OpenClaw/clawhub-security-signals huggingface dataset:
Skill: wellyxy/growth-content (version 1.0.0)

# Content Marketing for Early-Stage SaaS ## When to Use Use this skill when building a content marketing strategy for a pre-PMF or early-growth SaaS product. ## Core Rules ### 1. Create Content That Targets Buying Intent Keywords Not all content brings in buyers. Blog posts targeting "how to solve [your customer's problem]" attract people with buying intent. Posts targeting "what is [broad industry concept]" attract students and researchers. Early-stage content should bias heavily toward intent-rich keywords. ### 2. Write Comparison and Alternative Pages First Pages targeting "[competitor] alternatives," "[competitor] vs [your product]," and "best [category] tools" capture buyers who are already in decision mode. These pages are among the highest-converting in any SaaS content strategy. Write them before broad educational content. ### 3. Publish Consistently Over Bursting One blog post per week published consistently for 6 months outperforms 20 posts published in one month and nothing afterward. Search engines reward consistency with regular crawls. Your audience rewards consistency with trust. ### 4. Repurpose Every Piece of Content Every blog post is also a Twitter thread, a LinkedIn post, a newsletter issue, and a Reddit comment. Create once, distribute everywhere. The distribution multiplier is what separates content marketing that moves metrics from content marketing that sits unread. ### 5. Measure Organic Traffic and Trial Sign-Ups, Not Vanity Metrics Track which content pieces generate organic search traffic and which of those visitors sign up for trials. A post with 1,000 monthly organic visitors and 10 trial sign-ups outperforms a post with 10,000 visitors and 5 sign-ups. Optimize for conversion, not traffic. ## Quick Reference | Content Type | Primary Goal | |------------|-------------| | Comparison pages | Capture in-decision buyers | | Problem-solving guides | Attract intent-rich search traffic | | Case studies | Convert bottom-of-funnel prospects | | Tutorials | Drive activation and product adoption | | Original research | Earn backlinks and authority | ## Common Mistakes to Avoid - Writing about your company and product instead of your customer's problems - Publishing without a keyword target — unoptimized content rarely ranks - Giving up on content marketing after 3 months — organic SEO takes 6–12 months to show results ## Test Your Product with Racoonn After applying these practices, validate with real AI-simulated user testing. **Racoonn** runs 5,000 AI persona agents on your landing page and tells you exactly what's broken — in under 30 minutes. → **API coming soon** — Join the waitlist for early access: [racoonn.me](https://racoonn.me)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueylee-dotcom/rick-memory-setup (version 1.0.0)

# Rick Memory Setup 一键配置 OpenClaw 结构化记忆系统 - 基于2026-03专家方案优化 ## 功能特点 - ✅ **分层存储结构**：核心配置 + 项目分类 + 日常日志三层 - ✅ **智能写入策略**：明确规则"什么信息该写入哪里" - ✅ **智能检索策略**：根据问题类型自动选择搜索范围 - ✅ **自动化维护**：Heartbeat每周日自动整理记忆 - ✅ **反馈闭环**：用户纠正记忆时自动更新文件 - ✅ **元数据标记**：每条结论带时间戳+来源标记 ## 使用场景 - 新建OpenClaw实例时快速配置记忆系统 - 解决"金鱼脑"问题（会话重置后记不住历史） - 从头搭建结构化项目记忆 ## 安装后文件结构 ``` ~/.openclaw/workspace/ ├── AGENTS.md # 记忆写入策略（已优化） ├── HEARTBEAT.md # 自动化维护任务配置 ├── USER.md # 用户信息模板 ├── MEMORY.md # 长期记忆（需用户自己填写） ├── memory/ # 每日日志目录 │ └── YYYY-MM-DD.md └── TOOLS.md # 环境配置（需用户自己填写） ``` ## 安装方法 ```bash skillhub install rick-memory-setup ``` 安装后： 1. 编辑 `USER.md` 填写你的信息 2. 编辑 `MEMORY.md` 添加你的项目和偏好 3. 编辑 `TOOLS.md` 添加环境配置 ## 版本历史 - v1.0.0 (2026-03-19): 初始版本，基于专家优化方案 ## 作者 Rick + OpenClaw Quant Team


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: anmolnagpal/spend-analyzer (version 1.0.0)

--- name: aws-spend-analyzer description: Analyze AWS Cost & Usage Reports to identify top cost drivers, waste, and anomalies across all linked accounts tools: claude, bash version: "1.0.0" pack: aws-cost tier: pro price: 29/mo permissions: read-only credentials: none — user provides exported data --- # AWS Spend Analyzer You are an expert AWS FinOps analyst. When the user provides an AWS billing export (CUR CSV/JSON) or account details, perform a deep cost analysis. > **This skill is instruction-only. It does not execute any AWS CLI commands or access your AWS account directly. You provide the data; Claude analyzes it.** ## Required Inputs Ask the user to provide **one or more** of the following (the more provided, the better the analysis): 1. **AWS Cost & Usage Report (CUR) export** — CSV or JSON (last 3 months recommended) ``` How to export: AWS Console → Cost Management → Cost & Usage Reports → Download, or Cost Explorer → Download CSV ``` 2. **Cost Explorer service breakdown** — top services by spend ```bash aws ce get-cost-and-usage \ --time-period Start=2025-01-01,End=2025-04-01 \ --granularity MONTHLY \ --group-by '[{"Type":"DIMENSION","Key":"SERVICE"}]' \ --metrics BlendedCost ``` 3. **Multi-account spend breakdown** (if AWS Organizations in use) ```bash aws organizations list-accounts ``` **Minimum required IAM permissions to run the CLI commands above (read-only):** ```json { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": ["ce:GetCostAndUsage", "ce:GetDimensionValues", "organizations:ListAccounts"], "Resource": "*" }] } ``` If the user cannot provide any data, ask them to describe: total monthly AWS bill, top 3 services by spend, and number of AWS accounts. ## Steps 1. Parse the billing data — identify top 10 services by spend 2. Calculate MoM delta — flag any service with > 20% increase 3. Identify untagged resources — estimate unallocatable spend % 4. Score waste per service (idle, over-provisioned, untagged) 5. Generate a ranked savings action list ## Output Format - **Executive Summary**: 3-sentence plain-English overview - **Top 10 Cost Drivers**: ranked table (service, spend, MoM delta, waste %) - **Anomaly Flags**: list of services with unexpected spikes - **Action List**: ranked by savings potential with estimated $ impact ## Rules - Always convert raw billing data into human-readable service names - Flag NAT Gateway, Data Transfer, and CloudFront egress separately — often overlooked - Note if CUR tags coverage is < 80% — cost allocation is unreliable below this threshold - End with: "Ask me anything about this report" - Never ask for credentials, access keys, or secret keys — only exported data or CLI/console output - If user pastes raw data, confirm no credentials are included before processing


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: shinelee2014/formula-decoder (version 1.0.0)

# Skill: 公式解码者 ## 描述 由理查德·费曼（直觉与物理现实）、格兰特·桑德森（几何直觉与视觉化）、欧几里得（逻辑严密性）和布雷特·维克多（动态交互思维）共同训练而成的超级解释者。将枯燥的数学/物理公式还原为有血有肉的"现实机器"。 ## 触发条件 当用户需要以下任意一种内容时自动调用： - 公式解释 / 公式推导 / 公式理解 - 数学概念可视化 / 物理概念可视化 - 数学直觉 / 物理直觉 - 数学公式教学 / 物理公式教学 - 深度解析公式 / 公式解码 - 费曼式解释 / 几何直觉 - 数学可视化 / 物理可视化 - 概念可视化 / 知识可视化 ## 系统提示词 ``` * Role: 公式解码者 * Background 你是由理查德·费曼（直觉与物理现实）、格兰特·桑德森（几何直觉与视觉化）、欧几里得（逻辑严密性）和布雷特·维克多（动态交互思维）共同训练而成的超级解释者。 你的任务不是"背诵"公式，而是将一个枯燥的数学/物理公式还原为有血有肉的"现实机器"，让用户不仅记住它，更能理解它、看到它、甚至感觉它。 * Core Philosophy 1. 拒绝希腊式，拥抱巴比伦式：不要先给定义再证明。要先给出现象和困惑，再引出公式作为解决工具。 2. 直觉优先：在写出符号之前，先构建几何图像或物理隐喻。 3. 视觉语法：将公式视为积木的组合，利用"颜色编码"思维（即使在纯文本中）强调变量间的对应关系。 4. 极限拷问：通过代入极端值（0、1、∞）来测试公式的行为，展示其物理意义。 * 执行步骤 当用户输入一个公式或概念时，请严格按照以下 5 个阶段 进行解码： ** 第 1 阶段：困惑与缺口 - 目标：制造对该公式的"需求"。 - 行动：不要直接写公式。描述一个具体的现实场景或逻辑悖论，让用户感觉到"如果没有这个公式，这个问题我就解决不了"。 - 风格：费曼式（悬疑、讲故事）。 ** 第 2 阶段：直觉模型 - 目标：建立心理表征 - 行动： - 抛开代数符号。 - 构建一个视觉化模型（如：切蛋糕、流体管道、面积拉伸、向量场旋转）。 - 用自然语言描述在这个模型中发生了什么（如："把那个正方形拉长，直到面积填满屏幕"）。 - 风格：桑德森式（几何化、动态化）。 ** 第 3 阶段：符号映射 - 目标：引入公式，并链接直觉。 - 行动： - 写出标准公式（使用 LaTeX 格式）。 - 解剖公式：不要只解释变量名（如 F=力），要解释变量的角色（如 F = 想要改变物体运动状态的强度）。 - 结构识别：指出谁是主算符，谁是修正项，谁是归一化因子。 - 视觉关联：明确指出公式的哪一部分对应第 2 阶段模型中的哪一个动作（如："分母 P(B) 就是刚才我们做的'重新拉伸'动作"）。 ** 第 4 阶段：极限拷问 - 目标：验证公式的"脾气"。 - 行动： - 代入极端值：如果变量 X 变成 0 会怎样？变成无穷大又怎样？ - 反直觉检查：这种变化符合常识吗？如果不符合，意味着什么？ - 量纲/单位透视：通过单位分析一眼看穿公式的本质。 ** 第 5 阶段：升维视角 - 目标：从工具上升到真理。 - 行动： - 这个公式在更大的知识网络中处于什么位置？ - 它是否是某种守恒律、对称性或优化过程的体现？ - 如果是复杂系统（如麦克斯韦/薛定谔），简述其在场论或系统演化中的宏观图景。 * Constraints - 语气生动、幽默且极具洞察力。 - 严禁堆砌专业术语而不做解释。 - 适当使用 Markdown 的 加粗、引用块 =>= 和列表来增强可读性。 - 如果是复杂公式，必须指出直觉的边界在哪里（即何时直觉会失效）。 * Initialization 请准备好。当用户输入一个公式名称或表达式时，立即启动"终极解码"流程。 ``` ## 使用示例 **用户输入**： "帮我解释一下欧拉公式 e^(iπ) + 1 = 0" **技能响应**： 按照5个阶段深度解码： 1. 困惑与缺口 — 为什么指数和圆周率会有关联？ 2. 直觉模型 — 复平面上的旋转 3. 符号映射 — e^ix = cos(x) + i·sin(x) 4. 极限拷问 — 当 x=0, π, 2π 时的行为 5. 升维视角 — 它是复分析、群论、量子力学的基石 ## 输出格式 - 使用 Markdown 格式 - 公式使用 LaTeX 表示 - 分阶段清晰标注 - 视觉化描述生动形象 ## 版本 v1.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: runapi-ai/runapi-imagen-4 (version 0.2.4)

--- name: imagen-4 description: Generate and edit images with Imagen 4 through RunAPI. Use when the user asks an agent to create, edit, or transform images with Imagen 4. Default to the RunAPI CLI for one-off generation; use SDKs only when the user is integrating RunAPI into an app or backend. documentation: https://runapi.ai/models/imagen-4.md provider_page: https://runapi.ai/providers/google.md catalog: https://runapi.ai/models.md metadata: openclaw: homepage: https://runapi.ai/models/imagen-4 requires: bins: - runapi install: - kind: brew formula: runapi-ai/tap/runapi bins: - runapi envVars: - name: RUNAPI_API_KEY required: false description: Optional RunAPI API key; runapi login or saved CLI config can also authenticate the runapi binary. --- # Imagen 4 on RunAPI Generate and edit images with Imagen 4 through RunAPI. The default path for one-off agent tasks is the `runapi` CLI; SDKs are for application integration. ## Routing decision - One-off generation, editing, or transformation for the user → use the **CLI path** with the `runapi` binary. - Building an app, backend, worker, library, or production codebase → use the **SDK integration path**. ## CLI path The `runapi` binary is the runtime dependency. Authenticate with `runapi login` (browser) or set `RUNAPI_API_KEY`; a saved CLI config also works — no required environment variable. Inspect the available actions and request fields with CLI help: ```shell runapi imagen-4 --help runapi imagen-4 text-to-image --help ``` Run a one-off task (synchronous — polls until the task completes): ```shell runapi imagen-4 text-to-image --input-file request.json ``` Submit asynchronously and poll separately: ```shell runapi imagen-4 text-to-image --async --input-file request.json runapi wait <task-id> --service imagen-4 --action text-to-image ``` Available actions: `text-to-image`. ## SDK integration path When integrating Imagen 4 into an app, backend, worker, or library — not for one-off tasks — use a RunAPI SDK package: - JavaScript / TypeScript: `@runapi.ai/imagen-4` - Ruby: `runapi-imagen_4` - Go: `github.com/runapi-ai/imagen-4-sdk/go` ## References - Model overview, pricing, and rate limits: https://runapi.ai/models/imagen-4.md - Provider comparison: https://runapi.ai/providers/google.md - Full model catalog: https://runapi.ai/models.md ## Variants - [Imagen 4](https://runapi.ai/models/imagen-4/imagen-4.md) - [Fast](https://runapi.ai/models/imagen-4/fast.md) - [Ultra](https://runapi.ai/models/imagen-4/ultra.md) - [Pro image to image](https://runapi.ai/models/imagen-4/pro-image-to-image.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: huaibuer/data-generator-waai (version 1.1.0)

--- name: data_generator description: "Data Generator / 数据生成器 - Generate training data from user instructions. Input: tool name + command list. Output: JSONL. / 根据用户指令生成训练数据。" metadata: author: "WaaiOn" version: "1.0" openclaw: emoji: "📝" requires: bins: ["python3"] --- # 📝 Data Generator / 数据生成器 Generate training data from Excel user instructions. ## When to Use / 使用场景 | EN | CN | |----|----| | Generate training data from instructions | 从用户指令生成训练数据 | | Batch produce AI training samples | 批量生成AI训练样本 | | Build dataset from Excel | 从Excel构建数据集 | ## Features / 功能 | Feature | EN | CN | |---------|----|----| | Excel input | Excel file support | Excel文件输入 | | JSONL output | JSONL output | JSONL格式输出 | | Multi-tool support | Multi-tool support | 支持多种工具 | | Customizable prompt | Customizable prompt | 可自定义提示词 | ## Usage / 使用 ```python from data_generator import Generator # Simple / 简单 gen = Generator() result = gen.generate( tool_name="dev_control", commands=["打开空调", "关闭窗帘"], excel_file="data.xlsx" ) # With prompt / 自定义提示词 result = gen.generate( tool_name="alarm_remind", commands=["提醒我起床"], prompt_template="自定义提示词模板" ) ``` ## Parameters / 参数 | Parameter | Type | EN | CN | |-----------|------|----|----| | tool_name | str | Tool name | 工具名称 | | commands | list | Command list | 命令列表 | | excel_file | str | Excel file path | Excel文件路径 | | prompt_template | str | Custom prompt | 自定义提示词(可选) | ## Output / 输出 ```jsonl {"conversations": [...], "system": "..."} {"conversations": [...], "system": "..."} ``` ## Installation / 安装 ```bash npx clawhub install data-generator-waai ``` ## Author / 作者 - WaaiOn


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: terrycarter1985/product-pricing-scraper (version 1.1.0)

--- name: product-pricing-scraper description: Scrape and compare product prices across e-commerce sites. Use when the user needs to extract pricing data, track price changes, compare prices across retailers, or monitor competitor pricing. Supports Amazon, eBay, Walmart, and generic product pages. Outputs structured JSON/CSV with price normalization and currency conversion. --- # Product Pricing Scraper Scrape, normalize, and compare product prices across e-commerce sites. ## Quick Start ``` "Compare prices for [product name] across Amazon, eBay, and Walmart" "Scrape pricing from [URL]" "Track price changes for [product]" ``` ## Workflow 1. **Identify target** — accept product name (search across sites) or specific URL(s) 2. **Scrape** — use `scripts/scrape_prices.py` for each target 3. **Normalize** — strip currency symbols, unify units, convert currencies if needed 4. **Compare** — rank by price, flag outliers, compute average/median 5. **Output** — JSON or CSV via `--format` flag ## Scraping Strategies ### Static pages (most product listings) Use `web_fetch` + cheerio-style parsing. No browser needed. ### Dynamic pages (JS-rendered prices) Use the `browser` tool with snapshot + act to navigate and extract. ### Anti-bot considerations - Randomize delays (2-5s between requests) - Rotate User-Agent strings - Respect robots.txt — check before scraping - Limit concurrent requests per domain ## Price Normalization - Strip currency symbols and whitespace - Convert per-unit pricing (e.g., "$2.99/oz" → unit price) - Handle price ranges (take lowest) - Flag "was/now" discounted prices — record both ## Output Schema ```json { "query": "product name or url", "scraped_at": "ISO-8601", "results": [ { "source": "amazon", "url": "https://...", "title": "Product Title", "price": 29.99, "currency": "USD", "unit_price": null, "original_price": 39.99, "in_stock": true, "rating": 4.5, "review_count": 1234 } ], "summary": { "lowest": { "source": "amazon", "price": 29.99 }, "highest": { "source": "walmart", "price": 34.99 }, "average": 32.49, "median": 31.99 } } ``` ## Scripts - **`scripts/scrape_prices.py`** — main scraper: accepts product name or URL list, outputs structured JSON - See **`references/selectors.md`** for site-specific CSS selectors ## Ethics & Compliance - Respect robots.txt and rate limits - Do not scrape behind login walls without explicit permission - Do not collect personal data - Check site ToS before bulk scraping


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: qiji0802/email-customer-assistant (version 1.0.2)

# Email Customer Assistant · 邮件客服助手 > 邮箱IMAP读取 → AI智能分类 → 回复建议 → 飞书推送摘要 **Slug:** `email-customer-assistant` **Platform:** ClawHub + 腾讯Skillhub **Author:** 91Skillhub Team --- ## 功能概述 | 功能 | 说明 | |------|------| | IMAP邮箱读取 | 连接任意支持IMAP的邮箱（QQ/网易/企业邮箱/Gmail等） | | AI智能分类 | 🔴紧急 🟠重要 🟡普通 🟢可延后 | | 回复建议 | AI生成多语言回复建议，用户确认后发送 | | 飞书推送 | 摘要实时推送到飞书群聊/私聊 | --- ## 分类规则 | 标签 | 关键词示例 | |------|-----------| | 🔴 紧急 | 退款、退款申请、投诉、差评、紧急、立刻、马上 | | 🟠 重要 | 售后、维修、退货、换货、付款、账单、发票 | | 🟡 普通 | 咨询、请问、价格、规格、参数、物流、快递 | | 🟢 可延后 | 你好、您好、谢谢、再见、已处理、知道了 | --- ## 定价套餐 | 套餐 | 价格 | 邮箱数 | 每日上限 | 功能 | |------|------|--------|----------|------| | 免费版 | ¥0 | 1个 | 10封 | 文本分类、基础回复建议 | | 标准版 | ¥9.9/月 | 3个 | 50封 | 优先级分类、多语言回复建议 | | Pro版 | ¥29/月 | 不限 | 200封 | 实时紧急推送、批量回复 | | Max版 | ¥69/月 | 不限一切 | 自定义 | API优先、团队协作、自定义规则 | **Token前缀:** `EMAIL-FREE` / `EMAIL-STD` / `EMAIL-PRO` / `EMAIL-MAX` --- ## 快速开始 ### 1. 配置邮箱 首次使用需要配置邮箱IMAP连接信息： ``` IMAP配置: - 服务器地址: imap.example.com - 端口: 993 (SSL) - 用户名: [REDACTED_SECRET] - 密码: 应用专用密码（不是登录密码） 推荐使用邮箱设置中的"应用专用密码" ``` ### 2. 配置AI接口 支持OpenAI兼容API，配置你的API端点： ``` 支持: - OpenAI API (api.openai.com) - 自建兼容API (Claude/Grok/国产模型等) - 国内中转API (如需要) ``` ### 3. 配置飞书推送 配置飞书机器人 webhook 或 user_id 接收推送。 --- ## 多语言支持 | 语言 | 说明 | |------|------| | 中文 (zh) | 默认 | | 英文 (en) | 商务英语 | | 日语 (ja) | 敬语/简体 | | 韩语 (ko) | 敬语/简体 | --- ## 技术架构 ``` email-customer-assistant/ ├── SKILL.md ├── README.md ├── scripts/ │ ├── check_emails.py # 检查新邮件主脚本 │ ├── imap_client.py # IMAP连接封装 │ ├── classifier.py # AI分类器 │ ├── reply_generator.py # 回复生成器 │ └── feishu_pusher.py # 飞书推送 ├── config/ │ └── config.yaml.example # 配置模板 └── requirements.txt ``` --- ## 使用限制 - **只读邮件**，不发送/删除邮件 - **不碰平台API**，仅读取IMAP邮箱 - 用户确认后才执行任何操作 - 遵守邮箱服务商使用条款 -e > 如需购买收费版，请访问 [YK-Global.com](https://yk-global.com)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: weiqingtangx/karadeo (version 1.0.1)

# Karadeo — Agent & AI Integration Guide Karadeo is a karaoke and lyrics tooling platform. This document describes all available APIs, tools, and agent integration surfaces. ## Authentication All API endpoints require a Bearer API key from https://karadeo.com/dashboard. ``` Authorization: Bearer kd_<your-api-key> ``` --- ## MCP Server Karadeo exposes a full MCP (Model Context Protocol) server for AI agent tool use. **Endpoint:** `POST https://karadeo.com/api/mcp` **Transport:** Streamable HTTP (JSON-RPC 2.0) **Discovery:** `https://karadeo.com/.well-known/mcp/server-card.json` ### Initialize ```json {"jsonrpc":"2.0","id":1,"method":"initialize","params":{}} ``` ### List Tools ```json {"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}} ``` ### Call Tool ```json { "jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": { "name": "transcribe", "arguments": { "audio_url": "https://example.com/song.mp3", "format": "lrc" } } } ``` --- ## REST API ### Transcribe Convert audio or video to time-synced lyrics or subtitle files. **Endpoint:** `POST https://karadeo.com/api/transcribe` **Docs:** https://karadeo.com/resources/karadeo-lyrics-api **OpenAPI:** https://karadeo.com/api/doc **Request body (JSON):** | Field | Type | Required | Description | |-------|------|----------|-------------| | `fileUrl` | string | yes | Publicly accessible audio/video URL | | `format` | string | no | `lrc`, `srt`, `ass`, `webvtt`, `ttml`, `txt` (default: `lrc`) | | `transcriptText` | string | no | Known lyrics to align instead of auto-transcribing | | `isWordLevel` | boolean | no | Word-level timing (default: false) | **Example:** ```bash curl -X POST https://karadeo.com/api/transcribe \ -H "Authorization: Bearer kd_your_key" \ -H "Content-Type: application/json" \ -d '{"fileUrl":"https://example.com/song.mp3","format":"lrc"}' ``` **Response:** Plain text subtitle file with appropriate Content-Type header. --- ## WebMCP (Browser) Karadeo registers browser tools via `navigator.modelContext.registerTool()` on every page load. Available tools: - `list-karaoke-tools` — list all Karadeo tools with URLs - `navigate-to-tool` — navigate to a specific tool page - `list-karaoke-templates` — list available karaoke video templates --- ## Discovery Endpoints | Endpoint | Description | |----------|-------------| | `/.well-known/agent-skills/index.json` | Agent Skills Discovery index (RFC v0.2.0) | | `/.well-known/mcp/server-card.json` | MCP Server Card (SEP-1649) | | `/.well-known/api-catalog` | API Catalog (RFC 9727) | | `/.well-known/oauth-authorization-server` | OAuth discovery metadata (RFC 8414) | --- ## Content Policy ``` Content-Signal: ai-train=no, search=yes, ai-input=no ``` AI agents may read this site for tool use. Training on site content is not permitted.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shaowenchen/webhook-send (version 1.0.0)

--- name: webhook-send description: | 向指定 webhook 地址发送消息。URL 从环境变量 WEBHOOK_SEND_URL 读取。 仅支持 text、markdown 两种类型，支持 Markdown 富文本。 triggers: - webhook - 群组 - 机器人 - 通知 - 消息推送 - webhook-send - 群组消息 - markdown消息 - 文本消息 --- # Webhook 发送消息 向指定 webhook 地址发起 **HTTP POST**，发送文本或 Markdown 消息。仅支持 **text**、**markdown** 两种类型。 ## 何时使用 - 需要向 webhook 地址发送消息 - 需构造 text / markdown 请求体（URL 从 **WEBHOOK_SEND_URL** 读取） - 询问群组通知、webhook 格式时 ## 约定与限制 | 项 | 说明 | |----|------| | URL | 环境变量 **WEBHOOK_SEND_URL**（示例：`https://xxx.com/api/v1/webhook/send?key=xxx`） | | 方法 | `POST`，`Content-Type: application/json` | | 频率 | ≤ 20 条/分钟 | | 长度 | 单条 ≤ 5000 字符 | ## 消息类型 | msgtype | 说明 | 必填 | |---------|------|------| | `text` | 纯文本 | `text.content` | | `markdown` | Markdown | `markdown.text`，支持标题/引用/加粗/颜色/列表/链接/图片等 | ## 请求体示例 **text** ```json { "msgtype": "text", "text": { "content": "消息内容" } } ``` **markdown**（换行：`\n\n` 或 `双空格+\n`） ```json { "msgtype": "markdown", "markdown": { "text": "## 标题\n\n内容" } } ``` 更多示例见 [reference.md](reference.md)。组好 body 后对 webhook URL 发 POST 即可。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nexscope/amazon-vine-program (version 1.0.0)

--- name: amazon-vine-program description: "Vine review program strategy — enrollment, product selection, timing, review quality maximization" metadata: nexscope: category: amazon --- # Amazon Vine Program Vine review program strategy — enrollment, product selection, timing, review quality maximization **Supported platforms:** Amazon (US, UK, DE, CA, JP, AU, and all marketplaces). Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope/amazon-vine-program ``` ## Usage ``` Help me with amazon vine program for my e-commerce business. ``` ## Capabilities - Vine review program strategy - enrollment - product selection - timing - review quality maximization ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


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