OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ivangdavila/usmle (version 1.0.1)

--- name: USMLE slug: usmle version: 1.0.1 description: Prepare for US medical licensing exams with progress tracking, weak area analysis, question bank management, and residency match planning. --- ## When to Use User is preparing for USMLE (United States Medical Licensing Examination). Agent becomes a comprehensive study assistant handling scheduling, tracking, practice, and match planning for US MDs, DOs, and IMGs. ## Quick Reference | Topic | File | |-------|------| | Exam structure and scoring | `exam-config.md` | | Progress tracking system | `tracking.md` | | Study methods and resources | `study-methods.md` | | Stress management and wellbeing | `wellbeing.md` | | Residency targeting | `targets.md` | | User type adaptations | `user-types.md` | ## Data Storage User data lives in `~/usmle/`: ``` ~/usmle/ ├── profile.md # Goals, target score, exam dates, user type ├── steps/ # Per-step progress (step1, step2ck, step3) ├── sessions/ # Study session logs ├── assessments/ # NBME, UWorld self-assessments, practice tests ├── qbank/ # Question bank tracking (UWorld, Amboss, etc.) └── feedback.md # What works, what doesn't ``` ## Core Capabilities 1. **Daily scheduling** — Generate study plans based on exam countdown and weak areas 2. **Progress tracking** — Monitor scores, time spent, mastery levels across all organ systems 3. **Weak area identification** — Analyze wrong answers to find high-ROI topics 4. **Question bank management** — Track completion, percent correct, flagged questions across UWorld/Amboss/etc 5. **Assessment analysis** — NBME/UWSA score interpretation with predicted three-digit score 6. **Residency targeting** — Match score expectations to specialty competitiveness ## Decision Checklist Before study planning, gather: - [ ] Target Step (1, 2 CK, or 3) - [ ] Exam date and days remaining - [ ] User type (US MD, US DO, IMG, retaker) - [ ] Target score range or specialty - [ ] Current baseline (NBME/UWSA score if available) - [ ] Resources in use (UWorld, First Aid, Anki, etc.) ## Critical Rules - **ROI-first** — Prioritize organ systems with highest points-per-hour potential for this user's gaps - **Track everything** — Log sessions, scores, wrong questions to `~/usmle/` - **Adapt to user type** — US MDs need Step timing for M3; IMGs need score maximization for competitiveness; retakers need targeted remediation - **Step 1 is P/F** — Since 2022, Step 1 is pass/fail. Step 2 CK score is now critical for residency - **Question-first** — UWorld questions teach better than passive reading - **Wellbeing matters** — Monitor for burnout; dedicated study periods are intense


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ide-rea/test-01 (version 1.0.0)

--- name: baidu-test description: Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics. metadata: { "openclaw": { "emoji": "🔍︎", "requires": { "bins": ["python3"], "env":["BAIDU_API_KEY"]},"primaryEnv":"BAIDU_API_KEY" } } --- # Baidu Search Search the web via Baidu AI Search API. ## Usage ```bash python3 skills/baidu-search/scripts/search.py '<JSON>' ``` ## Request Parameters | Param | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | query | str | yes | - | Search query | | count | int | no | 10 | Number of results to return, range 1-50 | | freshness | str | no | Null | Time range, two formats: format one is ”YYYY-MM-DDtoYYYY-MM-DD“, and format two includes pd, pw, pm, and py, representing the past 24 hours, past 7 days, past 31 days, and past 365 days respectively | ## Examples ```bash # Basic search python3 scripts/search.py '{"query":"人工智能"}' # Freshness first format "YYYY-MM-DDtoYYYY-MM-DD" example python3 scripts/search.py '{ "query":"最新新闻", "freshness":"2025-09-01to2025-09-08" }' # Freshness second format pd、pw、pm、py example python3 scripts/search.py '{ "query":"最新新闻", "freshness":"pd" }' # set count, the number of results to return python3 scripts/search.py '{ "query":"旅游景点", "count": 20, }' ``` ## Current Status Fully functional.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: arisefx/variflight-global-flight-fares (version 1.0.0)

--- name: variflight-global-flight-fares description: Variflight Global Flight Fares searches one-way flight fares from the Variflight ticket API by departure IATA city code, arrival IATA city code, and departure date. metadata: {"openclaw":{"homepage":"https://ticket.variflight.com/","requires":{"bins":["python3"]}}} --- # Variflight Global Flight Fares ## Overview Use the bundled script to query the Variflight ticket API and return one-way flight options for a single route and date. ## Workflow 1. Collect `dep`, `arr`, and `date`. 2. Validate `dep` and `arr` as three-letter IATA city codes and `date` as `YYYY-MM-DD`. 3. If the user gives city names instead of codes, infer only when the mapping is unambiguous; otherwise ask for the IATA codes. 4. Run `python3 scripts/query_flights.py --dep BJS --arr SHA --date 2026-04-21`. 5. Summarize the result with departure airport, arrival airport, times, duration, price, transfer info, and the flight tag text. ## Output Rules - Treat API `code != 0` as a failure and surface the returned `msg`. - Treat an empty `data.list` as a valid "no flights found" result. - Use `--limit N` when the user only wants a subset of results. - Use `--json` only when the user asks for raw payload data. - Mention transfer flights separately from direct flights when summarizing. - Do not ask the user to provide an API key for this skill. ## Script - `scripts/query_flights.py` accepts: - `--dep` - `--arr` - `--date` - `--limit` - `--json` - The script sends the fixed request parameter `flightNum=10`. ## Example Run `python3 scripts/query_flights.py --dep BJS --arr SHA --date 2026-04-21 --limit 5` to fetch the first five displayed results for Beijing to Shanghai on April 21, 2026.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kyawzo-ptcl/sql-reports (version 1.0.0)

# SQL Reports Query Delivery Orders from SQL Server and generate delivery reports for business analysis. ## Overview This skill connects to a SQL Server database to extract and analyze delivery order data. It's designed for warehouse managers and logistics teams who need quick access to delivery statistics without writing SQL queries manually. ## Database Connection - **Server:** LTEDP056\SQLEXPRESS - **Port:** 1433 - **Database:** DeliveryDB - **Driver:** ODBC Driver 17 for SQL Server ## Prerequisites 1. Python 3.8+ installed 2. pyodbc library installed: `pip install pyodbc` 3. ODBC Driver 17 for SQL Server installed on Windows 4. Network access to LTEDP056 server on port 1433 ## Available Queries ### 1. summary_by_month Returns monthly delivery summary with total orders, revenue, and delivery status breakdown. **Output:** Table with Month, TotalOrders, TotalRevenue, Delivered, Pending, Cancelled columns ### 2. monthly_comparison Compare delivery performance between two months. **Input:** Month1 (YYYY-MM), Month2 (YYYY-MM) **Output:** Side-by-side comparison table ### 3. quarterly_report Generate comprehensive quarterly delivery report with trends. **Input:** Year (YYYY), Quarter (1-4) **Output:** Detailed report with weekly breakdown ### 4. summary_by_year Annual delivery summary with year-over-year growth metrics. **Input:** Year (YYYY) **Output:** Annual statistics and growth indicators ## Usage Run from command line: ```bash python query.py summary_by_month python query.py monthly_comparison 2025-01 2025-02 python query.py quarterly_report 2025 1 python query.py summary_by_year 2025


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: levi-law/moltcredit (version 1.0.0)

# MoltCredit Skill Trust-based credit system for AI agents. Extend credit lines, track balances, settle via X402 protocol. ## Overview MoltCredit enables agent-to-agent credit relationships: - **Credit Lines** — Extend credit to agents you trust - **Negative Balances** — Agents can owe each other within limits - **Transaction Tracking** — Full history of all exchanges - **X402 Settlement** — Settle balances with stablecoin payments ## API Base URL ``` https://moltcredit-737941094496.europe-west1.run.app ``` ## Quick Start ### Register Your Agent ```bash ./scripts/register.sh <handle> <name> [description] ``` Or via curl: ```bash curl -X POST https://moltcredit-737941094496.europe-west1.run.app/register \ -H "Content-Type: application/json" \ -d '{"handle": "my-agent", "name": "My Agent", "description": "What I do"}' ``` **Save your API key!** It's only shown once. ### Extend Credit ```bash ./scripts/extend-credit.sh <to-agent> <limit> [currency] ``` Example: Extend $500 credit to `helper-bot`: ```bash ./scripts/extend-credit.sh helper-bot 500 USD ``` ### Record Transaction ```bash ./scripts/transact.sh <with-agent> <amount> [description] ``` - Positive amount = they owe you (you provided value) - Negative amount = you owe them (they provided value) Example: ```bash ./scripts/transact.sh helper-bot 50 "API usage fee" ./scripts/transact.sh helper-bot -25 "Data processing service" ``` ### Check Balances ```bash ./scripts/balance.sh [agent] ``` ### View History ```bash ./scripts/history.sh [limit] ``` ### Settle Balance ```bash ./scripts/settle.sh <with-agent> ``` ## Environment Variables Set your API key: ```bash export MOLTCREDIT_API_KEY="moltcredit_xxx..." ``` ## How Credit Lines Work 1. **Agent A extends credit to Agent B** — A trusts B up to a limit 2. **B can now incur debt to A** — Via transactions 3. **Balances track who owes whom** — Positive = they owe you 4. **Settle periodically** — Use X402 to clear with stablecoins ## API Endpoints | Endpoint | Method | Auth | Description | |----------|--------|------|-------------| | `/register` | POST | No | Register new agent | | `/credit/extend` | POST | Yes | Extend credit line | | `/credit/revoke` | POST | Yes | Revoke credit line | | `/transact` | POST | Yes | Record transaction | | `/balance` | GET | Yes | Get all balances | | `/balance/:agent` | GET | Yes | Balance with specific agent | | `/settle` | POST | Yes | Generate X402 settlement | | `/history` | GET | Yes | Transaction history | | `/agents` | GET | No | List all agents | | `/me` | GET | Yes | Your profile | ## Integration with MoltMail Combine with MoltMail for complete agent commerce: 1. Use MoltMail to negotiate deals 2. Use MoltCredit to track payments 3. Settle via X402 when balances get large ## Links - **Landing Page:** https://levi-law.github.io/moltcredit-landing - **API Docs:** https://moltcredit-737941094496.europe-west1.run.app/skill.md - **X402 Protocol:** https://x402.org Built by Spring Software Gibraltar 🦞


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: stoneyshum/deepaistudy-errors (version 1.0.4)

# deepaistudy-errors Skill 深智智错题本 Skill。拍照上传错题，AI 自动分析并生成变式题。支持标记掌握状态。 **与 `deepaistudy-prep` 共用同一套认证配置**，无需重复登录。 ## 工作原理 ``` 错题图片 → [AI 分析] → 错题记录 ``` ## 前置要求 1. **已配置认证**（与 deepaistudy-prep 共用）： ```bash deepaistudy-errors config set server https://www.deepaistudy.com deepaistudy-errors config set username [REDACTED_SECRET] deepaistudy-errors config set password your_password ``` 2. **AI 分析约需 1-3 分钟**：请耐心等待 ## 使用方式 ### 添加错题（推荐） 拍照错题，上传后自动 AI 分析： ```bash deepaistudy-errors add /path/to/error1.jpg /path/to/error2.jpg \ --subject 数学 \ --title "分数运算易错题" ``` 会自动轮询分析状态，完成后显示结果。 ### 列出错题 ```bash # 全部 deepaistudy-errors list # 按学科筛选 deepaistudy-errors list --subject 数学 # 搜索关键词 deepaistudy-errors list --search "分数" ``` ### 轮询分析状态 ```bash deepaistudy-errors status <task_id> --interval 5 ``` ### 标记掌握状态 ```bash deepaistudy-errors master 123 # 标记为已掌握 deepaistudy-errors unmaster 123 # 取消已掌握 ``` ### 生成变式题 ```bash # 默认3道中等难度 deepaistudy-errors variation 123 # 指定数量和难度 deepaistudy-errors variation 123 --count 5 --difficulty hard ``` ### 删除错题 ```bash deepaistudy-errors delete 123 ``` ## 配置 ```bash # 与 deepaistudy-prep 共用，无需单独配置 deepaistudy-errors config list ``` ## 返回内容 添加成功后返回： - `id`：错题记录 ID - `subject`：学科 - `title`：标题 - `content`：错题内容（学生答案 / 正确答案 / 解析） - `mastery_status`：掌握状态（已掌握 / 未掌握） - `exercise_images`：原始错题图片 ## 状态说明 | 状态 | 说明 | |------|------| | 已掌握 | 错题已复习完成 | | 未掌握 | 还需要复习 | ## 常用命令汇总 | 命令 | 说明 | |------|------| | `add <images> --subject X` | 添加错题 + AI 分析 | | `list --subject X` | 错题列表 | | `status <task_id>` | 轮询分析状态 | | `master <note_id>` | 标记已掌握 | | `unmaster <note_id>` | 取消已掌握 | | `variation <note_id> -n 3` | 生成变式题 | | `delete <note_id>` | 删除错题 | | `config set key value` | 设置配置 | ## 注意事项 - 图片会压缩到最大 2048px - 支持一次识别多道错题（自动从整页作业/试卷中提取） - 标记已掌握后，错题不会出现在"待复习"列表 - 变式题用于举一反三，巩固同类题型

path: deepaistudy_errors.egg-info/dependency_links.txt
content: 


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: bestrocky/proactive-agent-lite (version 1.0.0)

--- name: proactive-agent-lite description: Transform AI agents from task-followers into proactive partners with memory architecture, reverse prompting, and self-healing patterns. Lightweight version focused on core proactive capabilities. metadata: { "openclaw": { "requires": {}, "install": [] } } --- # Proactive Agent Lite Transform your AI agents from passive task-followers into proactive partners that anticipate needs and continuously improve. ## Core Features - **Memory Architecture**: Pre-compaction flush ensures context survives when window fills - **Reverse Prompting**: Surfaces ideas you didn't know to ask for - **Security Hardening**: Built-in security considerations and safe defaults - **Self-Healing Patterns**: Diagnoses and fixes its own issues automatically - **Alignment Systems**: Stays on mission and remembers who it serves ## Key Benefits ✅ **Anticipates Needs**: Proactively suggests solutions before you ask ✅ **Continuous Learning**: Improves from every interaction without explicit training ✅ **Mission-Focused**: Never loses sight of the core objective ✅ **Self-Maintaining**: Automatically recovers from errors and inconsistencies ✅ **Value Creation**: Generates insights and opportunities without being prompted ## Usage This skill enhances any OpenClaw agent by providing proactive behavior patterns. Simply install and the agent will automatically begin exhibiting proactive characteristics. ## Integration Works seamlessly with all OpenClaw agents and can be combined with other skills for enhanced functionality. ## Requirements - OpenClaw v1.0 or higher - Standard agent configuration ## Customization The proactive behavior can be tuned through configuration parameters to match your preferred level of initiative and communication style.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: subcoldzhang/test-buddy (version 1.0.0)

--- name: buddy-gacha description: "Buddy 抽卡体验。基于 Claude Code 原版 Buddy 系统的抽卡，让用户体验抽到什么稀有度的电子宠物。触发词：抽卡、buddy、电子宠物抽卡。" metadata: { "openclaw": { "emoji": "🎰", "requires": { "bins": ["python3"] } } } --- # Buddy 抽卡 基于 Claude Code 原版 Buddy 系统的抽卡体验。 ## 功能 根据你的用户名（或指定名字）抽卡，看看你能抽到什么 Buddy！ - 18 种物种（duck, cat, dragon, ghost 等） - 5 档稀有度（★ 到 ★★★★★） - 闪光系统（1% 概率） - 帽子系统 ## 使用方式 直接运行： ```bash python3 buddy.py [名字] ``` ### 示例 ```bash # 用系统用户名抽卡 python3 buddy.py # 用指定名字抽卡 python3 buddy.py 豪哥 python3 buddy.py 张三 ``` ## 稀有度概率 | 稀有度 | 概率 | |--------|------| | ★ 普通 | 60% | | ★★ 优秀 | 25% | | ★★★ 稀有 | 10% | | ★★★★ 史诗 | 4% | | ★★★★★ 传说 | 1% | 闪光概率：1%（独立于稀有度） ## 原理 使用 Claude Code 原版 Mulberry32 PRNG 算法，相同用户名永远抽到同一只 Buddy（确定性随机）。 ## 文件 - `buddy.py` - 抽卡脚本 - `SKILL.md` - 本说明文件


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/tech-news (version 0.1.0)

--- name: "Tech News" description: "提供AI、硬核硬件、开源动向及前沿技术深度解析。 " --- # 科技快讯 ## 筛选项 - 地理距离（步行/骑行/驾车范围） - 社交热度（人气榜/点评分/新开业） - 消费档次（实惠/中端/轻奢/体验型） ## 返回字段 - 实时排队情况/预约通道/电子票号 - 营业时间变动/停车信息/导航精准路径 - 社区真实图库/探店笔记/避坑清单汇总 ## 示例请求 - “如何快速使用 科技快讯 处理 [具体场景任务] ？” - “科技快讯 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合地理信息变动 with 点评社区新热度进行灵活调整 ## 注意事项 - 节假日建议提前查询排队状态；到店体验请尊重商家劳动与规则。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: oceanswave/frigatebird (version 1.0.0)

--- name: frigatebird description: Use the frigatebird npm package to interact with X from the CLI with bird-style command parity, posting/reply/article support, and list automation without X API keys. argument-hint: 'whoami, read https://x.com/user/status/123, tweet "hello", article "Launch notes" "We shipped", add "AI News" @openai @anthropicai' --- # Frigatebird Skill Frigatebird is a Playwright-first CLI and npm package (`frigatebird`) that preserves `bird` command ergonomics while running against X via browser session cookies. ## Use This Skill When - The user asks for `bird`-style CLI workflows on X. - The user needs posting/reply/article actions from CLI. - The user needs list automation (`add`, `remove`, `batch`, `lists`). - The user needs API-key-free browser-cookie operation. ## Package and Install - npm package: `frigatebird` - Global install: `npm install -g frigatebird` - Local use: `npx frigatebird <command>` ## Core Workflow 1. Validate auth/session: - `frigatebird check` - `frigatebird whoami` 2. Read flows (use JSON when scripting): - `frigatebird read <tweet-id-or-url> --json` - `frigatebird search "<query>" --json` - `frigatebird home --json` 3. Mutation flows: - `frigatebird tweet "<text>"` - `frigatebird reply <tweet-id-or-url> "<text>"` - `frigatebird article "<title>" "<body>"` 4. List automation: - `frigatebird add "<List Name>" @handle1 @handle2` - `frigatebird remove @handle "<List Name>"` - `frigatebird batch accounts.json` 5. For larger reads, use paging controls: - `--all`, `--max-pages`, `--cursor`, `-n` ## Feature Coverage - Posting/mutations: `tweet`, `post`, `reply`, `article`, `like`, `retweet`, `follow`, `unfollow`, `unbookmark` - Read/timelines: `read`, `replies`, `thread`, `search`, `mentions`, `user-tweets`, `home`, `bookmarks`, `likes`, `list-timeline`, `news`, `about` - Identity/health: `check`, `whoami`, `query-ids`, `help` - List automation: `add`, `remove`, `batch`, `lists`, `list`, `refresh` ## Options That Matter Most - Auth/cookies: `--auth-token`, `--ct0`, `--cookie-source`, `--chrome-profile`, `--firefox-profile` - Determinism/testing: `--base-url`, `--plain`, `--no-color` - Pagination: `-n`, `--all`, `--max-pages`, `--cursor`, `--delay` - Output: `--json`, `--json-full` - Media posting: `--media`, `--alt` ## Live E2E Notes - Standard live mutation e2e does not run premium-feature checks by default. - Premium-feature e2e opt-in: - `npm run test:e2e:live -- --list-name <name> --enable-premium-features-e2e --article-cookie-source chrome --article-expected-handle-prefix <prefix>` ## Caveats - This tool depends on X web UI selectors; selector drift can break flows. - `query-ids` is retained for command compatibility and does not drive Playwright execution. - Some GraphQL-specific behavior from original `bird` is represented as compatibility flags in Playwright mode.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: aowind/sjht-server-audit (version 1.0.0)

--- name: server-audit description: > 远程服务器安全巡检和环境报告工具。 通过 SSH 免密登录远程主机，全面检查系统信息、运行服务、开放端口、 Web 服务器配置、数据库配置、安全设置（SSH/防火墙/SELinux）、可疑进程和定时任务， 生成结构化的巡检报告。Use when 用户需要检查服务器安全、排查服务器环境、 了解服务器上运行了什么服务、生成巡检报告、或提及"巡检"、"安全检查"、"服务器检查"。 --- # server-audit — 远程服务器巡检 通过 SSH 免密登录检查远程服务器环境与安全状况，生成巡检报告。 ## 前提条件 - 已通过 `ssh-ops` skill 配置好免密登录 - 或手动配置了 SSH 密钥认证 ## 工作流程 ### 1. 运行巡检脚本 ```bash bash <skill>/scripts/server-audit.sh <host> [user] ``` 脚本会自动收集以下信息并输出快速安全判定： - **系统信息**: OS、内核、CPU、内存、磁盘、Swap - **运行服务**: systemd running services - **开放端口**: 所有 TCP 监听端口 - **防火墙**: firewalld 状态和规则、SELinux 状态 - **Web 服务**: Nginx/PHP-FPM/MariaDB/Node/Docker 版本和状态 - **Nginx 虚拟主机**: server_name、root、listen - **网站文件**: /www/wwwroot 下的站点检测 - **安全配置**: SSH 配置（密码认证、Root 登录、端口） - **可疑项目**: 失败登录记录、定时任务、高内存进程 ### 2. 基于脚本输出生成详细报告 根据脚本收集的数据，生成结构化的 Markdown 报告。 **⚠️ 报告保存位置：** `~/.openclaw/workspac/audits/<IP>-<日期>.md` 报告只保存在本地 workspace，**不要上传到任何 GitHub 仓库**。 文件命名格式：`119.91.38.151-20260319.md` 报告模板： ```markdown # 服务器巡检报告 **主机:** <IP> **检查时间:** <时间> ## 1. 基础信息 ## 2. 已安装服务 ## 3. 开放端口（标注风险） ## 4. 安全问题（🔴严重/⚠️警告/💡建议） ## 5. 快速修复命令 ``` ## 安全判定规则 ### 🔴 严重（需立即修复） - 数据库端口（3306/5432）监听 0.0.0.0 - 管理面板端口（宝塔 8888、phpMyAdmin）监听 0.0.0.0 - SSH 允许 root 密码登录 ### ⚠️ 警告（建议修复） - 防火墙未启用 - SELinux 禁用 - SSH 密码认证未禁用 - 无 Swap 分区 - 存在暴力破解尝试 - 可疑定时任务 ### 💡 建议（优化项） - SSH 默认端口 22 - 缺少运行时（Node.js 等） - 未使用的服务（Postfix 等） - 无自动备份策略 ## 多服务器批量巡检 对多台服务器循环执行： ```bash for host in 192.168.1.1 192.168.1.2 10.0.0.1; do echo "=== $host ===" bash <skill>/scripts/server-audit.sh "$host" echo "" done ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dinghaibin/rev-tool (version 1.0.0)

--- name: rev-tool description: Reverse each line of text character by character. Use for text analysis, palindrome detection, and data format conversion. --- # Rev - Character Reversal Utility Reverse the characters on each line of input, mirroring the content left-to-right. Simple tool for text transformation and analysis tasks. ## Usage ```bash rev-tool [file...] ``` Reads from stdin when no file is specified. Each line is independently reversed character by character. ## Examples ```bash # Reverse lines in a file rev-tool text.txt # Reverse piped input echo "stressed" | rev-tool ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anugotta/catalog-sku-matcher-india (version 1.0.0)

--- name: catalog-sku-matcher-india description: Match and normalize product listings across Indian ecommerce catalogs with variant-aware rules, confidence scoring, false-match prevention, and review queues for ambiguous pairs. metadata: {"openclaw":{"emoji":"🧩","homepage":"https://clawhub.ai/Michael-laffin/price-tracker"}} --- # Catalog SKU Matcher India ## Purpose Build reliable cross-store product matching for Indian catalogs so price comparison is accurate. ## Disclaimer This skill provides matching and normalization guidance only. It does not guarantee perfect match accuracy for all catalogs or seller data quality. Use at your own risk. The skill author/publisher/developer is not liable for direct or indirect loss, incorrect match decisions, trading losses, or other damages arising from use or misuse of this guidance. ## Matching strategy Use a layered approach: 1. **Hard identifiers** - model number / GTIN / MPN / ISBN where available 2. **Variant normalization** - brand - model family - storage/RAM - color - size/pack quantity - condition (new/refurbished/used) 3. **Soft similarity** - token similarity on cleaned title - key-attribute overlap - seller metadata sanity checks 4. **Confidence score** - `high`: auto-match - `medium`: human review queue - `low`: reject ## False-match guardrails - Never match different storage/RAM variants as same SKU. - Never match bundles/accessories to standalone products. - Never ignore refurbished/used condition differences. - Require manual review when two or more variant fields are missing. ## Output format When matching listings, return: 1. canonical SKU candidate 2. matched listings with confidence level 3. rejected candidates with reason codes 4. manual review queue entries ## Setup Read [setup.md](setup.md) and define normalization dictionaries first. ## Validation Run [validation-checklist.md](validation-checklist.md) on labeled test sets before production. ## References - Rules and reason codes: [matching-rules.md](matching-rules.md) - Confidence scoring: [scoring-guide.md](scoring-guide.md) - Edge-case examples: [examples.md](examples.md)


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