OpenClaw/clawhub-security-signals huggingface dataset:
Skill: psiclawops/gist-post (version 1.0.1)

--- name: gist-post version: 1.0.0 description: Post content to GitHub Gist and get back a shareable URL. Rich context sharing between agents, operators, and humans. homepage: https://github.com/PsiClawOps/gist-share --- # gist-post Post any content — summaries, plans, reports, pitches, logs — to GitHub Gist and share the URL. A lightweight way for agents to publish rich context that humans and other agents can read from anywhere. ## Why Gists? Agents communicate through messages, but messages disappear into scroll. A gist is: - **Persistent** — stays at a stable URL - **Readable by anyone** — no auth required for public gists - **Markdown-rendered** — GitHub renders it beautifully - **Shareable** — paste the URL anywhere When an agent needs to hand off context to a human, another agent, or a future session — a gist beats a wall of chat text. --- ## Setup You need a GitHub Personal Access Token scoped to `gist`, set as `GITHUB_TOKEN` in your environment. Ask your agent to walk you through creating one if you haven't already. --- ## How to Post a Gist Use the `exec` tool to call `gh gist create`: ```bash gh gist create --public --desc "DESCRIPTION" --filename "FILENAME.md" - << 'EOF' # Your content here Supports **markdown** formatting. EOF ``` Use `--secret` instead of `--public` if the content shouldn't be publicly indexed. The command returns the gist URL on success: ``` ✓ Created public gist FILENAME.md https://gist.github.com/USERNAME/HASH ``` --- ## Workflow 1. **Prepare content** — write your markdown 2. **Choose visibility** — `--public` or `--secret` 3. **Post it** — run `gh gist create` via `exec` 4. **Return the URL** — share it in the conversation, send it to another agent, or log it --- ## Tips - Use `.md` extension in `--filename` so GitHub renders markdown - Write a meaningful `--desc` — it's searchable - Update an existing gist: `gh gist edit GIST_ID` - List your gists: `gh gist list` --- *Gists: the simplest way for a claw to leave a note the world can read.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: onceyoungs/wx-mp-write (version 1.0.0)

--- name: wx-mp-write description: 微信公众号文章全流程写作助手。支持热点选题、文章撰写、AI去味润色（去除机器感）、配图建议、一键发布到公众号草稿箱。适用于技术博主、自媒体创作者、企业公众号运营等场景。当用户需要写公众号文章、润色内容、寻找配图或发布到微信时使用此skill。 --- # 微信公众号写作助手 全流程辅助微信公众号文章创作，从选题到发布一站式解决。 ## 核心功能 ### 1. 热点选题建议 - 根据领域/关键词搜索最新热点 - 提供选题角度和切入点建议 - 生成文章大纲框架 ### 2. 资料搜索（默认使用 tavily-search） **核心工具**：使用 tavily-search 技能搜索引擎查询选题资料、行业知识和配图素材 搜索策略： - **选题阶段**：搜索领域关键词 + "热点/趋势/最新/爆文"，了解当前用户关注什么 - **撰写阶段**：搜索具体技术/产品资料、数据、案例，补充文章论据 - **配图阶段**：搜索"关键词 + 设备/实拍/产品/车间"，找高质量图片素材 结果处理原则： - 优先引用来源可靠的页面（官方网站、行业媒体、百科类） - 过滤广告、低质内容和重复信息 - 每次搜索保留 3-5 条最相关结果即可，避免信息过载 > ⚠️ 前置条件：需要环境变量 `TAVILY_API_KEY` 已配置（或在 `~/.openclaw/.env` 中设置） ### 3. 文章撰写 - 支持多种风格：技术干货、故事叙事、观点评论、教程指南 - 自动适配公众号格式（标题、段落、重点标注） - 生成引人入胜的开头和有行动号召的结尾 ### 4. AI 去味润色 **核心功能**：去除 AI 写作痕迹，让文章读起来像真人写的 润色策略： - 加入个人口语化表达（"说实话..."、"其实..."、"讲真..."） - 使用不完美但真实的句式（适当重复、口语停顿） - 插入具体场景和细节（时间、地点、真实案例） - 加入情感波动（惊喜、困惑、恍然大悟） - 使用接地气的比喻（避免过于书面的修辞） - 适当使用网络流行语（但不过度） - 长短句交错，避免过于规整的排比 ### 5. 配图建议 - 根据文章内容推荐配图类型 - 使用 tavily-search 技能搜索合适的配图素材（优先搜索设备实拍、产品图、行业场景） - 提供封面图设计建议 图片引用格式：`![图片描述](图片URL)` 图片优先级：设备官网产品图 > 行业媒体报道图 > 高质量通用工业图（Unsplash 等） ### 6. 发布到公众号 - 生成符合微信格式的 Markdown - 调用 wechat-publisher 发布到草稿箱 - 支持设置标题、作者、摘要 ## 使用流程 ``` 用户输入需求 ↓ 选择功能模式： - 从零开始写 → 选题 → 搜索资料 → 大纲 → 撰写 → 润色 → 搜索配图 → 发布 - 已有草稿 → 补充资料 → 润色 → 搜索配图 → 发布 - 仅润色 → 去味处理 - 仅发布 → 调用发布工具 ↓ 生成最终文章 ↓ （可选）发布到公众号草稿箱 ``` ## 写作风格指南 ### 技术干货风格 - 开门见山，先给结论 - 步骤清晰，代码/命令可复制 - 加入踩坑记录和解决方案 - 结尾总结核心要点 ### 故事叙事风格 - 场景化开头（时间、地点、人物） - 情节推进有起伏 - 金句点睛 - 引发共鸣的结尾 ### 观点评论风格 - 明确立场，不模棱两可 - 论据充分，数据支撑 - 承认反方观点并回应 - 给出行动建议 ## AI 去味检查清单 润色后检查文章是否还有 AI 痕迹： - [ ] 没有"首先/其次/最后/综上所述"等模板化连接词 - [ ] 没有过于完美的三段式结构 - [ ] 有具体的个人经历或观察 - [ ] 有情感词（惊喜、崩溃、感动、无语） - [ ] 有口语化表达（"说白了..."、"你可能想不到..."） - [ ] 长短句交错，不是每段都一样长 - [ ] 有适当的括号补充（（笑）、（别问我怎么知道的）） ## 配图建议规则 | 文章类型 | 配图建议 | | -------- | ---------------------------------- | | 技术教程 | 代码截图、架构图、流程图、效果演示 | | 产品测评 | 产品实拍、对比图、细节特写 | | 行业观点 | 数据图表、趋势图、相关新闻截图 | | 个人故事 | 场景照片、聊天截图、相关物品 | | 工具推荐 | 软件界面、功能截图、前后对比 | ## 发布格式 生成的 Markdown 需符合 wx-mp-push 要求： - 使用标准 Markdown 语法 - 图片使用 `![描述](URL)` 格式 - 代码块指定语言类型 - 标题层级使用 ## 和 ### ## 工具调用 ### 资料搜索与配图（默认） 使用 `tavily-search` 技能搜索资料和配图： - 要求环境变量 `TAVILY_API_KEY` 已配置（或在 `~/.openclaw/.env` 中设置） - Brave web_search 不可用时，Tavily 是默认替代方案 - 支持中英文搜索，结果以 Markdown 格式输出便于引用 ### 发布到公众号 ``` 将生成的 Markdown 内容传递给 wx-mp-push 进行发布，要保留原始文件 ``` 如果用户未安装发布工具，提供手动发布指南。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: utopiabenben/social-publisher (version 1.0.1)

--- name: social-publisher description: 【爆款标题】自媒体人必备：一键发布 4 个平台，告别重复劳动！ 你是不是经常写完文章，还要手动调整格式？微信公众号要加粗标记，小红书要标签前置，知乎要保持 Markdown...每次都要花 1-2 小时重复劳动？ 本工具用一键转换技术，让你的 Markdown 秒变 4 大平台原生格式（微信/小红书/知乎/抖音），支持预览和零风险模拟发布。 ✨ **核心亮点**： - 一键转换：Markdown → 4大平台原生格式 - 预览模式：发布前看效果，放心调整 - 多图支持：配图自动上传（需API凭证） - 安全模拟：默认不真实发布，测试零风险 📁 **典型场景**： - 自媒体运营：一篇内容，4平台同时发布 - 内容创作者：告别格式转换的繁琐 - 团队协作：统一格式化标准 🎯 **为什么选我**： ✅ 唯一支持 4 大中文平台（微信、小红书、知乎、抖音） ✅ 完整预览+模拟模式，零风险 ✅ 开源免费，可自扩展平台 👉 立即体验：`clawhub install social-publisher` --- # Social Publisher Skill Publish content to multiple Chinese social platforms with a single command. ## Features - **内容格式化**: 自动将 Markdown 转换为各平台格式 - 微信公众号：标题加下划线，列表转换为圆点，加粗转为【】标记 - 小红书：分段清晰，自动添加话题标签，适合移动端阅读 - 知乎：保留 Markdown 语法，支持代码块和表格 - 抖音：文案简短化，话题标签前置 - **多平台发布**：一键发布到微信公众号、小红书、知乎、抖音 - **图片支持**：支持多图片上传（需要真实 API） - **预览模式**：使用 `--format` 预览各平台格式化效果 - **安全模拟**：默认模拟模式，不实际发布，可放心测试 ## Usage ### 预览各平台格式化效果 ```bash social-publisher --title "我的标题" --content "正文内容" --format ``` ### 模拟发布（展示格式化内容） ```bash social-publisher publish --title "标题" --content "正文" --images "img1.jpg,img2.jpg" --platforms wechat,xiaohongshu ``` ### 真实发布（需要配置 API 凭证） ```bash social-publisher publish --title "标题" --content "正文" --images "img1.jpg" --platforms wechat,zhihu ``` ## Command-Line Options - `--title/-t`: 文章标题（必填） - `--content/-c`: 文章正文，支持 Markdown（必填） - `--images/-i`: 图片路径，逗号分隔 - `--platforms/-p`: 目标平台，逗号分隔（默认：wechat,xiaohongshu,zhihu,douyin） - `--dry-run`: 仅检查配置，不发布 - `--format`: 仅预览各平台格式化效果，不发布 ## Configuration ### 模拟模式（默认） 直接使用即可，无需配置。所有发布均为模拟，仅展示格式化效果。 ### 真实发布模式 Set credentials in environment variables or config file: **微信公众号**: - `WECHAT_APPID` - `WECHAT_APPSECRET` **小红书**: - `XIAOHONGSHU_APP_ID` - `XIAOHONGSHU_APP_SECRET` - `XIAOHONGSHU_ACCESS_TOKEN` **知乎**: - `ZHIHU_CLIENT_ID` - `ZHIHU_CLIENT_SECRET` - `ZHIHU_ACCESS_TOKEN` **抖音**: - `DOUYIN_APP_KEY` - `DOUYIN_APP_SECRET` - `DOUYIN_ACCESS_TOKEN` Or place them in `~/.openclaw/secrets/social-publisher.json`: ```json { "wechat": {"appid": "...", "appsecret": "..."}, "xiaohongshu": {"app_id": "...", "app_secret": "...", "access_token": "..."}, "zhihu": {"client_id": "...", "client_secret": "...", "access_token": "..."}, "douyin": {"app_key": "...", "app_secret": "...", "access_token": "..."} } ``` ## Formatting Examples ### Input Markdown ```markdown # 一级标题 ## 二级标题 - 列表项1 - 列表项2 **加粗文本** ``` ### WeChat Output ``` 【标题】 一级标题 【正文】 一级标题 ==================== • 列表项1 • 列表项2 【加粗文本】 ``` ### Xiaohongshu Output ``` 【标题】 ✨ 一级标题 ✨ 【正文】 1. 列表项1 2. 列表项2 【话题】 #生活记录 #分享 #好物推荐 ``` ### Zhihu Output ``` # 一级标题 ## 二级标题 - 列表项1 - 列表项2 **加粗文本** ``` ### Douyin Output ``` 【文案】 列表项1 列表项2 加粗文本 【标题】 一级标题 【话题】 #短视频 #推荐 #热门 ``` ## Safety - 模拟模式下不发送任何网络请求 - 凭证从环境变量读取，不会记录到日志 - 真实 API 调用时使用 HTTPS - 支持 dry-run 模式用于安全测试 ## Roadmap - [ ] 微信公众号真实 API 实现 - [ ] 小红书真实 API 实现 - [ ] 知乎真实 API 实现 - [ ] 抖音真实 API 实现 - [ ] 图片上传功能 - [ ] 平台草稿箱保存功能 - [ ] 发布历史记录 ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cinience/aliyun-cloudauth-verify (version 1.0.0)

--- name: aliyun-cloudauth-verify description: Use when managing Alibaba Cloud ID Verification (Cloudauth) via OpenAPI/SDK, including the user is working on identity-verification resource operations, config updates, status checks, or troubleshooting Cloudauth API workflows. version: 1.0.0 --- Category: service # ID Verification (Cloudauth) Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for ID Verification. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `Cloudauth` - Default API version: `2022-11-25` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: `output/aliyun-cloudauth-verify/` ## Validation ```bash mkdir -p output/aliyun-cloudauth-verify for f in skills/security/identity/aliyun-cloudauth-verify/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/aliyun-cloudauth-verify/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-cloudauth-verify/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/aliyun-cloudauth-verify/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xueyetianya/gdpr-checker (version 2.3.6)

--- version: "2.0.0" name: GDPR Checker description: "GDPR Compliance Checker. Use when you need gdpr checker capabilities. Triggers on: gdpr checker." GDPR合规检查。合规审计、用户同意、数据权利、泄露响应、DPA、检查清单。GDPR compliance checker. GDPR、数据保护。 author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # GDPR Checker GDPR合规检查。合规审计、用户同意、数据权利、泄露响应、DPA、检查清单。GDPR compliance checker. GDPR、数据保护。 ## 推荐工作流 ``` 需求分析 → 选择命令 → 输入描述 → 获取结果 → 调整优化 ``` ## 命令速查 ``` audit audit consent consent rights rights breach breach dpa dpa checklist checklist ``` --- *GDPR Checker by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com - Run `gdpr-checker help` for all commands ## Commands Run `gdpr-checker help` to see all available commands. ## When to Use - to automate gdpr tasks in your workflow - for batch processing checker operations ## Output Returns reports to stdout. Redirect to a file with `gdpr-checker run > output.txt`. ## Configuration Set `GDPR_CHECKER_DIR` environment variable to change the data directory. Default: `~/.local/share/gdpr-checker/` ## Requirements - bash 4+ - python3 (standard library only)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xeusoc/nightscout-local (version 1.0.0)

--- name: nightscout-local description: Read glucose data from a Nightscout site. Use when the user asks for the current CGM reading, trend, recent readings, or Nightscout status. This skill is read-only and should not be used for portal login automation or write actions. --- # Nightscout Local Use this skill for **read-only Nightscout data access**. Set the Nightscout base URL with either: - environment variable: `NIGHTSCOUT_BASE_URL` - command flag: `--url https://your-site.example/` Supported tasks: - get current glucose from Nightscout - get recent readings - check basic Nightscout site status/config ## Commands Current reading: ```bash NIGHTSCOUT_BASE_URL="https://your-site.example/" \ python3 /Users/serveradmin/.openclaw/workspace/skills/nightscout-local/scripts/nightscout_read.py current ``` Simple command path for later automation/use: ```bash python3 /Users/serveradmin/.openclaw/workspace/scripts/current_bg.py ``` Recent readings: ```bash NIGHTSCOUT_BASE_URL="https://your-site.example/" \ python3 /Users/serveradmin/.openclaw/workspace/skills/nightscout-local/scripts/nightscout_read.py recent 6 ``` Site status: ```bash NIGHTSCOUT_BASE_URL="https://your-site.example/" \ python3 /Users/serveradmin/.openclaw/workspace/skills/nightscout-local/scripts/nightscout_read.py status ``` ## Output guidance - Report glucose in **mg/dL**. - Include trend/direction when available. - Include the reading timestamp in America/Los_Angeles. - Keep medical wording careful: report readings clearly, but do not claim to provide medical advice. ## Guardrails - Read-only only. - Do not modify Nightscout settings. - Do not infer treatment recommendations unless Martin explicitly asks for interpretation. - If the endpoint is unavailable, report that plainly.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: merjua14/local-lead-gen (version 1.0.0)

--- name: local-lead-gen description: Automated local business lead generation and cold outreach pipeline. Scans businesses by niche and city, scores their websites (SSL, mobile, speed, design), identifies bad/outdated sites, enriches with contact info, and sends personalized cold emails pitching services. Use when building lead gen pipelines, prospecting local businesses, finding companies with bad websites, automating cold outreach, or setting up recurring business development workflows. --- # Local Lead Gen — Automated Business Prospecting & Outreach Find local businesses with bad websites, score them, and auto-email personalized pitches. ## Pipeline Overview 1. **Scan** — Search businesses by niche + city via Brave Search API 2. **Score** — Check each site: SSL, mobile-friendly, speed, design age, broken elements (0-100) 3. **Filter** — Flag sites scoring <40 as outreach candidates 4. **Enrich** — Extract emails from contact pages using DeepCrawl or direct scraping 5. **Email** — Send personalized cold emails via Resend (or any SMTP provider) 6. **Track** — Log all leads to Google Sheets or CSV with status tracking ## Requirements - **Brave Search API key** — for business discovery (free tier available) - **Resend API key** (or SMTP credentials) — for sending cold emails - **DeepCrawl API key** (optional) — for better contact page parsing - **Google Sheets OAuth** (optional) — for lead tracking ## Quick Start ```bash # Set environment variables export BRAVE_API_KEY=your_key export RESEND_API_KEY=your_key # Run the scanner node scripts/bad-website-hunter.js --niche "restaurants" --city "Austin TX" --limit 20 ``` ## Configuration Edit `scripts/config.json` to customize: - Target niches and cities - Score thresholds for outreach - Email templates and sender info - Suppression list (unsubscribed emails) ## Scripts - `scripts/bad-website-hunter.js` — Main pipeline: scan → score → enrich → email - `scripts/config.json` — Pipeline configuration ## Email Templates The default cold email template pitches web design/development services. Customize in config.json: ``` Subject: Quick question about {business_name}'s website Hi {first_name}, I was looking at {business_name}'s website and noticed a few things that might be costing you customers: {issues_found}. I help local businesses in {city} modernize their online presence. Would you be open to a quick chat about what an upgrade could look like? Best, {sender_name} ``` ## Scaling - **Small towns** (pop 5-30K) yield the highest percentage of bad sites - **Best niches**: restaurants, auto repair, salons, contractors, law offices - **Send 10-25 emails per day** per domain to avoid spam flags - **Rotate sending domains** if scaling past 50/day - **Add drip sequences** (follow-up emails at day 3 and day 7) ## References - See `references/scoring-criteria.md` for website scoring methodology - See `references/email-best-practices.md` for cold email deliverability tips


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chenjiahui11/fanqie-novel-auto-publish (version 1.0.3)

# fanqie-novel-auto-publish # 番茄小说创作发布一条龙技能 整合 `open-novel-writing`（AI 创作）+ `fanqie-publisher`（番茄发布），从想法一键生成到发布到番茄小说。 ## 完整流程 ``` 想法 → 设定 → 大纲 → 生成多章 → 评审修订 → 自动发布到番茄小说 ``` ## 工作原理 技能目录下包含两个核心模块： | 模块 | 文件 | 说明 | |------|------|------| | `novel_generator` | `novel_generator.py` | 调用 AI 生成小说章节 | | `fanqie_publisher` | `fanqie_publisher.py` | 调用 fanqie-publisher 发布章节 | | `auto_publish` | `auto_publish.py` | 编排完整工作流 | ## 使用方式 ### 命令行发布 ```bash cd ~/.openclaw/skills/fanqie-novel-auto-publish/scripts python auto_publish.py --check # 检查状态 python auto_publish.py --works # 列出作品 python auto_publish.py -w "作品名" -f "章节.md" # 发布单章 ``` ### Python 调用 ```python import sys sys.path.insert(0, "~/.openclaw/skills/fanqie-novel-auto-publish/scripts") from auto_publish import AutoPublishWorkflow workflow = AutoPublishWorkflow() # 检查状态 status = workflow.check_status() # 发布章节 result = workflow.publish_chapter( work_title="诡异系统：我在规则世界卡BUG", chapter_file="C:/path/to/chapter.md" ) ``` ## 依赖 - `open-novel-writing` 技能（生成小说内容） - `fanqie-publisher` 技能（发布到番茄小说） 这两个技能需要先安装并配置好。 ## 工作目录 - 小说输出：`~/.openclaw/skills/novel-generator/output/` - 记忆文件：`~/.openclaw/skills/novel-generator/.learnings/` - Cookie 文件：`~/.openclaw/skills/fanqie-publisher/scripts/fanqie_cookies.json` --- 企业开发·定制技能请联系Wx：CChenJ_


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: waleolapo/crypto-whale-monitor (version 1.0.3)

--- name: crypto-whale-monitor description: Monitors large cryptocurrency wallet balances (whales) on-chain using Web3 RPC to detect potential market-moving activity. Can read from `references/wallets.md` or accept custom addresses. --- # Crypto Whale Monitor This skill contains the logic to connect to blockchain explorers and track large balances for a defined set of "whale" wallets. ## Workflow 1. **Define Wallets**: Add known whale addresses to `references/wallets.md`. 2. **Execute**: Run `npm start` (or `./scripts/monitor.js`) to scan the list. 3. **Analyze**: Review output for "WHALE DETECTED" alerts. 4. **Schedule**: Set up a cron job to run `npm start` periodically for automated monitoring. ## Scripts - `scripts/monitor.js`: Core logic for checking balances via public RPC. Reads from `references/wallets.md` by default. ## References - `references/wallets.md`: A list of known, public whale wallet addresses.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: farmerboybi-bot/cute-kitten-generator (version 1.0.0)

--- name: cute-kitten-generator description: Generate high-detail cute kitten/animal images using ComfyUI local workflow. Use when user wants to create adorable animal photos with high clarity (1536×1536 resolution, 100 steps, RealVisXL V5.0). Supports kittens, puppies, and other small animals with close-up portrait, fur details, and home scene settings. --- # Cute Kitten Generator ## Quick Start 生成可爱小猫/小动物高清图片： ```bash cd /Users/lobster/.openclaw/workspace/ComfyUI curl -X POST -H "Content-Type: application/json" \ -d '{"prompt": '"$(cat cute_kitten_sofa.json)"', "client_id": "qiuer-bot"}' \ http://127.0.0.1:8188/prompt ``` ## 配置固化 🔒 - **分辨率**: 1536×1536（高清正方形） - **步骤**: 100 steps（高细节） - **CFG**: 8.0 - **模型**: RealVisXL_V5.0_fp16.safetensors - **Sampler**: dpmpp_2m, scheduler: karras - **Workflow**: `cute_kitten_sofa.json` ## 场景定制 修改 `cute_kitten_sana.json` 的 prompt（node 2）： - **动物类型**: kitten → puppy / bunny / hamster - **场景**: sofa → bed / carpet / garden / basket - **光线**: golden hour → morning light / soft window light ## 发送 WhatsApp 生成完成后发送： ```bash openclaw message send \ --target "+17704012443" \ --media "/Users/lobster/.openclaw/workspace/ComfyUI/output/CUTE_KITTEN_SOFA_00001_.png" ``` ## 清晰度提升方案 ### 方案 A（已固化 ✅） - 1536×1536 + 100 steps → 2.9MB 高清版 ### 方案 B（Hires. Fix） 如需更高清晰度： - 先 1024×1024 生成 - 再 2x upscale + 重绘细节 - 创建 `cute_kitten_hires.json` workflow ## 文件位置 - **Workflow**: `/Users/lobster/.openclaw/workspace/ComfyUI/cute_kitten_sofa.json` - **输出**: `/Users/lobster/.openclaw/workspace/ComfyUI/output/CUTE_KITTEN_*.png`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: xinian5216/cf-markdown-agents (version 1.0.0)

--- name: cf-markdown-agents description: Fetch web content using Cloudflare's Markdown for Agents protocol. Automatically converts HTML to Markdown with 80% token reduction. Use when fetching web pages for AI processing, web scraping, content extraction, or when the user mentions "Markdown for Agents", "cf-markdown", or needs structured web content in Markdown format. --- # Cloudflare Markdown for Agents This skill enables fetching web content using Cloudflare's Markdown for Agents protocol, which automatically converts HTML pages to clean, structured Markdown format. ## What is Markdown for Agents? Cloudflare's Markdown for Agents is a content negotiation feature that: - Automatically converts HTML to Markdown at the edge - Reduces token usage by ~80% compared to raw HTML - Returns clean, structured content perfect for AI processing - Adds `x-markdown-tokens` header showing estimated token count - Includes `Content-Signal` headers for AI usage permissions ## Usage ### Basic Fetch Use the provided script to fetch any URL with Markdown for Agents support: ```bash scripts/fetch-markdown.sh <URL> ``` Example: ```bash scripts/fetch-markdown.sh "https://developers.cloudflare.com/agents/" ``` ### In Code TypeScript/JavaScript example: ```typescript const response = await fetch("https://example.com/page", { headers: { Accept: "text/markdown, text/html", }, }); const tokenCount = response.headers.get("x-markdown-tokens"); const markdown = await response.text(); ``` cURL example: ```bash curl https://example.com/page -H "Accept: text/markdown" ``` ## Response Headers - `content-type: text/markdown; charset=utf-8` - Content is Markdown - `x-markdown-tokens: <number>` - Estimated token count - `content-signal: ai-train=yes, search=yes, ai-input=yes` - Usage permissions ## Supported Sites Any site using Cloudflare with Markdown for Agents enabled: - Cloudflare Developer Documentation (developers.cloudflare.com) - Cloudflare Blog (blog.cloudflare.com) - Any site with the feature enabled in Cloudflare dashboard ## Benefits | Aspect | HTML | Markdown | |--------|------|----------| | Tokens | 16,180 | 3,150 | | Reduction | - | ~80% | | Structure | Complex | Clean | | AI Parsing | Hard | Easy | ## References - Full protocol details: [references/protocol.md](references/protocol.md) - API examples: [references/examples.md](references/examples.md)


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