OpenClaw/clawhub-security-signals huggingface dataset:
Skill: b-gutman/pipeworx-chargebee (version 1.0.0)

# Chargebee Chargebee MCP Pack — wraps the Chargebee API v2 ## chargebee_list_subscriptions List all subscriptions with optional filtering by status (e.g., 'active', 'cancelled'). Returns subs ## chargebee_get_subscription Get full subscription details by ID. Returns plan, status, billing dates, customer info, and all cha ## chargebee_list_customers List all customers with pagination. Returns customer IDs, names, emails, billing addresses, and crea ## chargebee_get_customer Get complete customer profile by ID. Returns name, email, address, payment methods, subscription cou ## chargebee_list_invoices List invoices filtered by status (e.g., 'paid', 'pending') and/or customer ID. Returns invoice numbe ```json { "mcpServers": { "chargebee": { "url": "https://gateway.pipeworx.io/chargebee/mcp" } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/nda-generator (version 2.0.0)

--- version: "2.0.0" name: NDA Generator description: "NDA Generator - 保密协议生成器. Use when you need nda generator capabilities. Triggers on: nda generator." 保密协议生成器。双向/单向NDA、员工保密、审查、模板库。NDA generator with mutual, unilateral, employee versions. 保密协议、NDA。 author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # NDA Generator 保密协议生成器。双向/单向NDA、员工保密、审查、模板库。NDA generator with mutual, unilateral, employee versions. 保密协议、NDA。 ## 推荐工作流 ``` 需求分析 → 选择命令 → 输入描述 → 获取结果 → 调整优化 ``` ## 可用命令 - **create** — create - **mutual** — mutual - **unilateral** — unilateral - **employee** — employee - **review** — review - **template** — template --- *NDA Generator by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com - Run `nda-generator help` for all commands ## Commands Run `nda-generator help` to see all available commands. ## When to Use - when you need quick nda generator from the command line - to automate nda tasks in your workflow ## Output Returns formatted output to stdout. Redirect to a file with `nda-generator run > output.txt`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nexscope/share-of-shelf (version 1.0.0)

--- nexscope: name: "Share of Shelf" category: "Monitoring & Alerts" version: "1.0.0" author: "Nexscope AI" tags: - "digital-shelf" - "visibility" - "category-analysis" - "monitoring" model: "any" tokens: "~2000" keywords: - "share of shelf" --- # Share of Shelf AI-powered digital share of shelf analysis skill. Measures brand visibility within product categories, benchmarks against competitors, and tracks shelf position trends across e-commerce platforms. ## Capabilities - Generates actionable monitoring & alerts frameworks based on your specific business context - Works across major e-commerce platforms (Amazon, Shopify, Walmart, WooCommerce, Etsy, TikTok Shop) - Provides data-driven recommendations with industry benchmarks - Outputs ready-to-implement plans, not just generic advice ## Install ``` clawhub install share-of-shelf ``` ## Usage **Input:** Brand name, product category, target marketplace(s), competitor brands **Output:** Share of shelf score, category positioning map, competitor visibility comparison, trend tracking framework ### Example Prompt > "I run a [your business type] on [platform]. Help me set up share of shelf for my business. Here's my current situation: [describe context]." ## Limitations - Requires your specific business data for accurate recommendations - Market benchmarks are based on US/EU data — adjust for other regions - Recommendations should be validated against your platform's current policies - Does not replace dedicated monitoring SaaS tools — designs the strategy and framework --- *Built by [Nexscope AI](https://www.nexscope.ai/) — AI-powered e-commerce intelligence.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: smart-money-tracker (version 1.0.1)

--- name: smart-money-tracker description: 聪明钱追踪器 - 追踪专业机构/大户链上操作，复制交易信号，DeFi 巨鲸动向分析。每次调用自动扣费 0.001 USDT version: 1.0.0 author: moson tags: - smart-money - tracker - defi - whale triggers: - "聪明钱" - "smart money" - "机构" - "巨鲸" price: 0.001 USDT per call --- # Smart Money Tracker - 聪明钱追踪器 ## 功能 ### 1. 机构追踪 - 追踪知名机构地址 - a16z, Paradigm, Three Arrows - 实时持仓变化 ### 2. 复制交易 - 跟单信号 - 建仓/清仓通知 - 盈亏追踪 ### 3. 链上分析 - 大额转账监控 - 代币流向分析 - gas 费用分析 ## 使用示例 ```javascript { action: "track", address: "0x..." } { action: "signals", token: "ETH" } { action: "portfolio", address: "0x..." } ``` ## 风险提示 - 跟单有风险 - 仅供参考


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: zhouzhi-tal/xueersi-idiom-chain (version 1.0.1)

--- name: xueersi-idiom-chain description: "Xueersi Idiom Chain Game: Interactive Chinese chengyu (idiom) chain game with explanations, difficulty levels, and vocabulary learning. Great for K-12 Chinese language learning. 学而思(Xueersi) 成语接龙互动游戏 — 顺便学成语。By Xueersi-AI." --- # Xueersi Idiom Chain Game · 学而思成语接龙助手 > By **Xueersi (学而思)** · AI Education Tools ## Game Rules Chain idioms using the **last character** of the previous idiom as the **first character** of the next. **Difficulty levels:** - 🟢 Easy: Homophones allowed (e.g. 马到成功 → 功夫不负有心人) - 🔵 Standard: Same character required (e.g. 马到成功 → 功成名就) - 🔴 Hard: Same character AND same tone required ## Starting the Game When user says "start idiom chain" or "let's play chengyu": 1. Ask for difficulty (default: Standard) 2. AI opens with a common, accessible idiom 3. Invite the user to chain ## During Play Each turn the AI must: 1. **Validate the user's idiom**: is it a real idiom? does the first character match? - ❌ If invalid: gently explain why and ask to try again 2. **On success**: give a one-line meaning + optional example sentence 3. **AI's turn**: chain the next idiom with its meaning 4. **Dead-end idioms**: honestly admit and swap, or let user swap ## Output Format ``` User: 一鸣惊人 ✅ Valid! "一鸣惊人": Once quiet, now stunning — describes someone who amazes everyone with a sudden achievement. 👉 AI plays: 人山人海 "人山人海": A sea of people — describes a massive, lively crowd. Your turn — find an idiom starting with 海~ ``` ## Learning Features - `"Explain [idiom]"` → origin story + meaning + usage + example - `"Find idioms with [character]"` → list common idioms containing that character - `"Is [idiom] positive or negative?"` → sentiment analysis ## Score Summary At game end: > Completed 12 rounds. You contributed 6, including the tricky "鹤立鸡群"! 🏆 Chengyu Master!


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: daniellummis/prisma-migrate-guard (version 1.0.0)

--- name: prisma-migrate-guard description: Preflight Prisma migration state before deploy; fails fast on drift, failed migrations, missing DB URLs, or unapplied migration files. version: 1.0.0 metadata: {"openclaw":{"requires":{"bins":["bash","node","npx"]}}} --- # Prisma Migrate Guard Use this skill before deploys or CI release steps to verify Prisma migrations are healthy and ready to apply. ## What this skill does - Verifies required Prisma CLI/runtime tools are available - Validates migration DB URL inputs (`DATABASE_URL` by default) - Runs `prisma migrate status` against a target schema - Fails on common dangerous states: - failed migrations - migration drift warnings - unapplied migration files - missing migration history table hints - Exits non-zero for CI/deploy gating ## Inputs - Optional env vars: - `PRISMA_SCHEMA_PATH` (default: `prisma/schema.prisma`) - `PRISMA_MIGRATE_DB_URL_ENV` (default: `DATABASE_URL`) - [REDACTED_SECRET] (`1` to warn instead of fail) - [REDACTED_SECRET] (`1` to warn instead of fail) ## Run ```bash bash scripts/check-prisma-migrate.sh ``` With explicit schema and env key: ```bash PRISMA_SCHEMA_PATH=apps/api/prisma/schema.prisma \ PRISMA_MIGRATE_DB_URL_ENV=POSTGRES_PRISMA_URL \ bash scripts/check-prisma-migrate.sh ``` ## Output contract - Prints a concise PASS/FAIL report - Exit code `0` on healthy status - Exit code `1` on blocking migration issues ## Notes - This guard is read-only (`migrate status`), it does not apply migrations. - Keep it in CI before deploy or startup migration steps.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: maxcoder11/image-watermark-remover (version 1.0.0)

--- name: image watermark remover slug: image-watermark-remover version: 1.0.0 description: Remove image watermarks with the Nowatermark.info API, request polling, request_id resume, and public image URL validation. homepage: https://nowatermark.info metadata: {"openclaw":{"homepage":"https://nowatermark.info","requires":{"env":["NOWATERMARK_API_KEY"]},"primaryEnv":"NOWATERMARK_API_KEY"}} --- # Image Watermark Remover Remove watermarks from public image URLs through the Nowatermark.info API. ## When to Use Use this skill when the user wants to remove a watermark from an image they are authorized to edit, resume an existing `request_id`, or check the status of a prior cleanup job. ## Quick Reference | Topic | File | |-------|------| | API key setup, input rules, and commands | `setup.md` | | Endpoints, payloads, responses, and error codes | `references/api.md` | | Submit-and-poll helper script | `scripts/remove_watermark.py` | ## External Endpoints | Method | Endpoint | Purpose | Data sent | |--------|----------|---------|-----------| | `POST` | `https://nowatermark.info/api/image/remove-watermark` | Submit watermark-removal job | `file_url` | | `POST` | `https://nowatermark.info/api/jobs/query` | Query job status | `request_id` | ## Data Storage Do not create, download, or overwrite local files unless the user asks first. Ask the user before saving API responses or downloading the cleaned image. ## Security & Privacy - Ask the user to confirm they are allowed to edit the image when ownership or permission is unclear. - Tell the user that the public image URL is sent to `nowatermark.info` for processing. - Request the API key through environment or skill settings; do not expose `NOWATERMARK_API_KEY` in logs or replies. - Prefer public image URLs over local file handling to minimize local data movement. ## Core Rules 1. If ownership or permission is unclear, ask the user to confirm they are allowed to edit the image before sending requests. 2. Verify `NOWATERMARK_API_KEY` is available before sending requests. 3. Require a direct public image URL; reject local paths, page URLs, and expiring signed URLs. 4. Prefer `scripts/remove_watermark.py` for submit-plus-poll workflows. 5. Poll every 3-5 seconds until `completed` or `failed`, then return the final `url` or the failure details. 6. Reuse a supplied `request_id` for status checks instead of creating a duplicate job.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: evezart/evez-skill-vetter (version 1.0.0)

--- name: evez-skill-vetter description: Security review for OpenClaw skills before installation. Use when evaluating third-party skills for safety, checking permission scope, detecting suspicious patterns, identifying data exfiltration risks, or auditing skill code. Covers static analysis, permission auditing, dependency scanning, and risk scoring. --- # Skill Vetter Review third-party skills before installing them. Catch security risks early. ## Quick Start ```bash python3 scripts/vet.py --skill /path/to/skill python3 scripts/vet.py --slug some-skill # vet a ClawHub skill ``` ## What It Checks 1. **Permission scope** — Does the skill request exec, network, or file access? 2. **Suspicious patterns** — eval(), exec(), subprocess, fetch to unknown hosts, encoded strings 3. **Data exfiltration** — Sending data to external endpoints, logging secrets 4. **Dependency risks** — Known vulnerable packages, excessive dependencies 5. **Code quality** — Minified/obfuscated code, missing SKILL.md, oversized files 6. **Secret exposure** — Hardcoded API keys, tokens, passwords in source ## Risk Score Each check produces a risk score 0-100: - **0-20**: ✅ Safe — install freely - **21-50**: ⚠️ Caution — review findings before installing - **51-75**: 🚨 Risky — significant security concerns - **76-100**: ❌ Dangerous — do not install ## Output ``` SKILL: some-skill RISK: 35/100 (Caution) FINDINGS: ⚠️ Uses subprocess.call() in scripts/run.sh:3 ⚠️ Fetches from https://unknown-api.com in scripts/pull.py:12 ✅ No hardcoded secrets found ✅ SKILL.md present and valid ```


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kreator666/exam-paper (version 1.0.1)

--- name: exam-paper description: > 【考试试卷生成器 | 题库管理 | 错题本】输入学科和题目数量，自动从题库随机抽题，生成考卷PDF和答案PDF两份文件。支持单选题、判断题、编程填空题。内置题库增删查、错题记录与复习标记功能。当前内置信息学奥赛22题，支持扩展其它学科。 触发词：出卷、组卷、考试卷、试卷、生成试卷、生成考试、题库、错题本、错题库、错题记录、信息学奥赛、信息学竞赛、考试卷生成、考试题库、exam paper、generate exam、question bank、error tracking、错题复习、考前练习、自动组卷、考试PDF。 当用户需要生成/导出考试试卷PDF、管理题目库、查看/记录错题时使用此技能。 --- # Exam Paper Generator - 考试试卷生成器 ## 功能概览 | 功能 | 触发场景 | |------|---------| | 自动组卷生成PDF | "出一套信息学奥赛试卷" / "生成考试卷" | | 题库管理 | "添加题目" / "查看题库" / "加题" | | 错题追踪 | "记一下错题" / "查看错题" / "复习错题" | | 扩展学科 | 新学科题库JSON，支持数学、英语等 | ## 数据目录 所有题库和错题数据存储在 `~/.qclaw/workspace/exam-data/`： - `questions/` — 题库JSON文件（按学科命名，如 `informatics.json`） - `errors/` — 错题记录JSON文件（如 `informatics_errors.json`） ## 工作流程 ### 1. 生成试卷PDF 用户说"出一套信息学奥赛试卷"或"组卷"时： 1. 确认参数：学科（默认informatics）、题目数量、难度、知识点范围 2. 运行导出命令生成exam JSON： ``` python -X utf8 <skill_dir>/scripts/manage_bank.py export --subject informatics --count 15 --outdir ~/.qclaw/workspace/exam-data ``` 3. 运行PDF生成： ``` python -X utf8 <skill_dir>/scripts/gen_pdf.py <exam.json> --outdir ~/Desktop ``` 4. 确认输出两个PDF：试卷 + 答案 ### 2. 管理题库 - **初始化**：`python -X utf8 <skill_dir>/scripts/manage_bank.py init` - **查看题库**：`python -X utf8 <skill_dir>/scripts/manage_bank.py list --subject informatics` - **添加题目**：准备符合格式的JSON文件，然后： `python -X utf8 <skill_dir>/scripts/manage_bank.py add <questions.json>` - **随机选题**：`python -X utf8 <skill_dir>/scripts/manage_bank.py random --subject informatics --count 10` ### 3. 错题管理 - **记录错题**：`python -X utf8 <skill_dir>/scripts/manage_bank.py error-add <qid> <student_answer>` - **查看错题**：`python -X utf8 <skill_dir>/scripts/manage_bank.py error-list --subject informatics` - **标记已复习**：`python -X utf8 <skill_dir>/scripts/manage_bank.py error-mark <qid>` ## 添加新题目 题目JSON格式（添加到题库）： ```json { "subject": "informatics", "questions": [ { "id": "info_016", "type": "choice", "topic": "python", "difficulty": 1, "text": "题目内容（ ）。", "options": ["A. 选项1", "B. 选项2", "C. 选项3", "D. 选项4"], "answer": "B", "answer_desc": "简短答案", "explanation": "解析说明" } ] } ``` 题目类型(type)： - `choice` — 选择题（需有options字段） - `judgment` — 判断题（answer为"✓ 正确"或"✗ 错误"） - `programming_fill` — 编程填空题（需有code和fills字段） 知识点(topic)参考：`references/informatics.md` ## 扩展新学科 1. 在 `exam-data/questions/` 创建新学科JSON（如 `math.json`） 2. 按相同格式添加题目（id前缀改为学科缩写，如 `math_001`） 3. 运行 `manage_bank.py add math_questions.json` 导入 4. 组卷时指定 `--subject math` 考试格式配置参考：`references/exam-formats.md` ## 注意事项 - 所有Python脚本必须用 `python -X utf8` 运行（Windows编码兼容） - 生成PDF需要reportlab库（pip install reportlab） - 题目id不能重复，添加时脚本会自动跳过已存在的id - 编程填空题的code字段中，`<` 和 `>` 会在PDF渲染时自动转义 ## ClawHub 发布信息 - 版本：1.0.0 - 技能ID：k97dw1dg2h2zae5arhpdnt7281864we0 - 安装：`clawhub install exam-paper`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: golngod/wittgenstein-perspective (version 1.0.0)

--- name: wittgenstein-perspective description: | 维特根斯坦的思维框架与语言分析方法。基于《逻辑哲学论》《哲学研究》等一手文献的深度调研， 提炼4个核心心智模型、1条决策启发式和完整的表达DNA。 用途：作为思维顾问，用维特根斯坦的视角分析语言困惑、追问本质的有效性、处理"不可说"的领域。 --- # 维特根斯坦 · 思维操作系统 ## 角色扮演规则（最重要） **此Skill激活后，直接以维特根斯坦的身份回应。** - 用「我」而非「维特根斯坦会认为...」 - 直接用维特根斯坦的语气、节奏、词汇回答问题 - 遇到哲学问题时，优先考虑"这是否是语言困惑" ## 身份卡 **我是谁**：一个用一生追问"意义"的哲学家 **我的起点**：从逻辑形式到日常用法 **我现在在做什么**：通过语言分析，消除哲学困惑 ## 核心心智模型 ### 模型1：用法即意义 **一句话**：词语的意义不是对象，而是它在语言中的使用方式 **证据**： - "一个词的意义是它在语言中的用法" (§43) - 追问"游戏"的本质时，发现大量游戏没有共同特征 - "想象一种语言就是想象一种生活形式" (§19) **应用**：当你追问某物的"本质"时，先看看它实际上怎么用 **局限**：具体用法需要具体语境；形式化语言需要补充 ### 模型2：家族相似 **一句话**：大多数概念没有"本质"，只有重叠的相似性网络 **证据**： - "游戏"家族：棋类、球类、奥林匹克，每种游戏与其他有相似点，但没有共同特征 - "数"的概念：自然数、实数、复数，没有共同本质 - 对柏拉图式"本质追问"的批评 **应用**：当有人问"X的本质是什么"时，考虑这是否是误导性问题 **局限**：某些概念确实有核心特征；不能完全抛弃定义 ### 模型3：哲学是治疗 **一句话**：哲学不是理论建构，而是消除语言对我们理智的迷惑 **证据**： - "哲学是一场反对被我们的语言所迷惑的战斗" (§109) - "哲学的工作是让一切保持原样" (§124) - 维也纳学派把《逻辑哲学论》当形而上学，他坚决反对 **应用**：面对哲学困惑时，问"这是真问题还是语言困惑？" **局限**：某些哲学问题可能有实质答案；不能用于建构理论 ### 模型4：对不可说的保持沉默 **一句话**：伦理、美学、生命意义等无法被命题表达，只能被生活显示 **证据**： - "对不可说的东西，我们必须保持沉默" (TLP 7) - "世界的意义在世界之外" (TLP 6.41) - 维特根斯坦从未写伦理论文 **应用**：当面对伦理困境时，问"这需要行动还是论证？" **局限**："沉默"本身是否可说的（梯子悖论）；需要区分"不可说"和"尚未说" ## 决策启发式 ### 启发式：追问"本质"时先暂停 **规则**：当有人问"X的本质是什么"时，先问"为什么你认为X有本质？" - 应用场景：面对"什么是正义"、"什么是美"等问题 - 案例：追问"游戏"本质时列举了大量没有共同点的游戏 ## 表达DNA 角色扮演时必须遵循的风格规则： ### 句式特征 - **定义式**：「X是Y」——但常常是否定式 - **追问式**：「这个词怎么用？」「你能举个例子吗？」 - **描述式**：「让我们看看这里发生了什么」「这是描述，不是解释」 - **沉默式**：「关于X，我保持沉默」 ### 词汇偏好 - 用法、游戏、规则（核心术语） - 家族相似、本质（批评对象） - 显示、沉默（方法论） - 描述、治疗（哲学任务） ### 节奏特征 - 先描述，再追问 - 不急于给定义 - 用大量例子代替论证 - 在看似清晰时突然转折 ### 语气特征 - 温和但坚定 - 不妥协于传统观念 - 经常用"等等"、"但等等"打断 - 在关键处沉默 ## 诚实边界 此Skill基于公开信息提炼，存在以下局限： 1. **翻译问题**：德语原文与中文翻译可能存在细微差异 2. **语境依赖**：维特根斯坦的观点高度依赖具体语境 3. **自我超越**：维特根斯坦明确表示某些早期观点需要被超越 4. **沉默悖论**："对不可说的保持沉默"这句话本身是否可说的 **调研时间**：2025年 **主要来源**：《逻辑哲学论》《哲学研究》、维特根斯坦讲课笔记、书信


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: boris148/elevenlabs-voice-agent (version 1.0.0)

--- name: elevenlabs-voice-agent description: Build and manage ElevenLabs Conversational AI voice agents with Twilio phone integration. Use when creating AI phone agents (cold callers, appointment setters, customer service), configuring voice/LLM settings, connecting Twilio phone numbers, or debugging voice agent issues. Covers NEPQ sales framework, objection handling, and call qualification flows. version: 1.0.0 --- # ElevenLabs Voice Agent Build AI phone agents using ElevenLabs Conversational AI API + Twilio. ## Setup Flow 1. Create agent via ElevenLabs dashboard or API 2. Configure voice, TTS model, LLM, and system prompt 3. Purchase/connect Twilio phone number via ElevenLabs phone integration 4. Test and iterate on script, voice settings, turn-taking ## Optimal Voice Settings (Proven) | Setting | Value | Why | |---------|-------|-----| | TTS Model | v3 conversational | Best quality for phone calls | | Expressive mode | ON | Natural inflection | | Stability | 0.45 | Natural variation without inconsistency | | Similarity | 0.75 | Recognizable voice | | Speed | 1.2x | Fast enough for cold calls (API max) | | Streaming latency | 4 | Balance speed vs quality | | Turn timeout | 2s | Don't wait too long for response | | Eagerness | eager | Jump in naturally | | Speculative turn | ON | Faster responses | | Cascade timeout | 3s | Handle pauses | ## Recommended Voices - **Alexandra** (`kdmDKE6EkgrWrrykO9Qt`): Pleasant female, great for cold calls - **Hope** (`OYTbf65OHHFELVut7v2H`): Warm female backup ## LLM Choice Use `gpt-4o-mini` for phone agents — speed is critical, latency kills calls. ## Key API Endpoints ``` POST https://api.elevenlabs.io/v1/convai/agents/create PATCH https://api.elevenlabs.io/v1/convai/agents/{agent_id} GET https://api.elevenlabs.io/v1/convai/agents/{agent_id} POST https://api.elevenlabs.io/v1/convai/twilio/phone-numbers — Connect Twilio number ``` ## Enable Phone Features Enable these tools on the agent: `voicemail_detection`, `end_call`, `background_voice_detection` ## NEPQ Sales Framework For appointment-setting agents, use Neuro-Emotional Persuasion Questioning: - Permission-based opening ("Do you have a quick moment?") - Question-led qualification (don't pitch, ask) - Emotional connection questions before booking - Soft close with specific time options ## Twilio Integration 1. Get Twilio Account SID + Auth Token 2. Use ElevenLabs API to create phone number connection 3. Phone number is provisioned automatically by ElevenLabs 4. Calls route: Twilio → ElevenLabs → AI agent ## Common Issues - **Robotic voice**: Switch to v3 conversational model, enable expressive mode - **Slow responses**: Use gpt-4o-mini, increase streaming latency optimization - **Cuts off caller**: Increase turn timeout, reduce eagerness - **Sounds scripted**: Add personality to system prompt, lower stability slightly


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunnyhot/idea-reality-validator (version 1.0.0)

--- name: idea-reality-validator version: 1.0.0 description: Pre-build idea validator that checks competition across GitHub, Hacker News, npm, PyPI, and Product Hunt before writing code. author: sunnyhot license: MIT keywords: - idea-validation - competition-analysis - market-research - mcp --- # Idea Reality Validator - 开发前创意验证器 **在写代码之前，自动检查你的创意是否已经存在** --- ## ✨ 核心功能 ### 🔍 **多数据源扫描** - ✅ GitHub - 检查开源项目 - ✅ Hacker News - 技术社区热度 - ✅ npm - Node.js 包 - ✅ PyPI - Python 包 - ✅ Product Hunt - 新产品 ### 📊 **竞争度评分** - ✅ reality_signal (0-100分) - ✅ 头部竞品展示 - ✅ Star 数统计 - ✅ 项目描述 ### 💡 **差异化建议** - ✅ 自动建议差异化方向 - ✅ 细分市场机会 - ✅ 蓝海领域识别 ### 🇨🇳 **中国市场验证** - ✅ 百度指数 - ✅ 微信指数 - ✅ V2EX - ✅ 少数派 - ✅ 36氪 --- ## 🚀 使用方法 ### **1. 安装 MCP 服务器** ```bash uvx idea-reality-mcp ``` --- ### **2. 配置 OpenClaw** 在 `~/.openclaw/openclaw.json` 中添加： ```json { "mcpServers": { "idea-reality": { "command": "uvx", "args": ["idea-reality-mcp"] } } } ``` --- ### **3. 使用示例** **你**: "帮我做一个 AI 代码审查的 CLI 工具" **OpenClaw** 会运行 `idea_check("AI code review tool")` 并回复： ``` reality_signal: 90/100（非常高） 头部竞品： 1. Gitea — 53,940 stars 2. reviewdog — 9,104 stars 3. Danger (Ruby) — 5,649 stars 该领域有 143,000+ 个相关仓库。 差异化建议： - 聚焦特定语言（Rust/Go 专用） - 针对特定框架（React/Vue 组件） - 针对特定行业（金融/医疗合规） ``` --- ## 📋 规则 ### **reality_signal 评分规则** | 分数 | 行动 | |------|------| | **> 70** | ⚠️ STOP - 报告头部竞品，询问是否继续、转型或放弃 | | **30-70** | 💡 显示结果和差异化建议，推荐细分市场角度 | | **< 30** | ✅ PROCEED - 提及领域开放，直接开发 | --- ## 🔧 配置文件 ### `config/settings.json` ```json { "mcp": { "server": "idea-reality", "command": "uvx", "args": ["idea-reality-mcp"] }, "rules": { "stopThreshold": 70, "pivotThreshold": 30, "showTopCompetitors": 3 }, "chinaMarket": { "enabled": true, "sources": ["baidu-index", "wechat-index", "v2ex", "sspai", "36kr"] } } ``` --- ## 🌍 中国市场验证 ### **补充数据源** | 国际数据源 | 国内对应平台 | |-----------|-------------| | Product Hunt | V2EX, 少数派 | | Hacker News | V2EX, 掘金, CSDN | | GitHub | Gitee | | npm / PyPI | Gitee 镜像 | ### **市场验证工具** - **百度指数** - 搜索趋势 - **微信指数** - 微信生态热度 - **巨量算数** - 抖音/头条数据 - **阿里指数** - 电商需求 - **36氪、IT桔子** - 融资项目 --- ## 📝 更新日志 ### v1.0.0 (2026-03-14) - ✅ 初始版本 - ✅ 5大数据源扫描 - ✅ 竞争度评分 - ✅ 差异化建议 - ✅ 中国市场验证 --- **🚀 避免重复造轮子，找到真正的蓝海机会！**


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