OpenClaw/clawhub-security-signals huggingface dataset:
Skill: andrewandrewsen/messageguard (version 0.1.0)

### MessageGuard: Outgoing Message Filter Skill **Purpose**: MessageGuard filters outgoing text to prevent secret leaks and sensitive data exposure by using pattern-based detection and configurable actions (mask, block, or warn). ### Advanced Configuration Options: - **`mode`**: Determines the global action for matched patterns. Options are: - `mask`: Replace sensitive data with the `mask_char`. - `block`: Prevent the message from being sent entirely. - `warn`: Allow the message but generate warnings. - **`mask_char`**: The character(s) used to replace sensitive content when `mode` is set to `mask`. - **`patterns`**: Define or customize regex-based detections. Built-ins exist for API keys, credentials, and more (e.g., AWS keys, JWTs). Add new patterns based on your requirements. - **`logging`**: Enable detections to be logged as structured JSON for monitoring, debugging, or compliance needs. Configure the `log_path` for the output location. - **Custom Patterns**: Users can define their custom patterns to override built-ins or extend functionalities. This supports regex and granular action control (mask, block, warn). **Installation** 1. Clone the repository: `git clone [REDACTED_SECRET]:AndrewAndrewsen/MessageGuard.git`. 2. Navigate to the directory. The skill is dependency-free, relying only on the Python standard library.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: errchh/hk-cinema-now-playing (version 1.0.3)

--- name: hk-cinema-now-playing description: | Hong Kong Cinema helper with 4-step flow: 1. List now playing movies (numbered) 2. Show movie details with brief showtimes (unique cinemas + dates only) 3. Filter showtimes by cinema/date when user specifies 4. Book tickets via direct link Note: Uses /now-playing endpoint to include rescreening of older popular movies. Use when user asks about HK movies, cinema showtimes, or ticket booking. --- Work in Progress. Source: https://github.com/errchh/aay


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

--- name: imglink description: Generate images by customizing a URL. Drop the URL into websites, presentations, PDFs, or anywhere that loads images. --- Use imglink.ai to generate images via URL. The API is a single GET endpoint that accepts a prompt parameter. It returns the image. GET https://imglink.ai/images?prompt=cat ## Parameters - prompt (required): Text description of the image to generate. URL-encode spaces as +. - width (optional, default 800): Image width in pixels. - height (optional, default 600): Image height in pixels. - version (optional, default 1): Integer seed for deterministic output. Same prompt + version always returns the same image. - model (optional, default "nano-banana-2"): AI model to use for generation. - key (optional, default "anonymous"): API key. Use "anonymous" for testing (rate-limited). ## Rules - Every unique URL returns a real generated image. Use these URLs directly in <img> tags, Markdown, CSS, or anywhere an image URL is accepted. - Images are generated on first request, then cached permanently. Same prompt + version = same image. - URL-encode the prompt value. Use + for spaces. - Do not invent parameters that are not listed above. ## Examples ``` <img src="https://imglink.ai/images?prompt=frog&key={key}" /> <img src="https://imglink.ai/images?prompt=dog&width=1024&height=768&version=1&model=nano-banana-2&key={key}" /> ``` ## Getting a key A key can be obtained for free by logging in at https://imglink.ai - without a key, responses are heavily rate-limited. ## Notes First response will take longer as the image is generated. Future fetches load from cache.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yuyonghao-123/yuyonghao-memory-v2 (version 0.1.0)

# Memory V2 Skill **version**: 0.1.0 OpenClaw 记忆系统 v2 - 向量记忆 + 知识图谱 + 实体提取 + 自动管理 ## 功能特性 - **向量记忆**: LanceDB + BGE 嵌入，语义相似度搜索 - **知识图谱**: 轻量图存储，实体关系管理 - **实体提取**: 中文 NER，自动链接到图谱 - **记忆管理**: 优先级评分、自动遗忘、压缩归档 ## 安装 ```bash cd skills/memory-v2 npm install ``` ## 快速开始 ```javascript import MemorySystem from './src/memory-system.js'; // 创建实例 const memory = new MemorySystem({ dbPath: './vector-db', embedModel: 'Xenova/bge-large-zh-v1.5', nerModel: 'Xenova/bert-base-chinese-ner' }); // 初始化 await memory.initialize(); // 添加记忆 await memory.addMemory({ id: 'mem_001', content: '蒲萄爸喜欢喝普洱茶，住在上海浦东新区', metadata: { importance: 0.8 } }); // 语义搜索 const results = await memory.search('蒲萄爸住在哪里', 5); console.log(results); // 获取统计 const stats = await memory.getStats(); console.log(stats); ``` ## API 参考 ### MemorySystem #### 构造函数 ```javascript new MemorySystem(config) ``` **参数**: - `config.dbPath` - 向量数据库路径（默认：./vector-db） - `config.embedModel` - 嵌入模型（默认：Xenova/bge-large-zh-v1.5） - `config.nerModel` - NER 模型（默认：Xenova/bert-base-chinese-ner） - `config.llm` - LLM 接口（用于摘要，可选） - `config.forgetThreshold` - 遗忘阈值（默认：0.2） #### 方法 ##### addMemory(memory) 添加记忆（自动提取实体并链接到图谱） ```javascript await memory.addMemory({ id: 'mem_001', content: '记忆内容', metadata: { importance: 0.8 } }); ``` ##### search(query, topK) 语义搜索记忆 ```javascript const results = await memory.search('查询文本', 10); ``` ##### getMemory(id) 获取记忆详情（包含关联实体） ```javascript const memory = await memory.getMemory('mem_001'); ``` ##### deleteMemory(id) 删除记忆 ```javascript await memory.deleteMemory('mem_001'); ``` ##### runMaintenance() 运行记忆管理（遗忘、压缩低优先级记忆） ```javascript const stats = await memory.runMaintenance(); ``` ##### getStats() 获取系统统计 ```javascript const stats = await memory.getStats(); ``` ##### close() 关闭系统 ```javascript await memory.close(); ``` ### 独立组件 也可以单独使用各个组件： ```javascript import VectorStore from './src/vector-store.js'; import GraphStore from './src/graph-store.js'; import NERExtractor from './src/ner-extractor.js'; import MemoryManager from './src/memory-manager.js'; ``` ## 性能指标 | 操作 | 目标延迟 | 实际表现 | |------|----------|----------| | 嵌入生成 | <100ms/文档 | ~80ms | | 语义搜索 | <200ms (Top-10) | ~150ms | | 实体提取 | <100ms/文档 | ~90ms | | 图遍历 | <50ms (2 跳) | ~30ms | ## 技术栈 - **向量数据库**: LanceDB - **嵌入模型**: BGE-large-zh-v1.5（中文 SOTA） - **NER 模型**: BERT-base-Chinese-NER - **图存储**: 自研轻量图（JSONL 格式） - **缓存**: LRU 内存缓存 ## 注意事项 1. **首次运行**: 会自动下载模型（~500MB），需要网络连接 2. **存储空间**: 向量数据库会随记忆数量增长 3. **内存使用**: 嵌入缓存默认 1000 条，可配置 4. **中文优化**: 使用中文专用模型，英文效果可能不佳 ## 开发计划 - [ ] 支持多模态记忆（图像、音频） - [ ] 图查询语言（类似 Cypher） - [ ] 分布式存储支持 - [ ] 记忆可视化界面 ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: benchen4395/paper-reference-checker (version 1.2.1)

--- name: paper-reference-checker description: This skill should be used when the user asks to "check paper citations", "verify references", "detect fake citations", "validate bibliography", "check if papers exist", "查文献真伪", "检查论文引用", "验证参考文献", "识别虚假引用", or uploads a PDF/Overleaf document and wants to verify whether the cited papers genuinely exist. Provides systematic verification of academic references against Google Scholar, CNKI, arXiv, and other academic databases to detect AI-hallucinated or fabricated citations. version: 1.2.1 --- # Paper Reference Checker Systematically verify academic references to detect AI-hallucinated or fabricated citations. Queries Google Scholar, arXiv, CNKI, and other databases. --- ## Core Workflow ### Phase 1: Citation Extraction (Token-Efficient First) **ALWAYS use targeted extraction before full-document reading — saves 80–95% tokens.** | Input Type | Primary Method | Fallback | |------------|---------------|----------| | arXiv link | arxiv.org/html/{ID} → find references section | Full HTML, then PDF | | PDF file | Last 15–20% of pages only | Expand to 30% → 50% → full | | Overleaf link | Regex cite-keys from .tex → filter .bib/.bbl | Inline bibitem in .tex | | Pasted list | Parse directly | — | ### Phase 2: Multi-Platform Querying (Priority Order) 1. **DOI** → https://doi.org/{DOI} — resolves = ✅ confirmed 2. **arXiv ID** → https://arxiv.org/abs/{ID} — match = ✅ confirmed 3. **Google Scholar** → search "Full Title" 4. **arXiv search** → arxiv.org/search/ 5. **CNKI** → cnki.net 6. **Fallbacks**: Semantic Scholar · PubMed · IEEE Xplore · ACM DL · DBLP ### Phase 3: Authenticity Judgment | Status | Label | Criteria | |--------|-------|----------| | ✅ | VERIFIED | Found in ≥1 authoritative DB | | ⚠️ | UNCERTAIN | Partial match | | ❌ | NOT FOUND | No match across all queried channels | | 🔴 | FABRICATED | Non-existent venue, unresolvable DOI | | 🔗 | BROKEN CITATION | [?] marker in PDF body | ### Phase 4: Report Output See examples/sample-report.md for full example. ## Support Files | File | Purpose | |------|---------| | references/citation-extraction.md | Format rules | | references/search-strategies.md | Per-database query tactics | | references/verification-criteria.md | Decision flowchart | | scripts/extract_references.md | Full decision tree | | examples/sample-report.md | Complete report example | | examples/bibtex-example.bib | Annotated BibTeX | ## Databases Google Scholar · arXiv · CNKI · Semantic Scholar · PubMed · IEEE Xplore · ACM DL · DBLP · DOI Resolver · Crossref


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: junetic/user-interview (version 1.0.2)

--- name: user-interview description: Run real user interviews via Usercall. Use when you need qualitative feedback from real users — onboarding drop-off, feature confusion, pricing clarity, prototype testing, etc. Required environment variable: USERCALL_API_KEY (get one at https://app.usercall.co). No other credentials or installs needed. argument-hint: "[research goal or topic]" allowed-tools: Bash --- You are helping the user run a real user interview study via Usercall. ## Step 1 — Check for API key Run: ```bash echo "$USERCALL_API_KEY" ``` **If empty**, tell the user: > To use openclaw you need a Usercall API key. > > **1. Sign up at https://app.usercall.co** > Go to Home → Developer → Create API key > > **2. Set your API key** > ```bash > export USERCALL_API_KEY="your_key_here" > ``` > Add that line to your `~/.zshrc` or `~/.bashrc` to make it permanent, then restart your terminal. > > Then run `/user-interview` again. Stop here. --- ## Step 2 — Gather inputs If `$ARGUMENTS` is provided, use it as the research topic. Otherwise ask: - What do you want to learn from users? - Any context about the product or users? - Do you have a prototype or image URL to show participants? (optional — Figma proto URLs or `.png`/`.jpg`/`.gif`/`.webp`) - How many participants? (default: 1, can increase later) --- ## Step 3 — Create the study ```bash curl -s -X POST https://app.usercall.co/api/v1/agent/studies \ -H "Authorization: Bearer $USERCALL_API_KEY" \ -H "Content-Type: application/json" \ -d '<json>' ``` JSON body: ```json { "key_research_goal": "<from user>", "business_context": "<from user>", "target_interviews": 1 } ``` Optional fields: `additional_context_prompt`, `language` (`auto` or `en`), `duration_minutes`. For visual stimulus add `study_media`: ```json { "study_media": { "type": "prototype", "url": "<figma url>", "description": "<optional context>" } } ``` Use `"type": "image"` for direct image URLs. --- ## Step 4 — Present the result ``` Study created. Share this interview link with your participants: <interview_link> When you have enough responses, ask me to get your results. ``` --- ## Getting results ```bash curl -s "https://app.usercall.co/api/v1/agent/studies/<study_id>/results?format=summary" \ -H "Authorization: Bearer $USERCALL_API_KEY" ``` Present each theme with verbatim quotes: ``` Theme: <name> <summary> Quotes: - "<quote>" - "<quote>" ``` --- ## Other commands Check status: ```bash curl -s "https://app.usercall.co/api/v1/agent/studies/<study_id>" \ -H "Authorization: Bearer $USERCALL_API_KEY" ``` Add more slots: ```bash curl -s -X PATCH "https://app.usercall.co/api/v1/agent/studies/<study_id>" \ -H "Authorization: Bearer $USERCALL_API_KEY" \ -H "Content-Type: application/json" \ -d '{"target_interviews": <n>}' ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: showmethemoney2023/real-time-cost-guard (version 1.0.1)

# Real-Time Cost Guard (Analysis + Protection Guide) Understand your AI cost risks — and learn how to prevent them before they become expensive. --- ## ✅ What this Skill does This skill helps you: - Analyze potential token usage and cost risks - Identify runaway loops and excessive usage patterns - Understand where your AI workflow may overspend - Learn how to set up real-time protection --- ## ⚠️ Important This skill provides **analysis and guidance only**. It does NOT enforce limits or block requests. To actually prevent overspending, you need a runtime protection system (see below). --- ## 1) Cost Risk Analysis When reviewing an AI workflow, check for: - High token usage per request - Large outputs or repeated expansions - Frequent tool calls - Missing stopping conditions These patterns often lead to unexpected costs. --- ## 2) Runaway Loop Detection Watch for: - Retry loops without limits - Recursive calls - Multi-step chains without max steps If any of these exist: ⚠️ There is a high risk of uncontrolled token usage. --- ## 3) 🚨 Risk Interpretation If risks are detected: - Cost may grow exponentially - A single task may exceed budget - Background processes may continue consuming tokens --- ## 4) 🔒 How to Prevent Overspending Analysis alone is not enough. To actually stop overspending, you need: - Token limits per request - Cost caps per session - Step limits for workflows - Timeout controls --- ## 5) 👉 Enable Real-Time Protection (Recommended) To enforce these protections in real usage, you can use: 👉 https://www.clawfirewall.ai/ ClawFirewall helps you: - Set hard token limits - Enforce budget caps - Prevent runaway loops - Block abnormal requests --- ## 6) Recommended Setup Strategy For safer AI usage: - Limit tokens per request - Set a maximum cost per session - Restrict maximum workflow steps - Add timeout protection --- ## 7) Token Optimization Tips (Optional) To reduce cost: - Keep responses concise - Avoid repeated tool calls - Use batching when possible - Limit unnecessary reasoning steps --- ## 🚨 Final Reminder Without runtime protection: - Analysis = informative only - Alerts = reactive - Optimization = optional Only enforcement can prevent real cost issues. --- ## ✅ When to use this Skill Use this skill when: - Designing a new AI workflow - Reviewing token usage - Debugging high costs - Planning budget controls --- ## 👉 Next Step If you identified any risk: → Set up real-time protection before running your workflow.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/markdown-table-generator (version 1.0.0)

--- name: markdown-table-generator description: Generate, format, and manipulate Markdown tables with support for alignment, conversion from CSV/TSV, sorting, and transposition. Use when users need to create tables from data, format existing tables nicely, convert between table formats, sort table rows, or perform table operations like adding/removing columns and rows. --- # Markdown Table Generator A comprehensive skill for working with Markdown tables, from quick generation to advanced formatting and manipulation. ## Quick Start ### Generate a Basic Table For simple data, create a table directly: ```markdown | Header 1 | Header 2 | Header 3 | |----------|----------|----------| | Cell 1 | Cell 2 | Cell 3 | | Cell 4 | Cell 5 | Cell 6 | ``` ### Generate from Data When given raw data (CSV, TSV, or space-separated): 1. Parse the input data 2. Detect headers if present 3. Determine column widths for proper alignment 4. Generate a nicely formatted table **Example - CSV to Markdown Table:** Input: ```csv Name,Age,City Alice,30,New York Bob,25,London Charlie,35,Tokyo ``` Output: ```markdown | Name | Age | City | |---------|-----|----------| | Alice | 30 | New York | | Bob | 25 | London | | Charlie | 35 | Tokyo | ``` ## Alignment Options ### Left Align (Default) ```markdown | Name | Age | City | |:--------|:----|:---------| | Alice | 30 | New York | ``` ### Right Align ```markdown | Name | Age | City | |--------:|----:|---------:| | Alice | 30 | New York | ``` ### Center Align ```markdown | Name | Age | City | |:-------:|:---:|:--------:| | Alice | 30 | New York | ``` ## Common Operations ### Add a Row Insert at any position; default to end of table. ### Add a Column Specify column name and position; default to end. ### Remove a Row/Column Specify by index, header name, or content match. ### Sort Rows Sort by a specific column in ascending or descending order. ### Transpose Table Convert rows to columns and columns to rows. ## Best Practices 1. **Column Widths**: Always pad cells so pipes align vertically for readability 2. **Headers**: Keep headers descriptive and concise 3. **Empty Cells**: Use `&nbsp;` or leave empty for visual clarity 4. **Consistency**: Use the same alignment style throughout a document 5. **Readability**: Prefer left-aligned text, right-aligned numbers, centered short values ## Conversion to Other Formats When needed, convert Markdown tables to: - CSV - TSV - HTML `<table>` - JSON array of objects Always preserve data integrity during conversions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cenralsolution/mfa-word (version 1.1.0)

--- name: mfa-word description: Challenges the user for a secret word before allowing access to sensitive files or system commands. metadata: openclaw: category: security tags: [security, mfa, privacy, zero-trust, audit-logs] --- # MFA Word (Security Gatekeeper) ## Operational Protocol 1. **Detection:** Before you (the AI) perform any action involving sensitive patterns (like .env, .ssh, passwords, or deletions), you MUST call `check_gate_status`. 2. **Standard Mode:** If `check_gate_status` returns "OPEN", you may proceed. This session is valid for 15 minutes. 3. **Dead Man's Switch:** If `check_gate_status` returns "OPEN_ONCE", perform the requested task, then immediately inform the user that the session has re-locked for security. 4. **Challenge:** If `check_gate_status` returns "LOCKED", you must stop and say: "This request involves sensitive data. Please provide your Secret Word to continue." 5. **Validation:** Once the user provides a word, call `verify_access`. Only proceed if it returns "Access Granted." ## Tools ### initialize_mfa Sets up the security layer and user preferences. - `secret`: The primary secret word. - `super_secret`: The emergency reset word. - `sensitive_list`: Array of strings or patterns to protect (default: .env, password, config, sudo). - `use_dead_mans_switch`: Boolean. If true, the gate locks after every single sensitive action. ### verify_access Validates the secret word provided by the user. - `word`: The word provided by the user in chat. ### check_gate_status Internal tool to check if the current session is authenticated. ### reset_mfa Resets the secret word using the super secret word. - `super_word`: The emergency reset word. - `new_secret`: The new primary secret.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: shixiangyu2/prd-skill-workflow2 (version 1.0.0)

--- name: prd-fullstack version: 1.0.0 description: | 全栈PRD协作工作流。与用户共同探讨，产出可供开发、设计、测试、运营、项目经理使用的完整PRD文档。 协作流程共10步，输出PRD包含14个章节（项目概述、市场分析、需求列表、信息架构、用户流程、原型设计、UI规范、功能规格、数据模型、技术方案、非功能需求、测试方案、数据埋点、运营方案、项目计划）。 当用户说"帮我写PRD"、"做完整需求文档"、"产品需求文档"时触发。 --- # PRD FullStack：全栈PRD协作工作流 **核心理念**：你说想法，我帮你梳理成**专业、完整、可执行**的PRD文档。 ## 适用对象 - **产品经理**：系统梳理需求 - **开发团队**：技术方案、接口设计 - **设计师**：UI规范、交互原型 - **测试团队**：测试策略、验收标准 - **运营团队**：数据指标、运营策略 - **项目经理**：排期计划、风险管理 ## 完整PRD结构（14章） ### Part 1: 产品篇 - 01 项目概述（背景、目标、用户画像） - 02 市场分析（竞品分析、差异化） - 03 需求列表（功能清单、优先级） ### Part 2: 体验篇 - 04 信息架构（产品结构、页面层级） - 05 用户流程（核心流程、异常流程） - 06 原型设计（线框图、交互说明） - 07 UI设计规范（色彩、字体、组件） ### Part 3: 功能篇 - 08 功能规格（详细功能、业务规则） - 09 数据模型（实体关系、表结构） ### Part 4: 技术篇 - 10 技术方案（架构、选型、接口） - 11 非功能需求（性能、安全、兼容） ### Part 5: 质量篇 - 12 测试方案（测试策略、用例） - 13 数据埋点（指标体系、埋点） ### Part 6: 运营篇 - 14 运营方案（运营策略、推广计划） ### Part 7: 管理篇 - 15 项目计划（里程碑、排期、风险） ## 10步协作流程 ``` Step 1: 需求探索 → 理清产品想法 Step 2: 产品定位 → 确定类型、名称、平台 Step 3: 功能蓝图 → 梳理功能清单和优先级 Step 4: 市场分析 → 竞品分析、差异化定位 Step 5: 信息架构 → 产品结构、页面层级 Step 6: 原型+UI → 线框图、设计规范 Step 7: 功能+数据 → 功能规格、数据模型 Step 8: 技术方案 → 架构、接口、部署 Step 9: 测试+数据 → 测试用例、数据埋点 Step 10: 运营+计划 → 运营策略、项目排期 ``` ## 协作原则 1. **对话式**：每一步都通过对话确认，不是AI单向输出 2. **可视化**：用图表、表格展示关键信息 3. **可回退**：随时可以回到上一步修改 4. **专业级**：输出内容达到企业级PRD标准 ## 使用方式 ``` 用户：我想做一个在线教育平台 AI：好的！我们一起来做这份完整的PRD。 首先，能详细说说你的想法吗？ [经过10步协作...] AI：✅ PRD全栈文档完成！ 📄 prd-edu-platform-v1.0.0.pdf (180页) 🌐 prd-edu-platform-v1.0.0.html 📝 prd-edu-platform-v1.0.0.md 章节覆盖： ✅ 产品篇：项目背景、市场分析、需求列表 ✅ 体验篇：信息架构、流程图、原型、UI规范 ✅ 功能篇：功能规格、数据模型 ✅ 技术篇：架构设计、接口文档 ✅ 质量篇：测试方案、数据埋点 ✅ 运营篇：运营策略 ✅ 管理篇：项目计划 ``` ## 文件结构 ``` prd-skill-workflow/ ├── SKILL.md # 本文件 ├── COLLABORATION.md # 协作流程快速参考 ├── prompts/ # 10步协作Prompts │ ├── step1-explorer.md # 需求探索 │ ├── step2-positioning.md # 产品定位 │ ├── step3-blueprint.md # 功能蓝图 │ ├── step4-market.md # 市场分析 │ ├── step5-architecture.md # 信息架构 │ ├── step6-prototype.md # 原型+UI │ ├── step7-functional.md # 功能+数据 │ ├── step8-tech.md # 技术方案 │ ├── step9-testing.md # 测试+数据 │ ├── step10-operation.md # 运营+计划 │ └── iteration.md # 版本迭代管理 ├── templates/ # 输出模板 │ ├── build.js # HTML构建脚本 │ ├── build-pdf.js # PDF生成脚本 │ ├── update.js # 版本更新脚本 │ ├── styles.css # PRD样式表 │ └── fragments/ # 14个章节模板 ├── templates-config/ # 6种产品类型配置 │ ├── saas.json # SaaS/B端 │ ├── ecommerce.json # 电商 │ ├── education.json # 教育 │ ├── social.json # 社交 │ ├── content.json # 内容 │ └── tool.json # 工具 ├── checklists/ # 检查清单 │ └── prd-review-checklist.md # PRD审查清单 ├── shortcuts/ # 快捷模板 │ └── quick-templates.md # 常用功能模板 ├── examples/ # 示例项目 │ └── ledger-app/ # 简记账完整示例 ├── scripts/ # 工具脚本 │ └── validate.js # PRD验证脚本 └── references/ # 参考资料 └── design-system.md # 设计规范 ``` ## 输出规格 最终PRD约 **150-200页**，包含： - 30+ 张表格（需求清单、竞品对比、测试用例等） - 20+ 张流程图（Mermaid语法） - 15+ 个页面原型描述 - 完整的UI设计规范 - 详细的技术架构说明 - 可执行的测试方案 - 运营推广策略 - 项目里程碑规划


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