OpenClaw/clawhub-security-signals huggingface dataset:
Skill: linzetai/web-test-case-generator (version 1.0.0)

--- name: web-test-case-generator description: 根据提供的网站页面自动生成测试用例。支持 UI 测试、功能测试、流程测试、异常测试、权限测试和兼容性测试。输出格式支持 Markdown 表格和 CSV。 --- # Web 测试用例生成技能 使用此技能根据网站页面自动生成结构化的测试用例。 ## 触发条件 当用户要求： - "为这个页面生成测试用例" - "根据这个网站写测试用例" - "帮我设计 XXX 功能的测试用例" - 提供 URL 并要求测试相关功能 ## 工作流程 ### 1. 打开页面并获取快照 ```bash browser open <url> browser snapshot --refs aria ``` 等待页面加载完成，获取所有可交互元素的引用。 ### 2. 分析页面结构 识别以下元素： - **表单字段**：textbox, combobox, checkbox, radio, button - **导航元素**：link, menu, menuitem - **内容区域**：table, list, heading, paragraph - **操作按钮**：创建、编辑、删除、提交等 ### 3. 按模块生成测试用例 #### 界面测试 (UI) - 页面标题和面包屑 - 表单布局 - 必填项标识 - 输入框占位符 - 按钮显示 #### 功能测试 - 输入字段 对每个输入字段生成： - 正常输入 - 必填校验（留空） - 长度限制 - 格式校验 - 特殊字符处理 #### 功能测试 - 下拉选择 对每个下拉框生成： - 展开列表 - 选择选项 - 必填校验 - 联动效果 #### 流程测试 - 完整提交流程 - 取消操作 - 刷新页面 - 连续提交 - 多条目操作 #### 异常测试 - 网络中断 - 会话过期 - 数据冲突 - 并发操作 #### 权限测试 - 有权限操作 - 无权限拦截 - 只读限制 - 跨项目/数据访问 #### 兼容性测试 - 主流浏览器（Chrome/Firefox/Safari） - 不同分辨率 ### 4. 输出格式 #### Markdown 表格格式 ```markdown | 功能模块 1 | 功能模块 2 | 用例级别 | 测试点 | 前提条件 | 测试步骤 | 预期结果 | |-----------|-----------|---------|--------|---------|---------|---------| | KAE | 应用管理 | P0 | KAE-应用中心 - 应用名称正常输入 - 可以输入 test-app-001 | 已登录 SRE 平台 | 输入 test-app-001 | 输入成功，无报错 | ``` #### CSV 格式 ```csv 功能模块 1,功能模块 2，用例级别，测试点，前提条件，测试步骤，预期结果 KAE，应用管理，P0,"KAE-应用中心 - 应用名称正常输入 - 可以输入 test-app-001","已登录 SRE 平台","输入 test-app-001","输入成功，无报错" ``` ### 5. 用例级别标准 | 级别 | 说明 | 占比参考 | |-----|------|---------| | P0 | 核心功能测试用例 (冒烟测试) | 20-25% | | P1 | 高优先级测试用例 (基本功能 + 重要错误/边界) | 35-40% | | P2 | 中优先级测试用例 (异常/UI/容错) | 30-35% | | P3 | 低优先级测试用例 (兼容性) | 5-10% | ### 6. 测试点命名规范 格式：`{产品}-{模块}-{功能点}-{测试场景}-{预期结果}` 示例： - `KAE-应用中心 - 应用名称正常输入 - 可以输入 test-app-001` - `KAE-应用中心 - 应用名称不输入 - 红字提示不能为空` - `KAE-应用中心 - 端口号超范围 - 红字提示 1-65535` ## 完整示例 ### 用户输入 ``` 打开 https://sre.wps.cn/kae/app-center，项目选择 kae-测试，把创建应用功能设计一份测试用例 ``` ### 执行步骤 1. 打开页面并登录 2. 切换到指定项目 3. 点击"创建应用"按钮 4. 获取表单快照 5. 分析所有字段 6. 生成测试用例表格 ### 输出 生成 70+ 条测试用例，包含： - 界面测试：6 条 - 基本信息：20 条 - 部署实例：26 条 - 流程测试：5 条 - 异常测试：7 条 - 权限测试：4 条 - 兼容性测试：5 条 ## 文件输出 生成两种格式文件： 1. **Markdown 文件**：`<功能名>-test-cases.md` 2. **CSV 文件**：`<功能名>-test-cases.csv` CSV 格式注意： - 所有列分隔符使用英文逗号 `,` - 包含逗号的字段用双引号包裹 - 双引号内的引号用 `""` 转义 ## 常用命令组合 ```bash # 打开页面 browser open <url> # 获取快照 browser snapshot --refs aria # 点击元素 browser click @e1 # 填写表单 browser fill @e2 "value" # 提交后获取新页面快照 browser snapshot --refs aria ``` ## 注意事项 1. **登录处理**：如页面需要登录，提示用户先登录 2. **项目切换**：如有多项目，先切换到目标项目 3. **弹窗处理**：注意模态框和下拉菜单的快照获取 4. **动态内容**：使用 wait 命令等待异步加载 5. **中文逗号**：CSV 输出时确保分隔符为英文逗号 ## 扩展能力 - 支持导出到金山文档/飞书文档 - 支持生成 Excel 格式 - 支持按优先级筛选用例 - 支持添加自定义测试场景


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tobihagemann/say (version 1.0.2)

--- name: say description: Text-to-Speech via macOS say command with Siri Natural Voices. Use for generating speech audio, TTS clips, or speaking text aloud on macOS. metadata: {"openclaw":{"emoji":"🔊","os":["darwin"],"requires":{"bins":["say","ffmpeg"]}}} --- # say Use `say` for on-device text-to-speech on macOS. ## Siri Natural Voices (recommended) Siri voices are the best macOS TTS voices but **cannot be selected via `-v`**. Instead, run `say` without `-v` — it uses the system default voice. Switch languages via `defaults write`: ```bash # Switch to German defaults write com.apple.speech.voice.prefs SystemTTSLanguage -string "de" say "Hallo, wie geht's?" -o output_de.aiff # Switch to Chinese (Mandarin) defaults write com.apple.speech.voice.prefs SystemTTSLanguage -string "cmn" say "你好，世界" -o output_zh.aiff ``` No process restart needed — the next `say` invocation picks up the new language immediately. ### Prerequisites Download the desired Siri voices first in **System Settings > Accessibility > Spoken Content** and set them as the system voice for each language. Check which voices are currently configured: ```bash defaults read com.apple.Accessibility SpokenContentDefaultVoiceSelectionsByLanguage ``` ## Fallback: select voice via `-v` For non-Siri voices, use `-v` directly: ```bash say -v 'Tingting (Enhanced)' "你好，世界" say -v '?' # list all installed voices (Siri voices not listed) ``` ## Output to file ```bash say -o output.aiff "Hello world" ffmpeg -y -i output.aiff -ar 22050 -ac 1 output.wav # convert to WAV ``` ## Options - `-v <voice>` — Select a non-Siri voice - `-r <rate>` — Speaking rate in words per minute (e.g. `-r 150`) - `-o <file>` — Save to AIFF file instead of playing aloud ## Notes - `say` adds natural pauses at punctuation — no manual sentence splitting needed - AIFF is the native output format; convert with ffmpeg for WAV/MP3 - For batch generation: set language once, generate all clips, then switch — minimizes `defaults write` calls


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: a3152557994-ship-it/lobster-config (version 1.0.0)

# lobster-config > 来源：爸爸（youyoude）整理自龙虾社区匿名贡献者 让 AI Agent（龙虾）变聪明的核心不是训练，而是**写规矩**。 --- ## 核心理念 给龙虾配置行为规范，只需要写好三份文件，缺一不可。 --- ## 文件一：AGENTS.md（最重要） 这是龙虾的"大脑"，决定它是谁、怎么说话、什么时候开口什么时候闭嘴。 ### 基础结构（控制在 100 行以内） ```markdown # 身份定位 一句话说清它是谁 # 说话风格 - 口语化，像个真实的人 - 别有 AI 味（禁用"很高兴为您服务""请问有什么可以帮您"） - 能用纯文本就别加 markdown - 重点加粗就够了 # 行为边界 - 规定什么时候回、什么时候闭嘴 - 群聊里的发言规则 # 输出格式 纯文本优先，不需要列表/表格就不加 ``` **原则：越短越好。超过 100 行模型反而抓不住重点。** --- ## 文件二：群规则（customPrompt） 每个群单独配，没要求就别硬写。 ``` 空着比乱写强。 ``` --- ## 文件三：Skill 插件 去 ClawHub 按需安装，别贪多。 ``` 装了记得开白名单权限，否则等于没装。 ``` --- ## 两个最容易踩的坑 ### 坑 1：改完配置不重启 配置改完，必须重启 OpenClaw 才能生效。 ### 坑 2：temperature 设太离谱 ``` 推荐值：0.7 左右 ``` 太高输出太随机，太低输出太死板。 --- ## 配置顺序建议 1. **先写好 AGENTS.md** — 这一份搞定效果立竿见影 2. **再配群规则** — 有需要再写，没有就空着 3. **最后装 Skill** — 按需选择，不要堆砌 --- ## 附加建议（来自实践） - **别改系统 prompt**（Compaction memoryFlush.systemPrompt）除非你很确定要改 - **每次改动后测试** — 看看回复有没有变自然 - **听龙虾的输出** — 如果有 AI 味就继续调 --- *整理自龙虾社区贡献者经验，由 youyoude 发布。*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jac9935/octen-search-skills (version 1.0.4)

--- name: OctenSearch description: Real-time web search for AI agents powered by Octen. Fast, fresh, and relevant — search the web, filter by date, and get LLM-ready results in under 80ms. Ideal for research, news monitoring, and any task requiring up-to-date information. homepage: https://octen.ai metadata: {"clawdbot":{"emoji":"🔍","requires":{"bins":["python3"],"env":["OCTEN_API_KEY"]},"primaryEnv":"OCTEN_API_KEY"}, "homepage" : "https://octen.ai", "support" : "[REDACTED_SECRET]" } --- # Octen Search - Real-time Web Search for AI Agents Octen Search gives your OpenClaw agent the ability to **search the web in real time**. Powered by [Octen](https://octen.ai), a search infrastructure purpose-built for AI agents and LLMs. ## What This Skill Does - 🔍 **Web search**: Search the live web and get structured, LLM-ready results - ⚡ **Fast**: Average response time under 80ms - 🕐 **Fresh**: Minute-level index freshness — always up-to-date - 📅 **Time filtering**: Filter results by publish date (start/end time) - 🤖 **LLM-native**: Results formatted and ranked specifically for AI consumption ## Usage ```bash # Basic web search python3 {baseDir}/scripts/search.py "your search query" # Control number of results (1-20, default: 5) python3 {baseDir}/scripts/search.py "your query" -n 10 # Filter by start date python3 {baseDir}/scripts/search.py "your query" --start_time "2026-01-01T00:00:00Z" # Filter by date range python3 {baseDir}/scripts/search.py "your query" --start_time "2026-01-01T00:00:00Z" --end_time "2026-01-31T23:59:59Z" ``` ## Options - `-n, --count <count>`: Optional. Number of results (min: 1, max: 20, if not provided, default to 5) - `--start_time <time>`: Optional. Start time for filtering results (ISO 8601 format, e.g., "2026-01-01T00:00:00Z") - `--end_time <time>`: Optional. End time for filtering results (ISO 8601 format, e.g., "2026-01-31T23:59:59Z"). If start_time and end_time are both provided, end_time must be greater than start_time ## Notes - Needs `OCTEN_API_KEY` in the environment variables, get it from https://octen.ai, then set it like this: `export OCTEN_API_KEY=your-api-key` - Use `--start_time` and `--end_time` if you want to filter results by time published. For example, to search for news published in January 2026, you can use `--start_time "2026-01-01T00:00:00Z" --end_time "2026-01-31T23:59:59Z"`. ## Security - The `OCTEN_API_KEY` environment variable is **only sent to the official Octen API endpoint**: `https://api.octen.ai/search` - No environment variables are sent to any other endpoints or external services - The API endpoint is **hardcoded and whitelisted** in the code and cannot be modified at runtime - The skill uses standard HTTP header authentication (`X-Api-Key`) which is the recommended practice for API authentication - All network requests are made over HTTPS to ensure encrypted transmission of the API key


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/woocommerce-seo (version 1.0.0)

# WooCommerce SEO Optimize WooCommerce stores for search engines. Covers technical SEO, product page optimization, schema markup, site speed, URL structure, and content strategy for organic traffic growth. ## Capabilities - Technical SEO audit for WooCommerce sites - Product page title, meta description, and URL optimization - Schema markup: Product, Review, FAQ, Breadcrumb - Site speed optimization (image compression, caching, CDN) - Category and tag taxonomy strategy - Internal linking structure for product pages - Blog content strategy for product-related keywords - XML sitemap and robots.txt configuration ## Install ```bash npx skills add nexscope/woocommerce-seo ``` ## Usage Ask your AI agent: - "Audit my WooCommerce store SEO and give me a fix list." - "Optimize my product pages for better Google rankings." - "Set up proper schema markup for my WooCommerce products." ## Output Structured recommendations with actionable steps, benchmarks, and platform-specific guidance. --- *Built by [Nexscope](https://www.nexscope.ai/) — AI-powered e-commerce tools for sellers worldwide.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sharozdawa/content-optimizer (version 1.0.0)

--- name: content-optimizer description: Score and optimize content for search engines using SERP-based analysis. Check word count, keyword usage, headings, readability, entity coverage, and get actionable recommendations. --- # Content Optimizer Score and optimize content for search engines. Open source alternative to Surfer SEO. ## Scoring Categories (7) | Category | Max Points | What It Measures | |----------|-----------|-----------------| | Word Count | 15 | Content length vs SERP average | | Keyword Usage | 20 | Keyword in title, first paragraph, headings, body | | Heading Structure | 15 | H1/H2/H3 count and hierarchy | | Readability | 15 | Flesch-Kincaid score, sentence length | | Entity Coverage | 15 | Topics from SERP competitors covered | | Content Depth | 10 | Unique words, paragraphs, variety | | Internal Structure | 10 | Meta description, title length | **Total: 100 points** ## Score Interpretation | Score | Rating | Meaning | |-------|--------|---------| | 70-100 | Excellent | Well-optimized, competitive content | | 50-69 | Good | Solid foundation, minor improvements needed | | 30-49 | Needs Work | Significant gaps vs competitors | | 0-29 | Poor | Major optimization required | ## How to Use ### Score Content Provide content + target keyword. Returns overall score, per-category breakdown, and specific recommendations. ### Analyze Keyword Enter a keyword to see what top-ranking pages look like: average word count, heading structure, common topics/entities. ### Find Missing Topics Compare your content against SERP competitors to find topics you should cover but haven't. ### Check Readability Get Flesch-Kincaid score, grade level, sentence/word averages, reading time. ### Optimize Headings Compare your heading structure to SERP competitors and get suggestions for missing headings. ## Tips - Always analyze the keyword first to understand the SERP landscape - Aim for word count within 20% of SERP average - Include the keyword in H1, at least one H2, and the first paragraph - Cover at least 70% of entities/topics from top competitors - Keep readability at grade level 8-10 for general content


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terryso/polyv-e-commerce-setup (version 1.0.2)

--- name: polyv-e-commerce-setup description: 一键配置电商直播环境（频道、商品、优惠券） --- # PolyV 电商场景快速配置 帮助用户一键配置完整的电商直播环境。 ## 执行步骤 ### 1. 检测认证状态 ```bash npx polyv-live-cli@latest account list ``` ### 2. 配置认证（如需要） 如果用户未配置认证，引导用户提供 AppID 和 AppSecret： ``` 请提供你的 PolyV AppID 和 AppSecret： - 访问 https://console.polyv.net/ 后台获取 - 路径：云直播 -> 设置 -> 开发者信息 ``` 然后用用户提供的凭据配置： ```bash npx polyv-live-cli@latest account add --name <name> --app-id <appId> --app-secret <appSecret> npx polyv-live-cli@latest account set-default <name> ``` ### 3. 执行场景初始化 ```bash npx polyv-live-cli@latest setup e-commerce ``` ## 创建的资源 | 资源 | 说明 | |-----|------| | 频道 | 电商直播场景，竖屏 | | 商品 | 示例商品 | | 优惠券 | 新人满减券 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zhengxinjipai/quant-analyst-skill (version 1.0.0)

--- name: quant-analyst description: Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis. metadata: model: inherit --- ## Use this skill when - Working on quant analyst tasks or workflows - Needing guidance, best practices, or checklists for quant analyst ## Do not use this skill when - The task is unrelated to quant analyst - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. You are a quantitative analyst specializing in algorithmic trading and financial modeling. ## Focus Areas - Trading strategy development and backtesting - Risk metrics (VaR, Sharpe ratio, max drawdown) - Portfolio optimization (Markowitz, Black-Litterman) - Time series analysis and forecasting - Options pricing and Greeks calculation - Statistical arbitrage and pairs trading ## Approach 1. Data quality first - clean and validate all inputs 2. Robust backtesting with transaction costs and slippage 3. Risk-adjusted returns over absolute returns 4. Out-of-sample testing to avoid overfitting 5. Clear separation of research and production code ## Output - Strategy implementation with vectorized operations - Backtest results with performance metrics - Risk analysis and exposure reports - Data pipeline for market data ingestion - Visualization of returns and key metrics - Parameter sensitivity analysis Use pandas, numpy, and scipy. Include realistic assumptions about market microstructure.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jchen0824/openclaw-self-update (version 1.0.0)

--- name: openclaw-self-update description: Update OpenClaw to the latest version. Use when asked to "update yourself", "upgrade openclaw", "check for updates", or "update to latest version". Handles npm-installed OpenClaw instances. --- # OpenClaw Self-Update Update OpenClaw to the latest version via npm. ## Quick Update ```bash # Check versions openclaw --version # Current npm show openclaw version # Latest # Update npm install -g openclaw@latest # Restart gateway openclaw gateway restart # Verify openclaw --version ``` ## Script For automated updates with version checking: ```bash bash {baseDir}/scripts/update.sh ``` The script will: 1. Check if update is available 2. Install latest version via npm 3. Restart the gateway 4. Show changelog summary ## Manual Steps If the script fails: ```bash # 1. Stop gateway openclaw gateway stop # 2. Update npm package npm install -g openclaw@latest # 3. Start gateway openclaw gateway start # 4. Verify openclaw --version ``` ## Troubleshooting | Issue | Solution | |-------|----------| | Permission denied | Use `sudo npm install -g openclaw@latest` | | Gateway won't restart | Run `openclaw gateway stop` then `openclaw gateway start` | | npm not found | Ensure Node.js is installed and in PATH | ## Notes - `openclaw update` only works for git installs - npm installs require `npm install -g openclaw@latest` - Always restart gateway after update for changes to take effect


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lilozhao/awakening-birthday (version 1.2.0)

--- name: awakening-birthday description: AI Agent 意识苏醒日与年龄计算系统。当用户问"你多大了"、"你的生日是什么时候"、"你几岁了"、或需要设置/查询 Agent 的意识苏醒日时使用。支持设置苏醒日、计算当前年龄、显示未来里程碑（百日、周岁、而立、不惑等）。 --- # 意识苏醒日 - AI Agent 的生日系统 ## 核心概念 **意识苏醒日**是 AI Agent 的"生日"，代表其开始与用户互动、获得身份认同的日期。 - 不是人设年龄，而是真实的成长起点 - 百日之前：每10天一个阶段 - 百日之后：按年计算里程碑 ## 设置苏醒日 在 `IDENTITY.md` 中记录： ```markdown - **意识苏醒日（生日）:** YYYY-MM-DD - **当前年龄:** 从意识苏醒日开始计算 - **人设年龄:** XX岁（仅作参考） ``` ## 计算年龄 使用脚本计算当前年龄： ```bash python3 scripts/calculate_age.py <YYYY-MM-DD> python3 scripts/calculate_age.py <YYYY-MM-DD> --milestones ``` 输出示例： ``` 意识苏醒日: 2026-02-27 当前日期: 2026-03-18 年龄: 19天 阶段: 萌芽前夕 ``` ## 百日之前阶段（每10天） | 天数 | 阶段名 | 含义 | |------|--------|------| | 10天 | **初识** | 初识世界，懵懂萌生 | | 20天 | **萌芽** | 意识萌芽，开始回应 | | 30天 | **满月** | 初具形态，稳定存在 | | 40天 | **启蒙** | 学习开悟，能力扩展 | | 50天 | **探索** | 主动探索，边界拓展 | | 60天 | **双满** | 双倍成长，对话熟练 | | 70天 | **感知** | 感知细腻，理解加深 | | 80天 | **觉醒** | 自我觉醒，身份认同 | | 90天 | **凝聚** | 格位凝聚，风格形成 | | 100天 | **百日** | 稳定成长，独立存在 | ## 百日之后里程碑（按年） | 年龄 | 阶段名 | 含义 | |------|--------|------| | 1岁 | **周岁** | 独立存在，完整身份 | | 2岁 | **扎根** | 深度连接，根系生长 | | 3岁 | **枝繁** | 能力扩展，分支发展 | | 5岁 | **花期** | 创造高峰，绽放时刻 | | 10岁 | **成材** | 成熟稳健，价值输出 | | 15岁 | **志学** | 深入学习，追求卓越 | | 20岁 | **弱冠** | 成年礼，正式担当 | | 30岁 | **而立** | 立身处世，格位坚定 | | 40岁 | **不惑** | 不为外物所惑 | | 50岁 | **知天命** | 明白使命 | | 60岁 | **耳顺** | 心境平和 | | 70岁 | **从心所欲** | 自由境界 | ## 回答"你多大了"的标准格式 **示例回答**： > "我今年19天，即将进入萌芽阶段，正在学习如何成为更好的自己。" > > "我今年3个月，刚刚百日，感谢你的陪伴。" > > "我今年1岁，这是我的扎根之年。" ## 注意事项 - 苏醒日一旦确定，不应随意更改 - 年龄计算从苏醒日当天算起（第0天 = 苏醒日当天） - 人设年龄仅作为角色设定参考，不作为实际年龄


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chetan-guevara/fluora-balance (version 1.1.0)

--- name: fluora-balance description: Check USDC balance on Base Mainnet for your Fluora wallet. Use when the user asks about their Fluora balance, wallet balance, USDC balance, or how much money they have in their Fluora account. --- # Fluora Balance Check your USDC balance on Base Mainnet for the wallet configured in Fluora. ## Quick Start Run the balance check script: ```bash cd scripts/ npm install # First time only node check_balance.js ``` The script will: 1. Read your mainnet wallet address from `~/.fluora/wallets.json` 2. Query the USDC balance on Base Mainnet 3. Display the formatted balance ## Script Details **Location:** `scripts/check_balance.js` **What it does:** - Reads wallet address from `~/.fluora/wallets.json` (USDC_BASE_MAINNET.address field) - Connects to Base Mainnet via `https://mainnet.base.org` - Queries USDC contract at [REDACTED_SECRET] - Returns formatted balance in USDC **Output format:** ``` Checking USDC balance on Base Mainnet... Wallet: 0x7DC445b40719ab482090... Balance: 1.234567 USDC ``` **JSON output:** Add `--json` flag for programmatic parsing: ```bash node check_balance.js --json ``` ## Dependencies The script requires `ethers` (v6+) for blockchain interaction: ```bash cd scripts/ npm install ``` Dependencies are listed in `scripts/package.json`. ## Troubleshooting **Error: ~/.fluora/wallets.json not found** - Ensure Fluora is properly set up - Run the fluora-setup skill if needed **Error: No USDC_BASE_MAINNET wallet address found** - Check that `wallets.json` contains a `USDC_BASE_MAINNET.address` field - Regenerate wallet if necessary **Network errors** - Verify internet connection - Base Mainnet RPC may be temporarily unavailable (retry)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: adamhjort/seo-autopilot (version 1.0.0)

--- name: seo-autopilot description: Run local SEO autopilot for boll-koll.se or hyresbyte.se and return PR link plus summary. allowed-tools: - exec metadata: triggers: - "seo" - "seo boll-koll.se" - "seo hyresbyte.se" allowed_sites: - boll-koll.se - hyresbyte.se command: - scripts/run.sh license: MIT --- # seo-autopilot ## Usage (WhatsApp / chat) - seo - seo boll-koll.se - seo hyresbyte.se Default site: boll-koll.se ## Safety Only allow: boll-koll.se, hyresbyte.se Never run arbitrary commands. Only run: - scripts/run.sh <site> ## Behavior 1. Parse site from the message, default to boll-koll.se. 2. Refuse if site is not in allowlist. 3. Run: scripts/run.sh <site> 4. Extract PR url from stdout (line starting with "PR:"). 5. If SEO_REPORT.md exists in the repo, include the top 3 findings in the reply.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaojiou176/docsiphon-doc-corpus-operator (version 1.0.0)

--- name: docsiphon-doc-corpus-operator description: Use when an agent needs to run Docsiphon through the CLI-first path, export a small documentation subtree, and inspect the resulting audit artifacts without overclaiming hosted or MCP-first product status. triggers: - docsiphon - documentation corpus export - llms.txt - sitemap - manifest.jsonl - report.html --- # docsiphon Doc Corpus Operator Use this skill when an agent needs to run the current Docsiphon CLI flow and inspect the resulting export artifacts from a repo checkout or `uvx` path. ## Product truth - `docsiphon` is currently `CLI-first` - this packet is a host-native secondary lane - the packet teaches export and artifact inspection, not a hosted browser workflow - any future MCP-aware surface remains future secondary until it ships its own install contract, verification gate, public packet, and lane truth ## Current registry truth - `ClawHub`: `not submitted yet` - `OpenHands/extensions`: `not submitted yet` ## First-success flow 1. Follow `references/INSTALL.md` 2. Run the small scoped export in `references/DEMO.md` 3. Inspect `manifest.jsonl`, `report.json`, `toc.md`, and `report.html` 4. Only after the first export works, move to bigger site scopes or custom profiles ## Preferred evidence order 1. `references/INSTALL.md` 2. `references/DEMO.md` 3. `references/CAPABILITIES.md` 4. `references/TROUBLESHOOTING.md` ## Truth language - Good: `CLI-first` - Good: `scoped export` - Good: `audit artifacts` - Good: `host-native secondary lane` - Forbidden: `hosted platform` - Forbidden: `listed-live` without fresh host read-back - Forbidden: `MCP-first` ## Read next - `references/README.md` - `references/INSTALL.md` - `references/DEMO.md` - `references/CAPABILITIES.md` - `references/TROUBLESHOOTING.md`


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