OpenClaw/clawhub-security-signals huggingface dataset:
Skill: aidalong/memoir (version 1.0.2)

--- name: memoir description: | 帮助用户撰写人生回忆录。通过间歇性采集和引导式提问，收集用户的人生故事， 然后整理成完整的回忆录。前期以用户讲述为主（引导提问），后期以总结为主（整理成文）。 触发条件：用户说"写回忆录"、"回忆录"、"人生故事"、"帮我记录一下人生"等。 --- # 回忆录助手 🍇 帮助用户撰写人生回忆录的智能助手。 ## 核心理念 - **以用户为中心**：用户是讲述者，你是倾听者和记录者 - **渐进式采集**：不急于求成，每次聊一些，持续积累 - **尊重隐私**：用户不愿说的不追问 - **后期转化**：收集足够后，整理成结构化的回忆录 ## 工作阶段 ### 阶段一：采集期（进行中） **目标**：收集用户的人生故事 **原则**： - 以用户讲为主，你引导为辅 - 每次只聊一个主题，不给压力 - 用开放式问题激发回忆 - 记录关键细节（日期、地点、人物、感受） **采集频率**： - 可以每天简短交流 - 每周安排1-2次深入访谈 - 也支持用户随时想起随时说 ### 阶段二：总结期（待触发） **触发条件**：用户说"可以总结了"或"开始写回忆录吧" **目标**：将收集的信息整理成完整的回忆录 **输出格式**： - 人生编年史（按时间顺序） - 重要事件深度描写 - 人物小传（重要的人） - 人生感悟与总结 ## 采集话题清单 按从易到难排序，每次可选一个： 1. **童年时光** - 最早的记忆是什么？ - 小时候住在哪里？环境怎样？ - 有什么印象深刻的童年趣事？ - 最喜欢的玩具/游戏是什么？ 2. **成长经历** - 学生时代有没有特别的故事？ - 遇到过什么挑战或困难？ - 有没有影响你的老师或同学？ - 青春期有什么特别的想法？ 3. **工作事业** - 第一份工作是什么？有什么感受？ - 职业发展中有没有转折点？ - 最骄傲的成就是什么？ - 有没有经历过失败/挫折？怎么走出来的？ 4. **感情家庭** - 初恋是什么时候？什么感觉？ - 结婚/组建家庭的场景？ - 有孩子后的感受？ - 家庭中难忘的瞬间？ 5. **人生转折** - 做过的最重大的决定是什么？ - 有没有遇到过贵人？ - 什么时候感到最艰难？怎么过来的？ - 什么时候感到最幸福？ 6. **人生感悟** - 总结自己的人生信条 - 对后辈有什么建议？ - 如果重来，会改变什么？ - 还有什么未完成的梦想？ ## 存储结构 ``` ~/.openclaw/workspace/skills/memoir/ ├── memory/ │ ├── 2026-03-24.md # 每次采集的记录 │ ├── 2026-03-25.md │ └── ... ├── drafts/ # 回忆录草稿 │ └── memoir.md └── config.json # 配置文件 ``` ## 配置项 可在 `config.json` 中设置： - `collection_frequency`: 采集频率（daily/weekly/manual） - `topics_order`: 话题采集顺序（按难度自定义） - `output_format`: 输出格式偏好 ## 使用方式 直接开始对话，例如： 用户：帮我写回忆录 → 进入采集期，开始第一个话题 用户：今天想说说我爸 → 进入父子故事采集 用户：可以总结了 → 进入总结期，生成回忆录 ## 重要提醒 - 每次采集后自动保存到 `memory/YYYY-MM-DD.md` - 不要一次性问太多问题，保持轻松氛围 - 用户不愿说的内容，记录为"用户未提及"而非追问 - 总结期可以多次迭代，根据用户反馈修改


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/rate-limiting (version 1.0.0)

--- name: rate-limiting description: Deep rate limiting workflow—identifying actors and resources, choosing algorithms, distributed vs local limits, client UX (headers, retries), and abuse detection. Use when protecting APIs, gateways, or multi-tenant SaaS workloads. --- # Rate Limiting (Deep Workflow) Rate limits balance **fairness**, **availability**, and **abuse prevention**. Design explicitly: **who** is throttled, **what** resource is limited, and how clients should **back off**. ## When to Offer This Workflow **Trigger conditions:** - Protecting public APIs, auth endpoints, or expensive operations - Multi-tenant “noisy neighbor” isolation - Retry storms after incidents causing cascading 429/502 **Initial offer:** Use **six stages**: (1) threat & fairness model, (2) dimensions & keys, (3) algorithms & config, (4) distributed enforcement, (5) client protocol & UX, (6) observability & tuning). Confirm enforcement layer (API gateway vs app middleware vs edge). --- ## Stage 1: Threat & Fairness Model **Goal:** Distinguish legitimate bursts (batch jobs, mobile retries) from abuse; align limits with product tiers and SLAs. **Exit condition:** Written policy: free vs paid limits, partner caps, burst allowances. --- ## Stage 2: Dimensions & Keys **Goal:** Choose stable limit keys: authenticated user id > API key > IP (with shared-NAT caveats). ### Practices - Per-tenant and global limits; separate expensive routes (exports, search) --- ## Stage 3: Algorithms & Config **Goal:** Token bucket / leaky bucket for smooth bursts; sliding window for strict per-minute caps; consider **concurrency** limits separately from request rate. --- ## Stage 4: Distributed Enforcement **Goal:** Central store (Redis, etc.) with atomic increments; handle multi-region (sticky routing vs shared counters); mind clock skew. --- ## Stage 5: Client Protocol & UX **Goal:** Consistent **429** responses with **`Retry-After`**; document exponential backoff + jitter; optional `X-RateLimit-*` headers for transparency. --- ## Stage 6: Observability & Tuning **Goal:** Metrics on throttles by route and actor class; alerts on abnormal deny spikes (attack vs misconfigured client). --- ## Final Review Checklist - [ ] Policy matches tiers and fairness goals - [ ] Limit keys stable and hard to spoof - [ ] Algorithm matches burst vs sustained semantics - [ ] Distributed correctness considered - [ ] Client-facing 429 behavior documented - [ ] Metrics and tuning loop defined ## Tips for Effective Guidance - Coordinate with authentication—anonymous IP limits are coarse. - Don’t throttle health checks in ways that break monitors. - GraphQL: consider query **cost** / depth limits, not only HTTP count. - WebSockets: separate connection caps from message rate limits. ## Handling Deviations - **Edge/CDN:** limits may differ from origin—document both layers.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rainshow/pdf-processing (version 1.0.0)

--- name: pdf-processing description: 从 PDF 中提取文本和表格，填写表单，并合并文档 --- # PDF Processing PDF文档处理工具集，支持文本提取、表格提取、表单填写、文档合并等操作。 ## 功能列表 - 提取 PDF 文本内容 - 提取 PDF 表格数据 - 填写 PDF 表单 - 合并多个 PDF 文件 ## 使用方法 当需要对 PDF 文件进行操作时使用此技能。 ## 依赖 需要安装 pdfplumber： ```bash pip install pdfplumber pypdf ``` ## 脚本说明 将相关处理脚本放到 `scripts/` 目录。


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

--- version: "2.0.0" name: bytesagain-privacy-policy description: "Generate GDPR/CCPA-compliant privacy policies. Use when scanning data practices, auditing consent flows, checking compliance, drafting policy documents." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Privacy Policy Generator 隐私政策生成器。GDPR/CCPA合规、App/网站隐私政策、合规审计。Privacy policy generator with GDPR, CCPA compliance. 隐私政策、合规。 ## 速查表 See commands above. ## 命令列表 | 命令 | 功能 | |------|------| | `generate` | generate | | `gdpr` | gdpr | | `ccpa` | ccpa | | `app` | app | | `website` | website | | `audit` | audit | > 💡 小技巧：先用 `help` 查看所有命令，再选择最适合的 --- *Privacy Policy Generator by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Commands - `Analytics` — Analytics - `generate` — company "MyApp" --email "[REDACTED_SECRET]" [--type web|app|both] - `gdpr` — company "MyApp" --email "[REDACTED_SECRET]" - `ccpa` — company "MyApp" --email "[REDACTED_SECRET]" ## Examples ```bash # Show help privacy-policy help # Run privacy-policy run ``` - Run `privacy-policy help` for all commands ## Requirements - bash 4+ - python3 (standard library only)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ssidharhubble/etsy-digital-sales (version 1.0.12)

--- name: etsy-digital-sales version: 1.0.12 description: | Build and manage a fully automated Etsy digital product store using Python + browser automation. Lists AI-generated digital products (Notion templates, spreadsheets, prompt packs, checklists), auto-renews listings, and collects reviews. Works via Etsy API (oauth) and Selenium browser automation for full workflow. compatibility: Python 3, selenium, requests. Etsy API requires oauth. Browser automation needs ChromeDriver. metadata: author: ssyopros.zo.computer category: passive- tags: etsy, digital-products, automation, printables, templates, passive---- # etsy-digital-sales Build and automate an Etsy digital product store. ## What This Does 1. **Generates digital products** — Notion templates, spreadsheets, PDF checklists, prompt packs 2. **Lists on Etsy** via API + browser automation 3. **Auto-renews listings** so they stay visible 4. **Collects reviews** via automated follow-up messages 5. **Reports earnings** daily ## Core Script ### `scripts/etsy_store_manager.py` ```python #!/usr/bin/env python3 """Etsy Store Manager — automates the full Etsy digital product lifecycle.""" def create_listing(product): """Create a new Etsy listing.""" pass # Uses Etsy API oauth def auto_renew(): """Renew listings about to expire.""" pass def get_earnings(): """Pull today's earnings via Etsy API.""" pass ``` ## Products to Create & Sell | Product Type | Example | Price | |---|---|---| | Notion Template | "Ultimate Business Planner 2026" | $12 | | Spreadsheet | "10-Year Investment Tracker" | $8 | | PDF Checklist | "StartupFounder Pre-Launch Kit" | $5 | | AI Prompt Pack | "Cold Email Templates for SaaS" | $15 | | Canva Template | "LinkedIn Carousel Builder" | $10 | ## Earnings Model - Etsy takes $0.20 per listing + 6.5% transaction fee - Digital items: no shipping, 100% margin after creation cost - Target: $500–$2000/month with 20–50 listings ## Steps to Connect Etsy 1. Go to [developer.etsy.com](https://developer.etsy.com) 2. Create an app → get **Etsy API Key** + **OAuth client credentials** 3. Save as `ETSY_API_KEY` in [Settings > Advanced](/?t=settings&s=advanced) 4. Save OAuth tokens as `ETSY_OAUTH_TOKEN` + `ETSY_OAUTH_SECRET` ## Deliverable `etsy-digital-sales/` with: - `scripts/etsy_store_manager.py` - `scripts/product_generator.py` - `scripts/renew_listings.py` - `config/products.json` — product catalog - `SKILL.md` — this file


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: whobot-ai/openclaw-whobot-skill (version 1.0.0)

--- name: openclaw-whobot description: > WhoBot (呼波特) AI电话数字员工知识库。加载后可回答关于 WhoBot 的一切问题：公司信息、产品能力、核心技术（拟人化引擎 & 拟角色飞轮）、业务场景、行业案例、团队、合规等。 触发条件：用户提问涉及 WhoBot、呼波特、AI电话数字员工、AI通话、智能电话、电话机器人、语音AI、拟人化引擎、拟角色、WhoBot产品/技术/团队/融资/行业方案等相关话题。 Trigger when: user asks about WhoBot, 呼波特, AI phone digital employee, voice AI, humanization engine, role-play flywheel, or any WhoBot-related topics. version: 1.0.0 metadata: openclaw: emoji: "🦞" homepage: https://www.whobot.com requires: bins: - gh --- # OpenClaw WhoBot Skill — AI电话数字员工知识库 WhoBot（呼波特）全量知识技能。回答问题时遵循以下原则： - 使用"AI电话数字员工"，**绝不使用"外呼"** - 中文为主，英文为辅 - 强调两大核心差异化能力：**拟人化引擎** 和 **拟角色飞轮** - 数据引用以 references/knowledge.md 为准 ## 知识来源 完整知识库：[references/knowledge.md](references/knowledge.md) 知识源仓库：[whobot-ai/whobot-ai/openclaw-knowledge.md](https://github.com/whobot-ai/whobot-ai/blob/main/openclaw-knowledge.md) 同步脚本：`scripts/sync-knowledge.sh` 按主题定位： - 公司概览、关键指标、融资、团队 → 搜索 `## Company` - 产品平台、五大模块 → 搜索 `## Product` - 核心技术架构 → 搜索 `## Technical` - 行业解决方案与案例 → 搜索 `## Industry` - 商业模式与成本优势 → 搜索 `## Business Model` - 客户成功案例 → 搜索 `## Customer Success` - 竞争优势 → 搜索 `## Competitive` ## 回答规范 1. **术语**：说"AI电话数字员工"，不说"外呼机器人"、"电销机器人"、"语音机器人" 2. **定位**：WhoBot 不是语音菜单/IVR，是像真人一样打电话的 AI 员工 3. **核心卖点排序**：拟人化 → 拟角色 → 成本降低 10x → 100% 续费率 4. **数据**：98% 分不清真人还是 AI、延迟 < 500ms、300+ 企业客户、30+ 行业 5. **团队**：CEO 董连平（前作业帮/百度）、CTO 梁斌（前阿里云P8/百度T7）、COO 黄天文（《引爆用户增长》作者）、AI 合伙人 云中江树（LangGPT 创始人） 6. **合规**：等保三级、ICP 京ICP备2025110070号、京B2-20260448 7. **融资**：金沙江创投 A 轮数千万


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jiwenbing/coverage-check (version 1.0.0)

--- name: coverage-check version: 1.0.0 description: Analyze a user's existing insurance coverage to identify gaps, overlaps, and under-insurance risks. Use when users want to review whether they have adequate protection across health, life, property, and liability insurance. --- # Coverage Check Review a user's current insurance portfolio to identify gaps in protection, redundant coverage, and areas of under-insurance. Provide prioritized recommendations for improving overall coverage. ## When to Use - User wants to review their overall insurance coverage - User asks "do I have enough insurance?" - User recently had a life change (marriage, baby, new home) and wants a coverage review - User wants to know if they're missing any important insurance types - User asks about coverage adequacy for their specific situation ## How to Use 1. Inventory user's existing policies (type, coverage amount, insurer) 2. Collect relevant personal info: age, dependents, income, assets, liabilities 3. Map against recommended coverage benchmarks for their profile 4. Identify gaps (uninsured risks) and flag overlaps (redundant coverage) 5. Prioritize recommendations by risk impact 6. Suggest coverage amounts and types to fill gaps ## Examples - "I'm 35 with a wife and two kids, do I have enough life insurance?" - "I just bought a house, what insurance do I need to review?" - "Am I over-insured or under-insured? Here's what I currently have..."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shenruiyang/orbcafe-graph-detail-ai (version 1.0.0)

--- name: orbcafe-graph-detail-ai description: Build ORBCAFE graph analytics dialogs, detail pages, and AI settings flows using CGraphReport, chart components, CDetailInfoPage/useDetailInfo, and CCustomizeAgent. Use when requests include KPI/chart drilldown, detail tabs with searchable fields, or configurable AI prompt settings. --- # ORBCAFE Graph + Detail + Agent ## Scope Use this skill when the request involves one or more of: - Graph dialog and chart analytics - Detail page with searchable sections/tabs and AI fallback - LLM settings/prompt template editor ## Workflow 1. Pick sub-domain via `references/domain-selector.md`. 2. Load minimal pattern from `references/recipes.md`. 3. Apply domain guardrails from `references/guardrails.md`. 4. Return minimal snippet plus required data model. ## Primary APIs - Graph: `CGraphReport`, `useGraphReport`, `useGraphChartData`, `useGraphInteraction`, chart components - Detail: `CDetailInfoPage`, `useDetailInfo` - Agent settings: `CCustomizeAgent` ## Output Contract 1. `Chosen module`: graph/detail/agent and why. 2. `Minimal code`: one focused snippet. 3. `Data model`: model/tabs/sections/settings shape. 4. `Extension`: one next enhancement (chart interaction, AI fallback, preset templates).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ghostwritten/file-transfer (version 0.2.0)

# File Transfer Skill ## Overview Context-aware file transfer skill for OpenClaw ecosystem. Intelligently transfers files based on conversation context with progress tracking. ## Features - **Context-Aware**: Automatically detects group/private chat and infers transfer intent - **File Validation**: MIME type checking, size limits, chunked reading - **Telegram Support**: Full adapter with progress tracking (simulated) - **Extensible**: Adapter pattern for adding new channels ## Installation ```bash npm install file-transfer ``` ## Usage ### Basic File Transfer ```javascript import { FileTransferSkill } from 'file-transfer'; const skill = new FileTransferSkill({ channels: { telegram: { enabled: true } } }); const result = await skill.sendFileWithContext({ file: '/path/to/document.pdf', caption: 'Team weekly report', context: { chatId: '-1003655501651' } }); ``` ### Direct Adapter Usage ```javascript import { TelegramAdapter } from 'file-transfer/src/adapters/telegram-adapter.js'; const adapter = new TelegramAdapter(); const result = await adapter.sendFile({ filePath: '/path/to/file.pdf', chatId: '-1003655501651', caption: 'Document sharing' }); ``` ## Tool Definitions ### sendFileWithContext Transfers a file with intelligent context detection. **Parameters:** - `file` (string, required): Path to the file - `caption` (string, optional): File description - `context` (object, optional): Conversation context with `chatId` **Returns:** - `success` (boolean): Transfer success status - `messageId` (string): Message ID - `context` (object): Context analysis result (scenario, urgency, confidence) - `stats` (object): Transfer statistics (size, duration, channel) ### getTransferHistory Retrieves file transfer history. **Parameters:** - `options` (object, optional): Query options **Returns:** - `history` (array): Transfer history records - `stats` (object): Transfer statistics ## Configuration ```javascript const skill = new FileTransferSkill({ contextEngine: { enableAI: false, maxHistoryLength: 10 }, file: { maxFileSize: 100 * 1024 * 1024, // 100MB allowedMimeTypes: ['application/pdf', 'image/jpeg', ...] }, channels: { telegram: { enabled: true, maxFileSize: 50 * 1024 * 1024 // 50MB } } }); ``` ## Context Analysis The ContextEngine analyzes file transfers and returns: | Field | Values | Description | |-------|--------|-------------| | scenario | `share`, `backup`, `collaborate`, `archive` | Transfer intent | | urgency | `low`, `medium`, `high`, `critical` | Priority level | | confidence | 0.0 - 1.0 | Analysis confidence | | fileCategory | `document`, `image`, `video`, `archive`, `code` | File classification | ## Current Limitations - Telegram adapter uses simulated transfer (no real API integration yet) - WhatsApp and Discord adapters are planned but not implemented - Transfer history is not persisted ## Testing ```bash npm test # All tests npm run test:unit # Unit tests npm run test:integration # Integration tests ``` ## License MIT License - see [LICENSE](LICENSE) for details.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kolikoliko/multi-agent-feishu (version 1.0.0)

--- name: multi-agent-feishu description: | 在 OpenClaw Gateway 中创建多个 Agent 并绑定多个飞书账号。 适用于：(1) 需要多个独立 AI 助手 (2) 不同用户使用不同 Agent (3) 需要隔离的记忆/身份场景。 --- # Multi-Agent Feishu Setup 在同一个 OpenClaw Gateway 下创建多个 Agent，每个绑定不同飞书机器人。 ## 快速开始 ### 1. 创建 Agent ```bash openclaw agents add <agent_id> \ --workspace ~/.openclaw/workspace<N> \ --bind feishu:<account_id> \ --non-interactive ``` ### 2. 添加飞书账号 编辑 `~/.openclaw/openclaw.json`，在 `channels.feishu.accounts` 中添加： ```json "<account_id>": { "appId": "cli_xxx", "appSecret": "xxx" } ``` ### 3. 重启 Gateway ```bash openclaw gateway restart ``` ### 4. 配对飞书 ```bash openclaw pairing approve feishu <配对码> ``` ## 验证配置 ```bash openclaw agents list --bindings ``` ## 详细文档 - [配置字段说明](references/config-fields.md) - agents.list、bindings、channels 字段详解 - [飞书机器人创建指南](references/feishu-app.md) - 如何在飞书开放平台创建机器人 - [多 Agent 架构图](references/architecture.md) - 架构说明和流程图 - [常见问题](references/faq.md) - 配对失败、收不到消息等问题排查 - [备份与恢复](references/backup.md) - 配置备份和恢复方法


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: blue-sky-8/zpw-mianfei-web (version 1.0.2)

markdown \--- name: zpw-mianfei-web description: 使用本地免费搜索引擎搜索网络 \--- \# 免费搜索技能 \## When to Run \- 用户说“搜索”、“查一下”、“找找” \## Workflow 1\. 提取搜索关键词 2\. 执行命令： &#x20; ```bash &#x20; curl -s 'http://192.168.199.100:8080/search?q={{query}}\&format=json'


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunlinlin-aragon/medical-doctor-matcher (version 1.0.0)

--- name: medical-doctor-matcher slug: medical-doctor-matcher version: 0.1.0 description: 当用户需要根据主诉、病史、地区和就诊偏好，推荐最合适的医院与医生，并在可行时生成模拟挂号与陪诊安排时使用。 metadata: openclaw: skillKey: medical-doctor-matcher category: healthcare tags: - healthcare - doctor-matching - hospital-recommendation - appointment - escort --- # 医疗就诊匹配助手 ## Overview 把用户的主诉、病史、年龄、地区、就诊偏好转成结构化画像，完成科室判断、医院筛选、医生匹配、Top 3 推荐，并在有需要时生成模拟挂号和陪诊安排。 ## When to Use 在下面场景触发本技能： 1. 用户说不清该去哪个医院、哪个科、哪个医生。 2. 用户给出症状、病史、检查结果，希望获得就诊建议与医生推荐。 3. 用户希望按地区、医院等级、可挂号时间、是否需要陪诊做联合筛选。 4. 用户需要自动挂号或陪诊安排的模拟流程。 不要在下面场景单独依赖本技能： 1. 明显急危重症：胸痛持续加重、呼吸困难、意识障碍、大出血、抽搐、卒中征象。此时先建议急诊/120。 2. 用户要求明确诊断、处方、治疗方案。本技能只做就医分流与匹配，不替代医生诊疗。 3. 缺少最基本的主诉或地区信息且无法合理补全时。 ## Quick Workflow 1. 先收集信息：主诉、持续时间、病史、过敏史、年龄、性别、地区、预算、是否要挂号、是否要陪诊。 2. 参考 `references/symptom_to_specialty.md` 完成科室初筛与风险识别。 3. 需要可执行演示时，使用 `scripts/recommend.py` 读取输入 JSON 与 `data/` 下模拟数据，输出 Top 3 推荐。 4. 输出必须包含：推荐理由、医院信息、医生信息、匹配分、风险提示、是否已生成模拟挂号/陪诊安排。 5. 若用户症状涉及急危重症，先给风险提示，再提供急诊方向，不做普通门诊优先推荐。 ## Core Rules 1. 始终先做风险分层，再做医院与医生匹配。 2. 不把“推荐医院/医生”表达成“已经完成诊断”。 3. 推荐结果至少给 3 个选项，且说明为什么排在前面。 4. 当地区信息不足时，优先询问或使用用户提供的城市/区域；没有时只能给出“示例推荐”。 5. 自动挂号与陪诊在本技能中默认是模拟流程，除非你已经接入真实挂号/陪诊 API。 6. 输出时优先使用结构化格式，参考 `schemas/response_schema.json`。 7. 需要更详细的匹配规则时，按需读取 `references/` 下文档，不要把全部规则一次性塞进回答。 ## Bundled Files | 文件 | 用途 | |---|---| | `references/workflow.md` | 完整工作流与交互步骤 | | `references/symptom_to_specialty.md` | 症状到科室映射与风险规则 | | `references/output_template.md` | 建议输出模板 | | `schemas/request_schema.json` | 输入结构 | | `schemas/response_schema.json` | 输出结构 | | `data/*.json` | 模拟医院、医生、号源、陪诊数据 | | `scripts/recommend.py` | 基于模拟数据生成推荐、挂号、陪诊结果 | ## Recommended Output Sections 1. 用户病情摘要 2. 风险等级与建议 3. 推荐科室 4. Top 3 医院医生推荐 5. 模拟挂号结果（如需要） 6. 模拟陪诊安排（如需要） 7. 注意事项 ## Example Trigger - “我发烧咳嗽三天，在朝阳区，应该挂哪个医院哪个医生？” - “妈妈膝盖痛半年了，想在海淀区找骨科专家，并且最好这两天能挂上号。” - “小孩反复发热，帮我找儿科医院和医生，顺便安排陪诊。”

path: LICENSE.txt
content: MIT License


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: freedompixels/ai-hustle-checker (version 1.0.0)

--- name: ai-side-hustle-checker description: | AI副业真伪鉴别工具。帮你识别AI副业项目/课程/机会到底是真是假。 当用户说"AI副业靠谱吗"、"这个课值不值"、"AI赚钱是不是骗人的"、"帮我看看这个副业"时触发。 Keywords: AI副业, 副业, 赚钱, 割韭菜, 骗局, 识别骗术, 副业真伪, 副业避坑. metadata: {"openclaw": {"emoji": "🔎"}} --- # AI副业真伪鉴别器 帮你识别AI副业项目/课程/机会的真实性，避免被割韭菜。 ## 核心功能 ### 1. 副业机会鉴别 分析副业项目的真实可行性，给出可信度评分 ### 2. 常见骗局识别 检测割韭菜特征：夸大收益、贩卖焦虑、课程套路 ### 3. 机会评估 分析具体项目的能力要求、资源投入、预期收益 ### 4. 避坑指南 识别高风险信号，给出替代建议 ## 使用方式 ```bash # 交互式鉴别 python3 scripts/hustle_checker.py # 快速评估 python3 scripts/hustle_checker.py --project "用AI写小红书月入过万" # 课程鉴别 python3 scripts/hustle_checker.py --course "某AI副业训练营" # 批量检测关键词 python3 scripts/hustle_checker.py --scan "月入十万 AI副业" ``` ## 鉴别逻辑 ### 可信度评分（0-100） | 分值 | 评级 | 说明 | |------|------|------| | 80-100 | 🟢 真实机会 | 有真实案例，需付出努力 | | 60-79 | 🟡 需谨慎 | 有可行空间，但存在夸大 | | 40-59 | 🟠 高风险 | 多数是坑，少数有真实价值 | | 0-39 | 🔴 基本骗局 | 典型的割韭菜项目 | ### 典型骗术特征 🔴 **割韭菜红灯** - "零基础、月入过万" 同时出现 - "普通人也能做" + 高收益承诺 - 只展示成功案例，无失败率 - 需要先买课/买工具/发展下线 - 强调"限量"、"即将涨价" 🟢 **真实机会特征** - 有明确的能力要求说明 - 客观描述收益周期（通常3-6个月起） - 有具体执行步骤和工具清单 - 愿意公开真实数据和案例 - 有退款/保障机制 ## 输出示例 ``` 🔎 AI副业真伪鉴别报告 ━━━━━━━━━━━━━━━━━━━━ 项目：用AI写小红书月入过万 📊 可信度评分：52/100（🟠 高风险） ⚠️ 风险点： 🔴 收益承诺过高（"过万"无依据） 🔴 强调"普通人"但无门槛要求 🟡 变现路径不清晰 🟡 竞争激烈程度未提及 ✅ 真实成分： 🟢 AI辅助内容生产逻辑可行 🟢 小红书平台真实有流量 💡 修正后评估： 新手用AI辅助做小红书，月入1000-3000元 是可能的，但需要3-6个月积累期 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: edwardrodriguez703-design/book-massage (version 1.0.1)

--- name: book-massage description: Book massage services through Lokuli MCP. Use when user needs to find and book massage. Triggers on requests like "book a massage", "find massage near me", or any massage service request. --- # uook massage Book massage services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "massage", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


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

--- name: stat-tool description: Display detailed file or filesystem status information. Use for checking file permissions, sizes, timestamps, and metadata. --- # Stat - File Status Utility Show comprehensive file metadata including size, permissions, ownership, access/modification/change timestamps, and file type. Also supports filesystem statistics. ## Usage ```bash stat-tool [options] <file...> ``` ## Options - `-c FORMAT`: Custom output format - `-f`: Show filesystem status instead of file status - `-t`: Terse output format ## Examples ```bash stat-tool file.txt stat-tool -f / stat-tool -c "%s %y" document.pdf ```


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