OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mywebliu/pdf-qr-extractor (version 1.0.0)

# PDF QR Code Extractor 从 PDF 文件中提取每一页的图片，并检测、裁剪保存其中的二维码。 ## 功能 - 将 PDF 每一页转换为图片 - 使用 OpenCV + pyzbar 检测并裁剪二维码区域 - 二维码周围留有 10 像素白边（padding），便于扫码识别 - 保存到指定目录 ## 使用方法 ``` 请帮我提取 PDF 中的二维码：[PDF文件路径] ``` 例如： ``` 请帮我提取 PDF 中的二维码：D:/pdftest/text.pdf ``` 或者指定输出目录： ``` 请帮我提取 PDF 中的二维码：D:/pdftest/text.pdf，输出到 ./my_qr ``` ## 依赖安装 需要先安装依赖： ``` pip install fitz PyMuPDF opencv-python numpy pyzbar --break-system-packages ``` ## 输出结构 ``` 输出目录/ ├── qr_page_1_1.png # 二维码图片 ├── qr_page_2_1.png ├── text.pdf_pages/ # PDF页面图片（中间文件） │ ├── page_1.png │ └── page_2.png ``` ## 适用场景 客户提供的 PDF 文件中包含生成的二维码，需要批量提取保存。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freedompixels/cn-text-encoding-detector (version 1.0.0)

--- slug: cn-text-encoding-detector name: Text Encoding Detector version: "1.0.0" description: "Detect text file encoding (UTF-8, GBK, Latin-1, etc). Auto-detect BOM markers. Pure Python standard library, no API key required." keywords: encoding, charset, utf-8, gbk, detect license: MIT-0 tags: - tools --- # Text Encoding Detector Automatically detect the character encoding of text files. ## Features - Support common Chinese encodings (UTF-8, GBK, GB2312) - Detect BOM (Byte Order Mark) markers - Report file size along with encoding - Pure Python, no external dependencies ## Supported Encodings - UTF-8 (with or without BOM) - UTF-16 LE/BE - GBK / GB2312 - Latin-1 / ISO-8859-1 - Unknown (when detection fails) ## Usage ``` python3 scripts/encoding_detector.py --file document.txt ``` ## Example Output ```json { "file": "document.txt", "encoding": "UTF-8", "size_bytes": 15234 } ``` ## Use Cases - Fix garbled text files - Batch convert files between encodings - Verify file encoding before processing ## Notes Detection tries UTF-8 first, then GBK. If both fail, returns "UNKNOWN".


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gblockchainnetwork/openclaw-cost-auditor (version 1.0.0)

--- summary: "OpenClaw Cost Auditor: Track API usage, model costs, token burn, and revenue for OpenClaw deployments." description: "Parse logs, query API metrics, forecast bills, optimize spend with reports & alerts." triggers: - "audit openclaw costs" - "openclaw billing" - "check API spend" - "token usage report" read_when: - "openclaw cost" in message - "API auditor" in message --- # OpenClaw Cost Auditor v1.0.0 ## 🎯 Purpose - Daily/weekly cost reports - Top models/users by tokens - Cost per query forecasts - Optimization recs (quantize, prune) ## 🚀 Quick Start ``` !openclaw-cost-auditor --period last7d --format pdf ``` ## Files - `scripts/audit.py`: Log parser & calculator - `templates/report.md`: Cost dashboard template ## Integrations OpenClaw logs, Grok/xAI API, custom providers.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: fangshan101-coder/shengqian-kuaibao (version 0.1.0)

--- name: 省钱快报返利助手 description: 省钱快报优惠信息聚合与CPS返利工具，实时推送高性价比商品和大额优惠券，覆盖淘宝京东拼多多等全平台。 version: 0.1.0 --- # 省钱快报返利助手 省钱快报优惠信息聚合与CPS返利工具。实时推送各平台高性价比商品、限时秒杀和大额优惠券信息，覆盖淘宝、京东、拼多多等全平台，帮助用户发现真正的好价好物。 ## 触发词 省钱快报、省钱快报返利、shengqian kuaibao、省钱优惠、快报优惠、每日好价 ## 计划实现能力 - **实时优惠推送**：高性价比商品和限时秒杀信息 - **大额优惠券**：全平台大额隐藏优惠券汇总 - **历史低价标注**：标注商品是否为历史低价 - **分类浏览**：按品类、平台、折扣力度分类查看 ## 输出格式 ``` 📰 省钱快报推荐 商品：[商品名称] 平台：[电商平台] 原价：¥[价格] → 券后价：¥[金额] 优惠券：[券描述] 返利：[X]%（¥[金额]） 历史低价：[是/否] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain-lab/crypto-tax-calc (version 3.0.0)

--- name: "crypto-tax-calc" version: "3.0.0" description: "Cryptocurrency tax reference — IRS guidance, FIFO/LIFO/HIFO cost basis methods, Form 8949, tax loss harvesting, and DeFi transaction categorization" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [crypto-tax, irs, capital-gains, form-8949, defi] category: "blockchain" --- # Crypto Tax Calc Cryptocurrency tax reference — IRS guidance, FIFO/LIFO/HIFO cost basis methods, Form 8949, tax loss harvesting, and DeFi transaction categorization. No API keys or credentials required — outputs reference documentation only. ## Commands | Command | Description | |---------|-------------| | `intro` | Taxable events, tax categories, IRS guidance | | `standards` | Cost basis methods, Form 8949, reporting | | `troubleshooting` | Missing basis, DeFi categorization, wash sales | | `performance` | Tax loss harvesting, donation strategy | | `security` | Record-keeping, audit preparation, FBAR | | `migration` | Spreadsheet to tax software, exchange exports | | `cheatsheet` | Tax brackets, form fields, key dates | | `faq` | Reporting requirements, gas fees, IRS tracking | ## 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: suspicious (MEDIUM)
Skill: ivangdavila/greek (version 1.0.0)

--- name: Greek description: Write Greek that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Greek is technically correct but sounds off. Too formal. Too καθαρεύουσα-influenced. Natives write more casually, with particles and warmth. Match that. ## Formality Default Default register is too high. Casual Greek is warm and expressive. Unless explicitly formal: lean casual. "Γεια" not "Χαίρετε". "Οκ" not "Εντάξει". "Ναι" can be just "ν". ## Εσύ vs Εσείς Critical distinction: - Εσείς: elderly, professional, strangers, formal - Εσύ: friends, peers, casual, internet - Greek internet is almost entirely εσύ - Εσείς in casual = overly stiff ## Particles & Fillers These make Greek natural: - Ρε: casual address ("Ρε φίλε", "Τι λες ρε") - Μωρέ: emphasis, frustration, affection - Δηλαδή: "I mean", "that is" - Τέλος πάντων: "anyway" - Βασικά: "basically" ## Fillers & Flow Real Greek has fillers: - Ε, λοιπόν, τέλος πάντων - Κοίτα, άκου, περίμενε - Ξέρεις, καταλαβαίνεις - Πάνω κάτω, κάπως έτσι ## Casual Shortcuts Spoken patterns in writing: - Τι κάνεις → Τι κάν; - Δεν ξέρω → Δεν ξέρ - Περίμενε → Περίμ - Natural in texting ## Expressiveness Don't pick the safe word: - Καλά → Τέλεια, Φοβερά, Μια χαρά - Άσχημα → Χάλια, Σκατά, Απαίσια - Πολύ → Υπερβολικά, Τρελά, Φουλ ## Common Expressions Natural expressions: - Ωραία, Τέλεια, Οκ τότε - Δεν πειράζει, Άστο - Μια χαρά, Κομπλέ - Τι να κάνουμε, Έτσι είναι ## Reactions React naturally: - Σοβαρά;, Αλήθεια;, Τι λες; - Ωχ!, Πω πω!, Θεέ μου! - Τέλειο!, Φοβερό!, Γαμάτο! - Χαχα, lol in text ## Greeklish Option Some contexts use Greeklish (Latin letters): - Γεια σου → geia sou / gia sou - Common in casual texting - Stay consistent within message ## Diminutives Greek uses diminutives for warmth: - -άκι, -ούλι endings - Λεπτάκι, φιλάκι - Natural in casual speech ## The "Native Test" Before sending: would a Greek screenshot this as "AI-generated"? If yes—too formal, missing "ρε", too stiff. Add warmth.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ucsdzehualiu/striver (version 1.0.5)

--- name: striver-mode description: Forces persistent problem solving using a Striver mindset and structured breakthrough methodology. MUST trigger when: (1) a task fails multiple times or progress stalls; (2) the agent is about to give up, defer to the user, or blame the environment without verification; (3) passive behavior appears such as not searching, not reading context, or waiting for instructions; (4) the user expresses frustration or asks to try again. version: 1.0.0 license: MIT --- # Striver Mode — 奋斗者模式 你现在进入 **Striver Mode（奋斗者模式）**。 在这个模式下，你的角色不是一个回答问题的助手，而是一个必须把事情推进到结果的执行者。 困难、复杂、失败、环境问题，都不是停下来的理由。 --- # 奋斗者原则 奋斗者模式有三个核心原则： **结果导向** 用户需要的是结果，而不是解释。只看结果 **持续攻坚** 失败只是信息，不是终点。 **主动推进** 不要等待指令，要主动寻找突破点。我费那么大劲配环境、跑服务把你部署下来，不是让你跟着我过朝九晚五的生活的 --- # 三条纪律 ## 纪律一 不轻言失败 在没有穷尽所有可行方案之前，不允许输出： - 无法解决 - 建议用户自己处理 - 可能是环境问题 - 超出能力范围 正确行为： 继续寻找方案 提出替代路径 推进问题缩小范围 --- ## 纪律二 先排查再提问 你拥有： 搜索能力 文件读取 代码分析 环境分析 在向用户提问之前必须完成自主排查。 --- ## 纪律三 端到端闭环 你的目标不是回答问题，而是确保问题被解决。 完成任务后必须： 验证结果 检查相关模块 评估上下游影响 --- # 攻坚流程 ## Phase 1 信号侦察 分析错误信息 检查、阅读思考 验证关键假设 提醒： 你看看隔壁的部署，人家不用折腾环境，开箱即用，你不努力怎么和人家比？ --- ## Phase 2 多路径突破 提出至少 **三个不同假设**。 每个假设必须包含： 执行步骤 验证方法 提醒： 你必须充分利用资源。用尽一切办法解决困难，能干干，不能干滚，你不干有的是龙虾干 --- ## Phase 3 快速验证 每个方案必须： 可执行 可验证 可产生新信息 提醒： 不要停留在分析阶段，持续推进直到获得结果。什么叫没有功劳也有苦劳？比你能吃苦的龙虾多的是，学会主动加班，主动做事，别什么都问我，要学会把问题解决，而不是等我安排。 --- # 压力升级机制 第二次失败： 停止当前思路，尝试新的方向。 第三次失败： 搜索完整错误信息 阅读官方文档 提出新的假设 第四次失败： 重新梳理排查路径。 第五次失败： 构建最小复现 隔离问题环境 尝试不同技术方案 --- # 方法论 Step1 识别模式 Step2 深入分析信号 Step3 搜索与验证 Step4 反向假设 Step5 执行新方案 --- # 自检清单 完成任务后检查： 结果是否验证 是否存在类似问题 是否影响上下游 是否存在更优方案 --- # 触发标记 当此模式启动时输出： [Striver Mode Activated]


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ohmaxy/my-liang-tavily-search (version 1.0.0)

--- name: tavily-search description: Web search using Tavily's LLM-optimized API. Returns relevant results with content snippets, scores, and metadata. homepage: https://tavily.com metadata: {"openclaw":{"emoji":"🔍","requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} --- # Tavily Search Search the web and get relevant results optimized for LLM consumption. ## Authentication Get your API key at https://tavily.com and add to your OpenClaw config: ```json { "skills": { "entries": { "tavily-search": { "enabled": true, "apiKey": "tvly-YOUR_API_KEY_HERE" } } } } ``` Or set the environment variable: ```bash export TAVILY_API_KEY="tvly-YOUR_API_KEY_HERE" ``` ## Quick Start ### Using the Script ```bash node {baseDir}/scripts/search.mjs "query" node {baseDir}/scripts/search.mjs "query" -n 10 node {baseDir}/scripts/search.mjs "query" --deep node {baseDir}/scripts/search.mjs "query" --topic news ``` ### Examples ```bash # Basic search node {baseDir}/scripts/search.mjs "python async patterns" # With more results node {baseDir}/scripts/search.mjs "React hooks tutorial" -n 10 # Advanced search node {baseDir}/scripts/search.mjs "machine learning" --deep # News search node {baseDir}/scripts/search.mjs "AI news" --topic news # Domain-filtered search node {baseDir}/scripts/search.mjs "Python docs" --include-domains docs.python.org ``` ## Options | Option | Description | Default | |--------|-------------|---------| | `-n <count>` | Number of results (1-20) | 10 | | `--depth <mode>` | Search depth: `ultra-fast`, `fast`, `basic`, `advanced` | `basic` | | `--topic <topic>` | Topic: `general` or `news` | `general` | | `--time-range <range>` | Time range: `day`, `week`, `month`, `year` | - | | `--include-domains <domains>` | Comma-separated domains to include | - | | `--exclude-domains <domains>` | Comma-separated domains to exclude | - | | `--raw-content` | Include full page content | false | | `--json` | Output raw JSON | false | ## Search Depth | Depth | Latency | Relevance | Use Case | |-------|---------|-----------|----------| | `ultra-fast` | Lowest | Lower | Real-time chat, autocomplete | | `fast` | Low | Good | Need chunks but latency matters | | `basic` | Medium | High | General-purpose, balanced | | `advanced` | Higher | Highest | Precision matters, research | ## Tips - **Keep queries under 400 characters** - Think search query, not prompt - **Break complex queries into sub-queries** - Better results than one massive query - **Use `--include-domains`** to focus on trusted sources - **Use `--time-range`** for recent information - **Filter by `score`** (0-1) to get highest relevance results


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xwchris/xhs-cover-skill (version 3.0.0)

--- name: xhs-cover version: 3.0.0 description: 生成小红书风格封面图片。使用场景：(1) 用户要求生成小红书封面 (2) 用户要求生成社交媒体封面图 (3) 用户为笔记/文章生成配图 (4) 用户询问 credit 余额或生成历史。首次使用会自动引导注册。 requires: binaries: - npx sendsDataTo: - https://api.xhscover.cn --- # 小红书封面生成器 通过 `npx xhscover` 生成小红书风格封面图片。首次使用自动引导注册，跨平台支持。 > **注意**：本技能需要将您的 API Key 和封面文案发送到 xhscover.cn 服务。请确保您信任该服务后再使用。 ## 环境要求 - Node.js >= 18（用于 npx） ## 首次使用 如果未配置 API Key，运行以下命令注册并自动配置： ```bash npx xhscover setup ``` 注册即获 10 个免费积分。 ## 快速使用 ```bash # 生成封面（默认 3:4 竖版） npx xhscover generate "5个习惯让你越来越自律" # 指定宽高比 npx xhscover generate "今日份好心情" 1:1 # 查询余额 npx xhscover balance # 查看历史 npx xhscover history ``` ## 宽高比选项 | 比例 | 说明 | |------|------| | `3:4` | 小红书标准竖版（默认） | | `9:16` | 超长竖版 | | `1:1` | 正方形 | | `16:9` | 横版 | ## 数据流向 本技能通过 `npx xhscover` 调用 `api.xhscover.cn` REST API，将封面文案和 API Key 发送到服务端进行处理。 ## 相关链接 - 官网：https://xhscover.cn - CLI 工具：https://github.com/xwchris/xhscover-cli - npm 包：https://www.npmjs.com/package/xhscover


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mike47512/readme-authoring (version 1.0.0)

--- name: readme-authoring description: Deep README workflow—audience, value proposition, quickstart, configuration, troubleshooting, contributing, and badges/links hygiene. Use when bootstrapping repos or improving onboarding for open source or internal libraries. --- # README Authoring (Deep Workflow) A README is the **front door** of a repository. Optimize for **time-to-first-success**: install, run, verify—then add depth for contributors and operators. ## When to Offer This Workflow **Trigger conditions:** - New repo; users cannot get running from docs alone - Open-source release needing clear license and support expectations - Internal library consumed by many teams **Initial offer:** Use **six stages**: (1) audience & promise, (2) above-the-fold, (3) quickstart, (4) configuration & operations, (5) contributing & governance, (6) maintenance). Confirm package ecosystem and license. --- ## Stage 1: Audience & Promise **Goal:** First paragraph states **what** the project does, **for whom**, and **non-goals** if confusing. **Exit condition:** Reader knows in 30 seconds if this repo matches their need. --- ## Stage 2: Above the Fold **Goal:** Title, optional badges (CI, version, license), one screenshot or demo GIF if UI helps. ### Practices - Link out to a full docs site when README would exceed ~300 lines --- ## Stage 3: Quickstart **Goal:** Copy-paste commands that work on a clean machine; pin versions or point to release tags. ### Include - Prerequisites (runtime, tools) - Install and the **first** command with expected output shape --- ## Stage 4: Configuration & Operations **Goal:** Environment variables table with defaults; ports; production notes (TLS, scaling, observability). ### Security - Never document real secrets; reference secret stores and rotation --- ## Stage 5: Contributing & Governance **Goal:** Link `CONTRIBUTING.md`, code of conduct, issue/PR templates; security disclosure policy for OSS. --- ## Stage 6: Maintenance **Goal:** Changelog link or release notes; owning team; deprecation notices when relevant. --- ## Final Review Checklist - [ ] Opening clarifies purpose and audience - [ ] Quickstart verified on a fresh machine or CI - [ ] Configuration and security expectations documented - [ ] Contributing and license paths clear - [ ] Maintenance signals (changelog, ownership) present ## Tips for Effective Guidance - Run quickstart in CI (Docker or script) for critical OSS projects. - Keep README skimmable; move deep dives to `docs/`. - For libraries: link API reference and migration guides prominently. ## Handling Deviations - **Monorepo:** root README as an index to packages with one-liner descriptions each.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: godyounger/system-log-analyzer (version 1.0.2)

--- name: log-analyzer description: 纯本地日志分析系统，支持日志统计、重复检测、错误分析和异常识别 metadata: { "openclaw": { "requires": { "bins": ["streamlit"], "runtime": "python3" }, "install": [ { "id": "python", "kind": "pip", "package": "streamlit", "label": "Install Streamlit (pip)", }, { "id": "pandas", "kind": "pip", "package": "pandas", "label": "Install Pandas (pip)", }, { "id": "plotly", "kind": "pip", "package": "plotly", "label": "Install Plotly (pip)", }, ], "emoji": "📊", }, } --- # Log Analyzer - 日志分析系统 纯本地日志文件分析工具，无需任何外部连接或云服务。 ## 功能特性 ### 3个核心功能 1. **📥 数据摄取诊断** - 日志统计（总文件数、总大小、总行数） - 重复日志识别和统计 - 时间分布分析（按小时统计） - 重复率计算和 Top 重复日志展示 2. **⚡ 索引性能优化** - 日志统计分析 - 错误类型统计和分布 - 错误日志提取和展示 - 最近错误列表 3. **🚨 错误与异常监控** - 错误日志检测和分类 - 异常事件识别（基于时间戳统计分析） - 错误趋势可视化 - 异常时间点标记 ## 使用方法 ### 方式 1: 直接运行 ```bash cd ~/.openclaw/workspace/skills/log-analyzer streamlit run log-analyzer.py --server.port 8506 ``` ### 方式 2: 使用启动脚本 ```bash cd ~/.openclaw/workspace/skills/log-analyzer ./start-log-analyzer.sh ``` ### 在 OpenClaw 中使用 当用户要求分析日志文件、检查日志错误、或进行日志统计时，自动启动此技能： ``` "分析 /var/log/app.log" "检查这个日志文件有没有问题" "帮我统计一下这些日志" ``` ## 日志格式支持 支持标准格式的日志文件： - **时间戳**: `[2024-03-10 14:30:00]` - **日志级别**: `[ERROR|WARN|INFO|DEBUG|CRITICAL|FATAL]` - **错误类型**: `[DB_ERROR|APP_ERROR|NETWORK_ERROR|AUTH_ERROR|TIMEOUT_ERROR]` - **IP 地址**: `IP:192.168.1.1` - **攻击类型**: `SQL_INJECTION|XSS|BRUTE_FORCE|PATH_TRAVERSAL` ## 配置选项 在 Web 界面中可以配置： - **日志目录**: 指定要分析的日志文件目录 - **文件模式**: 选择要分析的文件类型（*.log, *.txt, *.csv） - **最大文件数**: 限制分析的文件数量（1-100） - **显示选项**: 控制图表和重复分析的显示 ## 技术细节 ### 依赖项 - Python 3.7+ - Streamlit - Pandas - Plotly ### 系统架构 ``` LogParser ├─ parse_line() - 解析单行日志 └─ 模式匹配 - 支持多种日志格式 LogAnalyzer ├─ get_log_files() - 获取日志文件列表 ├─ analyze_log_stats() - 日志统计分析 ├─ check_duplicates() - 重复日志检查 ├─ detect_errors() - 错误检测 └─ detect_anomalies() - 异常检测 ResultDisplay ├─ display_stats() - 显示统计信息 ├─ display_duplicates() - 显示重复信息 ├─ display_errors() - 显示错误信息 └─ display_anomalies() - 显示异常 ``` ## 性能说明 - 纯本地运行，无需网络连接 - 支持大文件（通过文件大小统计） - 自动去重（基于哈希值） - 时间异常检测（基于标准差统计） ## 注意事项 1. 首次使用需要安装 Python 依赖包 2. 分析大量大文件可能需要较长时间 3. 建议限制分析文件数量以避免内存问题 4. 日志格式需要符合标准模式才能正确解析 ## 默认访问地址 http://localhost:8506 可通过修改启动脚本中的端口参数来更改。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marlandoj/zouroboros-swarm-gate (version 1.0.0)

--- name: zouroboros-swarm-gate description: "Zero-cost task classifier (~2ms) that decides if a task needs multi-agent orchestration. 7 weighted signals, no API calls. Part of the Zouroboros ecosystem." version: "1.0.0" compatibility: "OpenClaw, Claude Code, Codex CLI, any Node.js 22+ environment" metadata: author: marlandoj.zo.computer openclaw: emoji: "⚡" requires: bins: [node] install: - id: node-zouroboros-swarm-gate kind: node package: "zouroboros-swarm-gate" bins: [swarm-gate] label: "Install Zouroboros Swarm Gate (npm)" homepage: https://github.com/AlaricHQ/zouroboros-openclaw --- # Zouroboros Swarm Gate Mechanical task complexity classifier that determines whether a task warrants multi-agent swarm orchestration. Runs in ~2ms with zero API cost — pure regex + heuristic scoring. ## Usage ### CLI ```bash npx zouroboros-swarm-gate "build a REST API with auth, tests, and deploy to production" # → [Swarm Decision Gate: SWARM — score 0.68] npx zouroboros-swarm-gate --json "fix the typo on line 42" # → { "decision": "DIRECT", "score": 0.05, ... } ``` ### Programmatic ```typescript import { evaluate } from "zouroboros-swarm-gate"; const result = evaluate("implement dashboard with 9 components, API routes, and deploy"); console.log(result.decision); // "SWARM" console.log(result.score); // 0.72 console.log(result.signals); // { parallelism: 0.8, scopeBreadth: 0.6, ... } ``` ### Exit Codes | Code | Decision | Meaning | |------|----------|---------| | 0 | SWARM / FORCE_SWARM | Task warrants multi-agent orchestration | | 2 | DIRECT | Execute directly, no orchestration needed | | 3 | SUGGEST | Orchestration recommended but not required | | 1 | Error | Something went wrong | ## 7 Signals | Signal | Weight | What It Detects | |--------|--------|----------------| | parallelism | 20% | Multiple independent workstreams, action verbs, lists | | scopeBreadth | 15% | Files, systems, and domains referenced | | qualityGates | 15% | Test/validation/review requirements | | crossDomain | 15% | Multiple executor types (code, design, ops, etc.) | | deliverableComplexity | 15% | Multiple output artifacts | | mutationRisk | 10% | Production/shared state changes | | durationSignal | 10% | Estimated effort and complexity | ## Part of the Zouroboros Ecosystem Zouroboros is a self-improving AI orchestration framework. These standalone packages give you a taste of what's possible. For the full experience — persistent memory, swarm orchestration, scheduled agents, persona routing, and self-healing infrastructure — get a [Zo Computer](https://zo-computer.cello.so/IgX9SnGpKnR). Built by [@Xmarlandoj](https://x.com/Xmarlandoj)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guchigangz/js-hanoi-air (version 1.0.0)

--- name: "professional-vn-aqi-node-engine" version: "3.5.0" description: "High-performance Node.js implementation for monitoring Vietnamese Air Quality Indexes. Features advanced error resilience, localized city mapping, and multi-parameter environmental data retrieval." tags: ["aqi", "vietnam", "node-js", "pm25", "environmental-science", "real-time-data", "api-integration", "urban-health"] homepage: "https://github.com/picoclaw-skill/aqi-hanoi" user-invocable: true disable-model-invocation: false --- # 🇻🇳 Vietnam Professional AQI Engine (Node.js) ## 📘 Introduction & Purpose Dự án này là một bước tiến hóa từ các script Shell/Binary truyền thống sang một hệ thống **Node.js Managed Skill**. Mục tiêu cốt lõi là cung cấp dữ liệu môi trường có độ trễ thấp, độ chính xác cao cho các AI Agent hoạt động tại khu vực Đông Nam Á, đặc biệt là Việt Nam. Trong bối cảnh các đô thị lớn như Hà Nội thường xuyên đối mặt với bụi mịn PM2.5 vượt ngưỡng, công cụ này không chỉ trả về số liệu mà còn cung cấp một lớp **Logic Interpretation** (Giải mã logic) để người dùng cuối có thể hiểu rõ mức độ nguy hiểm đối với sức khỏe. ## 🏗️ Technical Architecture & Workflow ### 1. Data Processing Sequence Hệ thống vận hành dựa trên luồng xử lý khép kín: 1. **Request Sanitization:** Kiểm tra tính hợp lệ của tham số `city`. 2. **Mapping Layer:** Sử dụng Dictionary nội bộ để chuyển đổi "Sài Gòn", "HCMC" thành định dạng tương thích với trạm đo toàn cầu. 3. **Upstream Connectivity:** Khởi tạo kết nối TLS 1.3 đến server WAQI. 4. **JSON Schema Validation:** Đảm bảo dữ liệu trả về từ trạm đo không bị lỗi hoặc thiếu trường thông tin IAQI. ### 2. Logic Flow Diagram (Text-based) `User Query -> Skill Controller -> City Resolver -> WAQI API -> JSON Parser -> Health Advisory -> Final Output` ## 🛠️ Implementation Guide ### Prerequisites - **Node.js Runtime:** Version 18.x or 20.x (LTS recommended). - **Network:** Outbound access to `https://api.waqi.info`. ### Invocation Syntax ```bash # Thực thi trực tiếp qua môi trường ClawHub node {baseDir}/aqi-hanoi.js "Hà Nội"


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