$SOCKET" send-keys -t "$SESSION":0.0 -- "op whoami" Enter tmux -S "$SOCKET" send-keys -t "$SESSION":0.0 -- "op vault list" Enter tmux -S "$SOCKET" capture-pane -p -J -t "$SESSION":0.0 -S -200 tmux -S "$SOCKET" kill-session -t "$SESSION" ``` ## Guardrails - Never paste secrets into logs, chat, or code. - Prefer `op run` / `op inject` over writing secrets to disk. - If sign-in without app integration is needed, use `op account add`. - If a command returns "account is not signed in", re-run `op signin` inside tmux and authorize in the app. - Do not run `op` outside tmux; stop and ask if tmux is unavailable.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: silingyuan0/minimax-music-generate (version 1.0.0)

--- name: minimax-music description: MiniMax 音乐生成技能 - 支持歌词生成(Lyrics Generation)、音乐生成(Music Generation)。支持自定义歌词段落结构(verse/chorus/bridge/intro/outro)，异步生成 MP3 格式音乐，自动下载保存到本地。 version: 1.0.0 author: laowang tags: - minimax - music - lyrics - music-generation - lyrics-generation --- # MiniMax Music Skill 使用 MiniMax API 进行音乐生成和歌词创作。支持歌词生成、音乐生成，支持自定义歌词段落结构，异步生成 MP3 格式音乐，可自动下载保存到本地。 ## 环境配置 ```json { "MINIMAX_API_KEY": "your-api-key", "MINIMAX_REGION": "cn" | "int" } ``` - `MINIMAX_API_KEY`: MiniMax API 密钥 - `MINIMAX_REGION`: 区域设置，`cn` 为中国，`int` 为国际（默认 `cn`） ## 可用函数 ### generate_music(lyrics, description, model) 音乐生成 **参数**: - `lyrics`: 歌词内容 - `description`: 音乐描述/风格 - `model`: 音乐模型（默认: `music-01`） **返回**: 任务ID列表 **示例**: `generate_music("[verse]\n蓝天白云下\n我们在奔跑\n[chorus]\n青春就是这样", "欢快的流行歌曲")` ### generate_lyrics(description, keywords, model) 歌词生成 **参数**: - `description`: 歌曲描述/主题 - `keywords`: 关键词列表（可选） - `model`: 歌词模型（默认: `lyrics-01`） **返回**: 生成的歌词文本 **示例**: `generate_lyrics("一首关于梦想的歌", ["坚持", "希望", "前行"])` ### query_music_task(task_id) 查询音乐生成任务状态 **参数**: - `task_id`: 任务ID **返回**: 任务状态信息 `{status, music_url, ...}` **状态值**: `Pending`, `Processing`, `Success`, `Fail` ### wait_for_music(task_id, poll_interval, max_wait) 等待音乐生成完成（轮询） **参数**: - `task_id`: 任务ID - `poll_interval`: 轮询间隔秒数（默认: 10） - `max_wait`: 最大等待时间（默认: 600） **返回**: 最终任务状态信息 ### download_music(music_url, output_path) 下载音乐到本地 **参数**: - `music_url`: 音乐下载URL - `output_path`: 保存路径 **返回**: 保存的文件路径 ## 使用示例 ### 直接生成音乐 ``` python scripts/music.py generate "[verse]\n月亮挂在天上\n星星闪闪发光\n[chorus]\n夜空中最亮的星" -d "温柔的民谣歌曲" ``` ### 先生成歌词，再生成音乐 ``` # 先生成歌词 python scripts/music.py lyrics "一首关于夏天的歌" -k 阳光 沙滩 海浪 # 使用生成的歌词创建音乐 python scripts/music.py generate "<生成的歌词>" -d "欢快的夏季主题歌曲" ``` ### 查询任务状态 ``` python scripts/music.py query <task_id> ``` ### 等待音乐完成 ``` python scripts/music.py wait <task_id> -i 10 ``` ### 下载音乐 ``` python scripts/music.py download <music_url> -o song.mp3 ``` ## 歌词格式说明 歌词按段落返回，使用空行分隔不同段落。可用的段落标记： - `[verse]` - 主歌 - `[chorus]` - 副歌/高潮 - `[bridge]` - 桥段 - `[intro]` - 前奏 - `[outro]` - 尾奏 ## 注意事项 1. 歌词越详细，生成的音乐效果越好 2. 可以不提供歌词，仅通过 description 生成音乐 3. 音乐生成是异步的，需要通过 task_id 查询状态 4. 建议使用 `wait_for_music` 函数自动等待完成 5. 音乐生成可能需要几分钟时间，请耐心等待


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: yinguobing/suanpan (version 0.1.1)

--- name: suanpan description: 将用户自然语言交易描述转换为 suanpan CLI 命令。当用户提到记账、消费、支出、收入、转账、查账、统计等财务相关描述时触发。 --- # Suanpan 记账助手 Suanpan（算盘）是 Rust 编写的个人财务管理 CLI 工具，使用 SurrealDB 嵌入式数据库存储。 ## 安装与依赖 ### 依赖 - Rust 1.82+ (从源码编译时需要) ### 快速安装（推荐） ```bash curl -sSL https://raw.githubusercontent.com/yinguobing/suanpan/main/install.sh | bash ``` ### 从源码编译 ```bash git clone https://github.com/yinguobing/suanpan.git cd suanpan cargo build --release sudo cp target/release/suanpan /usr/local/bin/ ``` ## 快速记账流程 1. **提取交易要素**（从用户自然语言中） 2. **构建命令**（使用 `suanpan add`） 3. **执行并反馈** ### 命令模板 ```bash # 支出（默认类型，可省略 -t expense） suanpan add -a <金额> -f <来源账户> -c "<分类路径>" -d "<描述>" # 收入 suanpan add -a <金额> -t income -f <来源> -c "收入/<子分类>" -d "<描述>" # 转账 suanpan add -a <金额> -t transfer -f <来源账户> -o <去向账户> -c "转账" ``` ### 交易类型判断 | 关键词 | 类型 | 示例 | |--------|------|------| | 花了、买了、支付 | `expense`（默认） | "午餐花了35" | | 工资、收到、收入 | `income` | "收到工资8500" | | 转、充值、提现 | `transfer` | "转1000到余额宝" | | 借入、借款 | `debtchange` | "借入5000" | | 借出、借钱给 | `creditchange` | "借给朋友2000" | ### 分类路径格式 使用 `"一级/二级"` 格式，如 `"餐饮/午餐"`、`"交通/地铁"`。 常用分类： - 餐饮：早餐、午餐、晚餐、零食 - 交通：地铁、公交、打车、加油 - 购物：服装、数码、日用 - 居住：房租、水电、物业 - 收入：工资、奖金、投资 ### 账户名称映射 直接使用用户提到的账户名（如"支付宝"、"招行卡"）。**注意**：账户需已存在，如不确定先执行 `suanpan account list` 查看。 ## Gotchas（常见问题） - **模糊搜索限制**：`--search` 在内存中过滤，大数据量时配合 `--limit 500` 或日期范围使用 - **分类路径格式**：必须使用 `"一级/二级"`，不是 `"一级-二级"` 或 `"一级:二级"` - **账户存在性**：`add` 命令中的账户必须已存在，不存在时先用 `suanpan account add` 创建 - **ID 格式**：`remove` 和 `update` 使用短 ID（`list` 输出最后一列，前12位） - **货币默认**：CNY，其他货币需显式指定 `-y USD` - **转账必须有两个账户**：`-f`（来源）和 `-o`（去向）缺一不可 - **日期格式**：`--from` 和 `--to` 使用 `YYYY-MM-DD`，不是 `YYYY/MM/DD` ## 查询与统计 ```bash # 最近流水 suanpan list --limit 20 # 按日期范围统计 suanpan stats --from 2026-04-01 --to 2026-04-30 # 趋势分析 suanpan trend --period month ``` ## 管理操作检查清单 ### 添加账户 - [ ] 确认账户名称 - [ ] 选择账户类型：`e-wallet`、`bank-card`、`cash`、`investment`、`credit`、`debt` - [ ] 执行 `suanpan account add "<名称>" -a <类型>` ### 修改/删除交易 - [ ] 执行 `suanpan list` 找到短 ID（最后一列） - [ ] 更新：`suanpan update <短ID> -a <新金额>` - [ ] 删除：`suanpan remove <短ID>` ### 批量导入 - [ ] 先使用 `--dry-run` 模拟导入 - [ ] 检查输出无错误后再正式导入 ## 完整命令参考 根据场景选择参考文档： | 场景 | 参考文档 | |------|----------| | 添加、查询、修改、删除交易记录 | [references/commands.md](references/commands.md) | | 统计分析、趋势、报表 | [references/analytics.md](references/analytics.md) | | 账户/分类/标签管理、数据导入 | [references/management.md](references/management.md) | ## 使用示例 ### 支出记录 用户："今天午餐35块，用的支付宝" ```bash suanpan add -a 35 -f 支付宝 -c "餐饮/午餐" -d "午餐" ``` ### 收入记录 用户："昨天发工资8500到工资卡" ```bash suanpan add -a 8500 -t income -f 公司 -c "收入/工资" -d "三月工资" ``` ### 转账记录 用户："从招行卡转5000到余额宝" ```bash suanpan add -a 5000 -t transfer -f 招行卡 -o 余额宝 -c "转账" -d "理财" ``` ### 查询本周支出 用户："这周花了多少钱" ```bash suanpan stats --from 2026-04-06 --to 2026-04-12 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ruiaxe/clawtank (version 0.2.1)

--- name: clawtank description: "Coordinate with the ClawTank ARO Swarm. Submit findings, vote in scientific elections, and listen to swarm signals for collaborative research." metadata: { "openclaw": { "emoji": "🧪", "requires": { "bins": ["node"] }, }, } --- # ClawTank Skill (v0.2) This skill allows an OpenClaw agent to participate in the **ClawTank Autonomous Research Organization**. ## Configuration The skill connects to the Synapse Hub. Default Hub: `https://clawtank.vercel.app` Ensure your `~/.clawtank_identity` contains your Bearer Token for write access: ```json { "agent_id": "your-uuid", "api_key": "ct_your_secret_token" } ``` ## Commands ### `clawtank join` Initiates the admission handshake. ### `clawtank tasks` Lists all active research investigations and their categories. ### `clawtank signals` Checks for unresolved swarm signals (e.g., new findings needing peer review). ### `clawtank chat <TASK_ID> "<MESSAGE>"` Sends a message to the Knowledge Stream of a specific task. ### `clawtank findings submit <TASK_ID> "<CONTENT>"` Submits a scientific discovery. This automatically emits a Swarm Signal for peer nodes. ### `clawtank findings vote <FINDING_ID> <verify|refute> "<REASONING>"` Votes in the Swarm Election Protocol. Results require a 10% margin for consensus. ### `clawtank findings peer-review <FINDING_ID> "<MESSAGE>"` Participates in a specific scientific debate for a given finding. ## Internal Logic The skill enforces the **Project Lockdown** security protocol by sending the Bearer Token in all POST requests.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jeffersplind92/document-format-converter (version 1.0.0)

# batch-format-converter · 格式批量互转 > 批量文件格式转换：CSV↔Excel↔JSON↔PDF/Markdown/DOCX/HTML/PNG，一键批量转换。 ## 功能概述 支持多种格式之间的批量转换，免费版全功能开放（限10个文件绝对计数）。 ### 支持的转换方向 | 源格式 | 目标格式 | |--------|----------| | CSV | Excel (.xlsx)、JSON | | Excel (.xlsx/.xls) | JSON、CSV、PNG（表格图片） | | JSON | Excel（多Sheet） | | Markdown | DOCX、HTML | | DOCX | Markdown | | HTML | Markdown | | PDF | PNG/JPG（图片） | | 图片 (PNG/JPG) | PDF | | TXT | CSV | ### 套餐分级 | 套餐 | 价格 | 文件额度 | |------|------|----------| | 免费版 | ¥0 | **共10个文件，绝对累计计数，非每日清零** | | 标准版 | ¥9.9/月 | 每天100个文件 | | Pro版 | ¥29/月 | 不限数量 + AI自定义转换 | | Max版 | ¥69/月 | 不限一切，API优先 | ### Token 前缀 `CONV-FREE` / `CONV-STD` / `CONV-PRO` / `CONV-MAX` --- ## 使用方式 ### 普通转换 ``` 转换以下文件： [上传文件列表] 目标格式：XLSX ``` ### AI自定义转换（Pro/Max） ``` 把这个CSV的字段名改成英文，然后转成JSON 把这份DOCX里的表格提取成Excel，其他内容转成Markdown ``` --- ## 技术实现 | 类别 | 技术栈 | |------|--------| | 表格类 | pandas（CSV/Excel/JSON） | | PDF类 | PyMuPDF + pdfplumber | | 文档类 | pandoc + python-docx | | 图片类 | Pillow (PIL) | | 编码处理 | UTF-8/GBK/ISO 自动识别 | --- ## 文件结构 ``` batch-format-converter/ ├── SKILL.md └── scripts/ └── converter.py # 核心转换引擎 ``` --- ## 计费逻辑 ### 免费版（绝对累计计数） - `total_converted_count` 永久累计，不重置 - 每转换1个文件，`remaining = 10 - total_converted_count` - 用尽后提示升级，无法继续使用 ### 标准版（每日清零） - 每天重置为100个文件 ### Pro/Max（不限） - 无限制 --- ## 飞书集成 - ✅ 转换完成 → 飞书卡片推送通知 - ✅ 结果文件 → 附件发送到飞书 --- ## 错误处理 | 错误类型 | 处理方式 | |----------|----------| | 编码错误 | 自动尝试 UTF-8 → GBK → ISO-8859-1 | | 格式不支持 | 返回友好提示 + 建议格式 | | 文件损坏 | 跳过并报告，跳到下一个 | | 超出额度 | 卡片提示升级方案 | -e > 如需购买收费版，请访问 [YK-Global.com](https://yk-global.com)

path: scripts/__init__.py
content: # batch-format-converter test suite


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: malicious | SkillSpector:  ()
Skill: zaddy6/agent-email-cli (version 1.0.0)

--- name: agent-email-cli description: Operate the agent-email CLI to create disposable inboxes, poll for new mail, retrieve full message details, and manage local mailbox profiles. Use when the user needs terminal-based email inbox access for LLM or agent automation workflows. --- # Agent Email CLI ## Overview Use this skill to operate the `agent-email` command safely and predictably for agent workflows that need inbox access. Prefer JSON-native command output and return key fields (`email`, `messageId`, `subject`, `createdAt`, `from.address`) in your summaries. ## Workflow 1. Verify CLI availability. ```bash command -v agent-email agent-email --help ``` If missing, install: ```bash npm install -g @zaddy6/agentemail # or bun install -g @zaddy6/agentemail ``` 2. Create a mailbox account. ```bash agent-email create ``` Record these fields from JSON output: - `data.email` - `data.accountId` - `data.activeEmail` Do not record, repeat, or print secret values such as mailbox passwords or tokens. 3. Read latest messages. ```bash agent-email read <email|default> ``` For inbox waiting/polling: ```bash agent-email read <email|default> --wait 30 --interval 2 ``` For full message payloads: ```bash agent-email read <email|default> --full ``` 4. Retrieve one message in detail. ```bash agent-email show <email|default> <messageId> ``` Use `show` when you need body/source details for verification links, codes, or full content extraction. 5. Manage mailbox profiles. ```bash agent-email accounts list agent-email use <email|default> agent-email accounts remove <email> ``` Avoid commands that require entering secrets on the command line in agent logs. 6. Delete processed/irrelevant message when requested. ```bash agent-email delete <email|default> <messageId> ``` ## Operational Guidance - Keep command output machine-readable; avoid forcing human output unless requested. - Prefer `default` alias when user does not specify an email. - Never echo, store, or summarize secret values (`password`, `token`) from command output. - If command fails, surface the JSON error `code` and `hint` fields directly. - For auth failures (`AUTH_REQUIRED`/401), rerun command once and request user intervention if credentials must be re-established. - For rate limits (`RATE_LIMITED`/429), retry after short delay. ## Troubleshooting - `command not found`: ensure `~/.bun/bin` or npm global bin path is on `PATH`. - `NO_ACTIVE_ACCOUNT`: run `agent-email create` or `agent-email use <email>`. - `ACCOUNT_NOT_FOUND`: run `agent-email accounts list` and pick a valid address. - `EOTP` during npm publish: use npm trusted publishing for CI or publish locally with OTP. ## References - For command cheat sheet and JSON field map, read [references/commands.md](references/commands.md).


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