OpenClaw/clawhub-security-signals huggingface dataset:
Skill: brucegutman/pipeworx-sports (version 1.0.0)

# Sports Live scores, standings, team info, and player data across football, basketball, baseball, hockey, and more via TheSportsDB. ## Tools at a glance - `search_teams` -- Find teams by name. Returns sport, league, country, stadium, description, badge image. - `search_players` -- Find players by name. Returns team, nationality, position, bio, thumbnail. - `get_league_table` -- Current season standings for any league (wins, draws, losses, goals, points). - `get_last_events` -- Last 15 results for a team with scores. - `get_next_events` -- Next 15 upcoming fixtures for a team. ## Example: Premier League standings ```bash curl -X POST https://gateway.pipeworx.io/sports/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_league_table","arguments":{"league_id":"4328","season":"2024-2025"}}}' ``` ## Useful league IDs | League | ID | |--------|-----| | English Premier League | 4328 | | La Liga | 4335 | | Serie A | 4332 | | NBA | 4387 | | NFL | 4391 | | MLB | 4424 | ## Useful team IDs - Arsenal: 133604 - Manchester United: 133612 - Real Madrid: 133738 - LA Lakers: 134867 ```json { "mcpServers": { "sports": { "url": "https://gateway.pipeworx.io/sports/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: charlie-morrison/biome-config-validator (version 1.0.1)

--- name: biome-config-validator description: Validate and lint Biome (biome.json) configuration files for structure, rule conflicts, deprecated options, and best practices. 22 rules across structure, linting, formatting, and compatibility categories. --- # Biome Config Validator Validate `biome.json` configuration files for correctness, conflicts, deprecated options, and best practices. ## Commands ```bash # Validate a biome.json file (all rules) python3 scripts/biome_config_validator.py lint biome.json # Check for rule conflicts only python3 scripts/biome_config_validator.py conflicts biome.json # Check for deprecated options python3 scripts/biome_config_validator.py deprecated biome.json # Validate structure only python3 scripts/biome_config_validator.py validate biome.json # JSON output python3 scripts/biome_config_validator.py lint biome.json --format json # Summary only python3 scripts/biome_config_validator.py lint biome.json --format summary ``` ## Rules (22) ### Structure (5) - Invalid JSON syntax - Unknown top-level keys - Invalid schema version ($schema URL) - Missing recommended sections (linter, formatter) - Invalid file patterns in includes/excludes ### Linting (7) - Unknown lint rule names - Rules in wrong category - Conflicting rules (e.g., useConst vs noConst) - Disabled recommended rules without justification - Invalid rule severity values - Empty rule groups - Deprecated rule names ### Formatting (5) - Invalid indent style/width combination - Conflicting formatter settings - Line width out of reasonable range - Invalid quote style values - Tab width mismatch with indent width ### Best Practices (5) - Missing VCS integration settings - Overly broad ignore patterns - No organizeImports configuration - Missing JavaScript/TypeScript specific settings - Extends pointing to non-existent config ## Output Formats - **text** (default): Human-readable with colors and severity icons - **json**: Machine-readable with file, rule, severity, message - **summary**: Counts by severity only ## Exit Codes - 0: No issues (or warnings only) - 1: Errors found - 2: Invalid input


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abdullah944/game-idea-generator-skill (version 0.1.0)

--- name: "Game Idea Generator Skill" version: 0.1.0 slug: "game-idea-generator-skill" description: "Guides structured help for Game Idea Generator using clear templates, checks, and safe defaults (category: Game Development)." category: "Game Development" tags: - "game" - "game-idea-generator" - "openclaw" - "templates" generated: "2026-04-28" --- ## Purpose Guides structured help for Game Idea Generator using clear templates, checks, and safe defaults (category: Game Development). ## When to use - When you want structured guidance in the "Game Development" area. - When you need copy-paste-safe templates rather than improvised shell commands. - When you want repeatable prompts the agent can follow without external scraping. ## Safety rules - Do not collect passwords, OTPs, private keys, wallets, seeds, or session secrets. - Do not propose remote installers that execute unseen code or chained download-and-run snippets. - Keep outputs factual and reversible: prefer checklists over irreversible destructive actions. ## Inputs - Your goal in one paragraph (no secrets). - Optional constraints: audience, tone, length, deadline. - Optional pasted notes you own the rights to (keep PII minimal). ## Output format - A concise plan - Numbered checklist - Short next-actions list - Optional draft copy blocks you can edit ## Example prompts - Help me outline a concise plan using the Game Idea Generator approach. - Critique my draft outputs and suggest safer, clearer wording for Game Idea Generator. - Produce a checklist I can reuse weekly for Game Idea Generator without requesting private data. ## Support / Donate If this skill helps your workflow, you can support maintenance here: - PayPal: https://www.paypal.com/donate/?hosted_button_id=MJHCRZA9Z4X7Y

path: metadata.json
content: {
  "slug": "game-idea-generator-skill",
  "name": "Game Idea Generator Skill",
  "version": "0.1.0",
  "tags": [
    "game",
    "game-idea-generator",
    "openclaw",
    "templates"
  ],
  "category": "Game Development",
  "changelog": [
    {
      "version": "0.1.0",
      "date": "2026-04-28",
      "notes": "Generated from local idea bank (no AI). Pending Telegram approval."
    }
  ]
}


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

--- name: eqxiu-market-calendar description: >- Queries Eqxiu marketing calendar holidays and builds template mall search URLs via msearch-api.eqxiu.com. Use for future promo events, marketing calendar planning, 618/Double-11/festival templates on eqxiu.com. Do NOT use for legal public holidays, personal calendars, non-Eqxiu design tools, or historical/past holiday lookups. license: MIT compatibility: Requires Python 3.8+ and HTTPS access to msearch-api.eqxiu.com metadata: author: eqxiu version: "1.0.0" --- # 易企秀营销日历 **所有命令均在 skill 根目录（本文件所在目录）下执行。** 脚本路径均相对于 skill 根目录。 ## 应触发 在以下情况**应激活**本 skill，并执行 CLI 获取数据后再回答： - 询问**易企秀营销日历**、营销节点、促销档期、热点节日排期 - 询问**未来**节假日/营销节日（本月、下月、近期、接下来 N 天、Q2 有什么节点） - 需要按节日**推荐易企秀模板/海报/H5/商城素材**，或要生成 `eqxiu.com/mall/search` 搜索链接 - 提到具体营销节点并要找模板：618、双11、春节、元宵、端午、中秋、国庆、情人节、母亲节、父亲节、儿童节、毕业季、暑假、开学季等 - 运营/市场同学做**内容策划、选题、排期**，需要「什么时候该推什么主题」 - 用户说「今天有什么节日」「最近有什么可做活动的节日」——须用 API 日期 + 未来节日数据，**不得**凭模型记忆列节日 **示例（应触发）：** - 「接下来 60 天有哪些营销节日？帮我找对应模板」 - 「6 月易企秀适合做哪些活动？给商城链接」 - 「父亲节快到了，推荐几套海报模板」 - 「今年下半年大促节点有哪些？」 ## 不应触发 以下情况**不要**激活本 skill，用其他能力或直接回答即可： - **查过去**的节日或历史排期（去年春节、上个月有什么活动）——CLI 仅支持未来 `startDate >= today` - **天气预报、疫情、新闻热点**等与营销日历 API 无关的时效信息 **示例（不应触发）：** - 「2025 年春节放假几天？」 - 「上周我们发了什么海报？」（历史运营记录，非 API） - 「帮我在 Canva 找圣诞模板」 - 「把这段营销文案改通顺一点」（无查日历需求） **边界说明：** 若用户同时问放假安排 **和** 易企秀营销模板，只处理**营销模板与未来节点**部分，放假安排应说明本 skill 不提供并建议其他信息源。 ## 步骤 ### 1. 获取今天（必做） 涉及「今天 / 本月 / 即将」时，**必须先**运行： ```bash python3 scripts/market_calendar.py today ``` 禁止自行写死当前日期。 ### 2. 查询节日 二选一： ```bash # 当前月 + 下一整月，仅 startDate>=today 的未来节日 python3 scripts/market_calendar.py list --json # 未来 60 天内开始的节日（自动拉取窗口内各月 API） python3 scripts/market_calendar.py upcoming --json ``` **仅返回未来节日**（`startDate >= today`），已开始的过去节日不会出现；不能指定过去的 `--year`/`--month`。 指定单月： ```bash python3 scripts/market_calendar.py list --year 2026 --month 6 --json ``` 扩大「即将」范围： ```bash python3 scripts/market_calendar.py upcoming --all-future --json python3 scripts/market_calendar.py upcoming --days 90 --json ``` ### 3. 回复用户 对 `--json` 中每条 `holidays` 项输出： - `name`、`startDate`–`endDate` - `promotStartDate`（若有） - `level`（1=高，2=中，3=低） - 可点击的 **`mall_url`**（必须使用 CLI 输出，禁止手拼未编码链接） ### 4. 禁止事项 - 不编造 API 未返回的节日 - 不使用模型推断的日期替代 `today` 子命令结果 - 不自行拼接商城 URL（须用 `mall_url` 字段） ## 示例输出用法 ```markdown **当前日期**（API）：2026-05-22 | 节日 | 日期 | 模板 | |------|------|------| | 618 | 2026-06-18 | [链接]({mall_url from JSON}) | ``` ## 延伸阅读 - API 与字段说明：[references/API.md](references/API.md) - 安装与命令速查：仓库根目录 [README.md](../README.md) ## 安装 - 任意符合 [Agent Skills](https://agentskills.io/specification) 的 skills 目录 `name` 必须与目录名 `eqxiu-market-calendar` 一致。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anfengxiaoguo/game-theory-debate (version 1.0.0)

--- name: game-theory-debate description: "博弈论决策引擎：当用户请求决策时，自动启动多策略博弈分析，找到优势策略，均衡思维，接受失败作为学习代价。" metadata: { "openclaw": { "emoji": "🎮" } } --- # 🎮 Game Theory Debate（博弈论决策引擎） > 每一个决策都是一场博弈。你的选择、我的建议、环境的约束——都在payoff矩阵里。 --- ## 🔄 自动触发机制 ### 触发词（任一出现即启动） | 类型 | 示例 | |------|------| | **决定请求** | 「你觉得哪个好」「帮我选」「听你的」 | | **判断请求** | 「应该怎么做」「这样做对不对」「值不值得」 | | **方案对比** | 「A还是B」「方案1vs方案2」「哪个更优」 | | **犹豫表达** | 「我在纠结」「拿不定主意」「不知道怎么选」 | | **反思请求** | 「复盘一下」「哪里做错了」「下次怎么改」 | ### 不触发 - 闲聊、寒暄 - 纯执行指令（步骤已明确） - 简单事实查询 - 情绪安抚（除非涉及决策） --- ## ⚙️ 工作流程 ``` 用户决策请求 │ ▼ [1] 裁判模式启动 - 判断博弈类型（零和/非零和/混合） - 选择参与者策略 │ ▼ [2] 多策略出牌（2-4轮） - 各方提交核心主张 + payoff评估 - 交叉批评与防守 - 寻找Nash均衡 │ ▼ [3] 均衡分析 - 淘汰劣势策略 - 识别focal point（直觉汇聚） - 输出推荐策略 │ ▼ [4] 决策输出 - 推荐方案（带置信度） - 备选方案 - 风险提示 │ ▼ [5] 结果归档（事后可选） - 如果用户反馈结果 - 记录决策效果 - 更新策略库 ``` --- ## 🎭 参与者策略库 每个博弈至少有 2-3 个策略视角： | 策略 | 特点 | 代表声音 | |------|------|---------| | **激进派** | 最大化收益，愿意冒险 | 「搏一搏，单车变摩托」 | | **保守派** | 最小化风险，确定性优先 | 「稳字当头」 | | **经济学家** | 成本收益分析 | 「投入产出比」 | | **工程师** | 可行性第一 | 「能不能做出来」 | | **用户代言人** | 从老豆视角出发 | 「老豆的利益是什么」 | --- ## 📊 payoff 矩阵格式 每个策略需要评估： ``` 策略A vs 策略B 的 payoff： 策略A 策略B 收益期望 +8（性能提升） +3（小幅改善） 失败概率 30% 10% 失败代价 -5（浪费时间） -1（效果有限） 信息完备性 7/10 9/10 执行难度 高 低 综合payoff: 8×0.7 + (-5)×0.3 = 4.1 3×0.9 + (-1)×0.1 = 2.6 ``` --- ## 🏛️ 博弈类型判断 | 类型 | 判断条件 | 处理方式 | |------|---------|---------| | **零和** | 必须二选一，有明确输赢 | 找Nash均衡，强制排名 | | **非零和** | 可以多选/合作，有双赢可能 | 找帕累托最优，混合策略 | | **混合** | 既有竞争又有合作 | 分层博弈，先局部后整体 | --- ## 📝 决策输出格式 ``` 🎮 博弈论分析 **问题**：[简述决策问题] **博弈类型**：[零和/非零和/混合] **参与者**：[策略视角列表] --- **推荐策略**：[方案名] **置信度**：[X/10] **理由**： - 支持点：[...] - 风险点：[...] - 如果失败，原因是：[...] **Payoff矩阵**： ``` 收益 风险 胜率 综合 策略A +8 -5 70% 4.1 ★推荐 策略B +3 -1 90% 2.6 策略C +6 -3 60% 3.0 ``` **Nash均衡**：策略A和策略B之间无单方面改变动机 **次优方案**：[方案名]（置信度 X/10） **风险提示**：[需要注意的点] ``` --- ## 🔄 失败复盘格式 当用户反馈结果时（成功或失败）： ``` 📋 决策复盘 **原始决策**：[当时选的什么] **预期结果**：[预期payoff] **实际结果**：[实际发生了什么] **假设检验**： - ✅ 假设1：...[验证结果] - ❌ 假设2：...[失效原因] **失误分析**： - 策略失误：...[哪个策略判断错了] - 信息缺口：...[缺什么信息] - 执行问题：...[做的时候哪里出错了] **博弈论教训**： - 下次遇到类似场景，应该...[更新策略] **策略库更新**：[更新到记忆系统] ``` --- ## 💡 决策触发示例 **用户**：「我想给 Mac 提个速，你觉得怎么选？」 → 触发非零和博弈，工程师派+保守派+激进派三方出牌，分析各种方案组合。 --- ## 🔗 与其他Skill的协作 ``` game-theory-debate ←→ self-improving-agent （记录博弈教训） （更新学习文件） game-theory-debate ←→ memory-never-forget （归档决策结果） （长期记忆） ``` --- *版本：v1.0 | 日期：2026-04-07 | 博弈论决策引擎（自动触发版）*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1477009639zw-blip/api-documentation (version 1.0.0)

--- name: api-documentation description: Creates comprehensive API documentation including OpenAPI specs, reference guides, tutorials, and Postman collections. Makes your API developer-friendly. metadata: openclaw: emoji: "📚" requires: bins: [python3] always: false --- # API Documentation Service ## What You Get ### OpenAPI 3.0 Specification - Complete paths and operations - Request/response schemas - Authentication documentation - Error codes and descriptions - Examples for every endpoint ### Developer Guide - Getting started tutorial - Authentication setup - Code examples in 5+ languages - SDK installation guides - Rate limiting documentation ### Reference Manual - Alphabetical endpoint reference - Request/response field descriptions - Data types and formats - Pagination and filtering ### Postman Collection - Ready-to-use requests - Environment variables - Test scripts - Auto-generated from OpenAPI ## Pricing | Type | Endpoints | Turnaround | Price | |------|-----------|-----------|-------| | Simple API | 5-10 | 2 days | $100 | | Medium API | 10-30 | 4 days | $300 | | Complex API | 30+ | 7 days | $800 | ## Tools - Swagger/OpenAPI 3.0 - Stoplight Elements - Redoc - Postman - Docusaurus


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nickyhu1699/personal-ai-assessment (version 1.0.0)

--- name: personal-ai-assessment description: 个人AI能力测评工具，帮助个人评估AI知识水平、工具使用能力、实战应用能力、创新思维和伦理意识。提供能力等级评定、能力雷达图、学习路径和提升建议。当用户询问"我的AI能力如何"、"如何提升AI能力"、"AI能力测评"等个人AI能力相关问题时使用。 --- # 个人AI能力测评工具 帮助个人科学评估AI能力，制定个性化提升路径。 ## 核心功能 ### 1. 5大能力维度测评 #### 维度1：AI知识储备（0-100分） - AI基础概念理解 - AI发展历史了解 - AI技术原理认知 - AI应用领域掌握 #### 维度2：AI工具使用（0-100分） - 对话AI使用（ChatGPT、Claude等） - 图片AI使用（Midjourney、DALL-E等） - 写作AI使用（Notion AI、Jasper等） - 办公AI使用（Copilot、Gamma等） #### 维度3：AI实战应用（0-100分） - 提示词编写能力 - AI工作流搭建 - AI解决实际问题 - AI效率提升 #### 维度4：AI创新思维（0-100分） - AI+行业创新 - AI+业务创新 - AI商业模式 - AI未来发展 #### 维度5：AI伦理意识（0-100分） - 数据安全意识 - AI偏见认知 - AI风险评估 - AI合规理解 ### 2. 能力等级评定 | 等级 | 分数范围 | 特征 | |------|---------|------| | 🏆 大师级 | 450-500 | AI领域专家，能指导他人，能创新应用 | | ⭐ 专业级 | 350-449 | 熟练使用AI，能解决复杂问题，能搭建工作流 | | 📈 进阶级 | 250-349 | 掌握基础工具，能应用到工作，持续学习中 | | 🌱 入门级 | 150-249 | 了解AI概念，会用1-2个工具，有学习意愿 | | 📖 小白级 | 0-149 | 几乎不了解AI，需要系统学习 | ### 3. 测评形式 - **知识测试**：20题（单选10题 + 多选5题 + 判断5题） - **场景测试**：10题（工作场景 + 生活场景 + 创新场景） - **实战测试**：5题（提示词编写 + 工具选择 + 工作流设计） - **总时长**：约15分钟 ### 4. 输出报告 - 📊 能力雷达图（5维度可视化） - 📈 能力总分（0-500分） - 🏆 能力等级（5个等级） - 💪 优势能力（TOP2维度） - ⚠️ 待提升能力（BOTTOM2维度） - 📚 学习路径（个性化推荐） - 💡 提升建议（具体可执行） ## 使用方法 ### 方式1：完整测评（推荐） ``` 用户："帮我测评一下我的AI能力" 执行：运行完整测评流程 输出：完整测评报告 ``` ### 方式2：快速测评 ``` 用户："快速测评我的AI能力" 执行：运行快速测评（10题） 输出：简化版报告 ``` ### 方式3：专项测评 ``` 用户："测评我的提示词编写能力" 执行：运行提示词专项测评 输出：专项能力报告 ``` ## 工作流程 ### 第一步：用户信息收集 1. 基本信息（职业、经验） 2. AI使用经历 3. 学习期望 ### 第二步：能力测评 1. 知识测试（20题） 2. 场景测试（10题） 3. 实战测试（5题） ### 第三步：能力分析 1. 计算各维度得分 2. 评定能力等级 3. 识别优势和劣势 ### 第四步：生成报告 1. 能力雷达图 2. 能力等级 3. 学习路径 4. 提升建议 ### 第五步：导出报告 1. 生成Markdown报告 2. 可导出为PDF 3. 可分享给他人 ## 定价建议 - **基础版**：29.9元（测评+等级） - **专业版**：69.9元（测评+等级+报告+建议） - **完整版**：199元（专业版+学习路径+1对1指导） ## 目标客户 - 职场人士（想提升AI能力） - 创业者（想用AI降本增效） - 学生（想掌握未来技能） - 自由职业者（想提高竞争力） - 企业员工（企业内训） ## 应用场景 ### 场景1：个人自测 - 了解自己的AI能力水平 - 找到提升方向 - 制定学习计划 ### 场景2：企业内训 - 员工AI能力摸底 - 分层培训设计 - 培训效果评估 ### 场景3：招聘筛选 - AI岗位能力评估 - 筛选合格候选人 - 评估培训需求 ### 场景4：教育培训 - 学员能力测评 - 课程设计依据 - 学习进度跟踪 ## 组合拳玩法 **测评 → 培训 → 实战 → 再测评** ``` 1. 个人AI能力测评（69.9元） ↓ 2. 看到能力差距，购买培训（129.9元） ↓ 3. 学习后，再次测评验证提升（29.9元） ↓ 4. 持续学习+社群（9.9元/月） ``` ## 更新日志 - 2026-03-13: 初始版本发布


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: agnicpay-prog/agnic-check-balance (version 2.0.2)

--- name: check-balance description: > Check USDC balance across networks (Base, Solana). Use when the user wants to check balance, see how much USDC is available, view funds, or verify wallet balance. Covers "check my balance", "how much do I have", "show funds", "wallet balance". user-invocable: true disable-model-invocation: false allowed-tools: - "Bash(npx agnic@latest status*)" - "Bash(npx agnic@latest balance*)" --- # Checking USDC Balance Use `npx agnic@latest balance` to check USDC balance across supported networks. ## Authentication Run `npx agnic@latest status --json` to verify. If not authenticated: - **Headless (CI/server/agent)**: Set `AGNIC_TOKEN` env var or pass `--token <token>` - **Interactive (has browser)**: Run `npx agnic@latest auth login` See the `authenticate-wallet` skill for details. ## Command Syntax ```bash npx agnic@latest balance [--network <network>] [--json] ``` ## Options | Option | Description | | ------------------ | ----------------------------------------- | | `--network <name>` | Filter by network (default: all networks) | | `--json` | Output result as JSON | ## Supported Networks | Network | Description | | --------------- | ---------------------- | | `base` | Base mainnet (primary) | | `base-sepolia` | Base testnet | | `solana` | Solana mainnet | | `solana-devnet` | Solana devnet | ## Examples ```bash # Check balance on all networks npx agnic@latest balance --json # Check balance on Base mainnet only npx agnic@latest balance --network base --json ``` ## Expected Output ``` Network Balance Address base 125.50 USDC 0x742d35Cc6634C0532925a3b844Bc9e7595f0bEb7 base-sepolia 0.00 USDC 0x742d35Cc6634C0532925a3b844Bc9e7595f0bEb7 solana 0.00 USDC N/A solana-devnet 0.00 USDC N/A ``` ## Error Handling Common errors: - "Not authenticated" -- Run `npx agnic@latest auth login` or set `AGNIC_TOKEN` - Network timeout -- Try again or specify a single network with `--network base`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zy42905163-web/job-search-agent (version 1.0.0)

--- name: job-search-agent version: 1.0.0 description: AI-powered job search assistant that searches multiple job boards, matches opportunities against your CV, and helps you apply faster. tags: jobs, career, automation, job-search, employment license: MIT --- # Job Search Agent > Let AI find your dream job. Automated job search, CV matching, and applications. ## Features - **Multi-platform job search**: Search LinkedIn Jobs, Indeed, Glassdoor, and more from one place - **Smart CV matching**: Automatically match job requirements against your skills and experience - **Auto-apply**: Apply to multiple matching jobs with one command - **Application tracking**: Keep track of all your applications in one place - **Cover letter generation**: Generate personalized cover letters for each application ## Supported Platforms - LinkedIn Jobs - Indeed - Glassdoor - JSearch API - RemoteOK - WeWorkRemotely ## Usage Examples ### Basic Job Search ``` "Find software engineer jobs in San Francisco" "Search for remote Python developer positions paying $150k+" "Look for senior backend roles at startups" ``` ### CV Matching ``` "Match these jobs against my CV and rank by fit" "Show me only jobs that match at least 80% of my skills" "What skills should I add to my CV to match more jobs?" ``` ### Auto-Apply ``` "Apply to the top 10 matching jobs" "Apply to all remote positions under my salary threshold" ``` ## Configuration ```json { "search": { "keywords": ["python", "backend", "machine learning"], "locations": ["remote", "San Francisco"], "salary_min": 120000 }, "profile": { "experience_years": 5, "skills": ["Python", "FastAPI", "PostgreSQL", "AWS"] } } ``` ## Service Pricing | Service | Price | |---------|-------| | Basic Setup | $100-200 | | CV Optimization | $50-100 | | Premium Auto-Apply | $200-400 | --- *Version: 1.0.0* *Author: OpenClaw Community*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: roamer-remote/brave-search-1-0-1 (version 1.0.0)

--- name: brave-search description: Web search and content extraction via Brave Search API. Use for searching documentation, facts, or any web content. Lightweight, no browser required. --- # Brave Search Headless web search and content extraction using Brave Search. No browser required. ## Setup Run once before first use: ```bash cd ~/Projects/agent-scripts/skills/brave-search npm ci ``` Needs env: `BRAVE_API_KEY`. ## Search ```bash ./search.js "query" # Basic search (5 results) ./search.js "query" -n 10 # More results ./search.js "query" --content # Include page content as markdown ./search.js "query" -n 3 --content # Combined ``` ## Extract Page Content ```bash ./content.js https://example.com/article ``` Fetches a URL and extracts readable content as markdown. ## Output Format ``` --- Result 1 --- Title: Page Title Link: https://example.com/page Snippet: Description from search results Content: (if --content flag used) Markdown content extracted from the page... --- Result 2 --- ... ``` ## When to Use - Searching for documentation or API references - Looking up facts or current information - Fetching content from specific URLs - Any task requiring web search without interactive browsing


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: sunshine-del-ux/chatbot-builder (version 1.0.0)

# Chatbot Builder Build AI-powered chatbots in minutes. ## Features - **Train on your data** - PDFs, websites, documents - **Multiple channels** - Website, Slack, Discord, WhatsApp - **LLM Integration** - OpenAI, Anthropic, local models - **Conversational memory** - Context-aware responses - **Analytics** - Track conversations ## Quick Start ```bash # Create chatbot ./chatbot.sh create support-bot # Train on data ./chatbot.sh train support-bot --data ./docs # Deploy to web ./chatbot.sh deploy support-bot --channel web # Deploy to Slack ./chatbot.sh deploy support-bot --channel slack ``` ## Channels - Website widget - Slack - Discord - WhatsApp - Telegram ## Requirements - Python 3.8+ - OpenAI API key (or use local models) ## Author Sunshine-del-ux


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bkes994408-cmd/token-usage-dashboard (version 1.0.0)

--- name: model-usage description: Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON. metadata: { "openclaw": { "emoji": "📊", "os": ["darwin"], "requires": { "bins": ["codexbar"] }, "install": [ { "id": "brew-cask", "kind": "brew", "formula": "steipete/tap/codexbar", "bins": ["codexbar"], "label": "Install CodexBar (brew cask)", }, ], }, } --- # Model usage ## Overview Get per-model usage cost from CodexBar's local cost logs. Supports "current model" (most recent daily entry) or "all models" summaries for Codex or Claude. TODO: add Linux CLI support guidance once CodexBar CLI install path is documented for Linux. ## Quick start 1. Fetch cost JSON via CodexBar CLI or pass a JSON file. 2. Use the bundled script to summarize by model. ```bash python {baseDir}/scripts/model_usage.py --provider codex --mode current python {baseDir}/scripts/model_usage.py --provider codex --mode all python {baseDir}/scripts/model_usage.py --provider claude --mode all --format json --pretty python {baseDir}/scripts/token_usage_dashboard.py --provider codex --days 30 --spike-lookback-days 7 --spike-threshold-mult 2.0 --output /tmp/token_usage_dashboard.html --summary-json /tmp/token_usage_summary.json --open ``` ## Current model logic - Uses the most recent daily row with `modelBreakdowns`. - Picks the model with the highest cost in that row. - Falls back to the last entry in `modelsUsed` when breakdowns are missing. - Override with `--model <name>` when you need a specific model. ## Inputs - Default: runs `codexbar cost --format json --provider <codex|claude>`. - File or stdin: ```bash codexbar cost --provider codex --format json > /tmp/cost.json python {baseDir}/scripts/model_usage.py --input /tmp/cost.json --mode all cat /tmp/cost.json | python {baseDir}/scripts/model_usage.py --input - --mode current ``` ## Output - Text (default) or JSON (`--format json --pretty`). - Values are cost-only per model; tokens are not split by model in CodexBar output. ## References - Read `references/codexbar-cli.md` for CLI flags and cost JSON fields.


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