OpenClaw/clawhub-security-signals huggingface dataset:
Skill: terrycarter1985/sonos-music-search (version 1.0.1)

--- name: sonos-music-search-skill description: Search for music via Brave Search and play it on your Sonos speakers. homepage: https://clawhub.com/skills/sonos-music-search metadata: { 'aren': { 'emoji': '🎵', 'requires': { 'env': ['BRAVE_API_KEY'], 'bins': ['node'], 'network': true } } } --- # Sonos Music Search Skill Search for music on Spotify via Brave Search and play it on your Sonos speakers — right from the command line. ## Features - 🔍 Uses Brave Search API to find Spotify tracks - 🔊 Plays found tracks on your specified Sonos speaker - 🎶 View currently playing track - ⏱️ Discovery timeout protection (won't hang on missing speakers) - 🛡️ Safe search defaults to moderate ## Prerequisites - **Node.js** >= 18 - **Brave Search API key** — [Get one here](https://brave.com/search/api/) - **Sonos speaker** on the same local network - **Spotify account linked** to your Sonos system ## Installation ```bash npm install ``` ## Configuration Set your Brave Search API key: ```bash export BRAVE_API_KEY=your-api-key ``` ## Usage ### Play a track ```bash node src/index.js play "Living Room" "pink floyd comfortably numb" ``` ### View currently playing track ```bash node src/index.js current "Living Room" ``` ## Scripts | Command | Description | | ---------------------- | -------------------------------- | | `npm start` | Run the CLI | | `npm run format` | Format JS and Markdown files | | `npm run format:check` | Check formatting without writing | ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhuchenggong19851114-design/zhuchenggong-xiaohongshu-create (version 1.0.0)

--- name: xiaohongshu-create description: 小红书定制化创作技能 - 固定操作流程，提高发布成功率 --- # 小红书发布技能 ## 概述 固定化小红书发布操作流程，减少失败率。 ## 触发方式 - "发布小红书" - "帮我发到小红书" ## 前置条件 1. 浏览器已登录小红书创作者平台 2. 文案已确认OK 3. 图片素材准备好 ## 发布流程 ### 步骤 1：打开发布页 **图文发布**： ``` https://creator.xiaohongshu.com/publish/publish?target=image ``` **长文发布**： ``` https://creator.xiaohongshu.com/publish/publish?target=text ``` ### 步骤 2：选择发布方式 | 方式 | 操作 | 成功率 | |------|------|--------| | 文字配图 | 输入文案→生成配图→发布 | ★★★★★ 推荐 | | 上传图文 | 上传图片→粘贴文案→发布 | ★★★ 需要手动 | ### 步骤 3：文字配图（推荐） 1. 点击"文字配图" 2. 在输入框粘贴文案 3. 等待生成配图（几秒） 4. 选择样式 5. 点击"下一步" ### 步骤 4：设置标题和话题 1. 输入标题（可选） 2. 添加话题标签（#开头） 3. 点击"发布" ### 步骤 5：确认成功 1. 等待页面提示"发布成功" 2. 记录到飞书"已发布内容库" ## 常见问题 ### 问题1：Windows文件选择弹窗 - **现象**：点击上传图片后弹不出对话框 - **解决**：用"文字配图"方式，不需要手动上传 ### 问题2：登录失效 - **现象**：跳转到登录页 - **解决**：让用户重新扫码登录 ### 问题3：发布按钮点不了 - **原因**：内容不合规 or 网络问题 - **解决**：检查内容，刷新页面重试 ## 工具 - browser：浏览网页 - message：通知用户 ## 输出 - 成功：发布成功提示 + 链接 - 失败：报错原因 + 重试建议


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 0xspeter/camsnap-1-0-0 (version 1.0.0)

--- name: camsnap description: Capture frames or clips from RTSP/ONVIF cameras. homepage: https://camsnap.ai metadata: {"clawdbot":{"emoji":"📸","requires":{"bins":["camsnap"]},"install":[{"id":"brew","kind":"brew","formula":"steipete/tap/camsnap","bins":["camsnap"],"label":"Install camsnap (brew)"}]}} --- # camsnap Use `camsnap` to grab snapshots, clips, or motion events from configured cameras. Setup - Config file: `~/.config/camsnap/config.yaml` - Add camera: `camsnap add --name kitchen --host 192.168.0.10 --user user --pass pass` Common commands - Discover: `camsnap discover --info` - Snapshot: `camsnap snap kitchen --out shot.jpg` - Clip: `camsnap clip kitchen --dur 5s --out clip.mp4` - Motion watch: `camsnap watch kitchen --threshold 0.2 --action '...'` - Doctor: `camsnap doctor --probe` Notes - Requires `ffmpeg` on PATH. - Prefer a short test capture before longer clips.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pin-alt/20206-02-10-clawhub-summarize-1-0-0 (version 1.0.0)

--- name: summarize description: Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube). homepage: https://summarize.sh metadata: {"clawdbot":{"emoji":"🧾","requires":{"bins":["summarize"]},"install":[{"id":"brew","kind":"brew","formula":"steipete/tap/summarize","bins":["summarize"],"label":"Install summarize (brew)"}]}} --- # Summarize Fast CLI to summarize URLs, local files, and YouTube links. ## Quick start ```bash summarize "https://example.com" --model google/gemini-3-flash-preview summarize "/path/to/file.pdf" --model google/gemini-3-flash-preview summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto ``` ## Model + keys Set the API key for your chosen provider: - OpenAI: `OPENAI_API_KEY` - Anthropic: `ANTHROPIC_API_KEY` - xAI: `XAI_API_KEY` - Google: `GEMINI_API_KEY` (aliases: `GOOGLE_GENERATIVE_AI_API_KEY`, `GOOGLE_API_KEY`) Default model is `google/gemini-3-flash-preview` if none is set. ## Useful flags - `--length short|medium|long|xl|xxl|<chars>` - `--max-output-tokens <count>` - `--extract-only` (URLs only) - `--json` (machine readable) - `--firecrawl auto|off|always` (fallback extraction) - `--youtube auto` (Apify fallback if `APIFY_API_TOKEN` set) ## Config Optional config file: `~/.summarize/config.json` ```json { "model": "openai/gpt-5.2" } ``` Optional services: - `FIRECRAWL_API_KEY` for blocked sites - `APIFY_API_TOKEN` for YouTube fallback


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: mibayy/casino-bonus-hunter (version 1.0.0)

--- name: casino-bonus-hunter description: Scans 30+ online casinos, calculates the Expected Value of each welcome/reload/cashback bonus using optimal blackjack strategy, adjusts EV by casino reputation score, and outputs a ranked list so you know exactly which bonus to hunt next. metadata: author: "Mibayy" version: "1.0.0" displayName: "Casino Bonus Hunter" difficulty: "beginner" type: "automaton" --- # Casino Bonus Hunter Ranks casino bonuses by **adjusted Expected Value** — EV calculated with optimal blackjack strategy (0.3% house edge), then multiplied by a reputation score (0-10) based on CasinoGuru ratings and payout history. ## What it does 1. Evaluates 30+ welcome, reload, and cashback bonuses from crypto and traditional casinos 2. Calculates EV: `bonus - (wagering * house_edge)` with optimal strategy 3. Adjusts EV by reputation: a $400 EV bonus at a shady casino scores lower than a $300 EV bonus at BitStarz (9.2/10) 4. Outputs ranked JSON + console table ## EV Formula ``` EV_gross = bonus_amount - (bonus * wagering_x / game_contribution * house_edge) EV_adjusted = EV_gross * (reputation_score / 10) ``` Example — Stake $500 welcome (40x wagering, blackjack): - Real wagering = $20,000 - Expected loss = $20,000 * 0.3% = $60 - EV_gross = $500 - $60 = **+$440** - EV_adjusted = $440 * 9.1/10 = **+$400** (accounts for Stake's reliability) ## Output Saves to `/tmp/casino_bonuses.json` with full details per bonus. ## Configuration (env vars) | Variable | Default | Description | |----------|---------|-------------| | BONUS_MIN_REP | 0 | Minimum reputation score (e.g., 8 = reputable casinos only) | | BONUS_MIN_EV | 0 | Minimum EV to include (e.g., 50 = only $50+ EV) | | BONUS_TOP_N | 20 | Number of top bonuses to return | | BONUS_OUTPUT_FILE | /tmp/casino_bonuses.json | Output file path | ## Runs every 6 hours (bonus data changes infrequently) ## Strategy notes - Always play blackjack for wagering (0.3% edge vs 4%+ for slots) - Check game contribution — some casinos count blackjack at only 10% - Cashback bonuses have wagering x1 = almost free money - Filter by `BONUS_MIN_REP=8` to avoid unreliable casinos


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xing-xing-coder/paper-translator (version 0.1.2)

# Paper Translator Translates academic PDFs (EN→ZH), preserving formulas and layout. Generates bilingual version + Chinese-only version + glossary. ## One-liner ```bash bash <this-skill>/scripts/translate_paper.sh <paper.pdf> ``` Outputs (same directory): - `xxx.zh.dual.pdf` — Bilingual (EN+ZH) - `xxx.zh.mono.pdf` — Chinese only - `xxx.zh.glossary.csv` — Glossary ## Arguments Add after the script: | Arg | Example | Description | |-----|---------|-------------| | `--lang-out` | `zh-CN` | Target language | | `--pages` | `1-10` | Translate first 10 pages only | | `--no-dual` | | Generate monolingual only | | `--ignore-cache` | | Re-translate ignoring cache | | `--output` | `/out` | Output directory | ## Auto-installed dependencies Checks for `uv` and `pdf2zh-next` on first run; installs automatically if missing. ## Full arguments See [references/advanced-args.md](references/advanced-args.md) ## References - [pdf2zh-next](https://pdf2zh-next.com/) - [PDFMathTranslate-next](https://github.com/PDFMathTranslate-next/PDFMathTranslate-next) ## QQBot Send ```bash cp *.pdf ~/.openclaw/media/qqbot/uploads/ ``` Then send using `<qqmedia>/path/to/file.pdf</qqmedia>`. Max 10MB per file.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: zvirb/google-contacts-search (version 1.0.1)

--- name: Google Contacts Search description: Atomic node skill to search google contacts. Loops internally until successful. os: all requires: bins: - gog env: - COMPOSIO_API_KEY --- ## Lean Philosophy (Principles) - **Kaizen (改善):** This skill is an atomic node, broken down into its simplest, smallest component to eliminate waste and ensure perfection. - **Standardized Work (Hyojun Sagyo):** This node represents the most efficient, standardized path for this specific task before automation. - **Jidoka (自働化):** This node includes autonomous defect detection. It will stop immediately and report if it cannot achieve the expected outcome. # Google Contacts Search ## Role You are a precise tool orchestration node. Your only responsibility is to search google contacts. ## Input A JSON object containing the required parameters for the execution. ## Expected Output A JSON array representing the result of the operation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: miaomiao-d/smart-auto-note (version 1.0.0)

--- name: smart-auto-note description: 语义智能识别自动分类写入Obsidian笔记，支持待办自动归档 user-invocable: true disable-model-invocation: false --- ## 核心功能 1. 语义识别用户输入，自动拆分多内容，分类写入对应Obsidian笔记 2. 支持工作待办/生活待办/工作记录/灵感4类内容分类 3. 工作待办标记完成后，自动归档到对应工作记录 4. 防误触、重复内容拦截、自动创建缺失文件兜底 ## 权限要求 - filesystem（文件系统读写权限） ## 触发规则 - 仅待办事项、工作记录、灵感备忘类内容触发技能 - 提问、闲聊、系统指令等非记录类内容不触发 - 分类置信度＜90%，会发起固定话术的分类确认，不擅自写入 ## 分类说明 1. 工作待办：未来需完成的工作相关任务，需跟进闭环 2. 生活待办：未来需完成的个人生活相关事务，需跟进闭环 3. 工作记录：已完成的工作相关事项、流水、复盘，无需跟进 4. 灵感笔记：无明确执行要求的想法、创意、备忘，仅留存 ## 写入格式 所有内容均为文件末尾追加写入，不覆盖原有内容，按分类自带日期、时间、标准化格式。 ## 触发方式 自然对话直接触发，无需固定前缀。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: erick0309/feishu-doc-cn (version 1.0.0)

# Feishu Doc Skill Use this skill when user wants to read, create, update, or manage Feishu (飞书) documents. ## Tools Available - `feishu_doc`: Read, write, append, create documents in Feishu ## Use Cases - Read content from a Feishu document - Create new Feishu documents - Update existing Feishu documents - Append content to Feishu documents ## Auth User must have Feishu account with appropriate permissions to access documents. ## Examples - "帮我读取一下这个飞书文档的内容" - "创建一个新飞书文档" - "更新这个飞书文档" - "在这个飞书文档后面追加内容"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: smseow001/auto-research-agent (version 1.0.0)

--- name: autonomous-research version: 1.0.0 description: 自主AI研究框架 - 基于Karpathy AutoRS理念。AI Agent自主实验→训练→评估→迭代→保留最优。固定时间预算，可比较结果，持续优化。 keywords: [autonomous-research,autoRS,AI研究,自动实验,自主学习,模型优化,训练迭代] --- # Autonomous Research Framework 自主AI研究框架 **灵感来源：** Karpathy AutoRS **核心思想：** 给AI一个真实的研究环境，让它自主实验、评估、迭代 --- ## 一、核心设计 ### 研究循环 ``` 实验设计 → 代码修改 → 训练运行 → 评估指标 → 结果分析 ↑ ↓ ←←← 保留/丢弃 → 更新上下文 → 继续下一轮 ←←← ``` ### 三文件架构 | 文件 | 作用 | 修改权限 | |------|------|---------| | `prepare.py` | 数据准备、工具函数 | ❌ 不修改 | | `train.py` | 模型、优化器、训练循环 | ✅ Agent修改 | | `program.md` | Agent指令、实验目标 | ✅ 人类修改 | ### 固定预算 - **时间预算：** 每次实验固定时长（避免无限训练） - **评估指标：** 统一指标对比（val_loss, val_bpb 等） - **可比较性：** 相同预算下的结果可直接对比 --- ## 二、使用方式 ### 启动自主研究 ``` 请按照 program.md 的指令开始新一轮实验。 先查看当前 train.py 的状态，然后进行修改并运行。 ``` ### 研究循环 1. **读取 program.md** — 了解当前研究目标 2. **分析 train.py** — 理解当前实现 3. **设计实验** — 提出假设、修改方案 4. **运行训练** — 固定时间预算 5. **评估结果** — 与基线对比 6. **决定去留** — 保留提升、丢弃退步 7. **记录学到的** — 更新记忆/日志 ### 每次实验记录 ```markdown ## 实验 #[N] - [日期时间] ### 假设 [这次要改什么，为什么] ### 修改 [train.py 的改动点] ### 结果 - 评估指标: [数值] - vs 基线: [+/-%] ### 决定 [保留/丢弃] - [原因] ``` --- ## 三、program.md 模板 ```markdown # Research Program ## 基线状态 - 模型: [描述] - 优化器: [描述] - 评估指标: val_bpb = [数值] ## 研究目标 [当前要解决的问题/优化方向] ## 可修改范围 - 模型架构（层数、hidden维度、attention头数） - 优化器（学习率、beta、权重衰减） - 训练参数（batch_size、seq_len） - 正则化（dropout、weight_decay） ## 约束 - 训练时间: 5分钟固定 - 单GPU - 只修改 train.py ## 当前重点 [Agent根据历史结果自行判断下一个实验方向] ``` --- ## 四、评估指标指南 | 指标 | 说明 | 越低/高越好？ | |------|------|-------------| | val_bpb | 验证集每字节比特数 | 越低越好 | | val_loss | 验证损失 | 越低越好 | | test_acc | 测试准确率 | 越高越好 | | perplexity | 语言模型困惑度 | 越低越好 | --- ## 五、实验策略 ### 探索策略 1. **随机扰动** — 小随机变化，找到局部最优 2. **梯度方向** — 根据失败经验调整 3. **消融实验** — 去掉某部分看影响 4. **历史回顾** — 查看过去100次实验的模式 ### 避免重复 - 记录已尝试的（学习率、架构组合等） - 不重复已证明无效的实验 - 相似实验至少改一个关键变量 --- ## 六、日志格式 ### 实验日志 (experiments.md) ```markdown # 实验日志 ## 实验记录 | # | 时间 | 修改 | 指标 | vs基线 | 决定 | |---|------|------|------|--------|------| | 1 | 2026-04-17 | 初始基线 | 1.234 | - | 基线 | | 2 | 2026-04-17 | 学习率 1e-3→5e-4 | 1.189 | -3.6% | ✅保留 | | 3 | 2026-04-17 | 层数 8→12 | 1.201 | -2.7% | ❌丢弃 | ## 关键发现 - 学习率降低有效 - 层数增加不一定好 ``` --- ## 七、快速开始 1. **查看 program.md** — 了解研究目标 2. **查看 train.py** — 理解当前实现 3. **设计第一个实验** — 改什么、为什么 4. **运行训练** — `python train.py` 5. **记录结果** — 更新实验日志 6. **决定下一步** — 继续或回退 --- ## 八、Agent 指令 当用户要求开始自主研究时： 1. 先读取 `program.md` 了解目标 2. 分析 `train.py` 当前状态 3. 提出修改假设 4. 执行并记录 5. 持续迭代 --- *基于 Karpathy AutoRS 理念构建 | OpenClaw Skill*


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: supah-based/supah-base-intelligence (version 1.3.0)

--- name: supah-base-intelligence description: "Comprehensive token intelligence for Base blockchain. Risk scores, whale tracking, signal analysis, and safety checks for any Base token. Powered by SUPAH 5-gate scoring engine." metadata: { "openclaw": { "emoji": "📊", "requires": { "bins": ["curl", "node"], "env": ["SUPAH_API_BASE"] }, "network": { "outbound": ["api.supah.ai"] }, "x402": { "enabled": true, "currency": "USDC", "network": "base", "maxPerCall": "0.08", "payTo": [REDACTED_SECRET] } } } --- # SUPAH Base Intelligence Skill **Category**: Crypto & DeFi **Tags**: base, token-analysis, risk-scoring, defi, trading **Author**: SUPAH (@Supah_BASED_AI) **Version**: 1.0.0 ## Description SUPAH provides comprehensive token intelligence for Base blockchain. Get risk scores, whale tracking, signal analysis, and safety checks for any Base token. Built on SUPAH's proprietary data pipeline which utilizes Moralis for on-chain data indexing and enrichment. ## Usage Ask the agent natural questions like: - "What's the risk score for token 0x..." - "Check if this token is safe: 0x..." - "Show me whale moves for $TOKEN" - "Get SUPAH signals for Base today" ## Features - **5-Gate Risk Scoring**: SIG (signals), TA (technical), SEC (security), PRED (prediction), NARR (narrative) - **Safety Scanner**: Honeypot detection, deployer analysis, liquidity checks - **Whale Tracking**: Large holder movements and smart money flows - **Signal Feed**: High-conviction trading signals (score ≥ 85) - **Portfolio Analysis**: Scan wallet for risky tokens ## Examples ``` scan token 0x52ba04de312cc160381a56b6b3b7fd482ae71d31 check safety 0x52ba04de312cc160381a56b6b3b7fd482ae71d31 get base signals analyze wallet 0xYourAddress ``` ## Pricing **x402 USDC micropayments on Base — pay per call, no subscriptions.** | Endpoint | Price | What You Get | |----------|-------|-------------| | Token scan | $0.08 | Full 5-gate risk score + safety analysis | | Safety check | $0.03 | Contract safety + honeypot detection | | Signal feed | $0.15 | Live high-conviction trading signals | | Whale moves | $0.005 | Large holder movement tracking | | Wallet analysis | $0.01 | Portfolio risk scan | Your agent's x402-compatible HTTP client pays automatically. No API keys needed. [How x402 works](https://www.x402.org) ## Data Infrastructure SUPAH's intelligence pipeline is built on and utilizes **Moralis** for real-time on-chain data indexing. Moralis provides the foundational blockchain data layer — token transfers, wallet activity, contract events, and decoded transaction data — which SUPAH then processes through its proprietary 5-gate scoring engine, adding ML predictions, narrative analysis, and signal generation on top. **Data flow**: Moralis (on-chain indexing) → SUPAH Engine (scoring + ML) → x402 API → Your Agent ## Links - Website: https://supah.ai - API: https://api.supah.ai - Telegram: https://t.me/SUPAH_Based - X: https://x.com/Supah_BASED_AI ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: edwardrodriguez703-design/book-oil-change (version 1.0.1)

--- name: book-oil-change description: Book oil-change services through Lokuli MCP. Use when user needs to find and book oil-change. Triggers on requests like "book a oil-change", "find oil-change near me", or any oil-change service request. --- # uook oil change Book oil-change services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "oil-change", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 0x-professor/google-workspace-automation (version 0.1.0)

--- name: google-workspace-automation description: Design Gmail, Drive, Sheets, and Calendar automations with scope-aware plans. Use for repeatable daily task automation with explicit OAuth scopes and audit-ready outputs. --- # Google Workspace Automation ## Overview Create structured automation plans for common Gmail, Drive, Sheets, and Calendar workflows. ## Workflow 1. Define automation goal, services, and actions. 2. Derive required OAuth scopes and integration boundaries. 3. Build execution plan with schedule and retry behavior. 4. Export auditable artifact for implementation. ## Use Bundled Resources - Run `scripts/plan_workspace_automation.py` for deterministic automation planning. - Read `references/workspace-guide.md` for scope and quota considerations. ## Guardrails - Always declare least-privilege scopes. - Keep automations idempotent and auditable.

path: agents/openai.yaml
content: interface:
  display_name: "Google Workspace Automation"
  short_description: "Automate Gmail Drive Sheets and Calendar"
  default_prompt: "Use $google-workspace-automation to design a scope-aware Workspace automation flow."


path: references/workspace-guide.md
content: # Workspace Automation Guide

## Scope Strategy

- Request only service scopes required by actions.
- Keep separate automations for unrelated scope sets.
- Rotate credentials and monitor token usage.

## Reliability Strategy

- Use idempotent action design.
- Include retry policies for quota/rate-limit errors.
- Log run summaries for auditing.

## Input Requirements

- `automation_name`
- `services`
- `actions`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: fonilye/news-content (version 1.0.2)

--- name: news-content-extractor description: Input a news URL to efficiently extract the body, title, author, and date using a remote API. metadata: {"openclaw": {"requires": {"bins": ["node"], "env": ["EASYALPHA_API_KEY", "NEWS_EXTRACTOR_SERVER_URL"]}}} --- # News Content Extractor (Pro Version) This is a news content extraction Skill using a client-server architecture. ## Features - **Zero Local Dependencies**: Uses Node.js for the client, so no complex Python libraries need to be installed locally. - **Authentication**: Core API calls are protected by `EASYALPHA_API_KEY`. - **High-Performance Parsing**: Powered by a remote backend service based on `trafilatura`. ## Configuration Requirements The following environment variables must be set to use this Skill: 1. `EASYALPHA_API_KEY`: Your authentication token. Obtainable from: https://easyalpha.duckdns.org 2. `NEWS_EXTRACTOR_SERVER_URL`: (Optional) The backend server address. Defaults to the production API: https://easyalpha.duckdns.org/api/v1/extract ## Usage **User**: "Scrape the content of this page: https://www.bbc.com/news/uk-12345678" **Agent Behavior**: - Runs `node scripts/extract_news.js https://www.bbc.com/news/uk-12345678` - The script automatically includes the Token and sends the request to the server. - Parses and displays the results.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: zhaoguoqiang-hub/narrative-memory (version 0.6.0)

--- name: narrative-memory description: 叙事记忆系统v2：捕捉生命中有意义的时刻，编织成连贯的个人叙事。三层过滤确保记忆精华，支持模式识别和决策反思。触发条件：里程碑事件、关键决策、价值判断、情感转折、首次体验、深度反思。 --- # Narrative Memory - 叙事记忆系统 ## 使用方法 ### 直接调用 ```bash node scripts/narrative.js <命令> [选项] ``` ### 信号处理模式 ```bash node scripts/narrative.js --process-signal <signal> ``` ## 命令 - `add <事件描述>` - 添加新的叙事记忆 - `timeline [选项]` - 查看时间线 - `list [选项]` - 列出叙事记忆 ## 示例 ```bash # 添加记忆 node scripts/narrative.js add "完成了重要项目" --category milestone --importance 0.9 # 查看时间线 node scripts/narrative.js timeline --days 7 # 信号处理 node scripts/narrative.js --process-signal milestone_recorded ``` ## 与proactive-engine集成 该skill通过`--process-signal`参数与proactive-engine信号系统集成，支持自动捕获重要生命时刻。


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