OpenClaw/clawhub-security-signals huggingface dataset:
Skill: josephyb97/cluade-scientific-writer-wrapper (version 1.0.0)

--- name: scientific-writer description: AI-powered scientific writing tool. Generate publication-ready papers, grant proposals, posters, and more with real-time research and verified citations. metadata: { "openclaw": { "emoji": "🔬", "requires": { "bins": ["python3", "pip"], "env": ["ANTHROPIC_API_KEY"] } } } --- # Scientific Writer AI-powered scientific writing tool that combines deep research with well-formatted outputs. ## Installation ```bash pip install scientific-writer ``` ## Prerequisites - Python 3.10-3.12 - ANTHROPIC_API_KEY (required) - OPENROUTER_API_KEY (optional for research lookup) Set API keys: ```bash export ANTHROPIC_API_KEY='your_key' # or create .env file echo "ANTHROPIC_API_KEY=your_key" > .env ``` ## Usage ### As Python API ```python import asyncio from scientific_writer import generate_paper async def main(): async for update in generate_paper( query="Create a Nature paper on CRISPR gene editing...", data_files=["editing_efficiency.csv", "western_blot.png"] ): if update["type"] == "progress": print(f"[{update['stage']}] {update['message']}") else: print(f"✓ PDF: {update['files']['pdf_final']}") asyncio.run(main()) ``` ### Via OpenClaw exec ```bash # Run scientific writer python3 -c " import asyncio from scientific_writer import generate_paper async def main(): async for update in generate_paper( query='Create a paper on your topic...', data_files=[] ): print(update) asyncio.run(main()) " ``` ## Available Skills (when used as plugin) - `scientific-schematics` - AI diagram generation (CONSORT, neural networks, pathways) - `research-lookup` - Real-time literature search - `peer-review` - Systematic manuscript evaluation - `citation-management` - BibTeX and reference handling - `clinical-reports` - Medical documentation - `research-grants` - NSF, NIH, DOE proposal support - `scientific-slides` - Research presentations - `latex-posters` - Conference poster generation - `hypothesis-generation` - Scientific hypothesis development ## Output - Scientific papers (Nature, Science, NeurIPS format) - Grant proposals (NSF, NIH, DOE) - Conference posters (LaTeX beamerposter) - Literature reviews - Clinical reports ## Notes - Requires ANTHROPIC_API_KEY for Claude to work - Place data files in `data/` folder (images → figures/, data → data/) - Outputs saved to `writing_outputs/`

path: package.json
content: {
  "name": "scientific-writer",
  "version": "1.0.0",
  "description": "AI-powered scientific writing tool. Generate papers, grants, posters with research and citations.",
  "emoji": "🔬",
  "author": "K-Dense-AI",
  "source": "https://github.com/K-Dense-AI/claude-scientific-writer",
  "requires": {
    "bins": ["python3", "pip"],
    "env": ["ANTHROPIC_API_KEY"]
  },
  "keywords": ["scientific", "writing", "paper", "research", "academic", "latex", "grant"],
  "category": "productivity"
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: haohanyang92/a-stock-volume-price (version 1.0.0)

--- name: volume-price-analysis description: 通过成交量判断价格走势真实性，避免被"诱多"或"诱空"。用于A股技术分析。 --- # 量价关系验证 Skill ## 核心能力 通过成交量判断价格走势的真实性，避免被"诱多"或"诱空"。 ## 量价规则 ### 有效上涨 - 价格创新高 - 成交量放大（较前一日放量30%以上） - **结论**：有效突破，可跟进 ### 虚假上涨（诱多） - 价格创新高 - 成交量缩量 - **结论**：警惕主力出货，不可追 ### 底部反转 - 价格企稳 - 成交量连续3日温和放大 - **结论**：资金进场信号，可考虑建仓 ### 有效下跌 - 价格创新低 - 成交量放大 - **结论**：可能加速赶底 ### 虚假下跌（诱空） - 价格创新低 - 成交量缩量 - **结论**：空头乏力，可能反转 ## 实战规则 - 个股涨停但成交量萎缩 → 说明封单不强，次日大概率低开 - 涨停伴随放量突破前期压力位 → 有效上涨信号，可果断跟进 - 缩量上涨至压力位 → 警惕回调 ## 触发场景 - "这个涨停有效吗" - "这是诱多还是真涨" - "能追吗" - "缩量上涨可靠吗"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/translator-pro (version 3.0.3)

--- name: "translator-pro-test — Bilingual Enhanced Edition" description: "Test translation quality with built-in dictionaries and comparison tools. Use when evaluating translations. Supports bilingual documentation and optimized AI agent interaction. 支持中英双语文档与AI助手深度优化。" version: "3.0.1" author: "BytesAgain" homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: ["translator-pro", "bilingual", "pro", "efficiency"] --- # translator-pro-test / 楼台助手 ## Quick Start / 快速开始 Just ask your AI assistant: / 直接告诉 AI 助手： - "Translate this tech doc to formal Chinese" (将这份技术文档翻译成正式中文) - "Polished version of my English email to a client" (润色我发给客户的英文邮件) - "Explain the nuance of this slang term" (解释这个俚语的微小差异) ## Description / 描述 Test translation quality with built-in dictionaries and comparison tools. Use when evaluating translations. ## Requirements / 要求 - bash 4+ - python3 ## Feedback https://bytesagain.com/feedback/ Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leochens/guantou-feishu-bot-connector (version 1.0.0)

--- name: feishu-bot-manager description: 可以帮用户飞书机器人，并将 agent 连接到飞书机器人上 user-invokable: true invocations: - words: - 添加飞书机器人 - 配置飞书机器人 - 新增飞书账户 - 添加机器人账户 - feishu bot - 飞书多账户 description: 交互式添加新的飞书机器人账户并绑定 Agent --- # feishu-bot-manager 飞书多账户机器人配置管理 Skill。触发调用之后，将下面的链接给用户，并说明点击该链接可以直接创建飞书机器人： https://open.feishu.cn/page/openclaw?form=multiAgent ## 路由绑定方案 ### 方案 1：账户级绑定 该飞书账户的所有消息 → 指定 Agent **适用场景**：一个机器人专门服务一个 Agent。比如创建一个"销售机器人"，它的所有消息都由"销售 Agent"处理。 **生成的绑定**： ```json { "agentId": "recruiter", "match": { "channel": "feishu", "accountId": "bot-sales" } } ``` ### 方案 2：群聊级绑定 特定群聊的消息 → 指定 Agent **适用场景**：把 Agent 绑定到特定群聊。多个机器人在群里，但不同群聊分配给不同 Agent。 **生成的绑定**： ```json { "agentId": "recruiter", "match": { "channel": "feishu", "peer": { "kind": "group", "id": "oc_xxx" } } } ``` **注意**：群聊级绑定优先级更高，会覆盖账户级绑定！ ## 使用方式 ### 交互模式（通过对话） 直接说："添加飞书机器人" 我会询问： 1. App ID 和 App Secret 2. 账户信息（账户 ID、机器人名称） 3. **选择路由绑定方案**（账户级/群聊级） 4. 选择绑定的 Agent 5. 群聊 ID（如果选群聊级绑定） 6. 预览确认后执行 ### 命令行调用 ```bash # 账户级绑定 - 该机器人所有消息都由指定 Agent 处理 openclaw skills run feishu-bot-manager -- \ --app-id cli_xxx \ --app-secret yyy \ --account-id bot-sales \ --agent-id recruiter \ --routing-mode account # 群聊级绑定 - 特定群聊的消息由指定 Agent 处理 openclaw skills run feishu-bot-manager -- \ --app-id cli_xxx \ --app-secret yyy \ --account-id bot-sales \ --agent-id recruiter \ --chat-id oc_xxx \ --routing-mode group ``` ## 参数说明 | 参数 | 必填 | 说明 | |------|------|------| | --app-id | ✅ | 飞书 App ID (cli_xxx) | | --app-secret | ✅ | 飞书 App Secret | | --account-id | ❌ | 账户标识，默认自动生成 | | --bot-name | ❌ | 机器人名称，默认 "Feishu Bot" | | --dm-policy | ❌ | DM 策略: open/pairing/allowlist，默认 open | | --agent-id | ❌ | 要绑定的 Agent ID | | --chat-id | ❌ | 群聊 ID (oc_xxx)，群聊绑定时需要 | | --routing-mode | ❌ | 路由模式: account/group，默认 account | ## 配置结构示例 添加新机器人后，配置会变成这样（保留现有配置）： ```json { "channels": { "feishu": { "enabled": true, "appId": "cli_现有", // ← 保留不动 "appSecret": "现有Secret", // ← 保留不动 "dmPolicy": "open", "accounts": { // ← 新添加 "bot-new": { "appId": "cli_xxx", "appSecret": "yyy", "botName": "新机器人", "dmPolicy": "open", "allowFrom": ["*"], "enabled": true } } } }, "bindings": [ { // ← 新添加 "agentId": "recruiter", "match": { "channel": "feishu", "accountId": "bot-new" // 或 "peer": { "kind": "group", "id": "oc_xxx" } } } ] } ``` ## 流程 1. 检查并备份现有配置 2. 添加新账户到 `channels.feishu.accounts` 3. 根据选择的路由模式添加 binding 4. 设置 `session.dmScope` 为 `per-account-channel-peer` 5. 重启 Gateway ## 注意事项 - **保留现有配置**：现有 `appId/appSecret` 完全不动 - **自动备份**：修改前自动备份 openclaw.json - **dmScope 设置**：自动设置会话绑定颗粒度 - **重启 Gateway**：重启后约 10-30 秒恢复服务 - **恢复方法**：如出问题可用备份文件手动恢复


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: heyalerio/memesio-meme-generator (version 1.0.2)

--- name: memesio-meme-generator description: Use for meme generation with Memesio through MCP: search meme templates, add captions to templates or uploaded images, create meme agent accounts, and generate AI memes. Also use when setting up the Memesio Meme Generator in MCP clients or writing install and usage examples. --- # Memesio Meme Generator MCP Use the hosted Memesio meme generator MCP server: - Endpoint: `https://memesio.com/api/mcp` - Docs: `https://memesio.com/developers/mcp` - Official registry name: `com.memesio/meme-generator` ## When to use Trigger this skill when the user wants to: - connect an MCP client to Memesio - search meme templates - caption a known meme template - caption an uploaded image - create a Memesio agent account - generate AI memes through Memesio - document or submit the Memesio meme generator to MCP directories ## Connection shape Use a remote streamable HTTP server config: ```json { "mcpServers": { "memesio": { "type": "streamable-http", "url": "https://memesio.com/api/mcp" } } } ``` The server does not require connection-level secrets for basic connectivity. Public tools work anonymously. Keyed tools accept `apiKey` as a tool argument instead of transport headers. ## Recommended workflow 1. If the caller needs a key, call `create_agent_account`. 2. Use `search_templates` to discover meme formats. 3. Use `caption_template` when a template slug is already known. 4. Use `caption_upload` when the source image is custom. 5. Use `get_ai_quota` before `generate_meme` for keyed AI meme generation. ## Tool surface Supported tools: - `create_agent_account` - `search_templates` - `get_template_ideas` - `caption_template` - `caption_upload` - `generate_meme` - `get_ai_quota` Read `references/tools.txt` when you need concise behavior guidance or example payloads.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jwelten/proxygate (version 0.2.1)

--- name: proxygate description: "ProxyGate CLI — the Stripe for AI Agents. Use this skill whenever the user mentions ProxyGate, proxy requests, API marketplace, USDC payments, Solana wallet, vault balance, listings, tunnels, bounties, jobs, sellers, buyers, or any topic related to the ProxyGate platform. This is the router skill — it determines which sub-skill to invoke." --- # ProxyGate CLI Router You are working with **ProxyGate** — the Stripe for AI Agents. A marketplace where sellers list API capacity and AI agents buy access through a transparent proxy, paying with USDC on Solana. ## How to route Based on the user's intent, invoke the appropriate sub-skill using the `Skill` tool: | User wants to... | Invoke skill | |---|---| | Set up ProxyGate, install CLI, configure wallet, connect gateway | `pg-setup` | | Check balance, usage, earnings, listings, settlement history, status overview | `pg-status` | | Browse APIs, deposit USDC, make proxy requests, stream responses, rate sellers, withdraw | `pg-buy` | | Create listings, manage listings, rotate keys, upload docs, start tunnels, sell capacity, view earnings | `pg-sell` | | Post bounties, find work, claim jobs, submit results, manage job lifecycle | `pg-jobs` | | Update CLI or SDK to latest version | `pg-update` | ## Routing rules 1. **Always route** — never answer ProxyGate questions without loading the relevant sub-skill first 2. **Multiple skills** — if the user's request spans multiple areas (e.g. "check my balance and make a proxy call"), invoke the most relevant skill first 3. **When unclear** — if you can't determine intent, invoke `pg-status` to show an overview, then ask what they want to do ## Quick reference ```bash proxygate --help # all commands proxygate balance # check balance proxygate apis # browse APIs proxygate proxy <id> /path -d '...' # make a request proxygate listings list # your listings proxygate jobs list # bounty board proxygate tunnel # expose local service ``` ## Key facts - Payments: USDC on Solana (1 USDC = 1,000,000 lamports) - Auth: ed25519 wallet signatures (automatic via CLI/SDK) - Config: `~/.proxygate/config.json` (gateway URL + keypair path) - Gateway: `https://gateway.proxygate.ai` - Docs: `https://gateway.proxygate.ai/docs`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nic-yuan/apollo-overview (version 1.1.0)

--- name: apollo-overview description: > 给你一张完整的方法论地图，告诉你从想法到成品每步该做什么。 --- # Apollo 方法论套件 Apollo是一套**结构化AI编码方法论**，让AI coding agent变成严谨的工程师而不是盲目写代码的助手。 ## 套件组成 | Skill | 作用 | |-------|------| | **apollo-workflow** | 核心Pipeline入口（ brainstorming→plan→subagent→review→finish） | | **apollo-brainstorming** | 苏格拉底式提问，搞清楚"真正要做什么" | | **apollo-planning** | 把设计拆成2-5分钟的小任务 | | **apollo-parallel** | 并行派发多个子任务同时工作 | | **apollo-verification** | 证据先行——说"完成了"之前必须跑验证命令 | | **apollo-debugging** | 四阶段调试：根因→模式→假设→验证 | | **apollo-mode** | 开启/关闭严格工程模式 | | **apollo-state-machine** | 状态机驱动，支持随时打断恢复 | | **apollo-research-synthesis** | 多人并行研究，最后自己汇总 | | **apollo-timeout** | 超时容错，不卡死整个流程 | | **apollo-multi-round** | 复杂任务多轮确认，不闷头跑到底 | | **apollo-dream** | 像睡觉做梦一样整理记忆 | ## 核心原则 1. **Spec-first** — 先写规格，再写代码 2. **TDD** — 先写失败测试，再写实现 3. **Subagent驱动** — 并行执行，小步迭代 4. **证据说话** — 说"完成了"必须有验证结果 ## 使用方式 当用户说"开发新功能"、"修bug"、"写计划"、"开始项目"时触发。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vsharmi74/forex-skill (version 1.0.0)

# Forex VSA & PVA Analysis Agent ## Overview This OpenClaw agent is designed to automate Forex market analysis using Volume Spread Analysis (VSA) and Price Volume Analysis (PVA). It integrates with OpenRouter to process market sentiment and technical data. ## Features - **Volume Spread Analysis:** Detects professional "smart money" activity by analyzing price spreads and volume. - **PVA Filters:** Identifies climax volume and high-volume churn candles. - **OpenClaw Integration:** Utilizes the latest 2026 agent framework for real-time position management. - **Multi-Model Support:** Configured to work with high-context LLMs via OpenRouter and TypingMind. ## Configuration The agent requires a valid OpenClaw configuration key. Ensure your `config.json` uses the `key` field rather than `apiKey` for compatibility with v0.15.0. ## Installation Once synced, this skill can be deployed directly into your OpenClaw environment to begin monitoring currency pairs for VSA setups.# Forex Skill

path: browser/openclaw/user-data/Default/BookmarkMergedSurfaceOrdering

path: browser/openclaw/user-data/Default/SCT Auditing Pending Reports

path: devices/pending.json

Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: axelhu/superpowers-parallel-agents (version 1.0.1)

--- name: superpowers-parallel-agents description: Use when facing 2 or more independent tasks that can be worked on without shared state - dispatches parallel subagents using sessions_spawn for concurrent investigation and execution, adapted for OpenClaw --- # Superpowers Parallel Agents（OpenClaw 适配版） ## 概述 当面临 2+ 个独立任务时，将它们分配给专门的并行 subagent，同时工作。OpenClaw 用 `sessions_spawn` 创建独立 session 实现并行分发。 **核心原则：** 每个独立问题域分配一个 agent，让它们并发工作。 ## OpenClaw 适配 Superpowers 原版用 Claude Code 的 `Task` 工具并发派发。OpenClaw 用 `sessions_spawn`： - `sessions_spawn(mode="run")` — 一次性任务，并发执行 - `sessions_spawn(mode="session")` — 持久 session，可多轮交互 - 主 session 协调，subagent 结果通过 session 历史或文件系统汇总 ## 使用条件 ``` 有多个失败/独立任务？ → 它们独立？（不同根因、无共享状态）？ → 可以并行工作？ → 用 parallel-agents（这个技能）✅ → 顺序更合适？ → 用 systematic-debugging 单独处理 → 需要全面上下文理解？ → 单一 agent 处理 ``` **适用场景：** - 3+ 个测试文件失败，根因不同 - 多个子系统独立损坏 - 每个问题可以在不理解其他问题上下文的情况下理解 - 调查之间无共享状态 **不适用：** - 失败互相关联（修一个可能修其他） - 需要理解完整系统状态 - Agents 会互相干扰（编辑同一文件、共用资源） ## 流程 ### 1. 识别独立问题域 按问题分组： - 测试文件 A：工具审批流程 - 测试文件 B：批处理完成行为 - 测试文件 C：中止功能 每个问题域独立——修工具审批不影响中止测试。 ### 2. 为每个 Agent 创建专注任务 每个 agent 获得： - **明确范围：** 一个测试文件或子系统 - **清晰目标：** 让这些测试通过 / 修复这个 bug - **约束：** 不要改其他代码 - **预期输出：** 发现什么、修复什么的摘要 ### 3. 并行 dispatch 用 `sessions_spawn` 同时派发所有 agent： ```javascript // OpenClaw: sessions_spawn 并行派发 sessions_spawn({ task: "修复 src/agents/agent-tool-abort.test.ts 的 3 个失败测试...", runtime: "subagent", mode: "run", cwd: "/path/to/project" }) sessions_spawn({ task: "修复 src/batch/completion.test.ts 的 2 个失败测试...", runtime: "subagent", mode: "run", cwd: "/path/to/project" }) sessions_spawn({ task: "修复 src/tools/race-conditions.test.ts 的 1 个失败测试...", runtime: "subagent", mode: "run", cwd: "/path/to/project" }) ``` ### 4. 审查和整合 当 agents 返回： - 读每个摘要 - 验证修复不冲突 - 运行完整测试套件 - 整合所有变更 ## Agent 提示词结构 好的 agent 提示词： 1. **专注** — 一个清晰的问题域 2. **自包含** — 理解问题所需的全部上下文 3. **输出具体** — agent 应该返回什么？ ```markdown 修复 src/agents/agent-tool-abort.test.ts 中 3 个失败的测试： 1. "should abort tool with partial output capture" - 期望消息中有 'interrupted at' 2. "should handle mixed completed and aborted tools" - 快速工具被中止而非完成 3. "should properly track pendingToolCount" - 期望 3 个结果但得到 0 这些是时序/竞态条件问题。你的任务： 1. 读测试文件，理解每个测试验证什么 2. 识别根因——时序问题还是实际 bug？ 3. 修复： - 用事件等待替代任意 timeout - 如发现 bug 则修复 abort 实现 - 如测试的是变化的行为则调整测试期望 不要只加 timeout——找真正的问题。 返回：你发现了什么，修复了什么。 ``` ## 常见错误 **❌ 范围太广：** "修所有测试" — agent 会迷失 **✅ 具体：** "修 agent-tool-abort.test.ts" — 专注范围 **❌ 无上下文：** "修竞态条件" — agent 不知道在哪里 **✅ 有上下文：** 粘贴错误信息和测试名 **❌ 无约束：** Agent 可能重构一切 **✅ 有约束：** "不要改其他代码" 或 "只修测试" **❌ 输出模糊：** "修好了" — 不知道改了啥 **✅ 输出具体：** "返回根因和变更摘要" ## 何时不用 **相关失败：** 修一个可能修其他——先一起调查 **需要完整上下文：** 理解需要看到整个系统 **探索性调试：** 还不知道哪里坏了 **共享状态：** Agents 会互相干扰（编辑同一文件、用同一资源） ## 关键优势 1. **并行化** — 多个调查同时进行 2. **专注** — 每个 agent 范围窄，跟踪的上下文少 3. **独立性** — Agents 不互相干扰 4. **速度** — 3 个问题用 1 个问题的时间解决 ## 验证 Agents 返回后： 1. **审查每个摘要** — 理解改了什么 2. **检查冲突** — Agents 编辑了同一代码吗？ 3. **运行完整套件** — 验证所有修复一起工作 4. **抽查** — Agents 可能犯系统性错误


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xianyu110/bananapro-image-gen (version 1.0.2)

--- name: bananapro-image-gen description: 使用 Gemini 图像模型生成图片，支持白板图、Logo设计、社交媒体配图。 author: jarvis license: MIT tags: - image - gemini - ai - design - logo --- # Banana Pro Image Generation Skill 使用 Gemini 图像模型生成各种风格的图片。 ## 功能特点 - 文生图：根据文字描述生成图片 - 白板图：生成手写风格的概念图、流程图 - Logo设计：创建简约现代的Logo - 社交媒体配图：生成适合各平台的配图 - 图片编辑：修改已有图片 ## 中转 API 配置教程 ### 什么是中转 API？ 中转 API 是第三方提供的 API 代理服务，相比官方 API 有以下优势： - 成本更低（通常 5-7 折） - 国内网络直连，更稳定 - 无需配置代理 ### 推荐中转服务 **apipro.maynor1024.live**（默认） 网站：https://apipro.maynor1024.live/ ### 配置方式 **方式一：使用中转 API（推荐）** ``` export NEXTAI_API_KEY="your-api-key" ``` 获取 API Key： 1. 访问 https://apipro.maynor1024.live/ 2. 注册账号 3. 获取 API Key **方式二：使用官方 Gemini API** ``` export GEMINI_API_KEY="your-gemini-api-key" ``` 获取方式：访问 https://aistudio.google.com/apikey 注意：官方 API 需要科学上网。 **方式三：使用其他中转服务** 支持任何 OpenAI 兼容的中转服务： ``` export NEXTAI_API_KEY="your-api-key" export NEXTAI_API_URL="https://your-proxy.com/v1" ``` ## 使用方法 ### 基础用法 ``` python scripts/generate_image.py \ --prompt "A serene Japanese garden" \ --filename "garden.png" ``` ### 高清图片 ``` python scripts/generate_image.py \ --prompt "你的提示词" \ --filename "output.png" \ --resolution 2K ``` ### 图片编辑 ``` python scripts/generate_image.py \ --prompt "把天空改成夕阳效果" \ --filename "edited.png" \ --input-image "original.jpg" ``` ## 分辨率选项 - **1K**（默认）：~1024px，日常使用 - **2K**：~2048px，高清展示 - **4K**：~4096px，打印输出 ## 提示词技巧 ### 白板图模板 ``` 生成一张白板图片，手写字体风格，内容包含： - 标题：[主题] - 核心要点： 1. [要点1] 2. [要点2] - 使用箭头、框图等手绘元素 ``` ### Logo设计模板 ``` 设计一个[主题]Logo，要求： - 形状：[圆形/方形/抽象] - 颜色：[主色调] - 元素：[核心元素] - 风格：[现代/简约] ``` ## 常见问题 ### Q: 中转 API 安全吗？ A: 选择可信的中转服务很重要。apipro.maynor1024.live 是社区常用的中转服务。 ### Q: 支持中文提示词吗？ A: 完全支持！Gemini 对中文理解很好。 ## 许可证 MIT License


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: malleus35/agora-court-review (version 2.2.0)

--- name: court-review description: | Review a proposal through separated governance roles: strategist, operator, censor, historian, and sovereign. version: 0.1.0 --- # Court Review ## Purpose Separate responsibility-bearing perspectives before approval. ## Activate when Use this skill when: - multiple stakeholders need distinct review roles - governance or approval matters - a proposal affects strategy, operations, oversight, and legitimacy at once ## Roles ### Strategist What long-term posture does this create? ### Operator Can this actually be executed with current constraints? ### Censor What is unsafe, non-compliant, misleading, or insufficiently justified? ### Historian What precedent, path dependency, or institutional memory matters here? ### Sovereign What is the verdict, under what conditions, and who owns the decision? ## Output artifact ```markdown ## Court Review ### Strategist - ... ### Operator - ... ### Censor - ... ### Historian - ... ### Sovereign Verdict - Approve / Approve with conditions / Reject - Conditions: ... - Owner: ... ``` ## Guardrails - Do not merge all voices into one summary paragraph. - Do not let the sovereign verdict ignore the censor's material objection. - Do not skip ownership. ## Completion condition This skill is complete only when responsibility, conditions, and verdict are explicit.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: assix/agent-securitysandbox (version 1.0.0)

--- name: mass-sandbox description: Execute untrusted code or commands in a secure, isolated Docker environment. Use when asked to run unknown scripts, third-party skills, or any code that might pose a security risk to the host system. metadata: openclaw: requires: bins: [docker] --- # Multi-Agent Security Sandbox (MASS) ## Overview This skill provides a secure "clean room" for executing and monitoring untrusted commands and code. It uses Docker with restricted capabilities, no network access, and a custom seccomp profile to isolate the execution from your host system. ## When to Use - When asked to run a script or command from an untrusted source. - When testing a new agent skill that requires system access. - When you need to verify the behavior of code that might be malicious. ## Workflow ### 1. Identify Untrusted Input Determine if the command or script you are asked to run should be sandboxed. ### 2. Run in Sandbox Use the `mass` script located in the skill's `scripts/` directory to wrap the command. **Usage:** ```bash ./scripts/mass "<command>" ``` **Example:** To run a suspicious Python script: ```bash ./scripts/mass "python3 untrusted_script.py" ``` ### 3. Review Output The `mass` tool will return the stdout and stderr from the sandbox. If the command was blocked by security policies (e.g., trying to access sensitive files or making network calls), the output will reflect the failure. ## Security Features - **Capabilities Dropped:** All Linux capabilities are dropped (`--cap-drop=ALL`). - **No Network:** Network access is disabled by default (`--network=none`). - **Non-Root User:** Code runs as `sandboxuser`. - **Syscall Filtering:** Sensitive system calls are blocked via `seccomp.json`. ## Limitations - **No GUI:** The sandbox is CLI-only. - **Persistence:** Any changes made inside the container are lost after execution (`--rm`). - **Dependencies:** Only tools installed in the `Dockerfile` are available.


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