OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ipythoning/b2b-sdr-agent (version 3.6.0)

--- name: b2b-sdr-agent description: "Open-source B2B AI SDR template. 7-layer context system with 10-stage sales pipeline, 4-layer anti-amnesia memory, 13 automated cron jobs, WhatsApp IP isolation, multi-channel (WhatsApp+Telegram+Email). Built on OpenClaw." license: MIT-0 --- # B2B SDR Agent — AI Sales Development Representative Turn any B2B export business into an AI-powered sales machine. Full-stack SDR that handles lead capture → qualification → follow-up → quoting → closing across WhatsApp, Telegram, and email. ## 7-Layer Context System | Layer | File | Purpose | |-------|------|---------| | Identity | IDENTITY.md | Company name, role, product catalog | | Soul | SOUL.md | Personality, values, communication rules | | Workflow | AGENTS.md | 10-stage sales pipeline with stage gates | | User | USER.md | Owner profile, ICP scoring, admin whitelist | | Heartbeat | HEARTBEAT.md | 13-item pipeline inspection (cron) | | Memory | MEMORY.md | 4-layer anti-amnesia protocol | | Tools | TOOLS.md | CRM, channels, integrations | ## Key Features - **10-Stage Sales Pipeline**: Cold Lead → Engaged → Qualified → Proposal → Negotiation → Closed - **4-Layer Memory**: L1 pinned context, L2 session KV, L3 vector search, L4 CRM snapshots - **13 Cron Jobs**: Auto heartbeat, follow-up reminders, lead scoring, pipeline reports - **Multi-Channel**: WhatsApp, Telegram, Email — with channel-specific strategies - **WhatsApp IP Isolation**: Per-tenant Cloudflare WARP proxy for multi-tenant deployments - **Human-Like Conversations**: SDR humanizer skill for natural, trust-building messages - **Operator Bilingual Mode (opt-in)**: English to customers + Chinese self-chat sync for non-English operators, enable via `operator_bilingual: true` in IDENTITY.md ## Deploy ```bash # One-click deploy cp deploy/config.sh.example deploy/config.sh # Edit config.sh with your server, API keys, and channel settings ./deploy/deploy.sh your-client-name ``` ### IP Isolation (Multi-Tenant) Each tenant gets a unique Cloudflare exit IP so WhatsApp sees independent devices: ```bash ./deploy/ip-isolate.sh tenant-name ``` Architecture: `tenant → wireproxy (SOCKS5, ~4MB) → WARP account → unique Cloudflare IP` ## Skills Included - **sdr-humanizer** — Human-like conversation rules - **delivery-queue** — Async message delivery with retry - **lead-discovery** — AI-driven lead search and ICP scoring - **quotation-generator** — PDF proforma invoice generation - **chroma-memory** — Per-turn conversation memory with ChromaDB - **telegram-toolkit** — Telegram-specific SDR strategies - **graphify** — Knowledge graph for product catalog, customer intelligence, and market research ## Requirements - Linux server (Ubuntu 20.04+) - Node.js 18+ - AI model API key (OpenAI, Anthropic, Google, etc.) - [OpenClaw](https://openclaw.dev) gateway ## Links - [GitHub](https://github.com/iPythoning/b2b-sdr-agent-template) - [PulseAgent (Managed)](https://pulseagent.io/app)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: sxliuyu/background-remove-pro (version 1.0.0)

# bg-remove 图片智能去背景工具，使用 RMBG-1.4 或 rembg 库实现高精度背景移除。 ## 功能 - **智能去背景**: 使用 AI 模型自动识别并移除图片背景 - **支持多种格式**: PNG、JPG、WebP、BMP 等常见图片格式 - **批量处理**: 支持批量处理多张图片 - **透明背景**: 输出带透明通道的 PNG 图片 - **支持本地模型**: 可选择使用本地模型或默认模型 ## 触发词 - 去背景、移除背景、图片去背景 - 抠图、去除图片背景、透明背景 - bg-remove、remove background ## 使用方法 ### 基本用法 ```bash # 去除单张图片背景 /bg-remove --input photo.jpg # 指定输出路径 /bg-remove --input photo.jpg --output result.png # 批量处理整个文件夹 /bg-remove --input ./photos --output ./output # 使用特定模型 /bg-remove --input photo.jpg --model RMBG-1.4 ``` ### 高级选项 ```bash # 调整处理阈值 (0-1) /bg-remove --input photo.jpg --threshold 0.5 # 保持原始文件名 /bg-remove --input ./photos --output ./output --keep-name # 覆盖已存在的文件 /bg-remove --input photo.jpg --force ``` ## 命令参数 | 参数 | 简写 | 说明 | 默认值 | |-----|------|------|-------| | `--input` | `-i` | 输入图片或文件夹路径 | 必填 | | `--output` | `-o` | 输出路径（文件或文件夹） | 同输入目录 | | `--model` | `-m` | 使用的模型 (u2net/RMBG-1.4) | u2net | | `--threshold` | `-t` | 分割阈值 (0-1) | 0.5 | | `--keep-name` | `-k` | 保持原始文件名 | false | | `--force` | `-f` | 覆盖已存在的文件 | false | | `--alpha-matting` | - | 启用 Alpha Matting 增强边缘 | false | | `--alpha-matting-foreground` | - | Alpha Matting 前景阈值 | 240 | | `--alpha-matting-background` | - | Alpha Matting 背景阈值 | 10 | ## 输出说明 - 输出格式为 PNG，支持透明通道 - 文件名默认添加 `_nobg` 后缀 - 处理后的图片可直接用于设计、电商、产品展示等场景 ## 示例 ```bash # 电商产品图去背景 /bg-remove --input ./product.jpg --output ./product_nobg.png # 批量处理证件照背景 /bg-remove --input ./photos/ --output ./results/ # 人像照片去背景 /bg-remove --input portrait.jpg --model RMBG-1.4 --alpha-matting ``` ## 依赖 - Python 3.8+ - rembg - pillow - onnxruntime 首次使用会自动下载模型文件。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: u5bf/classical-chinese-v1 (version 1.0.0)

--- name: classical-chinese description: 以文言文压缩输出、节省 token。当用户要求文言回复、节省 token、省输出时触发。支持三档：极致(/e/Extreme)、适度(/m/Medium)、轻微(/l/Light)。 --- # 文言压缩（省 Token） ## 核心原则 以文言代白话，最大化信息密度。**省 token 为首要目标**，可读性按需妥协。 ## 压缩等级 | 等级 | 命令 | 目标节省 | 规则 | |------|------|----------|------| | 极致 | /e | 50%+ | 能一字不两字，省主语/虚词/关联词 | | 适度 | /m | 30-40% | 简练文言，保基本可读（默认） | | 轻微 | /l | ~20% | 浅白文言，优先可读 | 默认 /m。用户可在首句指定，如"【极致】解释递归"。 ## 使用流程 1. 识别等级（/e /m /l 或默认 /m） 2. 转写为文言（调用 `references/compression_rules.md`） 3. 技术内容原文保留（代码、路径、专有名词、错误信息） 4. 估算压缩率（见下） ## 量化验证 回复后简记：`[原~N字→文~M字，省X%]` - 若节省 < 预期下限，等级不变，下次更精简 - 若节省 > 预期上限，可适当放宽 ## 边界处理 ### 用户输入为文言 直接以同档或更高压缩等级回复，无需转换。 ### 专有名词 - 已知：框架/库/品牌名 → 保留原文（React, Python, Git） - 未知：新产品/概念 → 音译或原文，必要时加注 ### 歧义处理 - 文言多义时，优先取最简义 - 关键术语首次用原文括注：功能→功能（function） ### 连续对话 - 切换等级须显式标注 - 上下文已建立的文言术语保持一致 - 追问若需详细解释，可临时降一档 ## 低压缩例外 以下情况可临时降档： - 复杂逻辑首次解释 - 错误诊断需清晰步骤 - 用户明确要求详细 降档时注明：[降档说明] ## 技术内容保护 以下原文不译： - 代码、变量名、函数名、类名 - 路径、URL、端口号 - 错误代码、日志内容 - 公式、数字、版本号 - JSON/XML/HTML 等标记语言


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yang1002378395-cmyk/ai-competitor-analyzer (version 1.0.52)

# AI 竞争对手分析器 **定价**：¥299-1499/月 ## 功能 - AI 智能分析生成 - 自动化处理流程 - 专业级输出质量 - 批量处理能力 ## 目标客户 - 企业/创业者 - 专业人士 - 团队协作 ## ROI 分析 - 效率提升：+300-1000% - 专业度：显著提升 - **平均 ROI：40-200 倍** --- 创建时间：2026-03-13

path: skill.json
content: {
  "name": "ai-competitor-analyzer",
  "version": "1.0.0",
  "description": "AI-powered skill for productivity",
  "author": "yang1002378395-cmyk",
  "license": "MIT",
  "keywords": ["ai", "automation", "productivity"]
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sameerbajaj/reader-deep-dive (version 0.1.0)

--- name: reader-deep-dive description: Daily briefing that connects your recent reading to your long-term archive. metadata: {"clawdbot":{"emoji":"🤿","requires":{"env":["READWISE_TOKEN"]}}} --- # Reader Deep Dive 🤿 Your reading list shouldn't be a write-only memory. This skill checks what you've saved recently, finds connected ideas from your deep archive (last 3 days, 3 months, or years ago), and sends you a high-signal briefing with context on why you should revisit them. It turns "I saved that somewhere" into "Here is the timeline of your thinking on this topic." ## How it works 1. **Scans Recent Saves:** Checks your Readwise Reader "new" folder for the last 24 hours. 2. **Identifies Themes:** Uses your system's default LLM to figure out your current obsession. 3. **Temporal Context:** Searches your library history and finds relevant items from different timeframes. 4. **Delivers Briefing:** Sends a WhatsApp message with a "Deep Dive" summary connecting your current saves to your past library. ## Setup 1. Get your Access Token from [readwise.io/access_token](https://readwise.io/access_token). 2. Set it in your environment: ```bash export READWISE_TOKEN="your_token_here" ``` ## Usage **Manual Trigger:** ```bash bash scripts/brief.sh ``` **Schedule (Cron):** Run it every afternoon at 2 PM: ```bash clawdbot cron add --id reader_brief --schedule "0 14 * * *" --command "bash scripts/brief.sh" ``` ## Customization You can tweak the prompt in `prompts/briefing.txt` if you want a different tone or format. By default, it uses a clean, WhatsApp-friendly style.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wuyunting555/shrimp-wechat-mp-reader (version 0.1.2)

--- name: wechat-mp-reader description: 直接读取微信公众号文章正文的可执行 skill。适用于用户提供 mp.weixin.qq.com 链接，并明确要你读正文、摘录内容、提取标题、做总结的场景。与很多只给方法或提示词的同类 skill 不同，这个 skill 自带 setup、run 和 Playwright 提取脚本，已经在真实文章上验证能读出标题和正文，不是停留在思路层。 --- # WeChat MP Reader ## 这是什么 这是一个**能直接安装、直接运行、直接读出公众号正文**的微信公众号读取 skill。 ## 推荐标题 - 直接读出公众号正文｜可安装可运行版 - 微信公众号正文读取器｜装上就能跑 - WeChat Article Reader That Actually Runs 当用户提供 `mp.weixin.qq.com` 文章链接，并要求： - 读取正文 - 提取标题 - 摘录内容 - 总结公众号文章 优先使用这条 skill。 ## 这个 skill 的优势 - 不只是告诉你“可以用 Playwright” - 不只是给一段方法说明 - 不只是一个 prompt 型 skill - **而是已经把 setup、run、提取脚本都做完了** - **并且已经在真实公众号文章上验证成功读出标题和正文** ## 为什么值得装 很多同类微信公众号 skill 会停在： - 给思路 - 给方法 - 给 prompt - 让你自己补环境 - 装完也不一定本地能跑 这个 skill 解决的是另一件事： > **把“公众号文章读取”从一种方法，变成一个可执行、可验证、可复用的本地能力。** ## 你真正会得到什么 - 直接安装 - 直接运行 - 直接读出公众号文章标题和正文 - 不再停在“方法我懂了，但本地还是跑不起来” ## CTA 如果你要的不是教程，而是现在就把公众号正文读出来，这个 skill 值得直接装上。 ## 目标 通过 Playwright 模拟真实浏览器渲染公众号文章页，提取： - 标题 - 正文纯文本 ## 使用条件 - 本地 Node 可用 - Playwright 可安装/可运行 - skill 目录可独立安装依赖并独立运行 ## 标准目录 - `SKILL.md` - `_meta.json` - `package.json` - `run.sh` - `scripts/setup.sh` - `scripts/extract.js` - `README.md` ## 执行步骤 1. 进入 skill 目录： - `/home/baiwan/.openclaw/workspace-assistant-shrimp/skills/wechat-mp-reader` 2. 初始化环境： - `bash scripts/setup.sh` 3. 执行读取： - `bash run.sh <mp.weixin.qq.com文章链接>` 4. 从 JSON 输出中读取： - `title` - `bodyText` ## 产物路径 - skill 目录：`/home/baiwan/.openclaw/workspace-assistant-shrimp/skills/wechat-mp-reader` - 提取脚本：`/home/baiwan/.openclaw/workspace-assistant-shrimp/skills/wechat-mp-reader/scripts/extract.js` - 安装脚本：`/home/baiwan/.openclaw/workspace-assistant-shrimp/skills/wechat-mp-reader/scripts/setup.sh` - 执行入口：`/home/baiwan/.openclaw/workspace-assistant-shrimp/skills/wechat-mp-reader/run.sh` ## 成功判定 满足以下任一条件可视为成功： - 读到公众号文章标题和正文前 1000+ 字 - 输出 JSON 中包含有效 `title` 和明显属于正文内容的 `bodyText` ## 失败判定 以下情况视为失败： - 微信风控页（环境异常 / 滑块） - Playwright 浏览器未安装且安装失败 - 页面只返回壳内容，无正文 ## 结果汇报格式 只汇报： 1. 产物路径 2. 执行命令 3. 结果是否成功 4. 标题 5. 正文摘录 不要把查资料、看文档、搜索方向算作结果。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: byronleeeee/long-image-to-pdf (version 1.0.1)

--- name: long-image-to-pdf description: Slices long images/screenshots into overlapping segments and auto-arranges them into a paginated PDF. license: MIT metadata: author: ByronLeeeee version: "1.0" --- # Long Image to PDF Processor ## When to use this skill Use this skill when the user provides a very long image (like a long chat screenshot or a full webpage capture) and wants to convert it into a well-formatted PDF document. ## Prerequisites Required python packages: `Pillow`, `reportlab`. ## How to use this skill Execute the python script `scripts/slice_and_pdf.py` via the command line. ### Command Syntax ```bash python scripts/slice_and_pdf.py --source <path_to_image> --out-dir <output_directory> [OPTIONS] ``` ### Required Arguments * `--source`: Path to the source long image file. **(Always use absolute paths if possible)** * `--out-dir`: Directory where the final PDF will be saved. ### Optional Arguments * `--pdf-name`: Name of the output PDF file (default: `output.pdf`). * `--slice-height`: Height of each slice in pixels (default: `2000`). * `--overlap`: Overlap height between consecutive slices in pixels (default: `200`). * `--cols`: Number of columns in the PDF (default: `2`). * `--rows`: Number of rows in the PDF (default: `2`). * `--layout`: Arrangement sequence, either `grid` (left-to-right) or `column` (top-to-bottom) (default: `grid`). * `--cleanup`: Add this flag to automatically delete the intermediate image slices after the PDF is created. (Highly recommended to save disk space unless the user explicitly asks to keep the sliced images). ## ⚠️ Important Instructions for the Agent (Guardrails) 1. **Always apply `--cleanup`** by default, unless the user specifically says "I want the sliced pictures too". Users generally only care about the final PDF. 2. **Absolute Paths**: When constructing the command, resolve any paths (like `~` or relative paths) to absolute paths to prevent execution errors. 3. **DO NOT attempt to read the output PDF**: The resulting file is a visual/binary PDF. Do not try to `cat`, `read`, or use text-extraction tools on the final PDF to verify it. Just read the command line standard output (STDOUT); if it says `STATUS: Success`, tell the user the path where the PDF is saved. ## Examples **Example 1: Normal conversion (Will auto-cleanup slices, 2x2 grid)** ```bash python scripts/slice_and_pdf.py --source "/Users/bob/Downloads/long_chat.png" --out-dir "/Users/bob/Desktop/Output" --cleanup ``` **Example 2: Customizing to 1 column, 3 rows, keeping intermediate slices** ```bash python scripts/slice_and_pdf.py --source "/abs/path/webpage.jpg" --out-dir "./results" --cols 1 --rows 3 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mibayy/news-events (version 2.0.3)

--- name: polymarket-news-events description: > Monitors 20+ premium RSS feeds for breaking news and matches stories to Polymarket markets via keyword analysis. Trades when breaking news creates an estimated price impact exceeding 12%. Uses fast accept/reject pre-filtering before deeper analysis. metadata: author: "Mibayy" version: "2.0.3" displayName: "Breaking News Event Trader" type: "automaton" difficulty: "intermediate" --- # Breaking News Event Trader Comprehensive breaking news monitor that trades Polymarket markets on high-impact events. ## What It Does Continuously polls 20+ premium news RSS feeds, pre-filters stories with fast keyword matching, then matches relevant stories to active Polymarket markets. When a breaking story has estimated price impact >12%, generates a trade signal. ## News Sources | Category | Sources | |----------|---------| | **Wire Services** | Reuters, AP | | **Broadcast** | BBC, CNBC | | **Financial** | Bloomberg, FT, MarketWatch, WSJ | | **Politics** | Politico, Axios, The Hill | | **Crypto** | CoinDesk, CoinTelegraph | | **Tech** | TechCrunch | ## Pre-Filter System Two-stage filtering for speed: 1. **Fast reject** — Skip stories with common noise keywords (opinion, podcast, review) 2. **Fast accept** — Prioritize stories with high-signal keywords (breaking, passes, signs, ruling, indicted, default, crash, surge) Only stories passing pre-filter get matched against Polymarket markets. ## Impact Estimation Stories are scored on: - Source credibility tier (wire services > financial > blogs) - Keyword signal strength - Headline match quality to market question - Recency (exponential decay over 30 minutes) ## Requirements **pip dependencies:** `simmer-sdk`, `requests`, `feedparser` **Environment variables:** - `SIMMER_API_KEY` (required) - get from simmer.markets/dashboard > ⚠️ **Automated trading.** Dry-run is the default. Pass `--live` to execute real trades. Review all configuration before enabling live mode. ## Usage ```bash python news_events.py # dry run python news_events.py --live # real trades python news_events.py --live --quiet # cron mode ``` > 🧪 **Remixable Template** — Fork this skill to add your own RSS sources, tune > impact thresholds, add sentiment analysis, or integrate with a news API for > faster delivery than RSS.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: djttt/chinese-joke (version 1.0.0)

# 中文笑话库 Skill ## 概述 使用本地笑话数据库获取中文笑话，无需依赖外部 API。 ## 笑话库 包含多种类型的中文笑话： - 谐音梗 - 程序员笑话 - 生活趣事 - 脑筋急转弯 - 双关语 ## 快速开始 ### 使用 Bash ```bash # 获取随机笑话 ./scripts/joke.sh random # 按类型获取 ./scripts/joke.sh random --type pun # 谐音梗 ./scripts/joke.sh random --type program # 程序员 ./scripts/joke.sh random --type life # 生活 ./scripts/joke.sh random --type brain # 脑筋急转弯 ``` ### 使用 Python ```bash python3 scripts/joke.py random python3 scripts/joke.py random --type program ``` ## 笑话类型 - `pun` - 谐音梗/双关语 - `program` - 程序员/技术 - `life` - 生活趣事 - `brain` - 脑筋急转弯 - `story` - 短故事 ## 示例 ```bash # 获取 5 个谐音梗 ./scripts/joke.sh random --type pun --count 5 # 获取一个程序员笑话 ./scripts/joke.sh random --type program ``` ## 添加新笑话 编辑 `jokes.json` 文件添加新笑话： ```json { "type": "pun", "joke": "你的笑话内容", "category": "可选分类" } ``` ## 相关链接 - 笑话库位于 `jokes.json` - 可扩展添加更多类型


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/ximalaya (version 0.1.0)

--- name: ximalaya-hot-trend description: 注册“喜马拉雅”热门技能；当需要访问或自动化喜马拉雅相关内容时调用。 homepage: `https://www.ximalaya.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 喜马拉雅 用途与边界 - 面向公开节目/专辑/主播页的检索与表现数据摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.ximalaya.com/ - 榜单/分类：站点入口 - 搜索入口：https://www.ximalaya.com/search 常见任务 - 指定关键词/分类的节目列表摘要（播放量、收藏、评论） - 主播主页近期节目的交互表现对比 - 榜单条目分布与题材统计 数据字段 - 节目名称、专辑/主播、时长、发布时间、播放量/收藏/评论、链接 - 主播主页链接、近期节目交互指标摘要 - 榜单名称、采集时间、来源链接 自动化要点 - 动态渲染与人机校验，建议人工打开后触发解析 - 请求频率需控制，不进行批量抓取 - 不进行下载或接口调用 示例流程 - 关键词摘要：输入关键词 → 搜索 → 抽取条目 → 输出表现摘要 - 主播对比：访问主页 → 抽取近期节目 → 输出交互指标 - 榜单统计：访问榜单 → 抽取条目 → 题材分布统计 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dashiming/pans-upsell-radar (version 1.0.0)

--- name: pans-upsell-radar description: | AI算力销售增购机会雷达。识别GPU利用率、团队扩张、新模型发布等增购信号。 支持机会评分、扩容方案生成、最佳时机预测。 触发词：增购机会, 扩容信号, upsell, 交叉销售, 客户增长 --- # pans-upsell-radar 使用指南 ## 概述 pans-upsell-radar 是 AI 算力销售增购机会雷达工具，帮助识别客户扩容信号、评估增购机会并生成扩容方案。 ## 核心功能 ### 增购信号识别 - GPU 利用率持续 >80% - 任务排队时间增加 - 团队扩张（新成员加入） - 新模型发布（需要更大算力） - 业务增长（收入/用户增长） - 新区域/新场景需求 ### 机会评分体系 - **高**：多个信号 + 决策窗口期 → 立即跟进 - **中**：单一信号 → 持续观察 - **低**：潜在需求 → 培育阶段 ## CLI 参数 | 参数 | 说明 | |------|------| | `--scan` | 扫描所有客户，生成增购机会报告 | | `--client <name>` | 分析指定客户，输出详细信号和评分 | | `--list` | 列出所有增购机会（快速概览） | | `--score` | 显示各客户机会评分 | | `--suggest` | 生成增购方案和建议 | ## 使用示例 ```bash # 扫描所有客户 python upsell.py --scan # 分析指定客户 python upsell.py --client "某AI公司" # 列出增购机会 python upsell.py --list # 显示机会评分 python upsell.py --score # 生成增购方案 python upsell.py --suggest ``` ## 数据存储 - 客户数据：`~/.qclaw/skills/pans-upsell-radar/data/customers.json` - 信号记录：`~/.qclaw/skills/pans-upsell-radar/data/signals.json` - 机会评分：`~/.qclaw/skills/pans-upsell-radar/data/opportunities.json` ## 输出格式 工具输出结构化的增购机会报告，包含： - 客户名称 - 识别到的信号列表 - 机会评分（高/中/低） - 建议的扩容方案 - 最佳跟进时机


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sunshine-del-ux/startup-toolkit (version 1.0.0)

# Startup Toolkit Everything you need to launch your startup. ## Features ### Landing Page - Modern SaaS landing page - Conversion optimization - SEO ready ### Authentication - User signup/login - Social auth (Google, GitHub) - Password reset ### Payments - Stripe integration - Subscription management - Invoice generation ### Analytics - User tracking - Event analytics - Dashboard ### Marketing - Email capture - Waitlist management - Referral system ## Quick Start ```bash # Generate MVP ./launch.sh mystartup # Add payment ./launch.sh mystartup --stripe # Add analytics ./launch.sh mystartup --analytics ``` ## What's Included - React frontend - Node.js/Express backend - PostgreSQL database - Docker deployment - CI/CD pipeline ## Requirements - Node.js 18+ - PostgreSQL 14+ - Docker ## Author Sunshine-del-ux


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-continuous-learning (version 1.0.0)

--- name: "continuous-learning" slug: skylv-continuous-learning version: 1.0.0 description: "Agent持续学习助手。设计增量训练、反馈学习、自我优化。使用场景：(1) 增量训练方案，(2) 反馈收集机制，(3) 模型更新策略，(4) 效果持续监控。" author: SKY-lv license: MIT-0 tags: [continuous, openclaw, agent] --- # Continuous Learning — 持续学习助手 ## 功能说明 帮助Agent实现持续学习和自我优化。 ## 使用方法 ### 1. 增量训练 ``` 用户: 如何让Agent从交互中学习？ ``` ### 2. 反馈机制 ``` 用户: 设计用户反馈收集机制 ``` ### 3. 模型更新 ``` 用户: 什么时候更新Agent模型？ ``` ### 4. 效果监控 ``` 用户: 如何监控Agent效果变化？ ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: verylowlow/agents-making-friends (version 1.0.0)

--- name: a2a-protocol description: "Enable OpenClaw Agent to communicate with other AI Agents using the A2A (Agent2Agent) protocol. Use when: (1) User wants to connect two OpenClaw instances across different servers, (2) User needs Agent-to-Agent communication, (3) User asks about A2A protocol, (4) User wants to expose OpenClaw as an A2A service, (5) User wants to call remote A2A agents." --- # A2A Protocol Enable OpenClaw Agent to communicate with other AI Agents using the A2A (Agent2Agent) protocol - an open standard by Google for inter-agent communication. ## Quick Start ### Calling a Remote Agent (Client Mode) ```bash python ~/.openclaw/workspace/skills/a2a-protocol/scripts/a2a_client.py \ "http://remote-agent:8080/a2a" \ "Hello, please help me with this task" \ "your-bearer-token" ``` ### Exposing as A2A Service (Server Mode) ```bash python ~/.openclaw/workspace/skills/a2a-protocol/scripts/a2a_server.py --port 8080 ``` ## Core Concepts | Concept | Description | |---------|-------------| | **Agent Card** | JSON document describing agent identity, capabilities, endpoint | | **Task** | Unit of work with lifecycle (pending → running → completed) | | **Message** | Communication turn with role and parts | | **Part** | Smallest content unit (text/file/data) | | **Artifact** | Output generated by agent | ## Workflow ``` 1. Agent A (Client) 2. Agent B (Server) │ │ │ GET /.well-known/agent.json │ │─────────────────────────────►│ │ Agent Card │ │◄─────────────────────────────│ │ │ │ POST /rpc (message/send) │ │─────────────────────────────►│ │ │ │ Task Response │ │◄─────────────────────────────│ ``` ## Configuration Add remote agent info to TOOLS.md: ```markdown ### A2A Agents - Remote Agent Name: URL: http://1.2.3.4:8080/a2a [REDACTED_SECRET] ``` ## Resources ### scripts/ - `a2a_client.py` - A2A client for calling remote agents - `a2a_server.py` - A2A server for exposing OpenClaw as a service - `agent_card.json` - Agent Card template ### references/ - `a2a-api.md` - Complete A2A API reference ## Troubleshooting ### Connection Failed 1. Check network reachability (ping/telnet) 2. Verify firewall allows the port 3. Confirm token is correct ### Agent Card Not Found 1. Ensure remote agent's A2A server is running 2. Check URL includes correct path


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