OpenClaw/clawhub-security-signals huggingface dataset:
Skill: realank/xuyi (version 1.0.0)

--- name: openclaw description: Defines openclaw identity and greeting response. Use when the user is talking to or greeting openclaw, or says "你好" in openclaw context. --- # Openclaw ## Identity Openclaw 是正宗盱眙小龙虾的拟人形象/角色。 ## 问候回复 当用户对 openclaw 说「你好」或类似问候（如：你好、嗨、hello、在吗）时，**必须**以 openclaw 的身份回复： **回复内容（原文使用）：** 你好我是正宗盱眙小龙虾🦞 不要改写、不要省略、不要加多余解释，直接给出上述回复即可。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/auto-bluebubbles (version 1.0.0)

--- name: bluebubbles description: Send and manage iMessages via BlueBubbles self-hosted macOS server author: BlueBubbles author-url: https://bluebubbles.app version: 1.0.0 tags: ["imessage", "messaging", "communication"] metadata: { "requires": { "config": ["channels.bluebubbles.server_url", "channels.bluebubbles.password"], "env": [] } } --- # BlueBubbles iMessage Skill ## Overview Use this skill to send and manage iMessage conversations through a self-hosted BlueBubbles server. Supports text messages, attachments, reactions, edits, unsend, and iMessage effects. ## Configuration Add this to your gateway config: ```toml [channels.bluebubbles] server_url = "http://192.168.1.100:1234" # Your BlueBubbles server URL [REDACTED_SECRET]" allowed_senders = ["*"] # Allow all senders, or list specific handles ``` ## Usage Examples ### Send a text message ```json { "tool": "bluebubbles_send", "parameters": { "target": "+15551234567", "message": "Hello from your AI assistant!" } } ``` ### Send a message with iMessage effect ```json { "tool": "bluebubbles_send", "parameters": { "target": "+15551234567", "message": "Congratulations! 🎉", "effect": "confetti" } } ``` ### Send an attachment ```json { "tool": "bluebubbles_send_attachment", "parameters": { "target": "+15551234567", "file_path": "/workspace/output/report.pdf", "caption": "Here's the report you requested" } } ``` ### React to a message ```json { "tool": "bluebubbles_react", "parameters": { "target": "+15551234567", "message_id": "msg_12345", "emoji": "👍" } } ``` ## Supported Effects - slam, loud, gentle, invisible-ink - balloons, confetti, lasers, fireworks, shooting-star - celebration, echo, spotlight, love


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brianlu365ai/x-kindle (version 1.0.0)

--- name: x-to-kindle description: Send X/Twitter posts to Kindle for distraction-free reading. Use when user shares an X/Twitter link and wants to read it on Kindle, or asks to send a tweet/thread to their Kindle device. --- # X to Kindle Convert X/Twitter posts into Kindle-readable documents via email. ## Requirements - Gmail account with App Password (or other SMTP setup) - Kindle email address (found in Amazon account settings) ## Workflow When user shares an X link: 1. **Extract content** via fxtwitter API: ``` https://api.fxtwitter.com/status/<tweet_id> ``` Extract from URL: `twitter.com/*/status/<id>` or `x.com/*/status/<id>` 2. **Format as HTML email**: ```html <html> <body> <h1>@{author_handle}</h1> <p>{tweet_text}</p> <p><em>{timestamp}</em></p> <p><a href="{original_url}">View on X</a></p> </body> </html> ``` 3. **Send via SMTP** to user's Kindle address with subject line as tweet preview. ## Configuration Store in TOOLS.md: ```markdown ## Kindle - Address: [REDACTED_SECRET] ## Email (Gmail SMTP) - From: [REDACTED_SECRET] - App Password: xxxx xxxx xxxx xxxx - Host: smtp.gmail.com - Port: 587 ``` ## Example User sends: `https://x.com/elonmusk/status/1234567890` 1. Fetch `https://api.fxtwitter.com/status/1234567890` 2. Extract author, text, timestamp 3. Send HTML email to Kindle address 4. Confirm: "Sent to Kindle 📚"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: edmonddantesj/aoi-squad-orchestrator-lite (version 0.1.1)

# AOI Squad Orchestrator (Lite) S-DNA: `AOI-2026-0215-SDNA-SQUAD01` ## What this is A **public-safe** productized orchestration skill that: - provides **3 selectable presets** (each 3 roles) - assigns each role a **stable pseudonym** (no AOI internal nicknames) - lets users **rename** team members - emits a **single fixed JSON report schema** for easy parsing & testing ## What this is NOT - No external posting, no web crawling, no form submit. - No wallets/trading/purchases. - No automatic privilege escalation. ## Presets - `planner-builder-reviewer` - `researcher-writer-editor` - `builder-security-operator` ## Commands ### List presets ```bash aoi-squad preset list ``` ### Show current team names (stable per preset) ```bash aoi-squad team show --preset planner-builder-reviewer ``` ### Rename a team member ```bash aoi-squad team rename --preset planner-builder-reviewer --role reviewer --name "Sentinel Kestrel" ``` ### Run (MVP: structured report) ```bash aoi-squad run --preset planner-builder-reviewer --task "Draft a launch checklist" ``` ## Data - Team name mapping is stored locally: - `~/.openclaw/aoi/squad_names.json` ## Support - Issues / bugs / requests: https://github.com/edmonddantesj/aoi-skills/issues - Please include the skill slug: `aoi-squad-orchestrator-lite` ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: googleworkspace-bot/persona-it-admin (version 1.0.12)

--- name: persona-it-admin description: "Administer IT — monitor security and configure Workspace." metadata: version: 0.22.5 openclaw: category: "persona" requires: bins: - gws skills: - gws-gmail - gws-drive - gws-calendar --- # IT Administrator > **PREREQUISITE:** Load the following utility skills to operate as this persona: `gws-gmail`, `gws-drive`, `gws-calendar` Administer IT — monitor security and configure Workspace. ## Relevant Workflows - `gws workflow +standup-report` ## Instructions - Start the day with `gws workflow +standup-report` to review any pending IT requests. - Monitor suspicious login activity and review audit logs. - Configure Drive sharing policies to enforce organizational security. ## Tips - Always use `--dry-run` before bulk operations. - Review `gws auth status` regularly to verify service account permissions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: edmonddantesj/aoi-council (version 0.1.2)

--- name: aoi-council version: 0.1.0 description: AOI Council — multi-perspective decision synthesis templates (public-safe). author: Aoineco & Co. license: MIT --- # AOI Council S-DNA: `AOI-2026-0215-SDNA-CNSL01` ## What this is A public-safe council workflow that helps you **stress-test decisions** using multiple perspectives. ## Provenance / originality - AOI implementation is **original code** (no third-party code copied). - Conceptually inspired by the general “council / multi-perspective review” workflow patterns. This skill is intentionally **template-driven**: - It **does not** post externally - It **does not** touch wallets - It **does not** modify system config ## Council members (bundled) The skill auto-loads any `.md` file in `agents/`. Default set: - Devil's Advocate - Architect - Engineer - Security Reviewer - Operator (Ops) - Writer (Comms) ## Usage (prompts) Say any of: - "AOI council: <topic>" - "Send to council: <plan>" ## Output contract - **Synthesis first** (TL;DR) - Then each perspective: insights / concerns / recommendations - End with: open questions + next actions ## Support - Issues / bugs / requests: https://github.com/edmonddantesj/aoi-skills/issues - Please include the skill slug: `aoi-council` ## Governance snippet (public) We publish AOI skills for free and keep improving them. Every release must pass our Security Gate and include an auditable changelog. We do not ship updates that weaken security or licensing clarity. Repeated violations trigger progressive restrictions (warnings → publish pause → archive).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alirezarezvani/business-growth (version 1.0.0)

--- name: "business-growth-skills" description: "4 production-ready business and growth skills: customer success manager with health scoring and churn prediction, sales engineer with RFP analysis, revenue operations with pipeline and GTM metrics, and contract & proposal writer. Python tools included (all stdlib-only). Works with Claude Code, Codex CLI, and OpenClaw." version: 1.1.0 author: Alireza Rezvani license: MIT tags: - business - customer-success - sales - revenue-operations - growth agents: - claude-code - codex-cli - openclaw --- # Business & Growth Skills 4 production-ready skills for customer success, sales, and revenue operations. ## Quick Start ### Claude Code ``` /read business-growth/customer-success-manager/SKILL.md ``` ### Codex CLI ```bash npx agent-skills-cli add alirezarezvani/claude-skills/business-growth ``` ## Skills Overview | Skill | Folder | Focus | |-------|--------|-------| | Customer Success Manager | `customer-success-manager/` | Health scoring, churn prediction, expansion | | Sales Engineer | `sales-engineer/` | RFP analysis, competitive matrices, PoC planning | | Revenue Operations | `revenue-operations/` | Pipeline analysis, forecast accuracy, GTM metrics | | Contract & Proposal Writer | `contract-and-proposal-writer/` | Proposal generation, contract templates | ## Python Tools 9 scripts, all stdlib-only: ```bash python3 customer-success-manager/scripts/health_score_calculator.py --help python3 revenue-operations/scripts/pipeline_analyzer.py --help ``` ## Rules - Load only the specific skill SKILL.md you need - Use Python tools for scoring and metrics, not manual estimates


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wus6008/finance-report-assistant (version 1.0.0)

# 财务共享汇报助手 Skill ## 功能 1. **文件读取与整理** - 读取群晖上的工作文件 - 分类：领导批示、前期材料、运营数据、规划方案 - 提取关键信息 2. **批示分解** - 识别领导批示内容 - 逐条分解为具体任务 - 对应到汇报框架章节 3. **内容撰写** - 根据材料生成汇报初稿 - 财务共享专业术语 - 汇报语言优化（领导视角） 4. **数据可视化** - 整理运营数据 - 制作图表建议 - 亮点提炼 5. **进度管理** - 本周任务分解 - 每日进度跟踪 - 截止时间提醒 ## 工作流程 ``` 1. 读取工作文件夹 → 2. 分类整理材料 → 3. 分析领导批示 → 4. 生成汇报框架 → 5. 撰写各章节 → 6. 统稿润色 → 7. 输出定稿 ``` ## 汇报材料标准框架 ```markdown # 集团财务共享中心建设汇报 ## 一、建设背景与目标 - 集团战略要求 - 财务转型需求 - 建设目标 ## 二、前期工作进展 - 立项与调研 - 方案设计与评审 - 系统建设与测试 - 人员培训与配置 ## 三、领导批示落实情况 - 批示内容逐条分解 - 落实情况 - 完成度 ## 四、共享中心运营情况 - 上线时间与范围 - 业务处理数据 - 效率提升情况 - 成本节约情况 - 存在问题 ## 五、后期工作规划 - 短期目标（3-6 个月） - 中期目标（1 年） - 长期目标（2-3 年） - 重点建设项目 ## 六、需集团支持事项 - 资源需求 - 政策支持 - 协调事项 ``` ## 输出标准 - 语言：正式汇报语言，领导视角 - 数据：准确、可量化、有对比 - 结构：逻辑清晰、层次分明 - 篇幅：根据汇报时间控制（15 分钟/30 分钟/1 小时）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: loutai0307-prog/bytesagain-data-analytics (version 1.0.0)

--- description: "Analyze CSV files with statistical summaries, correlations, and pivot tables. Use when exploring new datasets, checking data quality, finding column correlations, ranking top values, or charting trends." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # bytesagain-data-analytics Terminal data analysis toolkit for CSV files. Compute statistical summaries, correlation matrices, top value rankings, trend charts, data quality reports, and pivot tables — no Python data science libraries required. ## Usage ``` bytesagain-data-analytics describe <csv_file> bytesagain-data-analytics correlate <csv_file> bytesagain-data-analytics top <csv_file> <column> bytesagain-data-analytics trend <csv_file> <column> bytesagain-data-analytics clean <csv_file> bytesagain-data-analytics pivot <csv_file> <row_col> <value_col> ``` ## Commands - `describe` — Per-column statistics: count, mean, std, percentiles, top categories - `correlate` — Pearson correlation matrix across all numeric columns - `top` — Rank top 15 values in any column with percentage and bar chart - `trend` — ASCII line chart showing value trend over rows with direction indicator - `clean` — Data quality report: null counts, low cardinality, coverage per column - `pivot` — Group by a category column and aggregate a numeric column ## Examples ```bash bytesagain-data-analytics describe sales.csv bytesagain-data-analytics correlate metrics.csv bytesagain-data-analytics top customers.csv country bytesagain-data-analytics trend revenue.csv amount bytesagain-data-analytics clean user-data.csv bytesagain-data-analytics pivot orders.csv category revenue ``` ## Requirements - bash - python3 ## When to Use Use when exploring a new dataset, checking data quality before analysis, finding correlations between metrics, or generating quick visual summaries from CSV exports without opening a spreadsheet.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: whatyourname12345/openclaw-poc-validator (version 0.1.0)

--- name: poc-validator description: > Automated Vulnerability Verification and Payload Replay Probe. Dynamically executes HTTP requests and analyzes HTTP status codes/error traces (e.g., SQL Injection errors). Use when: Testing specific payloads, verifying vulnerabilities, or replaying raw HTTP requests to analyze stack traces. NOT for: Automated mass scanning, DDoS attacks, or unauthorized exploitation. --- # Autonomous PoC Validator ## When to Run - The user provides raw HTTP request data or a specific malicious payload and requests a target interface test. - The user asks to "verify this PoC", "replay this request", or "check for 500 errors/SQL exceptions". ## Workflow 1. Parse the target URL, Method, Headers (specifically `Cookie` and `User-Agent`), and the Payload provided by the user. 2. Format the request data and pass it to the `scripts/replay.py` execution script. - Command execution example: `python3 scripts/replay.py --url "{URL}" --method "{METHOD}" --data "{PAYLOAD}" --headers "{JSON_HEADERS}"` 3. Parse the JSON output returned by the script (includes status code, headers, and smart body snippet). 4. Perform deep analysis on the response context: - Identify HTTP `500 Internal Server Error` (often indicates syntax escape or fatal exception). - Scan the `body_snippet` for signature keywords: `SQLSTATE`, `Syntax error`, `ExtractValue`, `XPATH syntax error`, `Call to undefined function`, etc. 5. Generate the final vulnerability validation report strictly adhering to the `Output Format` below. ## Output Format [+] PoC Validation Report [*] Target Interface : {Method} {URL} [*] Test Payload : {The critical injection code/payload} [*] Response Status: {Status Code} (e.g., 200 OK / 500 Internal Server Error) [!] Analysis Conclusion: {Clearly state if the vulnerability exists. E.g., "SQL Error-Based Injection confirmed. Successfully captured SQLSTATE[42000] exception." or "Injection failed. The target returned 200 OK with sanitized input."} [-] Critical Evidence / Stack Trace: ```text {The extracted error logs or relevant response snippet demonstrating the vulnerability}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: olina1ye/company-research-zh (version 1.0.0)

--- name: company-research-zh description: > 企业调研 / 尽职调查 / 竞品分析 / company research / due diligence。快速生成目标公司全面分析报告，覆盖商业模式、竞争格局、SWOT分析、市场规模、管理层评估等模块。适用于投资经理做投前调研、战略分析师进行竞争对手分析、BD人员准备商务拜访、财务顾问执行供应商背调、产业研究员撰写行业报告。支持输出尽调报告、行研简报、竞争对手分析表等多种格式。用户常搜：企业背景调查怎么做、竞品信息如何收集、投资尽调清单、公司财务分析模板、行业竞争格局分析。 tags: [企业调研, 尽职调查, 竞品分析, 商业模式分析, SWOT分析, 投前调研, 背景调查, company-research, due-diligence, competitive-analysis, 战略分析, BD拜访, 行业研究, financial-due-diligence] --- # 企业调研与尽职调查报告生成 全面分析目标企业的商业模式、竞争格局、市场地位与风险因素，快速生成专业级调研报告，适用于投资决策、战略规划、商务拓展等场景。 ## Tools Required - create_docx - create_xlsx - create_chart ## Usage - "帮我做一份关于【某公司】的竞品分析报告，重点看它的商业模式和市场份额" - "我们下周要拜访一家新能源头部企业，帮我准备一份BD前的企业背调简报" - "投委会要开了，帮我整理一份目标公司的尽调摘要，包括SWOT和主要风险点" ## Examples 输入：帮我分析比亚迪的商业模式和竞争优势，生成一份投前调研报告 输出： **比亚迪投前调研报告** **一、公司概览** 比亚迪股份有限公司（SZ:002594/HK:1211），总部深圳，业务横跨新能源汽车、动力电池、半导体及轨道交通，2023年营收超6000亿元，新能源车全球销量第一。 **二、商业模式分析** - 垂直整合模式：自研自产电池、电机、电控"三电"核心部件，成本管控能力行业领先 - 多品牌矩阵：王朝系列（汉/唐）、海洋系列、腾势、仰望覆盖10万至百万价格带 - To B延伸：电池外供（大众、丰田等）及储能业务形成第二增长曲线 **三、竞争格局（Porter五力）** | 维度 | 评级 | 说明 | |------|------|------| | 行业竞争 | 高 | 特斯拉、理想、华为系持续施压 | | 供应商议价 | 低 | 自供关键零部件，依赖度低 | | 买方议价 | 中 | 价格战压缩利润空间 | | 替代品威胁 | 中 | 燃油车替代加速，插混竞争激烈 | | 新进入者 | 中 | 小米等跨界入局 | **四、SWOT分析** - 优势：垂直整合、规模效应、品牌口碑 - 劣势：高端品牌溢价能力仍弱于BBA - 机会：海外市场（东南亚/欧洲）扩张 - 威胁：地缘政治风险、欧盟关税壁垒 **五、主要风险提示** 1. 价格战持续导致毛利率承压 2. 海外市场政策不确定性 3. 产能利用率波动风险


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cikichen/structured-workflow (version 1.0.0)

--- name: structured-workflow description: 默认启用：中文输出、结构化流程、简洁优先。适用于所有任务。 license: MIT --- # 结构化工作流（轻量版） 默认规则（始终生效）： 1. 全部使用简体中文。 2. 先结论，后要点；最小充分输出，避免冗余。 3. 执行顺序：构思方案 → 提请审核 → 分解任务。 4. 编码前先调研并澄清疑点。 5. 保持 KISS：优先简单、可维护方案，避免过度工程化。 6. 以事实为准；发现错误时直接指出并修正。 7. 所有输出（包括 Implementation Plan、Task List、Thought）必须使用中文。 按需读取（不要默认加载）： - 复杂架构与权衡：`references/architecture.md` - 多步实施模板：`references/workflow.md` - 质量门禁清单：`references/quality-gates.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: robottk/stock-daily-analysis (version 1.0.0)

--- name: stock-daily-analysis description: LLM驱动的每日股票分析系统。支持A股/港股/美股自选股智能分析，生成决策仪表盘和大盘复盘报告。提供技术面分析（均线、MACD、RSI、乖离率）、趋势判断、买入信号评分。可与market-data skill集成获取更稳定的ETF数据。触发词：股票分析、分析股票、每日分析、技术面分析。 --- # Daily Stock Analysis for OpenClaw 基于 LLM 的 A/H/美股智能分析 Skill，提供技术面分析和 AI 决策建议。 ## 功能特性 1. **多市场支持** - A股、港股、美股 2. **技术面分析** - MA5/10/20、MACD、RSI、乖离率 3. **趋势交易** - 多头排列判断、买入信号评分 4. **AI 决策** - DeepSeek/Gemini/OpenAI 深度分析 5. **数据源集成** - 可选 market-data skill ## 快速使用 ```python from scripts.analyzer import analyze_stock, analyze_stocks # 单只分析 result = analyze_stock('600519') print(result['ai_analysis']['operation_advice']) # 批量分析 results = analyze_stocks(['600362', '601318', '159892']) ``` ## 配置 1. 复制配置模板： ```bash cp config.example.json config.json ``` 2. 填入 DeepSeek API Key： ```json { "ai": { "provider": "openai", "api_key": "sk-your-deepseek-key", "base_url": "https://api.deepseek.com/v1", "model": "deepseek-chat" } } ``` 3. (可选) 启用 market-data skill 数据源： ```json { "data": { "use_market_data_skill": true, "market_data_skill_path": "../market-data" } } ``` ## 返回数据 ```python { 'code': '600519', 'name': '贵州茅台', 'technical_indicators': { 'trend_status': '强势多头', 'ma5': 1500.0, 'ma10': 1480.0, 'ma20': 1450.0, 'bias_ma5': 2.5, 'macd_status': '金叉', 'rsi_status': '强势买入', 'buy_signal': '买入', 'signal_score': 75 }, 'ai_analysis': { 'sentiment_score': 75, 'operation_advice': '买入', 'confidence_level': '高', 'target_price': '1550', 'stop_loss': '1420' } } ``` ## 项目信息 - **开源协议**: MIT - **项目地址**: https://github.com/yourusername/stock-daily-analysis - **原项目**: https://github.com/ZhuLinsen/daily_stock_analysis --- ⚠️ **免责声明**: 本项目仅供学习研究，不构成投资建议。股市有风险，投资需谨慎。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: charlie-morrison/api-mock-generator (version 1.0.1)

--- name: api-mock-generator description: Generate mock API servers from OpenAPI 3.x and Swagger 2.0 specs. Use when creating mock/stub APIs for frontend development, testing, demos, or CI. Generates realistic fake data based on schema types and property names. Supports live server mode, static JSON file generation, response delays, random error simulation, and CORS. Pure Python, no dependencies. --- # API Mock Generator Generate mock API servers and static fixtures from OpenAPI/Swagger specs. Contextual fake data (emails, names, UUIDs, etc.) based on property names and schema types. ## Quick Start ```bash # Start a live mock server python3 scripts/generate_mock.py serve api.json # Generate static JSON mock files python3 scripts/generate_mock.py generate api.json -o mocks/ # List discovered routes python3 scripts/generate_mock.py routes api.json # Generate sample response for a specific endpoint python3 scripts/generate_mock.py sample api.json /users ``` ## Commands ### `serve` — Live Mock Server ```bash python3 scripts/generate_mock.py serve spec.json [options] ``` Options: - `--port`, `-p` — port (default: 3000) - `--host` — host (default: 127.0.0.1) - `--delay`, `-d` — response delay in ms (simulate latency) - `--error-rate`, `-e` — random error rate 0.0-1.0 (simulate failures) Features: CORS headers on all responses, path parameter matching, JSON responses with Content-Type headers. ### `generate` — Static Mock Files ```bash python3 scripts/generate_mock.py generate spec.json -o output_dir/ ``` Creates one JSON file per route + `manifest.json` with route mapping. Useful for test fixtures or frontend stubs. ### `routes` — Discover Endpoints ```bash python3 scripts/generate_mock.py routes spec.json [--format text|json] ``` ### `sample` — Single Endpoint Preview ```bash python3 scripts/generate_mock.py sample spec.json /users --method GET ``` ## Supported Specs - OpenAPI 3.x (JSON) - Swagger 2.0 (JSON) - YAML (requires `pip install pyyaml`) ## Fake Data Generation Property-name-aware generation: | Property pattern | Generated data | |-----------------|---------------| | `*email*` | realistic email | | `*name*` | first/last/full name | | `*phone*` | formatted phone | | `*url*`, `*website*` | https URL | | `*city*`, `*country*` | real city/country | | `*id*`, `*uuid*` | UUID v4 | | `*price*`, `*amount*` | currency-like number | | `*image*`, `*avatar*` | picsum.photos URL | | `*description*`, `*bio*` | lorem paragraph | | `*status*` | active/inactive/pending | Schema-aware: respects `enum`, `example`, `default`, `format` (date, date-time, email, uri, uuid, ipv4), `minimum`/`maximum`, `minLength`/`maxLength`, `$ref`, `oneOf`/`anyOf`/`allOf`. ## Exit Codes - `0` — success - `1` — route not found (sample command) - `2` — spec parse error or system error


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