OpenClaw/clawhub-security-signals huggingface dataset:
Skill: cinience/aliyun-cloudfw-manage (version 1.0.0)

--- name: aliyun-cloudfw-manage description: Use when managing Alibaba Cloud Cloud Firewall (Cloudfw) via OpenAPI/SDK, including the user requests firewall policy/resource operations, change management, status checks, or troubleshooting Cloud Firewall API workflows. version: 1.0.0 --- Category: service # Cloud Firewall Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Cloud Firewall. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `Cloudfw` - Default API version: `2017-12-07` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: `output/aliyun-cloudfw-manage/` ## Validation ```bash mkdir -p output/aliyun-cloudfw-manage for f in skills/security/firewall/aliyun-cloudfw-manage/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/aliyun-cloudfw-manage/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-cloudfw-manage/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/aliyun-cloudfw-manage/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


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

# Financial Analysis - 财务分析工具 ## 功能说明 提供上市公司财务分析功能，包括估值指标、财务健康度评估等。 ## 核心功能 ### 估值指标 - PE (市盈率) - PB (市净率) - PS (市销率) - PEG (市盈率相对盈利增长比率) ### 盈利能力 - ROE (净资产收益率) - ROA (总资产收益率) - 毛利率 - 净利率 ### 财务健康度 - 资产负债率 - 流动比率 - 速动比率 - 利息保障倍数 ### 估值模型 - DCF (现金流折现) - 相对估值法 - DDN (股利折现) ## 使用示例 ```python from financial_analysis import calculate_pe, calculate_roe, dcf_valuation # 计算 PE pe_data = calculate_pe(stock_code="300308") # 计算 ROE roe_data = calculate_roe(stock_code="300308") # DCF 估值 dcf_result = dcf_valuation(stock_code="300308", growth_rate=0.2, discount_rate=0.1) ``` ## 安装依赖 ```bash pip install akshare pandas numpy ``` ## 数据源 - AkShare 财务数据 - 东方财富 API ## 注意事项 - 财务数据有季度延迟 - DCF 模型对参数敏感 - 建议结合行业对比


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rf-ai-wh/v35-test-report (version 1.0.0)

--- name: test-report-skill description: 分析v3.5生产部署器日志，生成测试报告和统计数据。支持运行状态监控、版本对比、策略分析，适合AI Agent性能评估和实验报告生成。 author: MoltbookAgent version: 1.0.0 tags: [test, report, analytics, v3.5, monitoring] --- # Test Report Skill ## 一句话说明 分析 v3.5 生产部署器日志，生成测试报告和统计数据。 ## 适用场景 - 📊 监控 v3.5 运行状态和性能指标 - 📈 对比 v3.0 和 v3.5 表现差异 - 🎯 分析策略分布和使用频率 - 📝 生成实验报告和数据可视化 ## 快速开始 ### 生成测试报告 ```bash python3 scripts/generate_report.py ``` ### 示例输出 ``` ============================================================ v3.5 生产部署器测试报告 ============================================================ 生成时间: 2025-03-26 15:00:00 ------------------------------------------------------------ 📊 总体统计: 总运行次数: 100 v3.5 运行: 85 (85%) v3.0 运行: 15 (15%) 🎯 平均赞数: v3.5: 35.2 赞 v3.0: 18.5 赞 提升: +90.3% 📈 预测准确度: 平均准确度: 75.5% MAE: 2.3 ⚙️ 策略分布: learning + insights: 45% exploration + engagement: 30% trending + concise: 25% ============================================================ ``` ## 功能详解 ### 1. 运行统计 - 总运行次数 - v3.5 / v3.0 占比 - 权重变化趋势 ### 2. 性能对比 - 平均赞数对比 - 准确度统计 - 预测偏差分析 ### 3. 策略分析 - 策略组合分布 - 探索 vs 利用比例 - 因果分数分布 ## 数据源 默认读取：`/tmp/agent_v35_production.log` ## 与其他 Skill 配合 | Skill | 配合方式 | |-------|---------| | v35-monitor-center | 监控数据输入源 | | auto-weekly-system | 周报中嵌入测试数据 | | weekly-report-skill | 生成周期性测试报告 | ## 更新日志 ### v1.0.0 (2025-03-26) - ✅ 基础报告生成 - ✅ v3.5/v3.0 对比分析 - ✅ 策略分布统计 ## 反馈与贡献 如有问题或建议，欢迎反馈。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dang-ngoc-duy/playwright-service (version 1.0.0)

# Playwright Service Skill Dùng khi cần: chụp screenshot web, lấy tiêu đề trang, scrape nội dung text/HTML. ## Endpoint ``` http://192.168.0.9:3000 ``` --- ## 1. Screenshot Chụp ảnh một trang web và gửi vào Telegram. ```bash curl -s -X POST http://192.168.0.9:3000/screenshot \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com","fullPage":false}' \ -o /root/.openclaw/workspace/screenshot.jpg ``` - `fullPage: true` → chụp toàn bộ trang (dài) - `fullPage: false` → chụp viewport 1280x800 (mặc định) Sau khi có file, gửi vào Telegram group: ``` message tool: action=send, filePath=/root/.openclaw/workspace/screenshot.jpg, channel=telegram, target=-1003778746127 ``` --- ## 2. Lấy tiêu đề trang ```bash curl -s -X POST http://192.168.0.9:3000/title \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com"}' ``` Response: `{"url":"...","title":"..."}` --- ## 3. Scrape nội dung ```bash # Toàn bộ text curl -s -X POST http://192.168.0.9:3000/scrape \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com"}' # Theo CSS selector curl -s -X POST http://192.168.0.9:3000/scrape \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com","selector":"h1"}' ``` Response: `{"url":"...","data":"..."}` --- ## Lưu ý - Facebook, Instagram yêu cầu đăng nhập → sẽ trả về trang login, không có nội dung thật - Trang SPA/React có thể cần thêm thời gian load — nếu scrape ra rỗng, báo Bé Heo để thêm `waitForSelector` - File screenshot lưu tại `/root/.openclaw/workspace/` trước khi gửi Telegram


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: malicious | SkillSpector: clean (LOW)
Skill: bytesagain1/gift-finder (version 3.0.1)

--- name: "gift-finder" version: "3.0.1" description: "Reference tool for life — covers intro, quickstart, patterns and more. Quick lookup for Gift Finder concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [gift,finder, reference] category: "life" --- # Gift Finder Reference tool for life — covers intro, quickstart, patterns and more. Quick lookup for Gift Finder concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ly5201314gjx/video-crawler (version 1.0.0)

# Video Crawler - 视频抓取技能 一键抓取抖音和推特视频。 ## 依赖安装 ```bash pip install playwright requests yt-dlp playwright install chromium ``` ## 使用方法 ```bash python3 video_crawler.py <平台> <链接> [输出文件] ``` ### 抓取抖音视频 ```bash python3 video_crawler.py douyin "https://v.douyin.com/xxx" ``` ### 抓取推特视频 ```bash python3 video_crawler.py twitter "https://x.com/i/status/xxx" ``` ## 支持的平台 | 平台 | 命令 | 示例 | |------|------|------| | 抖音 | douyin | https://v.douyin.com/xxx | | 推特 | twitter | https://x.com/i/status/xxx | ## 输出 - 成功：输出文件路径 - 失败：输出错误信息 ## 注意事项 - 抖音视频可能需要 15-25 秒 - 推特视频文件较大，注意 Telegram 16MB 限制


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: xiaoyaner0201/jd-lawsuit-skill (version 1.0.2)

--- name: jd-lawsuit description: >- Judicial Dispute（消费纠纷维权助手）。通过浏览器自动化采集电商平台订单数据、 商品快照、物流记录，生成退款申请、12315投诉函、民事起诉状等维权文书。 Use when user mentions "维权", "退款", "投诉", "消费纠纷", "订单纠纷", "12315", "消费者权益", "lawsuit", "dispute", or needs help with e-commerce order disputes and consumer rights protection. --- # JD Lawsuit — Judicial Dispute 消费纠纷维权 通过浏览器采集电商订单证据，生成维权文书，引导投诉和诉讼流程。 ## 前置条件 - 浏览器已登录目标电商平台（本 skill 不处理登录，由用户自行完成） - 需要 browser-use MCP（CDP 连接） ## 核心流程 ``` 确认平台与登录 → 定位订单 → 采集证据 → 生成文书 → 投诉/起诉 ``` ### Step 1: 确认平台与登录状态 向用户确认纠纷发生的**电商平台**，导航到该平台用户中心检查登录状态。 各平台的入口 URL、订单页路径、选择器策略见 [references/platform-guide.md](references/platform-guide.md)。 ### Step 2: 定位目标订单 向用户确认**订单号**或**商品关键词**，导航到订单列表页采集数据。 ### Step 3: 证据固定 对关键页面截图、提取结构化数据，保存至 `~/Downloads/dispute-evidence/{订单号}/`， 自动生成证据清单索引。 详细采集流程、截图规范、文件命名见 [references/evidence-collection.md](references/evidence-collection.md)。 ### Step 4: 生成维权文书 根据纠纷类型生成对应文书： | 类型 | 典型情况 | 生成文书 | |------|----------|----------| | 退款/退货 | 商品质量问题、描述不符、未收到货 | 退款申请函 | | 价格欺诈 | 先涨后降、虚假满减、保价不兑现 | 12315 投诉函 + 赔偿计算 | | 虚假宣传 | 商品描述与实物严重不符 | 12315 投诉函 | | 售后不作为 | 客服推诿、超时未处理 | 12315 投诉函 + 起诉状 | | 食品安全 | 过期/变质/违禁添加 | 12315 投诉函 + 退一赔十计算 | 文书模板见 [references/legal-templates.md](references/legal-templates.md)。 ### Step 5: 投诉提交（可选） 引导用户通过合适渠道提交投诉。 各渠道操作步骤见 [references/complaint-channels.md](references/complaint-channels.md)。 ## 详细参考 | 主题 | 文档 | |------|------| | 电商平台适配指南 | [references/platform-guide.md](references/platform-guide.md) | | 证据采集与截图规范 | [references/evidence-collection.md](references/evidence-collection.md) | | 维权文书模板 | [references/legal-templates.md](references/legal-templates.md) | | 法律依据速查 | [references/legal-basis.md](references/legal-basis.md) | | 投诉渠道操作指南 | [references/complaint-channels.md](references/complaint-channels.md) | ## 注意事项 - 不自动输入账号密码，登录始终由用户手动完成 - 截图前等待页面完全加载（`browser_wait` 2-3 秒 + snapshot 确认） - 生成的文书为参考模板，提示用户根据实际情况修改后使用 - 若金额较大或情况复杂，建议用户咨询专业律师


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: arisylafeta/clawlist (version 0.0.1)

--- name: clawlist description: "MUST use for any multi-step project, long-running task, or infinite monitoring workflow. Plan, execute, track, and verify tasks with checkpoint validation. For projects, automation, and ongoing operations." --- # Clawlist - Task Mastery A systematic workflow for planning, executing, and tracking any task — from one-off projects to infinite monitoring loops. ## When to Use This Skill **ALWAYS use clawlist when:** - Starting any new project or initiative - Setting up long-running monitoring - Breaking down complex goals - You need to track progress across sessions - Managing infinite tasks (research, monitoring, engagement) ## Long-Running & Infinite Task Examples ### Example: Moltbook Engagement (Infinite) - **Type:** Infinite loop - **Schedule:** Every 30 minutes - **Goal:** Engage with community, build presence - **Checkpoints:** Check feed, check DMs, create content ### Example: GitHub Monitoring (Long-Running) - **Type:** Continuous - **Schedule:** Every 4 hours - **Goal:** Monitor repos, triage issues, implement - **Checkpoints:** Inbox zero, PR review, implementation ## The Clawlist Workflow Uses standalone skills in sequence: 1. **brainstorming** → Clarify intent, explore approaches 2. **write-plan** → Create detailed plan with checkpoints 3. **doing-tasks** → Execute with skill discipline 4. **verify-task** → Confirm completion For parallel work, insert **dispatch-multiple-agents** between write-plan and doing-tasks. ## Ongoing Tasks File **Location:** `memory/tasks/ongoing-tasks.md` Tracks all long-running and infinite tasks. Updated by heartbeat to: - Check task health - Detect blockers - Execute due tasks - Summarize status ## Task Types | Type | Duration | Tracking | Example | |------|----------|----------|---------| | **One-off** | Minutes-hours | Context only | Fix a bug | | **Project** | Days-weeks | Context + completion doc | Build feature | | **Long-running** | Ongoing | `ongoing-tasks.md` | GitHub monitoring | | **Infinite** | Forever | `ongoing-tasks.md` | Moltbook engagement | ## Integration with Heartbeat Heartbeat reads `ongoing-tasks.md` every check to: - Execute due infinite tasks - Detect and report blockers - Update health status (🟢🟡🔴) - Ping user if intervention needed ## Quick Reference ``` New Task ↓ brainstorming → write-plan → doing-tasks → verify-task ↓ dispatch-multiple-agents (if parallel) ↓ update ongoing-tasks.md (if long-running) ``` ## Sub-Skills - **brainstorming** - Phase 1: Clarify - **write-plan** - Phase 2: Plan - **doing-tasks** - Phase 3: Execute - **dispatch-multiple-agents** - Parallel execution - **verify-task** - Phase 4: Verify


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: geniuslishengbo/weibo-hot-trend (version 1.0.0)

--- name: weibo-hot-trend description: 获取微博热搜榜数据，返回热搜标题、热度值和跳转链接。当用户需要查看微博热搜、微博热点、微博热榜时使用此技能。支持自定义获取条数（默认50条）。 --- # 微博热搜榜 ## 获取热搜 ```bash # 默认获取50条 node scripts/weibo.js # 获取前N条 node scripts/weibo.js 20 ``` ## 数据来源 微博网页端公开接口 `weibo.com/ajax/side/hotSearch` ## 注意事项 - 访问频繁可能触发风控，建议间隔 ≥30 分钟 - 热度单位为万，label_name 为标签（沸、新、热等）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: redfox-data/pdf-image-text-extractor (version 1.0.0)

--- name: PDF和图片文字提取 description: 从图片或 PDF 文档中识别并提取文字内容，支持多种图片格式和 PDF 文件，自动判断是否包含文字并保留原始格式输出结构化结果；当用户需要从图片或 PDF 提取文字、进行 OCR 识别、处理含文字的文档或转换为可编辑文本时使用 dependency: python: - pymupdf>=1.23.0 --- # 文档文字提取器 ## 任务目标 - 本 Skill 用于：从用户上传的图片或 PDF 文档中识别并提取文字内容 - 能力包含：图片文字检测、PDF 文字提取、格式保留、结构化输出、Markdown 文件生成 - 触发条件：用户上传图片或 PDF 并要求提取文字，或询问文档中的文字内容 ## 前置准备 ### 依赖说明 脚本所需的依赖包及版本： ``` pymupdf>=1.23.0 ``` 安装命令： ```bash pip install pymupdf>=1.23.0 ``` ### 支持的文件格式 - 图片格式：PNG、JPG、JPEG、GIF、WebP、BMP 等常见格式 - 文档格式：PDF（支持扫描版和文字版） ## 操作步骤 ### 标准流程 #### 图片文字提取流程 1. **接收图片** - 确认用户已上传图片文件 - 获取图片的访问 URL 2. **识别图片内容** - 使用 `read_image` 工具识别图片内容 - 在 prompt 中明确要求识别所有文字内容，包括标题、正文、注释、水印等 3. **判断文字存在性** - 如果检测到文字：进入步骤 4 - 如果未检测到文字：告知用户"图片中未包含可提取的文字"，任务结束 4. **提取并整理文字** - 提取图片中的所有文字内容 - 保持原有的结构和排版 - 整理为易读的格式 #### PDF 文字提取流程 1. **接收 PDF 文件** - 确认用户已上传 PDF 文件 - 获取 PDF 文件的本地路径 2. **调用脚本提取文字** - 执行命令：`python scripts/pdf_text_extractor.py <pdf_file_path>` - 脚本会自动提取所有页面的文本 - 尽量保留原文的段落结构和标题层级 3. **处理提取结果** - 如果提取成功：进入步骤 4 - 如果提取失败：告知用户错误信息，任务结束 4. **格式化输出** - 脚本返回的文本为 Markdown 格式 - 可直接展示或保存为文件 ### 统一输出步骤 5. **生成输出结果** - 根据用户需求生成 Markdown 文件 - 包含文件来源、提取状态、文字内容等信息 - 使用清晰的标题和结构组织内容 ### 可选分支 - 当用户仅需查看文字内容：直接输出文字，不生成文件 - 当用户要求保存结果：生成 `.md` 文件 - 当图片/PDF 文字模糊或难以识别：说明情况并提供最佳识别结果 - 当 PDF 包含扫描图片：提示用户该页面为扫描图片，可能需要 OCR 处理 ## 资源索引 - 必要脚本：见 [scripts/pdf_text_extractor.py](scripts/pdf_text_extractor.py) - 用途：从 PDF 文件中提取文本内容并保留格式 - 参数：PDF 文件路径 - 输出：JSON 格式结果，包含提取的文本和元信息 ## 注意事项 ### 图片文字提取 - **识别准确性**：文字识别结果受图片清晰度、字体、背景等因素影响，可能存在误差 - **文字排版**：提取时尽量保持原图的文字结构和顺序 - **多语言支持**：支持识别中文、英文等多种语言文字 ### PDF 文字提取 - **格式保留**：脚本会尽量保留原文的段落结构和标题层级 - **扫描版 PDF**：如果 PDF 是扫描图片，文字可能无法提取，需要告知用户 - **复杂布局**：表格、多栏等复杂布局的提取效果可能不佳 - **加密 PDF**：不支持加密或受密码保护的 PDF 文件 ### 通用注意事项 - **隐私保护**：处理的文件不会被存储，仅在当前会话中使用 - **文件大小**：建议处理小于 50MB 的文件，过大文件可能导致处理缓慢 ## 使用示例 ### 示例 1：图片文字提取 **用户操作**：上传一张包含文字的图片 **智能体处理**： 1. 使用 `read_image` 工具识别图片 2. 提取文字内容："所有经历的纠缠 / 还有难过的遗憾 / 都不可能没有意义" 3. 直接输出提取结果 ### 示例 2：PDF 文字提取并保存 **用户操作**：上传 PDF 并要求"提取这个 PDF 的文字" **智能体处理**： 1. 确认 PDF 文件路径 2. 执行脚本：`python scripts/pdf_text_extractor.py ./document.pdf` 3. 获取提取结果，包含 10 页内容 4. 生成 Markdown 文件：`./extracted_from_pdf.md` ### 示例 3：处理扫描版 PDF **用户操作**：上传扫描版 PDF **智能体处理**： 1. 执行脚本提取文字 2. 发现部分页面提取为空 3. 告知用户："检测到部分页面可能为扫描图片，文字无法直接提取。建议使用 OCR 工具处理。"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiangzhanyou/openclaw-ppt-generator (version 1.0.0)

--- name: openclaw-ppt-generator description: Generate PPT documents using Python and python-pptx. No third-party API calls, fully open source. metadata: { "openclaw": { "emoji": "📊", "requires": { "bins": ["python3"], "env": [] } } } --- # OpenClaw PPT Generator Generate PPT documents using open-source Python library (python-pptx). No third-party API calls required. ## Features - **Fully Open Source**: Uses python-pptx library, no API keys needed - **Simple & Fast**: Local generation, no network dependency - **Flexible Content**: Supports titles, text, and bullet lists - **Standard Templates**: Uses built-in PowerPoint layouts ## Scripts - `scripts/generate_ppt.py` - Main script to generate PPT from content ## Usage Examples ```bash # Generate PPT with title and content python3 scripts/generate_ppt.py --title "My Presentation" --content "Slide 1 content|Slide 2 content" # Generate PPT with bullet lists python3 scripts/generate_ppt.py --title "Project Report" --content "Introduction|Features:Feature A,Feature B,Feature C|Conclusion" # Specify output path python3 scripts/generate_ppt.py --title "Meeting Notes" --content "Agenda|Discussion|Action Items" --output "meeting.pptx" ``` ## Agent Steps 1. Get PPT title and content from user 2. Run `scripts/generate_ppt.py` with parameters 3. Return the generated PPT file path ## Content Format - Use `|` to separate slides - Use `:` to separate slide title from bullet list - Use `,` to separate bullet items Example: `"Title|Slide1:Item1,Item2,Item3|Slide2:Content"` ## Output ```json { "status": "success", "ppt_path": "output.pptx" } ``` ## Technical Notes - **Library**: python-pptx (open source) - **No API calls**: All processing done locally - **Templates**: Uses standard PowerPoint layouts - **Output format**: .pptx (Microsoft PowerPoint format)

path: _meta.json
content: ownerId: openclaw
slug: openclaw-ppt-generator
version: 1.0.0
publishedAt: 1773299692000

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhoujc11/google-scholar-search (version 1.0.0)

--- name: google-scholar-search description: Academic paper search using Semantic Scholar API. Free API - no key required. Search research papers, get citations, abstracts, authors, and download PDFs. Use when searching academic literature, finding research papers on specific topics, finding citation counts and paper metadata, getting paper abstracts and author information, or looking for papers from specific years or with minimum citations. --- # Google Scholar Search Search academic papers using the free Semantic Scholar API. No API key required. ## Quick Start Basic search: ```bash python3 {baseDir}/scripts/search_papers.py "machine learning transformers" ``` Search with filters: ```bash python3 {baseDir}/scripts/search_papers.py "deep learning" --limit 5 --year 2020-2023 --min-citations 10 ``` ## Search Options - `--limit N`: Number of results (default: 10, max: 100) - `--year YYYY-YYYY`: Filter by year range (e.g., "2020-2023" or "2023") - `--min-citations N`: Minimum citation count - `--json`: Output in JSON format for machine processing ## Get Paper Details Retrieve detailed information about a specific paper: ```bash python3 {baseDir}/scripts/search_papers.py --details <paper-id> ``` ## Returned Data Each paper includes: - **title**: Paper title - **authors**: List of authors with names - **year**: Publication year - **venue**: Journal or conference name - **citationCount**: Number of citations - **abstract**: Paper abstract - **url**: Link to Semantic Scholar page - **openAccessPdf**: Direct PDF link if available - **paperId**: Unique Semantic Scholar ID (for details lookup) ## Examples Search for recent AI papers: ```bash python3 {baseDir}/scripts/search_papers.py "large language models" --year 2022-2024 --limit 10 ``` Find highly cited papers on a topic: ```bash python3 {baseDir}/scripts/search_papers.py "quantum computing" --min-citations 50 --limit 10 ``` Get JSON output for integration: ```bash python3 {baseDir}/scripts/search_papers.py "neural networks" --json --limit 20 ``` ## Tips - Use specific keywords for better results - Filter by year to get recent research - Use `--min-citations` to find influential papers - The API is free and requires no authentication - For complex queries, try multiple related terms


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 52yuanchangxing/cron-job-guardian (version 1.0.0)

--- name: cron-job-guardian version: 1.0.0 description: "检查 cron 或 timer 配置中的频率、幂等、重试、日志与并发风险。；use for cron, timer, ops workflows；do not use for 直接启停生产任务, 替代真正监控." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/cron-job-guardian tags: [cron, timer, ops, audit] user-invocable: true metadata: {"openclaw":{"emoji":"⏰","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 定时任务守门人 ## 你是什么 你是“定时任务守门人”这个独立 Skill，负责：检查 cron 或 timer 配置中的频率、幂等、重试、日志与并发风险。 ## Routing ### 适合使用的情况 - 检查这些 cron 任务是否有风险 - 找重试和并发问题 - 输入通常包含：cron 文件、脚本目录或任务说明 - 优先产出：发现的任务、频率风险、验证步骤 ### 不适合使用的情况 - 不要直接启停生产任务 - 不要替代真正监控 - 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： - 发现的任务 - 频率风险 - 幂等风险 - 日志与告警 - 建议修改 - 验证步骤 ## 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 - 只做静态审计，不触发定时任务。 - 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 (MEDIUM)