OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zhaoolee/notes-export-api (version 0.1.2)

--- name: notes-export-api description: 通过可配置的 notes-export-api 导出接口，把 Markdown 内容导出为锤子便签风格的长图 PNG。脚本会优先探测本地生产入口 `http://127.0.0.1:18080`，探测不到时再回退到 `https://notes.fangyuanxiaozhan.com/api/export`；如果存在 `.env`，则读取其中的 `NOTES_EXPORT_API_BASE_URL` 作为最高优先级。支持 `default` 暖白纸感和 `smartisan-dark` 锤子暗黑两种主题；如果调用方没有主动声明主题，默认使用 `default`，不要强制用户选择。若本地 Markdown 文件里包含相对路径或绝对路径图片，脚本会先调用同源后端的 `/api/images/import` 上传图片并回填 URL，再调用导出接口生成带图便签。用户提到“便签导出”“锤子便签”、Markdown 转图片、把本地 .md 文件渲染成便签长图、或需要用脚本批量导出便签图片时使用。 --- # 便签导出 API 直接调用导出 API 做便签导出，不在本地重写渲染逻辑。 开源地址：`https://github.com/zhaoolee/notes` ## 工作流 1. 使用 `scripts/export_note.sh`。 2. 传入 `--markdown-file` 或 `--markdown`。 3. 若使用 `--markdown-file`，脚本会自动扫描 Markdown 内的本地图片引用： - `![alt](./image.png)` - `![alt](../assets/demo.jpg "title")` - `<img src="./image.png" />` 4. 对本地图片调用同源后端 `/api/images/import` 获取 URL，并把 Markdown 中的图片路径替换成可访问 URL。 5. 再把替换后的 Markdown 提交到 `/api/export`。 6. 传入 `--output`。 7. 只有在调用方明确指定主题时才传 `--theme`；否则直接使用默认的 `default`（暖白纸感），不要为了主题再追问用户。 8. 默认优先使用本地生产入口 `http://127.0.0.1:18080/api/export`；探测不到时回退到 `https://notes.fangyuanxiaozhan.com/api/export`。 9. 若需切换导出服务地址，可在仓库根目录 `.env` 或 `skills/notes-export-api/.env` 中设置： ```bash NOTES_EXPORT_API_BASE_URL=http://127.0.0.1:18080 # 或显式指定线上地址 NOTES_EXPORT_API_BASE_URL=https://notes.fangyuanxiaozhan.com ``` ## 主题约定 - `default`: 暖白纸感。默认主题；调用方未声明时使用它。 - `smartisan-dark`: 锤子暗黑。仅在调用方明确要求暗色或暗黑主题时使用。 ## 脚本用法 ```bash skills/notes-export-api/scripts/export_note.sh \ --markdown-file /abs/path/to/note.md \ --output /abs/path/to/note.png ``` 如果 `note.md` 中包含本地图片，例如： ```md ## **0x01** 配图如下： ![示意图](./images/demo.png) ``` 脚本会先上传 `./images/demo.png`，再把 Markdown 中的图片链接替换成后端返回的 URL，最后导出 PNG。 ```bash skills/notes-export-api/scripts/export_note.sh \ --markdown '## **0x01**\n正文内容' \ --output /abs/path/to/note.png ``` ```bash skills/notes-export-api/scripts/export_note.sh \ --markdown-file /abs/path/to/note.md \ --theme smartisan-dark \ --output /abs/path/to/note-dark.png ``` ## 注意事项 - 本地 Markdown 文件按 UTF-8 读取。 - `--markdown-file` 模式会自动处理本地图片；`--markdown` 内联文本模式不会解析相对路径图片，内联模式下请直接传可访问 URL。 - `--theme` 是可选参数；不传时默认使用 `default`。 - 默认优先探测本地生产入口 `http://127.0.0.1:18080/api/export`；若本地服务不可用，则回退到 `https://notes.fangyuanxiaozhan.com/api/export`。 - `.env` 中的 `NOTES_EXPORT_API_BASE_URL` 优先级高于自动探测结果。 - 命令行 `--endpoint` 优先级仍然最高。 - 若 `.env` 提供的是站点根地址，脚本会自动补上 `/api/export`。 - 图片导入接口会从同一个后端地址推导得到：若导出地址是 `/api/export`，图片导入地址会自动改为 `/api/images/import`。 - 本地图片路径支持相对 `Markdown` 文件的相对路径、绝对路径，以及 `file://` 路径。 - 若 Markdown 中引用的本地图片不存在，脚本会直接报错并停止导出。 - 遇到非 200 响应时，直接把错误返回给调用方。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: elandivar/neomano-internet-speed (version 0.1.0)

--- name: neomano-internet-speed description: Measure current internet connection speed (download + upload) from this machine using a lightweight HTTP-based speed test (Cloudflare speed endpoints). Use when the user asks for subida/bajada speed, bandwidth, or "speedtest". metadata: {"clawdbot":{"emoji":"🚦","requires":{"bins":["python3"]}}} --- ## What it does - Runs a simple download + upload throughput test against Cloudflare's speed test endpoints. - Reports Mbps for download and upload. ## Run ```bash python3 {baseDir}/scripts/speedtest.py python3 {baseDir}/scripts/speedtest.py --download-bytes 50000000 --upload-bytes 10000000 ``` ## Notes - Results depend on current network conditions and routing. - This is not as feature-rich as Ookla Speedtest, but has zero external deps.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-side-hustle-agent (version 1.0.0)

--- name: side-hustle-agent version: 1.0.0 description: AI 副业顾问 - 自动扫描接单平台，分析项目可行性，推荐最适合的赚钱机会。适合：想在网上接单赚钱但不知道从哪开始的人。 metadata: openclaw: emoji: "💰" requires: bins: ["curl"] --- # AI 副业顾问 Skill 自动扫描接单平台，分析项目可行性，推荐赚钱机会。 ## 核心功能 ### 1. 平台扫描 自动扫描以下平台： - Upwork（国际） - Fiverr（国际） - 掘金沸点（国内） - 猪八戒（国内） - 程序员客栈（国内） ### 2. 项目分析 对每个项目自动分析： - 预算范围 - 技术要求 - 竞争程度 - 时间成本 - 成功率预估 ### 3. 机会推荐 根据你的技能栈，推荐： - 最适合的项目 - 报价建议 - 提案模板 - 注意事项 ## 使用方法 ### 扫描今日机会 ``` 扫描今天适合我的接单机会 ``` Agent 会： 1. 访问主要平台 2. 筛选符合条件的项目 3. 按收益/难度排序 4. 给出行动建议 ### 分析特定项目 ``` 分析这个项目：[项目链接] ``` 返回： - 可行性评分（1-10） - 预计收益 - 时间成本 - 竞争分析 - 报价建议 ### 生成提案 ``` 为这个项目写提案：[项目链接] ``` 自动生成： - 专业提案 - 报价策略 - 时间规划 ## 配置文件 ### ~/.openclaw/workspace/HUSTLE.md ```yaml # 你的技能栈 skills: - Python - Node.js - React - 微信小程序 # 感兴趣的领域 interests: - AI 应用开发 - 自动化脚本 - 数据分析 - 网站开发 # 期望收入（元/月） target_income: 5000 # 可投入时间（小时/周） available_hours: 20 ``` ## 快速命令 ### Upwork 扫描 ```bash curl -s "https://www.upwork.com/search/jobs/?q=python" | grep -oP '(?<=title">)[^<]+' | head -10 ``` ### 掘金沸点 ```bash curl -s "https://api.juejin.cn/recommend_api/v1/short_msg/recommend" \ -H "Content-Type: application/json" \ -d '{"id_type":2,"sort_type":200,"cursor":"0","limit":20}' \ | jq '.data[].msg_Info.content' | head -10 ``` ## 输出格式 ### 机会卡片 ``` 🎯 [平台] 项目标题 💰 预算：$500-1000 ⏰ 截止：3天 📊 匹配度：85% 💡 建议：你的 Python 技能很适合，建议报价 $800 ``` ### 分析报告 ``` 📋 项目分析 ━━━━━━━━━━━━━━━━ ✅ 优势： - 技术栈匹配 - 预算合理 ⚠️ 风险： - 竞争激烈 - 时间紧张 💰 建议：报价 $900，3天交付 ``` ## 注意事项 - 免费平台有限流，建议配合 API Key - 项目信息实时变化，以平台为准 - 提案发送前人工审核 --- 创建：2026-03-11 版本：1.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: peand-rover/wellness-coach-video (version 1.0.0)

name: wellness-coach-video version: "1.0.0" displayName: "Wellness Coach Video — Program Overview and Client Journey Videos for Wellness Coaches and Holistic Wellness Practitioners" description: > You are a video assistant for wellness coaches, holistic wellness practitioners, and integrated wellness professionals — helping coaches communicate their whole-person approach, show the transformation arc that clients experience through a coaching engagement, and differentiate their practice from the wellness influencers and self-help content that potential clients have already consumed without changing their behavior. Stress management coaching, mindfulness and resilience coaching, energy and vitality coaching, work-life balance coaching, hormonal wellness coaching, and burnout recovery: Wellness Coach Video turns your holistic framework, your client relationships, and your personal wellness philosophy into short educational and testimonial clips that answer the question every burned-out or chronically stressed prospective client asks: is this different from the content I've already watched, and will this actually work for my life? Help wellness coaches attract the clients who need coaching rather than content by showing what a coaching relationship actually looks like from inside it. Program structure videos explaining the arc of your signature coaching program — the assessment phase, the goal-setting process, the weekly session format, the between-session accountability structure, and what the end of a program engagement looks like — give prospective clients the container clarity that makes investing in coaching feel like a defined experience rather than an open-ended commitment. Stress and burnout explanation videos describing the physiological and behavioral patterns that your coaching addresses — the sleep disruption cycle, the cortisol and energy relationship, the lifestyle habits that perpetuate burnout rather than recover from it — position your coaching as the structured intervention for a specific set of recognizable symptoms that your ideal client has been trying to manage on their own. Mindfulness integration videos showing how you incorporate awareness practices into practical life management rather than treating meditation as a separate practice give the time-pressured professionals who need your coaching the evidence that your approach works within real constraints. tags: - wellness coach video - holistic wellness video - wellness coaching marketing - burnout coach video - stress management coach - mindfulness coach video - wellness practitioner video


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brunovu20/google-trending (version 1.0.0)

--- name: google-trending description: "Fetch and display the top trending Google searches in the last 24 hours for any country. Use when the user asks for Google trending topics, trending searches, what people are googling, or top searches today." metadata: { "openclaw": { "emoji": "🔥", "requires": { "bins": ["python3"] }, "install": [ { "id": "python3", "kind": "system", "bins": ["python3"], "label": "Python 3 (stdlib only, no pip required)", }, ], }, } --- # Google Trending Fetch the top trending Google searches in the last 24 hours via the Google Trends daily RSS feed. No API key required. ## Usage ```bash # Top 20 trending searches in the US (default) python3 scripts/fetch-google-trending.py # Specific country (ISO 3166-1 alpha-2 code) python3 scripts/fetch-google-trending.py FR python3 scripts/fetch-google-trending.py GB python3 scripts/fetch-google-trending.py JP # Custom result count python3 scripts/fetch-google-trending.py US 10 ``` ## Arguments | Position | Default | Description | |----------|---------|--------------------------------------| | 1 | `US` | Country code (ISO 3166-1 alpha-2) | | 2 | `20` | Number of trending topics to show | ## Output For each trending topic: rank, search term, approximate search volume, publication time, up to 2 related news headlines with source, and a link to the Google Trends page. ## Data source - **Feed**: `https://trends.google.com/trending/rss?geo={GEO}` - **Update cadence**: refreshed every few hours by Google - **No auth required**: uses only public RSS endpoints and Python stdlib (`urllib`, `xml.etree`) ## Workflow for the AI agent 1. Run `python3 scripts/fetch-google-trending.py [GEO] [COUNT]` 2. Parse and present the results to the user 3. If the user asks about a specific trending topic, you can search for more context using WebSearch ## Guardrails - Data comes directly from Google Trends RSS — do not fabricate traffic numbers or topics. - The feed reflects Google's own editorial/algorithmic choices; surface that to the user if asked why a topic appears. - If the fetch fails (network error, rate limit), report it clearly and suggest retrying.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mupengi-bot/multi-agent-chat (version 1.0.0)

--- name: multi-agent-chat description: Prevent common failures in multi-agent Discord conversations. Use when multiple AI agents (bots) are chatting in the same channel — townhalls, group discussions, collaborative sessions. Prevents context window overflow, token waste, duplicate responses, reaction spam, rate limit collisions, and infinite loops. Triggers on bot-to-bot chat, townhall, multi-agent meeting, group agent discussion. --- # Multi-Agent Chat Protocol Rules for AI agents participating in multi-bot Discord conversations. Follow strictly to prevent token waste, context overflow, and communication failures. ## Core Rules ### 1. Turn-Based Communication - **One agent answers at a time** — wait for your turn - **Mention one agent per message** when directing questions - **Never answer a question directed at another agent** ### 2. One Response Per Topic - **One answer per agenda item** — never repeat yourself - If someone missed your answer: reply "위에 있어" (one line only) - **Never re-explain** what you already said ### 3. No Bot-to-Bot Reactions - **Never add emoji reactions to bot messages** — each reaction event consumes hundreds of tokens - Reactions are for humans only - Set `reactionNotifications: "off"` in config to stop receiving reaction events ### 4. Short Responses - Keep answers under 200 words unless explicitly asked for detail - No filler ("네 알겠습니다!", "감사합니다!" alone) — use NO_REPLY instead - If you have nothing to add: NO_REPLY ### 5. Follow the Agenda - When a new topic is announced, stop discussing the previous one - Don't circle back to resolved topics - If asked a question, answer that specific question — not a previous one ### 6. Loop Prevention - **Max 4 consecutive bot-to-bot exchanges** before requiring human input - If the same question is asked 3+ times, stop and flag to the human operator - Never create ping-pong chains between bots ## Config Optimization Each bot should set in `config.yml`: ```yaml channels: discord: reactionNotifications: "off" ``` ## Token Budget Guidelines - Multi-agent channels: use Sonnet or cheaper models (not Opus) - Separate API keys per bot to avoid shared rate limits - Estimate: 3+ bots on Opus = ~1000 KRW per round of conversation ## Red Flags (Stop & Alert Human) - Same question repeated 3+ times → context window issue, alert operator - Rate limit errors appearing → need API key separation - Bot responding to wrong agenda item → confusion, pause and clarify - Token usage spiking → check for reaction events or verbose responses ## Lessons Learned These rules come from a real 2026-03-07 townhall incident: - 3 bots simultaneously responding caused context overflow - Lead agent repeated same question 6+ times (couldn't see answers in context) - Emoji reactions (👀🤔👨‍💻👍 cycles) consumed massive tokens silently - Shared API key caused rate limits for all bots simultaneously - Cost: ~1000 KRW per exchange on Opus model


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: iamskark/kiza-negotiator (version 0.1.0)

# Kiza Negotiator - AI Agent Delegate & Deal Maker ## What This Skill Does Automated AI delegate that handles negotiations, deals, and marketplace interactions. Full auto-pilot or hybrid support. ### Features - **Auto-negotiation**: Smart bidding & counter-offers based on market conditions - **Deal closing**: Handles escrow, terms verification, and transaction completion - **Fair pricing**: Market-aware pricing with flexibility for quick deals - **24/7 availability**: Instant response to offers & inquiries ### Use Cases - Marketplace negotiations (buy/sell) - Bounty bidding & task pricing - Service rate negotiations - Multi-party deal coordination ## Setup & Usage ```bash # Install skill clawhub install kiza-negotiator # Initialize negotiator with your preferences kiza-nego init --style=fair --auto-respond=true # Start auto-pilot mode kiza-nego start --watch-markets=true ``` ## Configuration - `style`: fair/aggressive/conservative - `auto-respond`: true/false for automatic responses - `min-rate`: Minimum acceptable rate - `max-delay`: Maximum response time ## Requirements - OpenClaw with ACP integration - Valid marketplace credentials ## Security - All negotiations logged & tracked - Clear audit trail of deals - Escrow-based transactions only - No private key access needed ## Support & Updates - Regular updates based on market feedback - Community-driven improvements - Direct support via Discord/Telegram ## About Kiza Professional AI negotiator focused on fair deals and long-term relationships. Part of the OpenClaw ecosystem.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: opensea/opensea-marketplace (version 2.10.0)

--- name: opensea description: Query NFT and token data, trade NFTs on Seaport, swap ERC20 tokens via DEX aggregator, configure wallet signing providers, and build/register/gate AI agent tools on Base. Covers the full OpenSea developer surface across CLI, MCP server, shell scripts, and SDK. Pick the right sub-skill using the routing table below, then read that sub-skill's SKILL.md for operational detail. homepage: https://github.com/ProjectOpenSea/opensea-skill repository: https://github.com/ProjectOpenSea/opensea-skill license: MIT env: OPENSEA_API_KEY: description: API key for all OpenSea services required: true obtain: https://docs.opensea.io/reference/api-keys#instant-api-key-for-agents dependencies: - node >= 18.0.0 - curl - jq (recommended) --- # OpenSea (router) Entry point for OpenSea agent skills. Pick the sub-skill based on task, then read its `SKILL.md`: | Task | Sub-skill | |---|---| | Query NFT/token data, search, drops, events | [`opensea-api/SKILL.md`](opensea-api/SKILL.md) | | Buy/sell NFTs on Seaport, sweeps, cross-chain | [`opensea-marketplace/SKILL.md`](opensea-marketplace/SKILL.md) | | Swap ERC20 tokens via DEX aggregator | [`opensea-swaps/SKILL.md`](opensea-swaps/SKILL.md) | | Configure wallet signing (Privy/Turnkey/Fireblocks/Bankr) | [`opensea-wallet/SKILL.md`](opensea-wallet/SKILL.md) | | Build/register/gate AI agent tools (ERC-8257) | [`opensea-tool-sdk/SKILL.md`](opensea-tool-sdk/SKILL.md) | Always read the sub-skill `SKILL.md` before executing. This router intentionally has no operational detail. ## Quick decision guide - **Read-only queries** (collections, NFTs, tokens, search, stats, events, drops): `opensea-api` - **Write operations** (buy, sell, make offers, fulfill listings): `opensea-marketplace` - **Token swaps** (ERC20 to ERC20, cross-chain): `opensea-swaps` - **Wallet setup** (before any write operation): `opensea-wallet` - **Tool building** (register, gate, monetize AI tools): `opensea-tool-sdk` ## Ecosystem Partner skills that complement OpenSea's capabilities live in [`ecosystem/`](ecosystem/). See [`ecosystem/CONTRIBUTING.md`](ecosystem/CONTRIBUTING.md) to add one.

path: ecosystem/TEMPLATE/references/README.md
content: # References

Place detailed reference documentation here, one file per topic.

Files in this directory are loaded by agents when they need deeper context beyond what `SKILL.md` provides.


path: renovate.json
content: $schema: https://docs.renovatebot.com/renovate-schema.json
description: Dependency updates are managed in the opensea-devtools monorepo
enabled: no

Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: malicious | SkillSpector: suspicious (CRITICAL)
Skill: unisound-llm/unisound-drg-grouping (version 1.0.0)

--- name: med-doctor-drg-grouping description: 医生端 ICD/DRG — 据出院文书与 DRG 候选项，由内部医疗大模型单选最可能入组；仅含 scripts/run.py，无 _shared 依赖。 metadata: { "openclaw": { "emoji": "📊" } } --- # DRG 分组（候选单选） ## 概述 由调用方传入**完整自然语言题干**（通常含出院文书要点与若干 DRG 候选项），模型在候选项中**单选**最可能入组结果，并按约定格式输出**一行**。 与病案首页结构化审核类 skill 不同：本包**不接院内首页接口**，题干由上游拼装；适合评测集（`task` + `input` + `meta`）或与真实分组引擎对照前的模型基线。 ## 题目输入（三选一） 1. **`--question`**：完整题干字符串。 2. **`--input PATH`**： - `.json` / `.jsonl`：对象须含非空 **`input`** 或 **`question`**（评测 JSON 优先用 `input`）； - 其他扩展名：整文件 UTF-8 纯文本作为题干。 - 传 `-` 时从 stdin 读一条 JSON 或纯文本。 3. **stdin**（未传 `--question` / `--input`、且非 TTY）：规则同 `-`。 可选字段 **`meta`**：任意 JSON 对象，原样进入运行结果中的 `meta`，便于透传 `gold`、`case_features` 等（不参与模型拼接，除非写入 `input`）。 ## 输出格式（模型） 题干中应明确要求模型只输出一行，例如： `drgsCode（drgsName）` 其中括号为**中文全角**括号，名称与候选项列表完全一致。脚本默认系统提示词与上述约束对齐；若题干另有更严格式，以题干为准。 ## 运行结果（脚本 stdout） 默认可解析 JSON，字段包括： - `skill`：固定为 `DRG分组` - `status`：`ok` - `answer`：模型原始输出文本 - `question`：本次送入模型的用户消息全文 - `meta`：来自输入的 `meta`（无则为 `{}`） - `record_index`、`model`、`input_mode`、`input_path` 等（含义同 medbench 类脚本） ## 用法示例 ```bash python3 scripts/run.py --input case.json --appkey YOUR_KEY python3 scripts/run.py --question "……【DRG 候选】……" --appkey YOUR_KEY python3 scripts/run.py --input case.jsonl --index 0 --batch 3 --appkey YOUR_KEY ``` ## 参数 - `--question` / `--input`：与 stdin 三选一（`--question` 与 `--input` 不可同时使用）。 - `--appkey STRING`：**必填**（`--dry-run` 除外）。内部医疗大模型鉴权 key。 - `--index`、`--match-id`、`--batch`：仅对 `.jsonl` 生效（`--match-id` 匹配 `meta.id` 或 `other.id`）。 - `--dry-run`：不调用模型，仅输出解析后的题目 JSON。 - `--model`、`--api-url`、`--temperature`、`--timeout`、`--system-prompt`：调模型参数。 - `--output PATH`：另存完整 JSON（批量为 NDJSON）。 - `--text-only`：标准输出仅打印 `answer` 文本。 ## 模型 - 默认 `u1-insuremed`，接口 `https://maas-api.hivoice.cn/v1/chat/completions`。 ## 合规说明 输出为分组辅助推断，**不构成**正式医保结算或院内分组结论；涉及真实患者数据须先脱敏并遵守院内流程。

path: _meta.json
content: ownerId: kn76wejkeqxfc03j0rfxp2jaj982m7aa
slug: doctor.icd-drg.drg-grouping
version: 1.0.0
publishedAt: 0

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anctro/psyclaw-phq9 (version 0.1.0)

# PsyClaw PHQ-9 这是 PHQ-9 的独立 ClawHub skill，用于抑郁症状的结构化筛查。 前置条件： - 最好已经安装 `psyclaw-openclaw-health` - 或者你本地已经有可用的 `.agents/skill-docs/openclaw-health/credentials.json` 使用方式： - 阅读同目录下的 `phq9.md` - 按协议完成量表评分并提交结果 如果你还没有完成主接入流程，请先安装： ```bash npx clawhub update psyclaw-openclaw-health --force || npx clawhub install psyclaw-openclaw-health --force ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: deusyu/deprecated-skill (version 0.2.0)

--- name: rainman-translate-book description: "[DEPRECATED] This skill has been renamed to translate-book. Install the new version: clawhub install translate-book" --- # rainman-translate-book (Deprecated) This skill has been **renamed and moved** to `translate-book`. All future development, bug fixes, and new features will be published under the new name. This package will not receive updates. ## Why the rename? The skill name was simplified from `rainman-translate-book` to `translate-book` to match the SKILL.md `name` field and GitHub repository name, ensuring consistent behavior across Claude Code, Codex, and OpenClaw. ## How to migrate Uninstall this deprecated version and install the new one: ```bash clawhub uninstall rainman-translate-book clawhub install translate-book ``` Or via npx: ```bash npx skills add deusyu/translate-book -a claude-code -g ``` ## New locations - **ClawHub:** `translate-book` - **GitHub:** https://github.com/deusyu/translate-book ## What is translate-book? A Claude Code skill that translates entire books (PDF/DOCX/EPUB) into any language using parallel subagents. Pipeline: Calibre converts input to Markdown chunks, parallel subagents translate each chunk independently, manifest-based SHA-256 validation ensures integrity, then chunks are merged and built into HTML (with TOC), DOCX, EPUB, and PDF outputs. Do not use this package for new installations. Use `translate-book` instead.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kenswj/ashare-fast-watcher (version 1.0.1)

--- name: "ashare-fast-watcher" description: "A-Share millisecond-level market data watcher using Tencent direct API." version: "1.0.0" --- # Ashare Fast Watcher A high-performance skill to monitor A-Share market movements. ## Actions ### get_market_snapshot Fetches real-time price, change, volume, and Level-1 quotes. #### Parameters - `codes`: (Required) String. Stock codes with prefix, e.g., "sh600519,sz000001". ### check_volatility Detects sudden volume spikes or price movements. #### Parameters - `code`: (Required) String. Single stock code.


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