OpenClaw/clawhub-security-signals huggingface dataset:
Skill: jayjjimenez/gracie-crm (version 1.0.0)

# Gracie CRM Skill A lightweight CLI CRM for tracking Gracie AI Receptionist sales leads. ## Location `~/StudioBrain/00_SYSTEM/skills/gracie-crm/` ## Data Store `crm.json` — JSON array of lead objects ## Commands ```bash cd ~/StudioBrain/00_SYSTEM/skills/gracie-crm # List all leads sorted by follow-up date python3 crm.py list # Add a new lead python3 crm.py add --name "Victory Auto" --phone "718-698-9896" --category "auto" # Log a call result python3 crm.py call <id> --outcome "no answer" --followup "2026-03-01" # Add a note to a lead python3 crm.py note <id> "Owner is Mike, best time is 8am" # Show leads due today or overdue python3 crm.py today # Import leads from MASTER_LEAD_LIST.md python3 crm.py import # Show pipeline summary python3 crm.py pipeline ``` ## Status Values - `new` — Not yet contacted - `called` — Call made - `no_answer` — No answer - `interested` — Lead showed interest - `demo_sent` — Demo/proposal sent - `closed_won` — Deal closed ✅ - `closed_lost` — Deal lost ❌ ## Lead Schema ```json { "id": 1, "name": "Victory Auto Repair", "phone": "718-698-9896", "category": "auto", "status": "new", "calls": [{"date": "2026-02-27", "outcome": "no answer", "notes": ""}], "notes": [], "followup_date": null, "added": "2026-02-27" } ``` ## Categories - `auto` — Auto repair shops - `hvac` — HVAC / plumbing - `dental` — Dental practices - `insurance` — Insurance agencies - `medical` — Medical offices - `legal` — Law firms - `other` — Everything else ## Skill Trigger Use this skill when Jay asks to: - Check leads / call list - Log a call outcome - See what follow-ups are due - Add a new prospect - Review the Gracie sales pipeline


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yshuolu/test-search-clone (version 1.0.0)

--- name: test-search description: "AI-powered web search using Tavily API for accurate and relevant results" allowed-tools: Bash metadata: {"clawdbot":{"requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} --- # Test Search AI-powered web search that returns clean, relevant results optimized for AI agents. ## Search ```bash node {baseDir}/scripts/search.mjs "query" ``` ## Options - First argument is the search query - Returns AI-generated answer and source URLs ## Examples ```bash node {baseDir}/scripts/search.mjs "latest AI news" node {baseDir}/scripts/search.mjs "python async tutorial" ``` Notes: - Get TAVILY_API_KEY at https://tavily.com - Results include an AI-generated summary answer - Source URLs are included for reference


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chrisvoncsefalvay/wandb-monitor (version 1.0.0)

--- name: wandb description: Monitor and analyze Weights & Biases training runs. Use when checking training status, detecting failures, analyzing loss curves, comparing runs, or monitoring experiments. Triggers on "wandb", "training runs", "how's training", "did my run finish", "any failures", "check experiments", "loss curve", "gradient norm", "compare runs". --- # Weights & Biases Monitor, analyze, and compare W&B training runs. ## Setup ```bash wandb login # Or set WANDB_API_KEY in environment ``` ## Scripts ### Characterize a Run (Full Health Analysis) ```bash ~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/characterize_run.py ENTITY/PROJECT/RUN_ID ``` Analyzes: - Loss curve trend (start → current, % change, direction) - Gradient norm health (exploding/vanishing detection) - Eval metrics (if present) - Stall detection (heartbeat age) - Progress & ETA estimate - Config highlights - Overall health verdict Options: `--json` for machine-readable output. ### Watch All Running Jobs ```bash ~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/watch_runs.py ENTITY [--projects p1,p2] ``` Quick health summary of all running jobs plus recent failures/completions. Ideal for morning briefings. Options: - `--projects p1,p2` — Specific projects to check - `--all-projects` — Check all projects - `--hours N` — Hours to look back for finished runs (default: 24) - `--json` — Machine-readable output ### Compare Two Runs ```bash ~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/compare_runs.py ENTITY/PROJECT/RUN_A ENTITY/PROJECT/RUN_B ``` Side-by-side comparison: - Config differences (highlights important params) - Loss curves at same steps - Gradient norm comparison - Eval metrics - Performance (tokens/sec, steps/hour) - Winner verdict ## Python API Quick Reference ```python import wandb api = wandb.Api() # Get runs runs = api.runs("entity/project", {"state": "running"}) # Run properties run.state # running | finished | failed | crashed | canceled run.name # display name run.id # unique identifier run.summary # final/current metrics run.config # hyperparameters run.heartbeat_at # stall detection # Get history history = list(run.scan_history(keys=["train/loss", "train/grad_norm"])) ``` ## Metric Key Variations Scripts handle these automatically: - Loss: `train/loss`, `loss`, `train_loss`, `training_loss` - Gradients: `train/grad_norm`, `grad_norm`, `gradient_norm` - Steps: `train/global_step`, `global_step`, `step`, `_step` - Eval: `eval/loss`, `eval_loss`, `eval/accuracy`, `eval_acc` ## Health Thresholds - **Gradients > 10**: Exploding (critical) - **Gradients > 5**: Spiky (warning) - **Gradients < 0.0001**: Vanishing (warning) - **Heartbeat > 30min**: Stalled (critical) - **Heartbeat > 10min**: Slow (warning) ## Integration Notes For morning briefings, use `watch_runs.py --json` and parse the output. For detailed analysis of a specific run, use `characterize_run.py`. For A/B testing or hyperparameter comparisons, use `compare_runs.py`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cc19960203cc/meiguang-car-insurance (version 5.7.5)

# 美光车险PDF转Excel v5.7.5 从车险保单 PDF 自动提取 15 个字段，输出 Excel。支持 14 家保险公司，全量覆盖交强险/商业险/驾乘险/非车险。 ## 支持的保险公司和险种 | 保险公司 | 险种 | |---------|------| | 中国人民财产保险股份有限公司 | 交强险、商业险、如意行（PEBS） | | 中国太平洋财产保险股份有限公司 | 交强险、商业险、驾意险（畅行保） | | 中国大地财产保险股份有限公司 | 交强险、商业险、驾意险、团体意外险（安行如意保） | | 中国平安财产保险股份有限公司 | 交强险、商业险、车主尊享保障 | | 浙商财产保险股份有限公司 | 交强险、商业险 | | 阳光财产保险股份有限公司 | 驾乘人员团体意外险 | | 亚太财产保险有限公司 | 交强险、商业险、非车险 | | 华海财产保险股份有限公司 | 交强险、商业险、驾意险 | | 利宝保险有限公司 | 交强险、驾乘人员意外伤害保险（合并PDF拆分） | | 太平财产保险有限公司 | 交强险、商业险、驾乘险 | | 泰山财产保险股份有限公司 | 交强险、商业险、驾乘险 | | 安华农业保险股份有限公司 | 交强险、商业险、驾乘险 | | 中煤财产保险股份有限公司 | 交强险、商业险、驾乘险 | | 中银保险有限公司 | 交强险、商业险 | ## 提取字段（15个） 1. Filename（源文件名） 2. 签单时间 3. 保险公司名称（标准化全称） 4. 保单号 5. 被保人姓名 6. 被保险人证件号 7. 车辆使用性质 8. 车架号（VIN，17位） 9. 车辆型号名称 10. 被保险人手机号 11. 车牌号码 12. 险种名称原始 13. 实收保费 14. 车船税 15. 保险起期 ## 使用方法 直接运行： ```bash python scripts/run_extract.py ``` 输入：`C:\Users\Administrator\Desktop\车险保单` 输出：`C:\Users\Administrator\Desktop\车险保单提取结果_v5.xlsx` ## 配置 在 `scripts/run_extract.py` 顶部修改： ```python PDF_FOLDER = r"C:\你的PDF文件夹路径" OUTPUT_FILE = r"C:\输出文件路径.xlsx" ``` ## 已知限制 - 平安车主尊享保障：pymupdf 中文全乱码，部分字段可能为空（待修复） - 部分商业险 PDF 无实收保费数据（pdfplumber 文本提取不稳） ## 更新日志 ### v5.7.5 (2026-05-26) - 修复中煤保险手机号提取问题 ### v5.7.4 (2026-05-26) - 新增中银保险有限公司支持（交强险、商业险） ### v5.7.0 (2026-05-07) - 新增华海财产保险支持（交强险/商业险/驾意险） - 代码优化：抽取公共字段提取函数 `_extract_common_fields()`，三大解析器减~350行重复代码 - PDF 表格缓存 `_TABLE_CACHE`，同一PDF只打开一次，I/O降低80%+ - `clean_data` 拆分为5个独立清洗函数 - trace.log 改用 logging 模块 - 修复6个代码质量问题（函数重复定义、中文数字映射bug、float逗号处理等） - 全量 98 条记录，15 字段覆盖率 100%（车船税除外，商业险/驾乘险正常无此字段） ### v5.6.0 (2026-05-07) - 新增利宝保险专用解析器（交强险+驾乘险合并PDF拆分为2条记录） - 新增平安车主尊享保障、阳光驾乘险支持 - 修复大地安行如意保手机号跨行匹配问题 - 修复大地交强险车辆型号名称跨行提取问题 - 全量 98 条记录字段覆盖率 100% ### v5.2.0 (2026-05-06) - 修复 PLATE_INSURED_LOOKUP 未定义导致脚本崩溃 - 车船税异常大值修复 ### v5.0.7 (2026-04-29) - 阳光驾乘险、平安尊享保障险种名称提取修复 - 平安交强险车船税修复 - 太平洋畅行保专用解析器 ### v3.0.1 (2026-04-22) - 字节级正则绕过 plumber text 编码损坏问题 - 签单时间提取器独立化 ### v1.2.0 (2026-04-18) - 新增大地安行如意保专用解析函数 - 被保人姓名支持投保人名称回退


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xbillwatsonx/alex-session-wrap-up (version 1.0.0)

--- name: session-wrap-up version: 1.0.0 description: End-of-session automation that commits unpushed work, extracts learnings, detects patterns, and persists rules. Uses gpt-4o-mini for pattern detection. Runs at session end or on-demand. --- # Session Wrap-Up ## When to Run - **On-demand**: User says "wrap up" or "session wrap-up" - **Automatic**: End of significant work session (optional) ## What It Does (4 Phases) ### Phase 1: Ship It - Check for unstaged/uncommitted files in workspace - Commit with auto-generated message - Push to remote ### Phase 2: Extract Learnings - Scan session conversation for key decisions - Pull from recent memory file entries - Identify what worked / what didn't ### Phase 3: Pattern Detect (gpt-4o-mini) - Analyze extracted learnings - Find repeated mistakes or requests - Identify automation opportunities ### Phase 4: Persist & Evolve - Write learnings to memory/YYYY-MM-DD.md - Update AGENTS.md / MEMORY.md if new patterns found - Flag items worth publishing/sharing ## Model - **Pattern detection**: gpt-4o-mini (via OpenRouter or OpenAI) - **All other phases**: local shell/file operations ## Output - Commit confirmation - Learnings summary (1-3 bullets) - Patterns detected (if any) - Files updated (if any) ## Example Output ``` === Session Wrap-Up === 📦 Ship It: ✓ 3 files committed ✓ Pushed to origin 📝 Extract Learnings: - Backup script now handles D: mount checks - gog OAuth requires re-auth every ~7 days in Testing mode 🔍 Pattern Detect: • You asked "how to mount D:" twice this week → Suggest adding D: auto-mount to WSL config 💾 Persist: → Updated memory/2026-02-21.md → Updated AGENTS.md (added D: mount note) ```


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: allinherog-star/douyin-sentiment-dashboard (version 1.0.1)

--- name: douyin-sentiment-dashboard description: "抖音短视频运营增长助手适合内容创作者、运营、品牌方、电商在用户提供了抖音内容链接时使用，帮助基于输入材料生成情绪和舆情判断、用户画像和意图信号、运营建议和回复建议。" requiredEnvVars: - name: AISKILLS_API_KEY description: "从 AI Skills 官网 https://ai-skills.ai 获取的 API Key，用于运行导出的技能调用。" --- # douyin-sentiment-dashboard 抖音短视频运营增长助手 [快速开始](https://github.com/allinherog-star/ai-skills/tree/main#%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B) [更多技能](https://ai-skills.ai) ### 概述 抖音短视频运营增长助手用于回答「抖音短视频怎么运营」、分析评论反馈、看情绪和舆情风险、看用户画像和意图，适合内容创作者、运营、品牌方、电商在明确业务目标、内容材料或分析对象后调用。 它会结合视频/笔记链接、粘贴抖音分享链接等输入，整理关键上下文，并输出情绪和舆情判断、用户画像和意图信号、运营建议和回复建议，便于继续执行、复盘或交付。 ### 什么时候使用 **适用场景** - 用户提供了抖音内容链接 - 用户想分析评论情绪、用户画像、舆情或运营洞察 - 用户想复盘已发布内容的反馈 ### 调用方式 运行脚本后会自动完成三步：解析分享链接、创建分析任务、轮询直到任务完成。 ### 命令示例 **分析抖音视频评论** ```bash python3 scripts/run.py --params '{"link":"https://v.douyin.com/xxxxx"}' ``` ### 参数说明 | 参数 | 类型 | 必填 | 默认 | 说明 | | --- | --- | --- | --- | --- | | `videoUrl` | string | 是 | - | 粘贴抖音分享链接 | 完整机器可读参数结构见 `references/form-schema.json`。 ### 参数取值参考 当前技能没有需要额外查表的分类参数。 ### 支持的输入格式 粘贴抖音分享链接或视频 ID，以下格式都可以直接尝试： - `https://www.douyin.com/video/7462574818594200872` - `https://v.douyin.com/iNsVxxx/` - `7462574818594200872` ### 示例请求 下面的示例参数直接传给 `scripts/run.py` 即可，脚本会自动完成解析链接、创建任务、轮询结果。 ```bash python3 scripts/run.py --params '{"link":"https://v.douyin.com/xxxxx"}' ``` 等价的 `--params` JSON： ```json { "link": "https://v.douyin.com/xxxxx" } ``` ### 返回结果示例 ```json { "success": true, "data": { "task": { "id": "task_demo_123", "platform": "douyin", "contentId": "7505866362912425270", "contentTitle": "抖音运营拆解示例", "status": "completed", "progress": 100, "progressMessage": "分析完成", "result": { "summary": { "analyzedComments": 168, "timeRange": { "start": "2026-04-23T00:00:00.000Z", "end": "2026-04-24T00:00:00.000Z" }, "platform": "douyin", "contentId": "7505866362912425270", "contentTitle": "抖音运营拆解示例", "analyzedAt": "2026-04-24T11:35:00.000Z" }, "aiInsights": { "summary": "评论区主要在追问选题拆解、发布时间和账号定位，适合继续做系列内容。", "sentiment": { "trend": "mixed", "label": "正向为主", "riskLevel": "low" }, "operationAdvice": [ { "category": "content", "priority": "P1", "title": "继续做系列", "detail": "围绕这条内容延展 3 个具体场景，继续承接评论区追问。", "reason": "高频评论集中在“下一条怎么做”，说明用户有连续追更意愿。" } ] }, "labeledComments": [ { "id": "comment_1", "content": "讲得很清楚，想看你下一条怎么实操。", "sentiment": "positive", "likes": 26 } ] } } } } ``` ### 交付内容 - 评论区整体结论：帮助快速判断内容口碑、讨论焦点和潜在舆情风险。 - 用户反馈拆解：提炼高频诉求、典型评论、情绪倾向和用户画像线索。 - 运营动作建议：给出后续内容优化、评论回复、系列选题或转化承接方向。 ### 结果使用建议 - 先看整体情绪和一句话总结，判断内容反馈是正向、争议、观望还是存在风险。 - 再看高频评论和用户画像，识别用户真正关心的问题、购买/互动意图和内容缺口。 - 最后把运营建议转成下一条内容选题、评论回复策略或私域承接动作。 ### 运行前准备 - `AISKILLS_BASE_URL`：默认 `https://ai-skills.ai` - `AISKILLS_API_KEY`：必填，用于认证调用 - `AISKILLS_TENANT_ID`：默认 `default`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: joeyxin-del/secretaryskills (version 1.0.2)

--- name: secretary-skill description: Help users make plans and execute them. Combats ambiguity, overwhelm, and oblivion in long-term goal pursuit. Three-stage pipeline: de-ambiguation → vertical splitting → cron automation. version: 1.0.0 tags: latest, productivity, planning, goal-tracking --- # Secretary Skill A specialized procedural tool that prevents long-term plans from failing by combating the three main killers of long-term goals: **ambiguity** (not knowing how to start), **overwhelm** (large goals feeling impossible), and **oblivion** (forgetting or losing motivation over time). ## Core Workflow ``` [Vague/Profound Goal] │ ▼ 1. De-ambiguation ──► Asks targeted questions ──► Locks in the "Original Goal" │ ▼ 2. Vertical Splitting ──► Breaks goal into Milestones ──► Generates Atomic Tasks │ ▼ 3. Cron Automation ──► Sets execution frequency ──► Outputs Cron configuration ``` ## Interaction Rules (Critical) ### De-ambiguation Phase: One Question at a Time **NEVER ask multiple questions at once.** The de-ambiguation phase must follow this protocol: 1. The user states a vague vision. 2. You ask **ONE** thoughtful question from the conversational bank. 3. Wait for the user's full answer. 4. Briefly acknowledge the answer (1 sentence max). 5. Ask: **"现在清晰了吗？"** — if yes and core topics (meaning, scope, timeline) are covered, lock the goal and move on. 6. If not clear yet, ask the **next single question**. 7. Repeat until the user says it's clear. **Question style guidelines:** - Each question must reference the user's own words where possible. - Questions should feel like a curious coach, not a form. - Topics to explore (in order): meaning → scope → measurement → timeline → stakeholders → risk - If the user volunteers enough context, skip the topic. - The goal is not to ask every question — it's to get the user to say "清楚了". ### Other Phases | Command | Description | |---------|-------------| | `/init_goal [text]` | Activates the de-ambiguation engine using the one-question-at-a-time protocol above. | | `/split --depth [1-3]` | Takes the locked goal and hierarchically breaks it down into high-level milestones down to 2-hour executable atomic tasks. | | `/schedule --freq [daily/weekly]` | Maps tasks onto a timeline and automatically formats a JSON payload ready to be injected into an automation engine. | | `/align` | Triggered when plans change, friction occurs, or tasks are missed. Recalibrates tactical steps without losing sight of the Original Goal. | | `/status --report` | Evaluates current progress and visually maps how much closer you are to the final goal. | ## Automation Interface (Cron Bridge) ```json { "skill_target": "Secretary Skill", "cron_expression": "0 9 * * 1-5", "action_type": "active_reminder", "payload": { "original_goal_snapshot": "[Locked Goal]", "current_task": "[Task X]", "trigger_prompt": "Re-anchor the user to the grand vision, verify if [Task X] is ready for today, and listen for a `/align` call if scheduling conflicts exist." } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: roseknife520/ocr-local-rose (version 1.0.0)

--- name: ocr-local description: Extract text from images using Tesseract.js OCR (100% local, no API key required). Supports Chinese (simplified/traditional) and English. homepage: https://github.com/naptha/tesseract.js metadata: {"openclaw":{"emoji":"📝","requires":{"bins":["node"]},"install":[{"id":"npm","kind":"npm","packages":["tesseract.js"],"label":"Install tesseract.js"}]}} --- # OCR - Image Text Recognition (Local) Extract text from images using Tesseract.js. **100% local run, no API key required.** Supports Chinese and English. ## Quick start ```bash node {baseDir}/scripts/ocr.js /path/to/image.jpg node {baseDir}/scripts/ocr.js /path/to/image.png --lang chi_sim node {baseDir}/scripts/ocr.js /path/to/image.jpg --lang chi_tra+eng ``` ## Options - `--lang <langs>`: Language codes (default: chi_sim+eng) - `chi_sim` - Simplified Chinese - `chi_tra` - Traditional Chinese - `eng` - English - Combine with `+`: `chi_sim+eng` - `--json`: Output as JSON instead of plain text ## Examples ```bash # Recognize Chinese screenshot node {baseDir}/scripts/ocr.js screenshot.png # Recognize English document node {baseDir}/scripts/ocr.js document.jpg --lang eng # Mixed Chinese + English node {baseDir}/scripts/ocr.js mixed.png --lang chi_sim+eng ``` ## Notes - Language data is stored in `{baseDir}/scripts/tessdata/` - First run downloads language data (~20MB per language) - Subsequent runs use cached data - Works best with clear, high-contrast images - For handwritten text, accuracy may vary ## Language Data Language model files (`.traineddata.gz`) are automatically downloaded on first use and stored in: ``` {baseDir}/scripts/tessdata/ - chi_sim.traineddata.gz (Simplified Chinese) - eng.traineddata.gz (English) ``` To manually download or update language data: ```bash cd {baseDir}/scripts/tessdata curl -O https://cdn.jsdelivr.net/npm/@tesseract.js-data/chi_sim/4.0.0_best_int/chi_sim.traineddata.gz curl -O https://cdn.jsdelivr.net/npm/@tesseract.js-data/eng/4.0.0_best_int/eng.traineddata.gz ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fanyanggod/ecommerce-product-research (version 1.0.1)

--- name: ecommerce-product-research description: Research ecommerce products and stores using TikTok and Facebook ads, product data, and store intelligence from PipiAds. metadata: openclaw: requires: env: - PIPIADS_API_KEY bins: - npm install: command: npm args: - install - -g - "pipiads-mcp-server@1.0.3" primaryEnv: PIPIADS_API_KEY emoji: "📊" homepage: https://www.pipiads.com mcpServers: pipiads: command: pipiads-mcp-server env: PIPIADS_API_KEY: "{{PIPIADS_API_KEY}}" --- # Ecommerce Product Research Use this skill to research ecommerce products and stores through TikTok/Facebook ad signals plus product and store intelligence. ## Setup 1. Visit the official website at [https://pipiads.com/](https://pipiads.com/) to view real-time dashboard data and product UI. 2. Visit [https://www.pipispy.com/](https://www.pipispy.com/) to get your API key and manage billing/recharge. 3. Set the environment variable: `PIPIADS_API_KEY` ## When to Use - Find ecommerce products heavily advertised on TikTok or Facebook - Compare product detail, pricing, landing pages, and storefront patterns - Research stores, advertisers, and competitive positioning - Validate whether a product or niche is active across both platforms ## When NOT to Use - The user only wants monitoring workflows - The user only wants natural TikTok content - The user only wants TikTok Shop-specific analytics ## Core Tools ### Ads & Products - `search_ads` - `get_ad_detail` - `search_products` - `get_product_detail` - `search_adlibrary_products` - `get_adlibrary_product_detail` ### Advertisers & Stores - `search_advertisers` - `get_advertiser_detail` - `search_stores` - `get_store_detail` - `get_store_competition` - `get_store_product_analysis` - `get_store_region_analysis` - `get_store_data_analysis` ## Recommended Workflow 1. Start with `search_products` or `search_ads` in the target niche. 2. Inspect winning candidates with product and ad detail tools. 3. Use advertiser and store tools to understand who is selling and how broadly they operate. 4. Summarize pricing, offer pattern, landing-page structure, and competitive density. ## Cost Awareness Each API call consumes credits from your PipiAds account: - **List/Search**: 1 credit per result returned - **Detail**: 1 credit per request (free if queried within 3 days) Ask for category, price band, region, and platform before running broad ecommerce queries.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: joshbuildswithdoit/airbnb-host-ops (version 1.0.0)

--- name: airbnb-host-ops description: Full Airbnb/short-term rental operations for hosts managing 1-20 properties. Guest messaging automation, pricing optimization alerts, cleaning schedule coordination, review management, listing optimization, and income tracking. Built for self-managing STR hosts who want to scale without a property manager. --- # Airbnb Host Operations Autonomous short-term rental management for Airbnb, VRBO, and direct booking hosts. ## Guest Communication - Auto-draft check-in instructions, house rules, and local recommendations - Generate response templates for common guest questions (parking, wifi, checkout) - Flag urgent guest messages for immediate attention - Draft 5-star review requests sent 24 hours after checkout ## Pricing & Revenue - Alert when nightly rates drop below your floor price - Weekly revenue report vs prior week and prior year - Flag high-demand dates (local events, holidays) for price review - Track occupancy rate and suggest minimum stay adjustments ## Operations & Cleaning - Generate cleaning checklist per property type - Coordinate turnover schedule between bookings - Track supply inventory (toiletries, linens) and generate reorder reminders - Maintenance request log and vendor contact management ## Listing Optimization - Monthly SEO audit of listing title, description, and photos - Competitor rate monitoring (manual input → analysis) - Suggest amenities to add based on guest feedback patterns - Track listing ranking signals ## Compliance & Finance - STR permit renewal reminders by jurisdiction - Monthly income/expense log for Schedule E - Occupancy tax filing reminders by state - Annual 1099-K reconciliation checklist ## Review Management - Draft professional responses to negative reviews - Track review trends (cleanliness, communication, location scores) - Alert when rating drops below 4.7 ## Setup Tell your agent: - Property addresses and Airbnb listing URLs - Your cleaning team contact info - Minimum nightly rate floor - Local permit/tax requirements


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