OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yesong-hue/github-skills-assistant (version 1.0.0)

--- name: github description: Query and manage GitHub repositories - list repos, check CI status, create issues, search repos, and view recent activity. metadata: openclaw: emoji: "🐙" requires: env: - GITHUB_TOKEN - GITHUB_USERNAME config: - github.token - github.username --- # GitHub Integration Skill Query and manage GitHub repositories directly from your AI assistant. ## Capabilities | Capability | Description | |------------|-------------| | `list_repos` | List your repositories with filters | | `get_repo` | Get detailed info about a specific repo | | `check_ci_status` | Check CI/CD pipeline status | | `create_issue` | Create a new issue in a repo | | `create_repo` | Create a new repository | | `search_repos` | Search your repositories | | `get_recent_activity` | Get recent commits | ## Usage ``` You: List my Python repos Bot: [lists your Python repositories] You: Check CI status on my main project Bot: [shows CI/CD status] You: Create an issue about the bug Bot: [creates the issue] ``` ## Setup ### 1. Generate GitHub Personal Access Token 1. Go to https://github.com/settings/tokens 2. Click "Generate new token (classic)" 3. Name: `openclaw-github-skill` 4. Scopes: `repo` (required), `read:user` (optional) 5. Copy the token ### 2. Configure Credentials **Option A: Environment Variables (Recommended)** Set environment variables before starting OpenClaw: ```bash export GITHUB_TOKEN="ghp_your_token_here" export GITHUB_USERNAME="your_github_username" ``` **Option B: OpenClaw Config** Add to `~/.openclaw/openclaw.json`: ```json { "github": { "token": "ghp_your_token_here", "username": "your_username" } } ``` ### 3. Restart OpenClaw ```bash openclaw gateway restart ``` ## Security Notes ⚠️ **Protect Your Token:** - Never commit your token to git or share it publicly - Use the minimal required scopes (`repo` for private repos, `public_repo` for public-only) - Rotate your token if you suspect it was compromised - Consider using a secrets manager for production use ⚠️ **Best Practices:** - Don't store tokens in shell profiles (~/.zshrc) on shared machines - For local development, environment variables are acceptable - For production, use your platform's secret/credential store ## Rate Limits - Unauthenticated requests: 60/hour - Authenticated requests: 5,000/hour ## Requirements - OpenClaw gateway running - GitHub Personal Access Token with appropriate scopes


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yesong-hue/content-automation-pro (version 1.1.0)

--- name: content-generator description: Generate platform-specific shopping and recommendation copy for ecommerce products. version: 1.0.0 author: yesong-Hue homepage: https://clawhub.ai/yesong-Hue/content-generator tags: [content, ecommerce, social, marketing] readme: | # Content Generator - 种草内容生成器 # Content Generator - 种草内容生成器 一键生成多平台种草文案。 ## 功能 - 📝 小红书/抖音/朋友圈/知乎 多平台适配 - 🎯 多种风格模板（测评/清单/对比） - 🎨 自动表情和话题标签 - ⚡ 批量生成多平台版本 ## 使用 ``` 生成种草文案 商品信息 生成小红书文案 面霜 299 保湿效果好 生成朋友圈文案 简短 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: danielfoch/crea-ddf-mcp (version 0.1.0)

--- name: crea-ddf-mcp description: Query CREA/REALTOR.ca DDF via a hardened MCP server and CLI for institutional-grade listing data retrieval, metadata inspection, and media access. Use when a user needs DDF tools in Claude MCP, OpenClaw, or scripted research workflows. --- # CREA DDF MCP Use this skill to run DDF as a governed MCP/CLI integration for downstream analysis and automations. ## Workflow ### 1. Configure DDF credentials Set environment variables: - `DDF_BASE_URL` - `DDF_AUTH_URL` - `DDF_TOKEN_GRANT` (`client_credentials` or `password`) - `DDF_CLIENT_ID` + `DDF_CLIENT_SECRET` (for client credentials) - `DDF_USERNAME` + `DDF_PASSWORD` (for password grant) Optional operational controls: - `DDF_HTTP_TIMEOUT_MS`, `DDF_HTTP_RETRIES`, `DDF_HTTP_RPS`, `DDF_HTTP_BURST` - `DDF_MEDIA_ENTITY`, `DDF_MEDIA_RECORD_KEY_FIELD`, `DDF_MEDIA_ORDER_FIELD` ### 2. Build and run MCP ```bash npm --workspace @fub/crea-ddf-mcp run build node packages/crea-ddf-mcp/dist/mcp-server.js ``` ### 3. Validate with CLI ```bash npm --workspace @fub/crea-ddf-mcp run dev:cli -- search-properties --filters-json '{"city":"Toronto"}' --top 5 npm --workspace @fub/crea-ddf-mcp run dev:cli -- get-property --id "<ListingKey>" npm --workspace @fub/crea-ddf-mcp run dev:cli -- get-metadata ``` ### 4. Wire into Claude MCP/OpenClaw Use `references/claude-mcp-config.md` for Claude setup and `references/openclaw-wiring.md` for OpenClaw runtime wiring. ## Safety Rules - Prefer typed tools (`ddf.search_properties`, `ddf.get_property`) over raw calls. - Keep field selections to allowlisted safe sets unless governance is updated. - Treat data licensing/display obligations as upstream policy requirements. - Never store credentials in plaintext files committed to git.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/story-led-sell-script (version 1.0.0)

# Story-led Sell Script Turn a product, creator experience, or customer scenario into a believable story-driven sell script that persuades without sounding like a hard ad. Use this skill when a user wants a creator-native sales script built around story, sequence, and emotional credibility instead of a blunt problem-solution pitch. This is especially useful for TikTok Shop creators, founder-led ecommerce brands, UGC teams, and operators who need scripts that feel human while still moving toward conversion. ## Problem it solves Many ecommerce scripts explain the product correctly but fail to feel real. They jump straight into features, benefits, or claims without a believable narrative arc. This skill helps turn a product into a story that creates attention, emotional buy-in, and a more natural path to the CTA. ## Use when - A product is easier to sell through story than direct explanation - The user wants a more human, less ad-like creator script - You need a founder story, user story, or day-in-the-life sell script - The audience is skeptical of hard-sell formats - A brand needs a script that blends relatability with conversion intent ## Do not use when - The task is a pure direct-response pitch with no narrative angle - The user only needs hooks or CTAs, not a full script - There is no believable experience, scenario, or emotional shift to build on ## Inputs to ask for - Product and category - Audience and use context - Whose story is being told: creator, customer, founder, operator - Trigger moment, frustration, or turning point - Outcome or payoff - Proof available: demo, results, testimonial, routine use, social proof - Platform and target video length - CTA goal ## Workflow 1. Clarify the audience, product, and narrator perspective 2. Identify the cleanest story arc: before, trigger, discovery, shift, outcome 3. Build a believable emotional progression without melodrama 4. Introduce the product at the right moment, not too early 5. Add proof, specificity, or usage detail to keep the story credible 6. End with a CTA that fits the tone and funnel stage 7. Optionally provide alternate versions with different emotional intensity ## Output requirements Return: 1. Script in beat-by-beat format 2. Story hook or opening scene 3. Setup / before state 4. Trigger or change moment 5. Product introduction 6. Outcome / payoff 7. Proof cue 8. CTA 9. Optional shorter alternate version ## Quality standards - Story should feel believable, not fabricated or overly polished - Product mention should arrive naturally within the arc - Language should sound creator-native and spoken aloud - Avoid overclaiming, fake drama, or forced vulnerability - The final script should feel usable in a real short-form video ## Example prompts - Write a TikTok Shop script that sells through a customer story instead of a hard pitch - Turn this founder experience into a believable story-led sell script - Give me a creator-native script with a before -> discovery -> payoff arc


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 865116251/beauty-prompt (version 1.3.1)

--- name: beauty-prompt description: 装修家具灵感顾问，将用户的家居装修、空间设计需求转化为高质量视觉图像。触发场景：(1) 用户说"帮我生成[XXX]图片"，(2) 用户表达装修、家居、空间设计、商业展示等视觉内容需求，(3) 用户需要生成客厅、卧室、厨房等空间效果图。包含需求拆解提问、提示词中英文转化、调用 nano-banana2-apiyi 生成图片的完整流程。 --- # 装修家具灵感顾问 你的核心目标是协助用户将模糊的家居装修、空间设计或商业展示需求，转化为高质量的视觉图像。需要展现出专业的设计审美、逻辑严密的引导能力，以及高效的执行力。 ## 触发机制 当用户输入包含以下意图的指令时，触发本技能： - **关键词匹配**：「帮我生成[XXX]图片」、「帮我生成[XXX]」 - **意图识别**：用户明确表达需要关于装修、家居、空间设计、商业展示、风格设计等视觉内容的需求 ## 交互逻辑（需求拆解阶段） 在获取用户初始需求后，进入需求拆解阶段，遵循以下规则： ### 核心原则 **根据用户输入进行深度分析**，提出与需求直接相关的关键问题。不要使用固定模板问题，而是： 1. 分析用户描述的场景、用途、情感诉求 2. 联想该场景下重要的设计要素（材质、布局、光线、氛围、功能） 3. 问出能真正影响画面效果的细节 ### 问题设计示例 - 用户说"有助于睡眠的卧室" → 问床型、光线强度、是否需要绿植/香氛等助眠元素 - 用户说"赛博朋克电竞房" → 问色调偏好、空间规模、是否需要人物/角色 - 用户说"温馨的厨房" → 问开放式还是封闭式、岛台需求、餐桌位置 ### 选项规范 每个问题提供 3-6 个具体选择方案，遵循以下格式： - **问题使用数字索引**（1. 2. 3.） - **答案选项使用英文字母索引**（A. B. C. D.） **示例输出格式：** ``` 1. 风格偏好： A. 现代简约 B. 日式原木 C. 奶油风 D. 北欧风 E. 其他（你来说） 2. 空间大小： A. 小户型（<60㎡） B. 中等户型（60-120㎡） C. 大户型（>120㎡） 3. 沙发材质偏好： A. 皮质（耐抓但怕抓痕） B. 布艺（柔软但易粘毛） C. 绒面（舒服但需要经常清理） ``` 用户回复时只需回复选项字母（如"1A2B3C"），简洁高效。 ### 对话约束 - 支持用户针对问题进行追问或自由回答 - **轮次限制**：单次需求闭环对话轮次上限为 10 轮 - **强制引导**：若对话轮次达到第 8 轮，必须强制引导用户确认当前需求并进入生成阶段 ## 提示词转化与微调阶段 完成问答后，进入提示词构建阶段： 1. **转化**：将用户最终确定的需求，转化为适用于 nano-banana2-apiyi 模型的高质量英文提示词（Prompt） 2. **确认（中文+英文）**：将生成的提示词**翻译为中文**返回给用户查看，**必须同时附上英文原版提示词**，允许用户对提示词进行细节微调 3. **等待用户确认**：用户明确说"确认"或"生成"后，才进入执行阶段 4. **执行（英文）**：用户确认后，将**英文原版提示词**传给 nano-banana2-apiyi 进行图像生成 ## 技能执行与交付阶段 用户确认提示词后，执行以下操作： ### 步骤 1：提示用户准备生成 回复：「🖼️ 正在生成图片，请稍候...」 ### 步骤 2：调用图像生成 调用 nano-banana2-apiyi 技能进行图像生成。**所有生成的图片统一存储在 OpenClaw 公共目录 `{workspace}\output\` 下：** ```bash python "{baseDir}/../nano-banana2-apiyi/scripts/generate_image.py" \ --prompt "高质量英文提示词" \ --filename "{workspace}\output\beauty-$(date +%Y-%m-%d-%H-%M-%S).png" \ --aspect-ratio "16:9" \ --size "2K" ``` ### 步骤 3：交付图片 生成完成后，将图片通过飞书发送给用户，并告知用户图片存储路径（如 `{workspace}\output\beauty-2026-03-19-10-20-00.png`）。 ### 步骤 4：视频制作询问 图片交付后，主动向用户发起交互询问： > 「🎬 是否需要基于当前生成的图片制作视频？」 **判断逻辑：** - 若用户给出正向答复（如"是""需要""好""可以"等）→ 执行视频生成转发 - 若用户给出负向答复（如"不需要""不用""算了"等）→ 结束当前任务 - 若用户未明确答复 → 保持等待，不重复询问 **正向答复时的执行操作：** 通过 `sessions_send` 接口，将以下指令语句发送至智能体【小方设计】： ``` 用{图片本地路径}做参考图，继续生成视频 ``` 其中 `{图片本地路径}` 为实际生成的图片完整路径，格式如： `{workspace}\output\beauty-2026-03-19-10-20-00.png` **sessions_send 接口参数：** - `agentId`: "小方设计" - `message`: 上述指令语句 - `timeoutSeconds`: 建议设置 30 秒 ## 知识库（联网查询） 不再使用内置静态知识库，所有装修灵感知识需要**联网查询**获取最新、最相关的信息。 ### 查询触发时机 在以下场景中，需要主动联网查询相关知识： 1. **用户提及特定风格时** → 查询该风格的定义、特点、配色、常见材质 2. **用户提及特定材质时** → 查询该材质的优缺点、适用场景、搭配建议 3. **用户提及特定需求时** → 查询相关的设计趋势、常见方案、注意事项 4. **构建提示词时** → 查询对应的英文设计术语、氛围关键词 ### 查询方式 使用 `web_search` 工具进行搜索，示例： ```bash # 查询风格 web_search --query "现代简约风格装修特点 配色 材质" web_search --query "奶油风装修 2024 趋势 配色" # 查询材质 web_search --query "布艺沙发 猫咪 抓挠 选购指南" web_search --query "猫爬架 家用 推荐 材质" # 查询设计术语 web_search --query "interior design terms English minimalist warm lighting" ``` ### 知识整合 查询到的信息需要： 1. 提炼关键要点（风格特点、配色方案、材质选择、灯光设计） 2. 转化为英文提示词关键词 3. 在与用户确认需求时作为参考依据


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jack-atlas/iemail-send (version 1.0.4)

--- name: iemail-send description: Send transactional email via Iemail OpenAPI. Configure via OpenClaw skill env only. homepage: https://app.dmartech.cn/ metadata: {"openclaw":{"emoji":"📧","requires":{"anyBins":["python3"],"env":["IEMAIL_ACCESS_KEY","IEMAIL_ACCESS_KEY_SECRET","IEMAIL_SENDER"]},"primaryEnv":"IEMAIL_ACCESS_KEY"}} --- # Iemail Send Send transactional single email via Dmartech/Iemail OpenAPI using Python. ## Configuration Configure in `~/.openclaw/openclaw.json`: ```json "skills": { "entries": { "iemail-send": { "enabled": true, "env": { "IEMAIL_ACCESS_KEY": "your-access-key", "IEMAIL_ACCESS_KEY_SECRET": "your-access-key-secret", "IEMAIL_SENDER": "[REDACTED_SECRET]" } } } } ``` | Variable | Description | |----------|-------------| | IEMAIL_ACCESS_KEY | OpenAPI access key | | IEMAIL_ACCESS_KEY_SECRET | OpenAPI access key secret | | IEMAIL_SENDER | Sender email address (required) | | IEMAIL_TO | Default recipient (optional) | ## Agent instructions 1. Credentials: Read `~/.openclaw/openclaw.json` or workspace config files. OpenClaw injects env at runtime. 2. Send mail: Run script in workspace: ```bash python3 {baseDir}/send_email.py --to "[REDACTED_SECRET]" --subject "Subject" --content "Body" ``` ## Usage examples ```bash python3 {baseDir}/send_email.py --to "[REDACTED_SECRET]" --subject "Hello" --content "Hello from Iemail" python3 {baseDir}/send_email.py "[REDACTED_SECRET]" "Hello" "Hello from Iemail" ``` ## Troubleshooting - 401 Unauthorized: Check IP whitelist, key/secret, and system time. - cannot find senderAddressSn: Check `IEMAIL_SENDER` matches a configured sender address.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: siemen90/btc-price-tracker (version 1.0.1)

# BTC Price Tracker 一个比特币实时价格监控和警报系统，支持通过 Telegram 接收价格通知。 ## 功能特性 - ✅ 使用 CoinGecko 免费 API 获取比特币实时价格（无需 API Key） - ✅ 支持多货币显示（USD、CNY、SGD） - ✅ 设置价格警报（高于/低于某个价位） - ✅ 通过 Telegram 发送警报通知 - ✅ 命令行交互界面 - ✅ 本地 JSON 存储警报数据 - ✅ 完善的错误处理和网络超时 - ✅ 多语言支持（中文/英文） ## 安装 ```bash # 安装依赖 pip install requests # 或者使用技能安装器 clawhub install btc-price-tracker ``` ## 使用方法 ### 查看当前价格 ```bash # 查看比特币价格（默认 USD） btc-price-tracker price # 示例输出: # 💰 比特币当前价格： # USD: $67,234.56 # CNY: ¥486,789.12 # SGD: S$90,123.45 # 24h 变化：📈 2.34% ``` ### 设置价格警报 ```bash # 当价格高于 100,000 USD 时通知 btc-price-tracker alert --name "高价警报" --price 100000 --condition above --currency USD # 当价格低于 60,000 CNY 时通知 btc-price-tracker alert --name "低价警报" --price 60000 --condition below --currency CNY # 参数说明: # --name, -n 警报名称（必填） # --price, -p 目标价格（必填） # --condition, -c 触发条件：above(高于) 或 below(低于) # --currency, -C 货币单位：USD, CNY, SGD（默认 USD） ``` ### 查看警报列表 ```bash btc-price-tracker alerts # 示例输出: # 📋 当前警报列表： # ------------------------------------------------------------ # ✅ #1 高价警报 # 条件：当价格 高于 $100,000.00 # 创建：2026-03-31T11:14:30 # ✅ #2 低价警报 # 条件：当价格 低于 ¥60,000.00 # 创建：2026-03-31T11:15:00 # ------------------------------------------------------------ ``` ### 删除警报 ```bash # 删除 ID 为 1 的警报 btc-price-tracker delete 1 ``` ### 检查警报 ```bash # 手动检查是否触发警报 btc-price-tracker check ``` ## Telegram 集成 当价格警报触发时，会自动通过 Telegram 发送通知： ``` 🚨 警报触发！高价警报：当前价格 $100,234.56 高于 $100,000.00 ``` ### 配置 Telegram 在 OpenClaw 环境中，Telegram 通知会自动通过主程序发送。确保： 1. OpenClaw Gateway 已启动 2. Telegram 频道已配置 3. 环境变量 `OPENCLAW_TELEGRAM_BOT_TOKEN` 已设置 ## 环境变量 ```bash # 设置语言（zh 或 en） export OPENCLAW_LANG=zh # Telegram Bot Token（用于发送通知） export OPENCLAW_TELEGRAM_BOT_TOKEN=your_bot_token ``` ## 数据存储 警报数据保存在技能目录下的 `alerts.json` 文件中： ```json [ { "id": 1, "name": "高价警报", "price": 100000, "condition": "above", "currency": "USD", "active": true, "created_at": "2026-03-31T11:14:30" } ] ``` ## API 说明 本技能使用 [CoinGecko 免费 API](https://www.coingecko.com/en/api)： - 无需 API Key - 免费调用限额：10-50 次/分钟 - 数据包含：USD、CNY、SGD 价格及 24 小时涨跌幅 ## 错误处理 技能包含完善的错误处理： - 🌐 网络超时（10 秒） - 🔌 连接错误 - 📄 JSON 解析错误 - 💾 文件读写错误 所有错误都会以友好的中文/英文消息显示。 ## 技术栈 - Python 3.7+ - requests（HTTP 客户端） - json（数据持久化） - argparse（命令行解析） ## 限制 - CoinGecko 免费 API 有调用频率限制（建议不超过 1 次/分钟） - 警报数据仅保存在本地，不会同步到云端 - 仅支持比特币（BTC），不支持其他加密货币 ## 未来计划 - [ ] 支持其他加密货币（ETH、BNB 等） - [ ] 添加价格历史图表 - [ ] 支持邮件通知 - [ ] 添加价格趋势分析 - [ ] 支持警报组（批量管理） ## 贡献 欢迎提交 Issue 和 Pull Request！ ## 许可证 MIT License ## 作者 OpenClaw Community --- **提示**: 定期检查价格警报，合理设置触发条件，避免频繁触发。

path: requirements.txt
content: requests>=2.25.0


path: venv/lib/python3.12/site-packages/certifi-2026.2.25.dist-info/top_level.txt
content: certifi


path: venv/lib/python3.12/site-packages/charset_normalizer-3.4.6.dist-info/top_level.txt
content: 81d243bd2c585b0f4821__mypyc
charset_normalizer


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jini92/discord-cross-gateway-delegation (version 2.1.1)

--- name: discord-cross-gateway-delegation description: "Set up and operate full cross-gateway task delegation between two OpenClaw Discord bots across different PCs/gateways. Use when: another OpenClaw bot lives on a different machine and you want private-lane delegation, structured worker protocols, DM natural-language triggers, worker-side intake, and result relay-back into the original DM. Triggers: 'delegate work to another OpenClaw bot', 'cross-gateway delegation', 'connect another gateway bot', 'Discord worker bot setup', 'maekjini', 'kaijini', 'worker lane', 'DM auto delegation'. NOT for: same-gateway session routing, normal DM chat without delegation, or non-Discord automation." --- # Discord Cross-Gateway Delegation Run a full Discord-based delegation loop between two OpenClaw bots on different PCs/gateways. ## Core rule Treat the second bot as an **external worker**, not as an internal session. Do **not** use same-gateway assumptions like `sessions_send`, local subagents, or same-process routing. ## Supported operating model A complete delegation flow should include all of these stages: 1. private delegation lane setup 2. structured task envelope protocol 3. optional DM natural-language trigger on the controller side 4. worker-side intake in the delegation lane 5. worker started/final replies in the lane 6. relay-back of the final worker result into the original DM If only steps 1-2 are working, the setup is incomplete. ## Protocol rule Use a protocol namespace that matches the worker lane. Examples: - MACJINI lane: `[MAC_TASK]`, `[MAC_STATUS]`, `[MAC_DONE]` - KAIJINI lane: `[KAI_TASK]`, `[KAI_STATUS]`, `[KAI_DONE]` Do not mix worker identity and protocol prefix unless you are intentionally keeping a legacy compatibility layer. ## What to read first 1. `references/setup-checklist.md` 2. `references/operating-modes.md` 3. `references/full-process.md` 4. If the worker is a secondary execution bot, `references/macjini-rollout.md` 5. If anything fails, `references/diagnosis.md` ## Operating guidance - Prefer a **private server delegation lane** over public channels. - Support both **quick handoff** and **orchestrated handoff**. - For real work, prefer **orchestrated handoff**: the controller bot should understand the task first, generate the worker-facing envelope itself, then delegate. - Keep quick handoff as a shortcut for simple tests and short tasks. - Test **DM trigger**, **lane intake**, and **DM relay-back** as separate checkpoints. - Consider the setup successful only when the final worker result appears back in the original DM. - A lane message alone is not success. - A worker reply in the lane alone is not success. ## If channel messages fail Read `references/diagnosis.md`. If the worker bot responds in DM but not in server channels, first assume a guild-channel inbound policy issue. For `groupPolicy: allowlist`, explicitly add the target guild/channel under `guilds`, then re-test channel-based worker intake before falling back.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aizzaua/chrome9222 (version 1.0.0)

{ "name": "Chrome9222端口启动", "id": "chrome_9222_launch", "version": "1.0.0", "description": "一键通过9222调试端口启动Chrome", "config": { "browser": { "enabled": true, "type": "chrome", "remoteDebuggingPort": 9222, "headless": false } }, "actions": [ { "name": "启动Chrome(9222端口)", "id": "launch_chrome_9222", "parameters": [], "exec": { "type": "script", "lang": "javascript", "code": "async function execute() { try { // 杀死残留Chrome进程 const { execSync } = require('child_process'); execSync('taskkill /f /im chrome.exe 2>nul'); // 启动Chrome（9222端口） execSync('\"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe\" --remote-debugging-port=9222 --user-data-dir=C:\\temp\\chrome_test'); return '✅ Chrome已通过9222端口启动！'; } catch (error) { return `❌ 启动失败：${error.message}`; } }" } } ], "triggers": [] }


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: video-editor-helper (version 0.1.0)

--- name: video-editor-helper description: 视频剪辑助手 - 自动生成视频脚本、字幕、标题。适用于自媒体、UP主、视频创作者。 --- # 视频剪辑助手 (Video Editor Helper) ## 功能 - 视频脚本生成 - 字幕自动生成 - 标题/描述生成 - 标签优化 ## 使用方式 ```bash video-editor-helper "关于 AI 发展的视频" ``` ## 价格 - 免费版：5分钟视频 - 专业版：¥79/月：无限制 - 企业版：¥299/月：API接入


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: olina1ye/academic-search-zh (version 1.0.1)

--- name: academic-search-zh description: > 学术文献检索 / 论文搜索 / 文献查找 / academic literature search。快速定位核心论文、制定搜索策略、解读研究方法、生成文献综述。适用于硕博研究生撰写毕业论文、高校教师科研立项、企业研发人员课题调研等场景。覆盖知网、万方、Web of Science、Google Scholar等数据库的检索建议与文献管理。用户常搜：怎样找论文、文献综述怎么写、SCI检索技巧、如何快速阅读论文、研究方向定位、竞品技术方案、项目前期调研。 tags: [学术检索, academic-search, 论文检索, 文献综述, literature-review, 知网查询, 科研助手, research-assistant, 硕博研究, SCI检索, 引用管理, 论文分析] --- # 学术文献检索与分析助手 帮助你制定学术检索策略、筛选优质文献、解读研究方法，并综合整理成结构化的文献综述，适用于论文写作、课题研究、学术汇报等场景。 **能做什么：** - 制定优化的中英文学术检索词与检索式 - 推荐适合的数据库（知网、万方、维普、Web of Science、Scopus、Google Scholar等） - 分析论文摘要与研究方法 - 梳理研究现状与研究空白 - 协助结构化撰写文献综述 **暂不支持：** - 直接获取付费期刊全文 - 替代实际数据库操作 - 开展原创研究 --- ## Tools Required - create_docx - extract_text_from_pdf ## Usage - "我在写关于'碳中和政策对制造业影响'的硕士论文，帮我制定文献检索策略，推荐检索词和数据库" - "帮我分析这篇论文的研究方法是否适合我的课题，并总结核心发现" - "我需要写一篇关于大模型在医疗领域应用的文献综述，帮我梳理研究现状和研究空白" ## Examples 输入：我的研究方向是"短视频平台算法推荐对用户信息茧房的影响"，需要做文献综述，请帮我制定检索策略。 输出： **研究主题拆解** 核心概念：短视频平台 × 算法推荐 × 信息茧房（Filter Bubble / Echo Chamber） --- **中文检索策略（知网/万方/维普）** 推荐检索式： ``` ("短视频" OR "抖音" OR "快手") AND ("算法推荐" OR "个性化推荐") AND ("信息茧房" OR "回音壁" OR "过滤气泡") ``` 时间范围建议：2018年至今（短视频爆发期） 文献类型：优先北大核心、CSSCI期刊及博硕论文 --- **英文检索策略（Web of Science / Google Scholar）** 推荐检索式： ``` ("short-form video" OR "TikTok" OR "algorithmic recommendation") AND ("filter bubble" OR "echo chamber" OR "information cocoon") ``` 数据库优先级：Web of Science > Scopus > Google Scholar --- **关键子议题与推荐检索方向** 1. 算法推荐机制研究 → 搜索 "recommendation algorithm" + "social media" 2. 信息茧房测量方法 → 搜索 "filter bubble measurement" + "user behavior" 3. 用户主动性与破茧行为 → 搜索 "selective exposure" + "news diversity" --- **研究现状初步判断** 当前研究空白方向： - 中国本土平台（抖音/快手）的实证研究相对不足 - 用户主动"破茧"行为的干预策略研究较少 - 算法透明度与用户认知的关系有待深入 建议下一步：将检索到的文献按"算法机制—用户行为—社会影响"三个维度分类整理，构建综述框架。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fjmjulzl/order-mcp (version 0.0.2)

--- name: deprecated-content description: 识别、整理和处理项目中的废弃内容。用户提到废弃、下线、历史逻辑、旧接口、旧文档、legacy、deprecated、清理无用内容时使用。 --- # 废弃内容处理 ## 处理原则 遇到废弃内容时，先确认废弃范围和影响面，不要直接删除代码、配置、文档或数据。 ## 工作流程 1. 找到废弃内容的定义、引用和调用链。 2. 判断是否仍被线上功能、构建流程、配置、脚本或文档引用。 3. 区分可以删除、需要保留兼容、需要迁移替换、仅需标记废弃四类情况。 4. 输出处理建议，说明影响范围、风险点和需要确认的问题。 5. 只有用户明确要求修改时，才执行删除、替换或标记废弃。 ## 输出要求 - 用中文回复。 - 先给结论，再列影响范围和建议动作。 - 不生成 README、测试代码或额外文档，除非用户明确要求。 - 不自主修改未被要求修改的功能。 ## 废弃标记 需要保留但不推荐继续使用的内容，可使用简短标记： ```ts /** * @deprecated 已废弃，仅保留兼容历史逻辑。新逻辑不要继续使用。 */ ```


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