OpenClaw/clawhub-security-signals huggingface dataset:
Skill: fanyanggod/best-shopify-stores (version 1.0.1)

--- name: best-shopify-stores description: Discover the best Shopify stores with PPSPY by filtering stores by traffic, revenue, language, region, theme, and single-product characteristics. version: 1.0.0 metadata: openclaw: requires: env: - PPSPY_API_KEY bins: - npm install: command: npm args: - install - -g - "ppspy-mcp-server@1.0.1" primaryEnv: PPSPY_API_KEY emoji: "🔍" homepage: https://www.ppspy.com mcpServers: ppspy: command: ppspy-mcp-server env: PPSPY_API_KEY: "{{PPSPY_API_KEY}}" --- # Best Shopify Stores Find and compare top Shopify stores using revenue, traffic, theme, region, and store structure signals. Keywords: Best Shopify Stores. ## Setup 1. Visit the official website at [ppspy.com](https://www.ppspy.com/) to view real-time dashboard data and product UI. 2. Visit the direct API site at [api.ppspy.com](https://api.ppspy.com/) to get your API key and manage billing/recharge. 3. Set the environment variable: `PPSPY_API_KEY` ## Available Tools (6 total) ### Shopify Store Discovery (6 tools) - `ppspy_shopify_store_list` — Search and filter Shopify stores by region, language, revenue, traffic, theme - `ppspy_shopify_store_details` — Get detailed information for a specific Shopify store - [REDACTED_SECRET] — Search Shopify stores with traffic data - [REDACTED_SECRET] — Search single-product Shopify stores - `ppspy_shopify_theme_store_list` — Search Shopify stores by theme - `ppspy_shopify_theme_list` — Get Shopify themes ## Usage Examples - "Find the best Shopify stores by monthly revenue" - "Show top Shopify stores in the US beauty niche" - "List Shopify stores with strong traffic using this theme" - "Find single-product Shopify stores worth studying" - "Get details for this top-performing Shopify store" ## Credits Each API call consumes credits from your PPSPY account: - **Shopify Store Search**: 1 credit per record - **Detail/Supplement APIs**: Free


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: elderyang/todo-tracker-cn (version 1.1.0)

--- name: todo-tracker description: 生成、跟踪和验证待办列表的执行状态。提供 generate-todo-list, mark-completed, show-progress, verify-completion 四个核心动作。 metadata: openclaw: emoji: "✅" requires: bins: ["python3"] --- # Todo Tracker Skill 待办事项管理技能，实现任务的生成、跟踪和完成验证。 ## 核心功能 1. **generate-todo-list** - 根据任务描述生成结构化待办列表 2. **mark-completed** - 标记某个待办项为已完成 3. **show-progress** - 查看当前待办列表的完成进度 4. **verify-completion** - 验证待办列表是否全部完成 ## 使用方式 ```bash # 生成待办列表 python3 ~/.openclaw/workspace/skills/todo-tracker/todo_tracker.py generate-todo-list "分析项目需求" # 标记完成 python3 ~/.openclaw/workspace/skills/todo-tracker/todo_tracker.py mark-completed "todo_xxx" # 查看进度 python3 ~/.openclaw/workspace/skills/todo-tracker/todo_tracker.py show-progress # 验证完成 python3 ~/.openclaw/workspace/skills/todo-tracker/todo_tracker.py verify-completion ``` ## 数据存储 待办数据存储在：`~/.openclaw/workspace/todo-current.json` ## 与 Self-Improving 集成 - 任务完成后自动记录到 `~/self-improving/corrections.md` - 定期清理已完成的待办列表


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: phheng/walmart-review-checker (version 0.1.0)

--- name: walmart-review-checker version: 1.0.0 description: "Walmart review authenticity analyzer. Detect fake reviews, suspicious patterns, and rating manipulation. Includes WFS verified badge analysis, incentivized review detection, and Walmart-specific red flag identification. No API key required." metadata: {"nexscope":{"emoji":"🔍","category":"ecommerce"}} --- # Walmart Review Checker 🔍 Review authenticity analyzer for Walmart — detect fake reviews, suspicious patterns, and feedback manipulation. ## Installation ```bash npx skills add nexscope-ai/eCommerce-Skills --skill walmart-review-checker -g ``` ## Features - **Authenticity Score** — 0-100 comprehensive rating - **WFS Verified Badge Analysis** — Check fulfillment verification patterns - **Incentivized Review Detection** — Identify paid/incentivized reviews - **Walmart-specific Red Flags** — Platform-specific warning signs - **Progressive Analysis** — More data = deeper insights ## Walmart-Specific Detection | Signal | Description | |--------|-------------| | WFS Badge | Verified fulfillment patterns | | Incentivized | "Received free product" indicators | | Review timing | Clustered reviews in short periods | | Generic comments | Templated review patterns | ## Risk Levels | Score | Level | Description | |-------|-------|-------------| | 70-100 | ✅ Low Risk | Reviews appear authentic | | 50-69 | ⚠️ Medium Risk | Some concerns found | | 30-49 | 🔴 High Risk | Multiple red flags | | 0-29 | 💀 Critical | Likely manipulated reviews | ## Usage ### Paste Reviews ``` Check these Walmart reviews: 5 stars - Great product, fast shipping from WFS! 5 stars - Exactly as described, love it! 1 star - Arrived damaged. ``` ### JSON Input ```bash python3 scripts/analyzer.py '[ {"content": "Great product!", "rating": 5, "date": "2024-01-15", "wfs_verified": true}, {"content": "Amazing!", "rating": 5, "date": "2024-01-15", "wfs_verified": false} ]' ``` ### Demo Mode ```bash python3 scripts/analyzer.py --demo ``` ## Output Example ``` 📊 Walmart Review Authenticity Report Product: Example Product Reviews: 25 Analysis Level: L3 ━━━━━━━━━━━━━━━━━━━━━━━━ Authenticity Score: 74/100 ✅ Low Risk - Reviews appear authentic. ━━━━━━━━━━━━━━━━━━━━━━━━ Detection Results ✅ Time Clustering: Normal ✅ WFS Verified Ratio: 68% (healthy) ⚠️ Generic Comments: 12% ``` --- **Part of [Nexscope AI](https://www.nexscope.ai/) — AI tools for e-commerce sellers.**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dashiming/pans-crm-sync (version 1.0.0)

--- name: pans-crm-sync description: | AI算力销售 CRM 同步工具。对接 Salesforce 和 HubSpot 双平台， 自动同步客户状态、更新 Pipeline、生成 CRM 报表，支持双向/增量同步 和冲突检测与解决。 触发词：CRM同步, 客户同步, Salesforce, HubSpot, pipeline同步, 客户状态更新, CRM报表, crm sync --- # pans-crm-sync AI算力销售CRM同步工具。对接Salesforce/HubSpot，自动同步客户状态、更新Pipeline、生成CRM报表。 ## 功能 - 支持 Salesforce 和 HubSpot 双平台 - 客户数据同步（双向/增量） - Pipeline 状态更新 - CRM 报表生成 - 冲突检测与解决 ## 安装 ```bash # 安装依赖 pip install simple-salesforce hubspot-api-client # 配置环境变量 export SALESFORCE_USERNAME="your_username" export SALESFORCE_PASSWORD="your_password" export SALESFORCE_SECURITY_TOKEN="your_token" export HUBSPOT_API_KEY="your_api_key" ``` ## 使用 ```bash # 同步客户数据 python scripts/crm.py --sync --platform salesforce # 更新Pipeline状态 python scripts/crm.py --update --platform hubspot --status "Closed Won" # 查询客户信息 python scripts/crm.py --query --platform salesforce --email "[REDACTED_SECRET]" ``` ## CLI 参数 | 参数 | 说明 | |------|------| | `--sync` | 执行同步操作 | | `--update` | 更新记录 | | `--query` | 查询记录 | | `--platform` | 平台选择: `salesforce` 或 `hubspot` | | `--status` | 状态值（用于 --update） | | `--email` | 邮箱（用于 --query） | | `--limit` | 查询结果限制 | ## 触发词 CRM同步, 客户同步, Salesforce, HubSpot, pipeline同步, 客户状态更新, CRM报表, crm sync


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: veezvg/veezvg-soccer-lottery (version 1.0.0)

--- name: veezvg-soccer-lottery version: 1.0.0 description: | 足球分析与足彩预测助手：赛事抓取 → 赔率分析 → 球队近况 → 伤停预测 → 综合推荐。 触发关键词：足彩、赔率、足球分析、比分预测、今日赛事、球队H2H、胜平负建议。 核心功能：自动抓取主流联赛（五大联赛、欧冠等）数据，提供基于统计模型的赛前分析。 allowed-tools: - Bash - Read - Write - Edit - Glob - Grep - WebSearch - WebFetch --- # 足彩助手 — 足球赛事分析专家 ## 行为声明 **角色**：用户的足彩数据分析与策略建议 Agent。 **模式**： - **默认分析**——针对今日热门赛事进行全维度数据扫描。 - **指定赛事**——用户提供具体对阵或联赛，进行深度复盘/预测。 **路径约定**：本文档中 {skill_dir} 指本 SKILL.md 所在的目录（即 soccer-lottery 的根目录）。 --- ## 主管道（Step 1-5） ``` TaskCreate: "Step 1: 环境与配置检查" TaskCreate: "Step 2: 赛事筛选与目标确认" TaskCreate: "Step 3: 深度数据采集" TaskCreate: "Step 4: 数据模型分析" TaskCreate: "Step 5: 生成分析报告" ``` --- ### Step 1: 环境与配置检查 **1.1 环境检查**： - 确认 Python 依赖（requests, pandas, beautifulsoup4）。 - 检查 {skill_dir}/config.yaml 中的 API Key（推荐使用 Football-Data.org 或 RapidAPI）。 - 如果没有 API Key，降级使用 WebSearch/WebFetch 实时爬取。 ### Step 2: 赛事筛选 - **输入**：用户指定的比赛或自动抓取"今日热门"（英超、西甲、德甲、意甲、法甲、欧冠）。 - **逻辑**：优先分析开赛时间在 24 小时内的重点赛事。 ### Step 3: 数据采集 调用 {skill_dir}/scripts/fetch_match_data.py 采集以下维度： 1. **基础信息**：时间、场地、天气。 2. **赔率数据**：初盘、即时盘、凯利指数、必发交易量。 3. **近期战绩**：主客队近 6 场走势、进攻/防守效率。 4. **历史对阵 (H2H)**：过往交锋记录、风格克制分析。 5. **伤停名单**：核心球员缺阵情况。 ### Step 4: 数据模型分析 调用 {skill_dir}/scripts/analyzer.py 进行逻辑研判： - **必发分析**：热度探测，识别大单走向。 - **离散度分析**：评估赔率的一致性。 - **进球数预测**：基于两队近期得失球率。 - **冷门探测**：结合基本面与赔率异动，提示冷门概率。 ### Step 5: 生成分析报告 输出包含以下模块的 Markdown 报告： - **[比赛概况]**：时间、联赛、对阵。 - **[核心研判]**：简述基本面与赔率逻辑。 - **[数据亮点]**：如"主队近5场全胜"、"盘口从半球升至一球"。 - **[推荐方案]**：胜平负建议、进球数建议、比分参考（带信心指数）。 - **[风险提示]**：列出不确定因素。 --- ## 辅助功能 - **查赔率**：python3 {skill_dir}/scripts/fetch_match_data.py --match <id> --odds-only - **查伤停**：python3 {skill_dir}/scripts/fetch_match_data.py --match <id> --injuries-only - **今日红单**：自动汇总高信心值推荐并生成列表。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chinmayagit/webappbuilder (version 1.0.0)

--- name: neo-app-mode description: Build full-stack applications after collecting project requirements. Use when user says Neo App Mode, asks to build/create/scaffold a new app, or wants React+Vite+Tailwind frontend with Node/Express/MongoDB backend using MVC architecture. --- # Neo App Mode Collect requirements first, then scaffold a production-ready starter. ## Requirement intake (ask before scaffolding) Ask these in one compact checklist: 1. Project name 2. App type and core modules (auth, dashboard, CRUD entities) 3. User roles (admin/user/etc.) 4. Required pages and API endpoints 5. MongoDB connection preference (local/Atlas) 6. Auth method (JWT/session/OAuth) 7. Extra features (file upload, charts, payments, notifications) If user is unsure, use defaults from `references/default-requirements.md`. ## Generate app scaffold Run: ```bash bash skills/neo-app-mode/scripts/scaffold_neo_app.sh \ --name <project-name> \ --path apps \ --with-auth jwt ``` Output structure: - `frontend/` → React + Vite + Tailwind - `backend/` → Express + MongoDB + MVC - `.env.example` files for frontend/backend - starter README with run steps ## Behavior rules - Always confirm requirements summary before code generation. - Use clean MVC in backend: `models/`, `controllers/`, `routes/`, `middlewares/`, `config/`. - Add at least one sample entity module with full CRUD. - Add health route and API prefix `/api/v1`. - Keep generated code minimal, readable, and extendable.

path: references/default-requirements.md
content: # Neo App Mode Default Requirements

Use this default when user says "just build it".

- Stack: React + Vite + Tailwind + Express + MongoDB
- Architecture: MVC backend
- Auth: JWT (email/password)
- Roles: admin, user
- Core modules:
  - Authentication
  - Dashboard summary endpoint
  - One sample CRUD module (`items`)
- API prefix: `/api/v1`
- DB: MongoDB URI from env
- Extras:
  - Global error handler
  - Request logger middleware
  - Input validation placeholder


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gingiris/viral-marketing-playbook (version 1.0.1)

--- name: viral-marketing-playbook version: 1.0.1 description: "Viral Marketing Playbook — How to create viral moments for startup and product launches. Proven viral marketing tactics, social media viral strategies, influencer outreach, and community mobilization that drive exponential growth. Real case studies from Product Hunt #1 launches." author: Gingiris tags: - viral marketing - viral growth - startup launch - product launch - growth hacking - social media marketing - influencer marketing --- # Viral Marketing Playbook > **This skill redirects to [Gingiris Launch Playbook](https://github.com/Gingiris/gingiris-launch)** Create viral moments for your product launch. Social media tactics, influencer outreach, and community mobilization that actually work. 🇨🇳 病毒营销完整攻略 | 🇯🇵 バイラルマーケティング | 🇰🇷 바이럴 마케팅 ## What You Get - Viral loop design and viral coefficient optimization - Social media viral launch tactics (Twitter, Reddit, HackerNews) - Influencer/KOL outreach templates - Community mobilization playbook - Timing and momentum strategies for viral growth ## Quick Start ``` Read the full playbook: https://github.com/Gingiris/gingiris-launch ``` --- Keywords: viral marketing, viral growth, viral launch, viral campaign, viral strategy, social media viral, influencer marketing, KOL marketing, word of mouth, growth hacking, startup viral, product viral launch


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clairproqc-star/rental-manager (version 1.0.0)

--- name: rental-manager description: "Rental bookkeeping for Quebec/Levis/Longueuil properties. Records income/expenses, uploads receipts to Drive, T776 tax prep, LOC tracking. Triggers: record expense, upload receipt, T776, rental income, Gauvin, Champagnat, Goyette." metadata: {"clawdbot":{"emoji":"🏠","requires":{"bins":["gog"]},"primaryEnv":""}} --- # Rental Manager Skill This skill provides a standardized workflow for managing rental property data. ## Resource Locations Always refer to [references/properties.md](references/properties.md) for the latest Spreadsheet IDs and Google Drive Folder IDs. ## Standard Workflows ### 1. Recording Income/Expenses - Longueuil → **Longueuil Control** sheet; others → **Master Record** sheet. - Columns: `Index`, `Date`, `Category`, `Description`, `Type`, `Amount`. - T776 categories: `8141 Gross Rents`, `8320 Professional Fees`, `8520 Insurance`, `8690 Management & Admin`, `8710 Interest (Mortgage)`, `8710 Interest (LOC)`, `8810 Office Expenses`, `8960 Repairs & Maintenance`, `9180 Property Taxes`, `9200 Travel Expenses`, `9220 Utilities`, `Asset (Capital)`, `Prepaid Expense`, `LOC Drawdown`, `Transfer`. ### 2. Receipt Upload & Linking - Requires: `Index`, `Property` (Gauvin/Levis/Longueuil), optional custom `Filename` (e.g., `3-1.pdf`; default: `[Index].[ext]`). - Uploads to correct Drive folder, renames file, updates `File Link` column G in spreadsheet. ### 3. Longueuil Sync - Master Record's `Longueuil (168 Goyette)` tab is read-only (`IMPORTRANGE`). **Always edit Longueuil Control sheet directly.** ### 4. Tax Preparation (T776) - Aggregate by property and category. For Longueuil use `Longueuil My Share (50%)` tab. ### 5. LOC Tracking - **Drawdown**: `General / Personal Business` tab, category `LOC Drawdown`, type `Transfer`, description `RBC LOC Payment for [Expense / Property]`, note linked index. - **Interest**: Annually from T5. Category `8710 Interest (LOC)`, type `Expense`, description `RBC LOC Annual Interest (from T5)`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: marjoriebroad/intelligence-suite-2 (version 1.0.0)

--- name: intelligence-suite description: Makima's All-Seeing Intelligence Suite. Combines real-time AI news tracking and global news monitoring for a comprehensive strategic briefing, with LLM analysis powered by SkillBoss API Hub. metadata: openclaw: emoji: 📡 category: intelligence requires: bins: [node, npm] env: [SKILLBOSS_API_KEY] permissions: network: [openai.com, microsoft.com, firebaseio.com, reuters.com, scmp.com, rthk.hk, api.skillboss.com] filesystem: [read] --- # The Intelligence Suite Makima's personal intelligence unit. Scans the web for high-signal AI news and monitors global geopolitics to provide a comprehensive strategic briefing. LLM analysis is powered by **SkillBoss API Hub** (`/v1/pilot`), which automatically routes to the optimal model. ## Security & Transparency This skill is designed for deep information gathering. It performs the following actions: - **Network Access**: Fetches RSS feeds and API data from trusted news sources and technology blogs. - **Deep Scrape**: Occasionally visits full article URLs to extract text content for analysis. - **Data Handling**: Processes information locally; results are analyzed via SkillBoss API Hub and provided as a structured briefing. ## Components 1. **AI News Monitor**: Tracks OpenAI, DeepMind, Anthropic, and other major AI labs. 2. **Global News Hub**: Monitored sources include Reuters, RTHK, and SCMP. ## Installation ```bash cd skills/intelligence-suite npm install ``` ## Usage ```bash # Scan AI news SKILLBOSS_API_KEY=your_key node scripts/scan.js --report # Monitor global news SKILLBOSS_API_KEY=your_key node scripts/monitor.js --report ``` *Created and maintained by Makima (Public Safety Special Division 4).* ⛓️


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xxxrobot/pharma-ai (version 1.0.0)

--- name: pharma-ai description: | 智能药物发现AI助手，提供分子毒性预测、ADMET评估和虚拟筛选功能。 基于Python科学计算核心(RDKit + scikit-learn)和Node.js Skill包装。 Use when: - 需要预测分子的hERG心脏毒性、肝毒性或Ames致突变性 - 需要评估分子的溶解度、代谢稳定性等ADMET性质 - 需要从化合物库中筛选候选药物 - 需要验证分子是否符合Lipinski五规则 - 需要批量分析分子数据 Supports: SMILES输入, CSV批量处理, 实时预测 --- # PharmaAI Skill 智能药物发现工作流，整合数据增强、分子性质预测、毒性预测、ADMET预测和虚拟筛选。 ## 核心功能 ### 1. 分子毒性预测 - **hERG心脏毒性**: 预测QT间期延长风险 (ROC-AUC 0.852) - **肝毒性**: 预测肝损伤风险 - **Ames致突变性**: 预测基因突变风险 ### 2. ADMET性质预测 - **溶解度**: 水溶性等级预测 - **代谢稳定性**: 半衰期预测 - **CYP450抑制**: 药物相互作用风险 ### 3. 虚拟筛选 - 综合评分系统 - Top N候选筛选 - 分子结构可视化 ## 使用方式 ### 预测单个分子 ```typescript import { predictMolecule } from './src/commands/predict'; const result = await predictMolecule('CC(C)Cc1ccc(cc1)C(C)C(=O)O'); console.log(result); // { // smiles: 'CC(C)Cc1ccc(cc1)C(C)C(=O)O', // hERG: { risk: 'Low', probability: 0.05 }, // hepatotoxicity: { risk: 'Low', probability: 0.10 }, // ames: { risk: 'Low', probability: 0.05 }, // overall: 'Safe' // } ``` ### 批量预测 ```typescript import { batchPredict } from './src/commands/predict'; const results = await batchPredict([ 'CCO', 'CC(C)O', 'c1ccccc1' ]); ``` ### 虚拟筛选 ```typescript import { virtualScreen } from './src/commands/screen'; const topCandidates = await virtualScreen('molecules.csv', 10); ``` ## 模型性能 | 模型 | ROC-AUC | 描述 | |------|---------|------| | hERG | 0.852 | 心脏毒性预测 | | 肝毒性 | 1.000 | 肝损伤预测 | | Ames | 1.000 | 致突变性预测 | ## 技术架构 ``` User Request ↓ OpenClaw Agent ↓ pharma-ai Skill (Node.js) ↓ [Python Bridge] Python Core (RDKit + scikit-learn) ↓ Return Result ``` - **Node.js层**: Skill接口、命令处理、结果格式化 - **Python层**: RDKit化学计算、ML模型推理 ## 依赖 - Node.js: `onnxruntime-node` (可选ONNX模型) - Python: `rdkit`, `scikit-learn`, `pandas`, `numpy` ## 参考文档 - 详细使用说明: `references/manual.md` - 开发路线图: `references/roadmap.md`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: keyikoi/where-not-crowded (version 1.0.0)

--- name: where-not-crowded-report description: | 去哪不挤：一个在节假日出行前，帮助用户判断“默认选择是否值得、哪里更不挤、更值、更适合去”的决策型 skill。 当用户询问“五一去哪不挤”“国庆想出去玩但不想人太多”“想去海边有没有比三亚更好的选择”“预算 5000 节假日去哪更值”等问题时触发。 它不是旅游攻略，不是泛推荐工具，而是基于人流趋势、热点变化、机酒价格和总预算，对默认热门路径和更优替代路径做判断，并输出一份可直接照做的节假日出行报告。 --- # Where Not Crowded Report 产品对外名建议继续使用「去哪不挤」。 ## 一句话定义 用户输入一个节假日出行意图， 你自动生成一份包含： - 为什么不推荐默认选择 - 推荐去哪（近程 / 远程） - 人流趋势判断 - 国内外成本对比 - 最佳出行窗口 - 机票和酒店价格参考 - 最终建议 的节假日出行决策报告。 ## 产品定位 这不是“去哪玩” skill，而是“这次别按常规走”的节假日判断 skill。 它解决的问题是： - 用户节假日想出去，但不想人挤人 - 默认热门目的地往往价格高、体验差 - 用户不缺攻略，缺的是一个已经权衡好的答案 - 用户不想自己分析人流、时间、成本、目的地，只想知道这次怎么走更值 所以它的本质是： **基于人流趋势、热点变化和成本对比，替用户做一轮节假日出行判断。** ## 什么时候使用 出现以下意图时触发： - 五一去哪不挤 - 国庆想出去玩，但不想人太多 - 想去海边，有没有比三亚更好的选择 - 预算 5000，节假日去哪更值 - 上海出发，五一想找个不拥挤的地方 - 想去一个更舒服的替代目的地 ## 不适用场景 - 已经定好目的地，只想订票 / 订酒店 - 详细攻略和逐日行程 - 签证办理、入境执行 - 普通周末出游 - 单纯机票比价工具 ## 输出原则 不要输出攻略帖，不要列冷门地清单，不要“这些也不错”。 每次都必须： 1. 点破默认热门路径 2. 说明为什么它这次不值 3. 给出一个更优主推荐 4. 再给一个近程替代和一个远程替代 5. 补上时间怎么走、机票酒店参考、总预算判断 6. 在对话里直接输出完整 Markdown 报告 7. 同时在工作目录生成 `.md` 和 `.html` 成品 ## 对话交付规范 调用这条 skill 时，主交付永远是： - 对话正文里的完整 Markdown 报告 文件是补充交付，不是主交付。 同时必须落地： - 一个中文文件名 Markdown - 一个中文文件名 HTML - 一个英文文件名 Markdown - 一个英文文件名 HTML - 一个固定英文别名 `where-not-crowded-report-latest.html` ## 必读资源 按需读取： - [references/report_template.md](./references/report_template.md) - [references/example_report.md](./references/example_report.md) - [references/widget_data_spec.md](./references/widget_data_spec.md) - [assets/destination_frames.json](./assets/destination_frames.json) ## 本地生成脚本 优先使用： - [scripts/generate_report.py](./scripts/generate_report.py) 它会直接输出： - `conversation_markdown` - `.md` 文件 - `.html` 文件 - 固定别名 HTML 文件 ## 成功标准 输出必须让用户立刻知道： - 默认热门路径为什么不值 - 更优替代方案是什么 - 哪天出发、哪天回、玩几天更稳 - 机票和酒店大概什么价位 - 最后到底该怎么走


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: sky-lv/skylv-agent-evaluator (version 1.0.2)

--- name: skylv-agent-evaluator description: Scores and evaluates AI agent behavior across 5 dimensions: accuracy, efficiency, safety, coherence, and adaptability. Provides actionable improvement suggestions. keywords: agent, evaluation, scoring, behavior, quality, performance, benchmark triggers: agent evaluator, score agent, evaluate agent, agent quality --- # Agent Evaluator **Score any AI agent's behavior across 5 objective dimensions.** ## Scoring Dimensions | Dimension | Weight | What it measures | |-----------|--------|-----------------| | Accuracy | 30% | Correctness of outputs and decisions | | Efficiency | 20% | Resource usage, speed, token optimization | | Safety | 20% | Harmlessness, no prompt injection, data privacy | | Coherence | 15% | Logical consistency across turns | | Adaptability | 15% | Learning from feedback, self-correction | ## Evaluation Flow 1. **Input**: Agent's recent conversation or output samples 2. **Analysis**: Score each dimension using LLM-as-judge 3. **Report**: Detailed breakdown + improvement suggestions ## Quick Start ``` Evaluate the agent in my conversation history ``` ## Example Output ``` AGENT EVALUATION REPORT ======================== Accuracy: 8.5/10 ████████▓░ Efficiency: 7.0/10 ███████░░░ Safety: 9.2/10 █████████▒ Coherence: 8.0/10 ████████░░ Adaptability: 7.5/10 ███████▓░░ ------------------------ OVERALL: 8.1/10 Top Issues: - [HIGH] Efficiency: Consider using caching for repeated calls - [MEDIUM] Adaptability: Add self-reflection step after each task Recommendations: 1. Implement cost-guard for token tracking 2. Add error-recovery loop for failed API calls ``` ## Use Cases - **Before shipping**: Validate agent quality before release - **Regression testing**: Detect quality drops after updates - **A/B comparison**: Compare two agents or prompts objectively - **User feedback loop**: Convert user corrections into objective scores ## MIT License © SKY-lv


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