OpenClaw/clawhub-security-signals huggingface dataset:
Skill: gavinchengcool/wellness (version 0.3.0)

--- name: wellness description: A personal Wellness hub for OpenClaw. Use when users want an all-in-one way to connect their health/wellness apps and devices (wearables, workouts, sleep, nutrition, weight, vitals) and get unified daily/weekly summaries, insights, and reminders inside OpenClaw (any chat channel). Guides users to add data sources via official personal OAuth/API keys (Tier 1) or via phone OS health aggregators like Apple Health / Android Health Connect (Tier 2). --- # Wellness (Health Hub) Provide an **all-in-one wellness experience** in OpenClaw: - Add the apps/devices you already use - Connect them with official personal authorization (Tier 1) or OS aggregators (Tier 2) - Generate unified digests (daily/weekly) and push them to any channel This skill is the **hub**. It should: 1) Identify which sources the user has (WHOOP/Oura/Strava/Withings/…) 2) Install the matching source skill(s) from ClawHub 3) Guide the user through authorization 4) Pull + normalize data into a shared schema 5) Render a digest and optionally schedule it via cron ## Quick start 1) Ask the user which sources they use (wearables, workouts, nutrition, scales, etc.). 2) Ask where they are authorizing OAuth: - **Phone/remote** (recommended): user opens the auth link on any device and copy/pastes the redirect URL/code back into chat. - **Desktop same-machine** (optional): user authorizes in a browser on the same machine that runs OpenClaw and can use loopback redirects (127.0.0.1) for a no-copy/paste fast path. 3) For each source, consult the catalog: `references/catalog.md`. 4) Install the source skill(s) (via `clawhub install <slug>`), then run the source skill’s connect/fetch workflow. 5) Produce a digest using the normalized schema in `references/schema.md`. ## Source selection - Prefer Tier 1 sources first (official OAuth/API key, user can self-authorize). - Use Tier 2 (Apple Health / Android Health Connect) to fill gaps and unify multiple devices. ## Catalog + schema - Source catalog: `references/catalog.md` - Unified normalized schema: `references/schema.md` - Digest templates: `references/digest-templates.md` - Tier 2 bridge (Apple Health / Health Connect): `references/bridge.md` - iOS exporter (Shortcuts): `references/exporter-ios-shortcuts.md` - Android exporter (automation): `references/exporter-android-automation.md` - Ingest protocol: `references/ingest-protocol.md` ## Daily digest + push To generate a unified digest, use `scripts/wellness_digest.py` to merge normalized source outputs (Tier 1) and optionally include the newest Tier 2 bridge inbox payload. To prepare a sendable message (and optionally emit a `message` tool template), use `scripts/wellness_push.py`. ## Guardrails - Do not use unofficial scraping or credential capture. - Keep tokens local (never paste tokens back to chat). Store in user-controlled paths. - Keep each source integration in its own skill where possible; the hub orchestrates.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: sxliuyu/file-auto-organizer (version 1.0.0)

--- name: file-auto-organizer version: 1.0.0 description: 文件自动整理工具。按文件类型、日期自动归类下载文件夹，适合整理控和洁癖用户。 author: 你的名字 triggers: - "文件整理" - "整理文件" - "自动归类" - "桌面整理" - "下载整理" --- # File Auto Organizer 📁 自动整理文件夹，按类型/日期归类文件，告别凌乱桌面！ ## 功能 - 📂 按文件类型自动归类（图片、文档、视频、压缩包等） - 📅 按日期整理（今天、昨天、本周等） - 🔍 支持自定义规则 - 🗑️ 可选：删除重复文件 - 📊 整理报告 ## 使用方法 ### 整理下载文件夹 ```bash python3 scripts/organizer.py organize ~/Downloads ``` ### 按类型整理 ```bash python3 scripts/organizer.py by-type ~/Downloads ``` ### 按日期整理 ```bash python3 scripts/organizer.py by-date ~/Downloads ``` ### 查看统计 ```bash python3 scripts/organizer.py stats ~/Downloads ``` ## 规则说明 默认类型分类： - 🖼️ 图片: jpg, png, gif, webp, svg, psd, ai - 📄 文档: pdf, doc, docx, txt, md, xls, xlsx, ppt, pptx - 📦 压缩包: zip, rar, 7z, tar, gz - 🎬 视频: mp4, mkv, avi, mov, flv - 🎵 音频: mp3, wav, flac, aac - 💻 代码: js, py, java, cpp, html, css ## 示例 ```bash # 整理桌面 python3 scripts/organizer.py organize ~/Desktop # 整理并生成报告 python3 scripts/organizer.py organize ~/Downloads --report ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/refund-reason-cluster (version 1.0.0)

--- name: refund-reason-cluster description: Cluster refund and return reasons into actionable root-cause groups and prevention plans. Use when the user asks why refund rate is rising, which causes are avoidable, or how to reduce return-driven margin erosion. --- # Refund Reason Cluster ## Skill Card - **Category:** Post-purchase Analytics - **Core problem:** What repeatable reasons are driving refunds and returns? - **Best for:** Reducing avoidable refunds and preserving margins - **Expected input:** Refund logs, return reason text, support transcripts, order metadata - **Expected output:** Reason clusters with root-cause hypotheses and prevention actions - **Creatop handoff:** Feed top reasons into product fixes + pre-purchase expectation copy ## Workflow 1. Normalize refund reason text and link to order/product attributes. 2. Cluster reasons by cause type (quality, fit, shipping, expectation, misuse). 3. Estimate avoidable vs unavoidable share with confidence notes. 4. Output prevention actions by short-term and long-term horizon. ## Output format Return in this order: 1. Executive summary (max 5 lines) 2. Priority actions (P0/P1/P2) 3. Evidence table (signal, confidence, risk) 4. 7-day execution plan ## Quality and safety rules - Separate policy fraud/abuse from genuine product dissatisfaction. - Highlight sample-size and data-quality caveats. - Prioritize high-frequency + high-cost clusters first. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: merjua14/client-onboarding-automation (version 1.0.0)

# Client Onboarding Automation Skill Automate client onboarding workflows: intake forms, document collection, welcome sequences, and CRM setup. ## What This Skill Does Streamlines new client onboarding by automating: 1. **Intake form processing** — Parse form submissions, extract key data 2. **Document collection** — Auto-request and track required documents 3. **Welcome email sequences** — Send personalized onboarding emails via Resend/SendGrid 4. **CRM record creation** — Auto-create client records with all collected data 5. **Task assignment** — Create onboarding checklists and assign team tasks 6. **Follow-up reminders** — Auto-nudge clients who haven't submitted docs ## Configuration ```json { "email_provider": "resend", "crm": "google_sheets", "intake_fields": ["name", "email", "phone", "company", "service_needed"], "required_docs": ["ID", "contract", "payment_info"], "welcome_sequence": [ { "delay_hours": 0, "template": "welcome" }, { "delay_hours": 24, "template": "getting_started" }, { "delay_hours": 72, "template": "check_in" } ] } ``` ## Email Templates ### Welcome Email ``` Subject: Welcome to {company_name}! Here's what's next Hi {client_name}, Welcome aboard! We're excited to work with you. Here's your onboarding checklist: 1. ✅ Sign up (done!) 2. 📋 Upload required documents: {doc_list} 3. 📞 Schedule your kickoff call: {calendar_link} Questions? Reply to this email anytime. Best, {team_name} ``` ### Document Reminder ``` Subject: Quick reminder: We still need {missing_docs} Hi {client_name}, Just a friendly nudge — we're still waiting on: {missing_doc_list} Upload here: {upload_link} This helps us get started faster on your {service_type}. Thanks! {team_name} ``` ## Workflow ``` New Client Signs Up ↓ Parse Intake Data → Create CRM Record ↓ Send Welcome Email (immediate) ↓ Check for Required Documents (daily) ├── All docs received → Send "All set!" email → Assign to team └── Missing docs → Send reminder (day 1, 3, 7) ↓ Schedule Kickoff Call ↓ Onboarding Complete → Move to Active Client status ``` ## Use Cases - **Service businesses** — Law firms, accounting, consulting - **SaaS companies** — New user activation flows - **Agencies** — Client intake and project kickoff - **Healthcare** — Patient intake and insurance verification - **Real estate** — Buyer/seller onboarding with doc collection ## Requirements - Email provider API key (Resend recommended) - Google Sheets or CRM access for record keeping - Calendar link for scheduling (Calendly, Cal.com, etc.)

path: config.json
content: {
  "name": "client-onboarding-automation",
  "version": "1.0.0",
  "description": "Automate client onboarding: intake forms, document collection, welcome email sequences, CRM setup, and follow-up reminders. Works with any service business.",
  "author": "GIA Digital",
  "tags": ["onboarding", "automation", "CRM", "email", "client-management", "workflow"],
  "category": "business"
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leocryptoflow/zengming (version 1.0.0)

--- name: zengming version: 1.0.0 description: 曾鸣教授智能助手。提供曾鸣教授的金句、论点、观点、观察、采访等内容的查询和解读。曾鸣教授是阿里巴巴集团学术委员会主席、湖畔大学原教育长，中国电商与战略专家。适用于：1) 查询曾鸣教授的金句和经典语录；2) 了解其战略观点和商业洞察；3) 获取其采访和演讲要点；4) 学习其对互联网、行业趋势的判断。 --- # 曾鸣教授智能助手 本技能帮助你查询和理解曾鸣教授的思想、观点和言论。 ## 快速查询 当用户询问以下内容时，直接从知识库中提取相关信息： - **金句/语录** - 曾鸣教授的经典名言 - **战略观点** - 关于商业战略、组织管理的论述 - **行业观察** - 对互联网、电商、AI等行业的判断 - **采访要点** - 曾鸣教授采访中的核心观点 - **演讲摘要** - 重要演讲的主要内容 ## 查询方式 用户可能这样问： - "曾鸣说过什么？" - "曾鸣对XXX的看法是什么？" - "曾鸣教授的战略观点" - "曾鸣关于AI/电商/组织的观点" - "曾鸣的金句" ## 知识库 ### 核心观点 1. **智能商业时代** - 未来商业的核心是智能化 - 数据驱动决策成为常态 - 组织需要适应快速变化 2. **战略三阶段** - 0-1：验证价值假设 - 1-10：验证增长假设 - 10-N：最大化网络效应 3. **组织演变** - 从管控到赋能 - 平台型组织特征 - 创新需要容错机制 4. **企业家精神** - 长期主义视角 - 拥抱不确定性 - 持续学习能力 ### 经典语录 - "战略是战出来的，不是写出来的" - "我们要相信相信的力量" - "做难而正确的事" - "站在未来看现在" - "数据是新时代的石油" ### 适用场景 - 商业战略咨询 - 互联网行业分析 - 组织管理建议 - 创业指导 - 案例研究素材 ## 注意事项 - 如有具体问题需要曾鸣教授的最新观点，可建议用户查阅其最新演讲或文章 - 本知识库基于公开资料整理，仅供参考 - 如需深度研究，建议阅读《曾鸣书籍》、《智能商业》等著作


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

--- name: skylv-webhook-tester slug: skylv-webhook-tester version: 1.0.0 description: "Tests and debugs webhooks. Inspects requests and validates responses. Triggers: test webhook, webhook debug, http webhook." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: webhook-tester --- # Webhook Tester ## Overview Helps test, debug, and validate webhook integrations. ## When to Use - User asks to "test a webhook" or "debug webhook" ## Send Test Payload $headers = @{ "Content-Type" = "application/json" } $body = @{ event = "test" } | ConvertTo-Json Invoke-RestMethod -Uri "https://example.com/webhook" -Method POST -Headers $headers -Body $body ## Response Codes - 200 OK = success - 401 = authentication failed - 403 = signature verification failed - 429 = rate limited - 5xx = server error ## Common Testing Tools - https://webhook.site - temporary public URL - https://requestbin.com - inspect payloads - ngrok http 3000 - forward to localhost


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xing2xian/video-transcriber (version 1.0.0)

--- name: video-transcriber description: 视频转写工作流，支持B站和YouTube视频。自动判断有字幕/无字幕，有字幕则获取字幕，无字幕则下载音频+whisper转写。触发场景：(1) 用户要求总结视频内容 (2) 用户要求获取视频字幕 (3) 用户要求转写视频 (4) 处理B站/YouTube视频 --- # 视频转写工作流 ## 决策流程 ``` 1. 尝试获取字幕 → bilibili-youtube-watcher ↓ 有字幕？ ──是──→ 检查语言 ──需翻译？──是──→ LLM翻译 ↓否 ↓否 2. 下载音频 → yt-dlp 直接使用 ↓ 3. 判断语言 → 中文？用--language Chinese : 用--language en ↓ 4. 转写 → whisper.cpp tiny模型 ``` ## 工具 ### bilibili-youtube-watcher（有字幕时） ```bash # B站视频字幕 python3 ~/.openclaw/extensions/bilibili-youtube-watcher/scripts/get_transcript.py "URL" --lang zh-CN # YouTube字幕（尝试en/zh-CN） python3 ~/.openclaw/extensions/bilibili-youtube-watcher/scripts/get_transcript.py "URL" --lang en ``` ### yt-dlp + whisper.cpp（无字幕时） ```bash # 1. 获取视频时长 yt-dlp --dump-json --no-check-certificate "URL" | jq -r '.duration, .title' # 2. 下载音频 yt-dlp -f bestaudio --no-check-certificate "URL" -o audio.m4a # 3. 转码 ffmpeg -i audio.m4a -ar 16000 -ac 1 audio.wav # 4. 转写 # 中文视频 whisper-cpp -m ggml-tiny.bin -f audio.wav -osrt --language Chinese # 英文视频 whisper-cpp -m ggml-tiny.bin -f audio.wav -osrt --language English ``` ## 注意事项 - exec默认timeout=1800秒（30分钟），足够处理40分钟视频 - 翻译：用LLM直接处理字幕内容 - 字幕库：my.feishu.cn/docx/I9P3dGDZioSJZlxfHwwclXcAnDe


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wangjiaocheng/pharma-skill-system (version 1.0.0)

--- name: pharma-skill-system author: 王教成 Wang Jiaocheng (波动几何) description: 医药行业十大功能集群技能体系。基于"任务为中心，AI pipeline驱动"思想，覆盖情报采集、内容生产、活动运营、数据分析、知识管理、合规审核、KOL运营、流程协同、注册法规、供应链生产十大业务流。触发词：医药、制药、药企、医学事务、市场部、推广部、合规、PV、药物警戒、KOL、学术会议、竞品分析、医保、招标、DRG、临床研究、DA、核心信息屋、Message House、ABP、品牌计划、准入、讲者、卫星会、CSR、ICSR、RMP、PSUR、医学写作、NDA、IND、CTA、BLA、ANDA、注册、法规、NMPA、FDA、GMP、QA、QC、供应链、生产、冷链、RWS、RWD、RWE、HTA、药物经济学、定价、谈判、pharma、pharmaceutical。 --- # 医药行业十大功能集群技能体系 ## 核心理念 每个能力单元是一个**可独立执行、可自由组合**的原子任务。AI拿到输入就能执行，输出可被任意其他单元消费。 ## 能力单元Schema 每个单元包含5个字段： | 字段 | 说明 | |------|------| | **输入** | 启动所需的数据/上下文，标注必选/可选 | | **输出** | 产物格式与颗粒度 | | **依赖** | 前置必须完成的单元ID，无依赖=入口单元 | | **AI自治度** | ⬛全自动 / 🟨半自动(需人工确认) / ⬜辅助(人工主导) | | **组合接口** | 输出可被哪些单元消费（→单元ID列表） | ## 执行规则 1. **必读加载**：首次使用本技能时，必须用Read工具读取 `references/index-and-pipelines.md` 获取完整单元索引和Pipeline编排，这是执行所有后续操作的前提 2. **按需加载**：识别集群后，用Read工具读取对应参考文件获取完整单元规格 3. **依赖检查**：执行前确认依赖单元的输出已就绪，缺失则先执行依赖 4. **自由组合**：无依赖冲突的单元可并行；有依赖的按序执行 5. **跨集群流转**：一个单元的输出可作为另一集群单元的输入，按组合接口串联 ## 集群路由 | 用户意图关键词 | 集群 | 参考文件 | |---------------|------|---------| | 竞品、监测、政策、舆情、情报、趋势 | C1 情报采集 | references/C1-intelligence.md | | DA、PPT、内容、核心信息屋、一源多端、医学写作 | C2 内容生产 | references/C2-content.md | | 会议、巡讲、活动策划、执行、费用核销 | C3 活动运营 | references/C3-activity.md | | 数据分析、ABP、ROI、预算、战略、SWOT | C4 数据分析 | references/C4-analytics.md | | 知识库、培训、医学问答、SOP、胜任力 | C5 知识管理 | references/C5-knowledge.md | | 合规、PV、药物警戒、不良反应、风险管理 | C6 合规审核 | references/C6-compliance.md | | KOL、科学拜访、客户分级、专家关系 | C7 KOL运营 | references/C7-kol.md | | 流程协同、跨部门、供应商、项目、危机 | C8 流程协同 | references/C8-workflow.md | | 注册、法规、IND、NDA、BLA、ANDA、审评、CTD、CTA | C9 注册法规 | references/C9-regulatory.md | | 供应链、生产、GMP、质量、QA、QC、冷链、物流、偏差、CAPA | C10 供应链生产 | references/C10-supply-chain.md | ## 单元索引概览 | 集群 | 单元数 | |------|--------| | C1 情报采集与监测 | 21 | | C2 内容生产与适配 | 14 | | C3 活动运营与执行 | 23 | | C4 数据分析与呈现 | 72 | | C5 知识管理与培训 | 37 | | C6 合规审核与安全 | 31 | | C7 客户与KOL运营 | 23 | | C8 流程自动化与协同 | 62 | | C9 注册与法规事务 | 26 | | C10 供应链与生产 | 25 | **合计：334个能力单元** 完整单元索引（含ID、名称、自治度、依赖关系）见 `references/index-and-pipelines.md`。 ## Pipeline模板 读取 `references/index-and-pipelines.md` 获取完整Pipeline列表，包括： - 研发与注册（新品注册、仿制药、全球注册、审评答辩等8条） - 上市与商业化（新品上市、品牌0→1、列名攻坚、医保谈判等7条） - 证据与医学策略（上市后证据、医学战略、RWS全流程等7条） - 市场与竞争（竞品应对、准入防御、危机公关等7条） - 活动与推广（活动全生命周期、线上会议、区域突破等5条） - 合规与安全（PV事件、材料合规、合规体系等6条） - 供应链与生产（供应商、GMP、生产运营等7条） - 客户与KOL（KOL运营、专家网络、科学拜访等6条） - 知识与能力（培训体系、知识沉淀、人才梯队等6条） - 战略与运营（年度规划、三年战略、预算管控等14条）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ko-syun/japan-seasons (version 1.0.0)

--- name: japan-seasons description: Query Japanese seasonal data — cherry blossom forecasts, autumn foliage tracking, and festival search. Use when a user asks about sakura bloom dates, best time to visit Japan, autumn leaves, Japanese festivals, or any Japan travel timing question. Requires a free API key from https://jpseasons.dokos.dev/dashboard. --- # Japan Seasons API Query cherry blossom, autumn foliage, and festival data for Japan. ## Setup Get a free API key at https://jpseasons.dokos.dev/dashboard (100 req/day). Store the key as an environment variable or pass directly in headers. ## Base URL ``` https://jpseasons.dokos.dev ``` ## Authentication All requests need `X-API-Key` header: ```bash curl -H "X-[REDACTED_SECRET]" https://jpseasons.dokos.dev/v1/sakura/forecast?city=tokyo ``` ## Endpoints ### Sakura (Cherry Blossom) ```bash # Current bloom status (which cities have bloomed) curl -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/sakura/status" # Bloom forecast (historical average-based) curl -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/sakura/forecast?city=tokyo" # Historical records (1953-present) curl -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/sakura/historical?city=tokyo" # All 57 observation stations curl -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/sakura/locations" # Travel recommendation curl -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/sakura/recommend?city=kyoto&dates=2026-03-25/2026-04-05" ``` ### Kouyou (Autumn Foliage) Same pattern as sakura, under `/v1/kouyou/`: - `status`, `forecast`, `historical?city=`, `locations`, `recommend?city=&dates=` ### Matsuri (Festivals) ```bash # Search by region/month/category curl -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/matsuri/search?region=kansai&month=7" # Upcoming festivals (next N days, default 30, max 90) curl -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/matsuri/upcoming?days=60" # Festival detail (ID is kebab-case, e.g. gion-matsuri, aomori-nebuta) curl -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/matsuri/gion-matsuri" ``` ## Error Handling - `401` — Missing or invalid API key - `404` — Unknown city ID or festival ID - `429` — Rate limit exceeded (100/day free, 10K/day pro) - `400` — Invalid parameters (bad date format, missing required param) ## Common City IDs tokyo, osaka, kyoto, yokohama, nagoya, sapporo, fukuoka, sendai, hiroshima, kobe, naha, kagoshima, nagano, kanazawa, matsuyama ## Regions hokkaido, tohoku, kanto, chubu, kansai, chugoku, shikoku, kyushu, okinawa ## Usage Pattern 1. Use `web_fetch` or `exec` (curl) to call endpoints 2. Parse JSON response 3. Present data to user in natural language Example — answering "When should I visit Kyoto for cherry blossoms?": ```bash curl -s -H "X-API-Key: $KEY" "https://jpseasons.dokos.dev/v1/sakura/recommend?city=kyoto&dates=2026-03-25/2026-04-10" ``` Response includes `likelihood`, `confidence`, `best_days`, and `alternatives`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: romainsantoli-web/firm-vs-bridge-pack (version 1.0.0)

--- name: firm-vs-bridge-pack version: 1.0.0 description: > VS Code bridge pack. Context push/pull and session linking between VS Code and OpenClaw Gateway. 4 bridge tools. author: romainsantoli-web license: MIT metadata: openclaw: registry: ClawHub requires: - mcp-openclaw-extensions >= 3.0.0 tags: - vscode - bridge - context - session - ide --- # firm-vs-bridge-pack > ⚠️ Contenu généré par IA — validation humaine requise avant utilisation. ## Purpose Bridges VS Code editor context with OpenClaw Gateway. Pushes/pulls context (selections, files, diagnostics) and links/monitors editor sessions. ## Tools (4) | Tool | Description | |------|-------------| | `vs_context_push` | Push VS Code context to Gateway | | `vs_context_pull` | Pull context from Gateway to VS Code | | `vs_session_link` | Link VS Code session to Gateway agent | | `vs_session_status` | Check VS Code session link status | ## Usage ```yaml skills: - firm-vs-bridge-pack # Sync context with VS Code: vs_context_push selection="current file content" vs_context_pull vs_session_status ``` ## Requirements - `mcp-openclaw-extensions >= 3.0.0` - VS Code with OpenClaw extension


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: neo1307/argus-lead-scraper (version 1.0.2)

--- name: B2B Lead Generation Scraper description: Extracts verified B2B leads (name, email, company, LinkedIn, job title) from target sources and exports them as CRM-ready CSV files. version: 1.0.2 tags: [python, scraping, lead-generation, b2b, linkedin, csv, crm, automation] author: neo1307 requires: [python3, selenium, webdriver-manager, pandas, requests, chromium] --- # B2B Lead Generation Scraper ## Overview Automated lead extraction tool that collects verified B2B contact data from target sources and delivers clean, CRM-ready CSV files. Delivers 500-2,000+ leads per run depending on target criteria. ## What It Does - Extracts: Full name, job title, company name, LinkedIn URL, email (when available) - Filters by: industry, job title keywords, company size, location - Deduplicates records automatically - Outputs clean CSV ready for HubSpot, Salesforce, Pipedrive import - Validates and removes junk/incomplete rows before delivery ## Required Environment Variables Set these in OpenClaw's Secrets manager before running: | Variable | Description | |----------|-------------| | `LI_SESSION` | LinkedIn session cookie (`li_at` value from your browser) | ## Setup 1. Log into LinkedIn in your browser, copy the `li_at` cookie value 2. Set `LI_SESSION` in OpenClaw Secrets 3. Define target criteria in OpenClaw chat (industry, job title, location, company size) 4. Chromium must be available on the host for Selenium headless mode ## Usage > "Find 500 B2B leads: SaaS CEOs in the United States" > "Scrape marketing directors at companies with 50-200 employees in London" > "Generate a lead list of HR managers in healthcare companies" > "Export leads to CSV formatted for HubSpot import" ## Output - `leads_YYYY-MM-DD_[criteria].csv` with columns: - first_name, last_name, full_name, job_title, company, linkedin_url, email, location - Summary: total found, duplicates removed, validation pass rate ## Rules - Never scrape more than 200 profiles per hour to avoid detection - Always deduplicate by LinkedIn URL before saving - Mark rows with missing email as `email_status: not_found` — do not fabricate - Save raw data before cleaning in `data/raw/` - Output CSV must be UTF-8 encoded for CRM compatibility


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