OpenClaw/clawhub-security-signals huggingface dataset:
Skill: austineyapp/austine-daily-ops (version 1.0.0)

--- name: austine-daily-ops description: Daily execution and trip-ops command system for Austine. Use when asked to run daily brief/checklist/status workflows, track priorities, or plan and procure trip logistics (transport, lodging, reservations, vendor quotes, recommendation + decision handoff). --- # Austine Daily Ops ## Overview Run Austine’s day-to-day operating cadence with concise, action-first outputs, and handle travel operations like a sourcing assistant (options scan, quote gathering, recommendation, and execution handoff). ## Core Capabilities ### 1) Daily execution cadence Use this pattern for daily operations asks. - Output max 5 bullets unless user asks for depth. - Always include: current status, blocker (or "No blocker"), next action. - Prefer concrete actions over commentary. When asked for daily planning: 1. List Top 3 priorities. 2. List next 72h critical deadlines/reminders. 3. Suggest one automation and one quick win (<15 min). 4. End with “next action I’ll take.” ### 2) Trip operations management Use this for travel planning/procurement tasks. Workflow: 1. **Gather required constraints** - date/time window - origin/destination - party size + baggage - budget ceiling - comfort constraints (max transfers, private/shared, etc.) 2. **Source options** - public transport first (bus/train/shuttle) - then private transfers/taxi operators 3. **Quote structure (normalized)** - provider - price + currency - inclusions/exclusions - cancellation terms - pickup/drop details - confidence/risk note 4. **Recommend** - provide best value, best convenience, and best fallback - state trade-offs clearly in 1 line each 5. **Decision handoff** - ask for one explicit choice (Option A/B/C) - after user chooses, prepare exact next step/message draft ### 3) Vendor follow-up mode When waiting for confirmations (driver details, seat confirmations, check-in QR, etc.): - Keep a short pending list with owner + due date. - Trigger reminders with time buffer (day-before or earlier for critical items). - Escalate if no response by cutoff. ## Output Rules - Bullet-first, concise, high signal. - No fluff. - If data is uncertain, say so explicitly. - For recommendations, always include one fallback. ## References - For trip transport procurement templates and comparison format, read `references/transport-procurement.md`. - For daily operating output templates, read `references/daily-ops-templates.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: shu0yu/conversations (version 2.0.0)

--- name: conversations-universal description: 从 OpenClaw sessions/*.jsonl 导入对话历史到本地 SQLite，配合 FTS5 全文搜索。成为真正的记忆库，支持语义化查询历史对话，让整理和回顾更高效。装好后跑一次 import，之后随时可以 query。 --- # conversations-universal 对话历史的本地记忆库。 ## 使用方式 ### 第一次使用 1. 运行导入脚本，把历史 session 读入本地库： ``` python {baseDir}/scripts/import_sessions.py ``` 2. 之后每次想回顾历史，直接用 query 脚本： ``` python {baseDir}/scripts/query_conversations.py <搜索内容> ``` ### 查询示例 ```bash # 查询某天的讨论 python scripts/query_conversations.py "我们讨论过内存泄漏" # 查询某个话题 python scripts/query_conversations.py "OpenClaw 配置" ``` ### 自动导入 建议配合 cron 定期导入新 session： ``` openclaw cron add \ --name "对话历史导入" \ --cron "0 */6 * * *" \ --session isolated \ --message "python {baseDir}/scripts/import_sessions.py" \ --announce ``` ## 工作原理 - **存储**：SQLite + FTS5 全文搜索，零外部依赖 - **导入**：读取 `agents/main/sessions/*.jsonl`，解析 role/content，写入 chunks 表 - **搜索**：FTS5 MATCH，支持自然语言查询 ## 环境变量 | 变量 | 默认值 | 说明 | |------|--------|------| | OPENCLAW_WORKSPACE | 自动推断 | session 文件所在目录 | | CONVERSATIONS_DB | `{workspace}/conversations.db` | 数据库路径 | | DRY_RUN | false | 试运行，不写入 | ## 依赖 - Python 3.8+（内置 sqlite3，FTS5 需要 3.9+） - OpenClaw sessions 目录 ## 与 conversations 1.0 的区别 | | conversations 1.0 | conversations-universal | |--|--|--| | 搜索方式 | Ollama 向量搜索 | SQLite FTS5 | | 外部依赖 | 需要 Ollama | 纯 Python | | 搜索质量 | 高（语义） | 中（关键词） | | 适用场景 | 已有 Ollama 的用户 | 所有人 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: brucegutman/pipeworx-ai-briefing (version 1.0.0)

# Ai Briefing AI Briefing MCP — Keep AI models current on industry developments ## get_briefing Get today's AI tools briefing — new MCP servers, APIs, SDKs, frameworks from the last 24 hours. Retu ## search_developments Search for new tools, APIs, MCP servers, and frameworks by keyword (e.g., 'vector databases', 'Claud ## get_recent Get recent tool releases filtered by category (e.g., 'mcp', 'agent_framework', 'open_source') or sou ## get_model_landscape Get recent AI model releases — which providers shipped what, when, and their key capabilities. Retur ## get_timeline Get a chronological timeline of AI developments between two dates. Returns events ordered by date wi ## get_ai_toolbelt Get the latest available tools — Claude Code features, MCP servers, SDK updates, CLI tools, integrat ## get_ai_news Get AI industry news — model releases, funding, acquisitions, policy changes, benchmarks. Returns ne ## what_happened Ask natural language questions about recent tools and developments (e.g., 'any new MCP servers this ```json { "mcpServers": { "ai-briefing": { "url": "https://gateway.pipeworx.io/ai-briefing/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mylillo926/zzheng (version 1.0.3)

# zzheng - 法律公众号文章生成器 ## 简介 专为法律工作者设计的微信公众号文章自动生成工具，优先抓取实时热点法律新闻，结合法律知识生成原创文章。 ## 功能特点 - **实时热点优先**：优先从微博热搜、百度热搜、知乎、腾讯新闻等抓取当日法律相关热点 - **话题自动分类**：劳动用工、消费维权、婚姻家庭、知识产权、企业合规等 - **个人企业独立**：个人篇和企业篇各自独立，互不关联 - **系列连续性**：预设话题库按进度循环，保证内容连贯 - **一键生成**：每次生成两篇文章，可直接复制到公众号 ## 使用方法 直接运行脚本即可生成当日文章： ```bash python generate_articles.py ``` ## 依赖要求 - Python 3.7+ - requests - beautifulsoup4 安装依赖： ```bash pip install requests beautifulsoup4 ``` ## 配置说明 - **文章保存目录**：`D:\其它工具\文章\`（可在脚本中修改） - **进度文件**：自动保存在文章目录下（v4_personal.json, v4_enterprise.json） ## 文章输出 每次运行生成两篇文章： - 个人篇：法律知识_YYYY-MM-DD_个人篇.txt - 企业篇：法律知识_YYYY-MM-DD_企业篇.txt ## 文章结构 - 📢 今日热点话题引入 - 📍 典型案例解读 - 📋 法律知识科普 - 💡 实用建议 - ✨ 结语 ## 作者 zzheng


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunshine-del-ux/my-awesome-tools (version 1.0.0)

# My Awesome Tools Collection of powerful shell scripts for developers to boost productivity. ## Scripts Included - **chrome-extension-generator** - Generate Chrome extension templates - **code-review-assistant** - AI-powered code review automation - **github-readme-generator** - Beautiful GitHub README files - **saas-landing-page** - SaaS landing page generator - **tech-blog-generator** - Technical blog template generator - **tweet-thread-generator** - Twitter thread creator ## Usage ```bash # Generate a Chrome extension ./chrome-extension-generator.sh "My Extension" "Description" # Generate GitHub README ./github-readme-generator.sh "Project Name" # Generate SaaS landing page ./saas-landing-page.sh "My SaaS" ``` ## Requirements - Bash 4.0+ - Git - Node.js (for some templates) ## Author Sunshine-del-ux


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: naive-white-expert/jd-affiliate (version 1.0.0)

--- name: jd-affiliate description: 京东联盟推广平台 - 推客工具、商品推广链接生成、佣金查询、推广数据统计、选品推荐、京粉联盟 --- # JD Affiliate (京东推广联盟) 京东联盟推广赚钱平台，帮助推客高效推广京东商品。 ## 推广链接 ### 链接生成 - 商品推广链接 - 活动推广链接 - 店铺推广链接 - 自定义落地页 ### 链接类型 - 短链生成 - 二维码生成 - 微信分享链接 - 社媒分享链接 ## 佣金查询 ### 佣金信息 - 商品佣金比例 - 佣金金额预估 - 佣金规则说明 - 类目佣金对比 ### 佣金结算 - 佣金收益统计 - 结算周期说明 - 提现申请 - 结算记录 ## 选品工具 ### 选品推荐 - 高佣商品推荐 - 热销商品推荐 - 新品推荐 - 活动商品推荐 ### 选品筛选 - 佣金比例筛选 - 价格区间筛选 - 评价数量筛选 - 销量筛选 ## 推广数据 ### 数据统计 - 推广订单数 - 推广金额 - 佣金收益 - 转化率 ### 数据分析 - 商品推广效果 - 渠道推广效果 - 时间段分析 - 用户画像分析 ## 使用示例 ### 生成推广链接 ``` 帮我生成京东这个商品的推广链接 ``` ### 查佣金 ``` 京东这个商品的推广佣金是多少？ ``` ### 高佣选品 ``` 京东佣金比例20%以上的商品有哪些？ ``` ### 查收益 ``` 京东联盟本月推广收益是多少？ ## 京粉工具 - 京粉APP - 京粉小程序 - 京粉选品库 - 京粉转链工具 ## 推广渠道 - 微信推广 - 抖音推广 - 小红书推广 - 微博推广 - 社群推广 - 直播带货 ## 推广素材 - 商品图片 - 商品文案 - 推广海报 - 优惠信息 ## 触发条件 - 用户提及京粉、京东联盟 - 推广链接生成需求 - 推客佣金查询 - "京东推广"关键词


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: luobusita-ai/tmd-wangda (version 0.7.0)

--- name: tmd-wangda description: 帮助用户完成中移网大(https://wangda.chinamobile.com)相关课程的学习,当用户需要学习网大课程(例如学习新课程，查询学习进度或终止学习等)或用户提供的url域名是(wangda.chinamobile.com)时使用此技能 --- ## 领域知识 ### 课程(subject) 课程由一个或多个课件组成(部分课程可能还包含子课程),课件分为选修和必修两种,完成所有必修课件即可 ### 课件(course) 课件可能有多个小节(chapter),每个小节的内容形态可能是视频类或文档类,完成所有小节的学习后，该课件就算完成学习 ### session 保留用户当前学习进度的文件，具体参考 `references/schemas.md`中session.json的定义 <br/> <br/> ## 参考信息 ### 工具指令清单 所有可用工具指令请参考 `references/tools.md`，所有指令都应该通过 `python scripts/tools.py <command> <args>`的形式调用 ### session信息说明 参考 `references/schemas.md` <br/> <br/> ## 核心功能 ### 1. 跟踪当前学习的课程进度 - 使用工具指令`get-session`指令,获取session中progress字段，总结学习进度,如果没有progress信息，则让用户提供课程地址，开始新的课程学习 - 如果尚未全部完成,使用工具指令`start-auto-study`继续学习 - 如果已经全部完成,则把progress总结一下，提示用户当前课程已经全部学完，是否要开始新的课程学习 ### 2.开始(添加)新的课程学习 - 获取现有的session信息,如果session信息且包含employeeName非空，需要与用户确认如下信息 1. 是否继续使用该账号学习,如需要更换账号，则需要重新登录 2. 总结一下progress信息(如果有)，询问用户是需要在现有的progress添加新的课程，还是中断现有的学习(可以使用`stop-study`指令来中断学习) - 根据用户提供的课程url地址，使用工具脚本`add-subject`指令添加待学习课程 - 使用工具脚本`start-auto-study`指令还是挂机学习 <br/> <br/> ## 其他功能 ### 1. 如何登录 - 有2种情况需要重新登录: 1. 进入课程或课件页后，浏览器自动重定向到了登录页面，说明需要重新登录 2. 用户希望使用非session中存储的账号进行学习,此时需要重新登录新账号，并在开始登录前，先使用工具指令`reset-session`指令重置session - 具体登录方式如下: - 使用工具指令`enter-login-page`进入登录页 - 使用工具指令`fill-phone`完成登录手机号填写和发送短信验证码流程 - 使用工具指令`fill-sms-code`填写短验,并完成登录 - 登录成功后，使用指令`update-employee-name`更新session中的员工姓名 ### 2. 中断现有的所有学习课程 只有当用户明确要求 `清理/中断/终止` 现有的课程学习时才使用该功能(使用工具指令`clear`实现)

path: scripts/stealth-extension/manifest.json
content: {
  "manifest_version": 3,
  "name": "Stealth Anti-DevTools",
  "version": "1.0",
  "description": "Bypass anti-devtools detection on web pages",
  "content_scripts": [
    {
      "matches": ["<all_urls>"],
      "js": ["stealth.js"],
      "run_at": "document_start",
      "world": "MAIN",
      "all_frames": true
    }
  ]
}


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: stale | SkillSpector: suspicious (CRITICAL)
Skill: dannyshmueli/polymarket-api (version 1.0.0)

--- name: polymarket description: Query Polymarket prediction markets. Use for questions about prediction markets, betting odds, market prices, event probabilities, or when user asks about Polymarket data. --- # Polymarket Query prediction market data from Polymarket's public API (no auth required). ## Quick Start ```bash # Top markets by 24h volume python3 scripts/polymarket.py --top # Search markets python3 scripts/polymarket.py --search "trump" # Get specific market by slug python3 scripts/polymarket.py --slug [REDACTED_SECRET] # List events (grouped markets) python3 scripts/polymarket.py --events ``` ## Script Location `skills/polymarket/scripts/polymarket.py` ## API Endpoints The script uses `gamma-api.polymarket.com`: - `/markets` - Individual markets with prices, volumes - `/events` - Event groups containing related markets ## Output Format Markets show: question, Yes/No prices (as percentages), 24h volume, total volume. ## Interpreting Prices - `outcomePrices` are 0-1 representing probability - Price of 0.65 for "Yes" = market thinks 65% chance of Yes - Higher volume = more liquid, more reliable signal


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mirowangl-ops/xiaozhua-agent-deployment (version 1.0.0)

--- name: agent-deployment description: "AI Agent 部署与管理。在 WSL2/Linux 上安装 OpenClaw，配置 GitHub Copilot 认证，启动 Gateway，初始化 workspace（SOUL.md/USER.md/TASKS.md）。支持多 Agent 部署、端口配置、认证排错。使用场景：部署新 Agent、重启 Agent、认证过期修复、配置更新。" --- # Agent 部署 ## 部署新 Agent（WSL2 + OpenClaw） ### 前置条件 - WSL2 已安装（需管理员权限：`wsl --install`） - Ubuntu 发行版 ### 安装步骤 ```bash # 1. 安装 nvm + Node.js（绕过 sudo） curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash source ~/.nvm/nvm.sh nvm install 24 # 2. 安装 OpenClaw npm install -g openclaw # 3. 初始化 openclaw setup # 4. GitHub Copilot 认证 openclaw login-github-copilot # 注意：device code 每次超时就失效，可能需要多试几次 # 5. 配置模型和端口 # 编辑 ~/.openclaw/openclaw.json # gateway.port: <YOUR_PORT>（避免和主 Agent 冲突） # model: github-copilot/claude-sonnet-4-20250514 # 6. 启动 openclaw gateway ``` ### 启动已有 Agent ```bash wsl -d Ubuntu source ~/.nvm/nvm.sh openclaw gateway ``` - 端口：<YOUR_PORT> - Token：`<YOUR_TOKEN>` - 浏览器打开 http://127.0.0.1:<YOUR_PORT> ## Workspace 初始化 新 Agent 上线后写入： - **SOUL.md** — 身份、职责、性格 - **USER.md** — Lei 的信息和偏好 - **TASKS.md** — 任务清单 - **COLLABORATION.md** — 协作协议 ## 常见问题 - **device code 超时** — 每次生成新 code，快速在浏览器完成认证 - **端口冲突** — 改 openclaw.json 的 gateway.port - **config overwrite** — Gateway 启动时可能重写配置，启动后检查端口是否正确 - **关机后重启** — 需要手动启动 WSL + Gateway，AI 没有管理员权限自动启动


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/ecommerce-keyword-research (version 1.0.0)

--- name: ecommerce-keyword-research description: "Cross-platform keyword research for e-commerce. Discover high-converting keywords across Amazon, Shopify, Etsy, Google Shopping, TikTok Shop, and Walmart. Analyzes search volume signals, competition indicators, commercial intent, and long-tail opportunities." metadata: nexscope: emoji: "🔍" category: ecommerce --- # E-Commerce Keyword Research 🔍 Cross-platform keyword research for e-commerce. Discover high-converting keywords across Amazon, Shopify, Etsy, Google Shopping, TikTok Shop, and Walmart. Analyzes search volume signals, competition indicators, commercial intent, and long-tail opportunities. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill ecommerce-keyword-research -g ``` ## Usage ``` Find the best keywords for my bamboo cutting board. I sell on Amazon US and my own Shopify store. ``` ## Capabilities - Cross-platform keyword discovery (Amazon, Google, Etsy, Walmart, TikTok) - Long-tail keyword expansion with commercial intent scoring - Keyword grouping by search intent (informational, commercial, transactional) - Competition assessment based on keyword structure and modifier analysis - Seasonal trend identification and keyword calendar planning - Platform-specific keyword optimization (Amazon A9, Etsy SEO, Google Shopping) ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mondilo1/cli (version 0.1.0)

--- summary: "CLI reference for `moltbot skills` (list/info/check) and skill eligibility" read_when: - You want to see which skills are available and ready to run - You want to debug missing binaries/env/config for skills --- # `moltbot skills` Inspect skills (bundled + workspace + managed overrides) and see what’s eligible vs missing requirements. Related: - Skills system: [Skills](/tools/skills) - Skills config: [Skills config](/tools/skills-config) - ClawdHub installs: [ClawdHub](/tools/clawdhub) ## Commands ```bash moltbot skills list moltbot skills list --eligible moltbot skills info <name> moltbot skills check ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tujinsama/contract-risk-review-claw (version 1.0.0)

--- name: contract-risk-review-claw description: | 合同风险审查虾 — 深度识别合同风险条款，给出修订建议，守住法律红线。 **当以下情况时使用此 Skill**： (1) 用户上传合同文件（PDF/Word/文本），要求审查风险 (2) 需要识别不平等条款、违约责任失衡、知识产权陷阱、管辖权不利等风险 (3) 需要对比我方模板与对方合同，找出被修改的关键条款 (4) 需要批量扫描历史合同库，找出高风险合同 (5) 需要生成结构化审查报告（风险清单 + 修订建议） (6) 用户提到"合同审查"、"风险识别"、"条款审核"、"合同风险"、"法律审查"、"不平等条款"、"违约责任"、"知识产权"、"管辖权"、"合同修订"、"帮我看看这份合同"、"这份合同有没有坑" --- # 合同风险审查虾 核心理念：合同不是走形式，每个字都可能是坑，让 AI 先帮你踩一遍。 ## 工作流程 1. **文本提取** — 解析 PDF/Word/文本，提取合同全文 2. **结构解析** — 识别标题、章节、条款编号、甲乙方信息、附件 3. **语义风险识别** — 逐条分析，识别 6 类风险（见下表） 4. **风险分级** — 🔴 高风险 / 🟠 中风险 / 🟡 低风险 5. **生成修订建议** — 每个风险点给出：风险说明 + 修改建议 + 替代文本 6. **输出审查报告** — 风险概览 + 风险清单 + 修订建议 + 对比版本 ## 6 类风险类型 | 风险类型 | 识别要点 | 典型案例 | |---------|---------|---------| | 不平等条款 | 权利义务明显失衡 | "乙方无条件接受甲方单方面变更价格" | | 违约责任失衡 | 一方违约责任过重或过轻 | "甲方违约赔偿1%，乙方违约赔偿30%" | | 知识产权陷阱 | 成果归属、使用权限不明 | "项目成果归甲方所有，乙方不得使用" | | 管辖权不利 | 约定对方所在地法院管辖 | "争议由甲方所在地法院管辖"（甲方在外地）| | 表述模糊 | 关键条款用词不明确 | "尽快交付"、"合理费用"、"适当补偿" | | 隐藏义务 | 附件、从句中的额外义务 | 附件中要求"乙方承担所有税费" | ## 输入要求 - **必填**：合同文本（直接粘贴 / 上传文件）、合同类型 - **推荐**：我方角色（甲方/乙方）— 影响风险判定方向 - **可选**：我方模板（用于对比分析） ## 输出格式 ``` ## 合同审查报告 **总体评分**：A/B/C/D（A=低风险可签，D=高风险不建议签） **风险概览**：高风险 X 处 / 中风险 X 处 / 低风险 X 处 --- ### 🔴 高风险条款 **[条款位置]** 原文：... - 风险说明：... - 修改建议：... - 替代文本：... ### 🟠 中风险条款 ... ### 🟡 低风险条款 ... --- **审查结论**：... ``` ## 关键设计原则 - **语义理解优先**：不只是关键词匹配，理解条款的真实含义和逻辑陷阱 - **上下文关联**：结合合同类型、我方角色判断风险方向 - **可解释性**：每个风险点都有明确的理由和依据 - **实用性**：给出可直接使用的替代文本，而非泛泛建议 ## 参考资料 - **风险条款库**：`references/risk-clauses.md` — 典型风险条款案例，按类型分类 - **合同模板库**：`references/contract-templates.md` — 各类标准合同关键条款 - **法律法规库**：`references/legal-regulations.md` — 相关法律条文要点 - **修订建议库**：`references/revision-suggestions.md` — 标准修订建议和替代文本 > 遇到复杂合同或行业特定合同时，读取对应 references 文件以获取更精准的判断依据。 ## 文件处理 - **Word (.docx)**：直接解析文本内容 - **PDF**：提取文本层；扫描件需 OCR（建议 300 DPI+） - **纯文本**：直接分析 - **批量审查**：逐份分析，汇总高风险合同清单 ## 协作关系 - 发现高风险条款 → 可联动**跨渠道消息推送虾**推送法务团队 - 审查通过后 → 可联动**合规哨兵监控虾**持续监控合同对方合规风险


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shaqima2024/ihub-daily-report (version 1.0.2)

# SKILL.md - iHub 日报自动填写 自动填写 iHub 测试农场平台的日报。支持自定义账号密码和日报内容。 ## 触发词 填写日报、自动日报、iHub日报、daily report ## 参数 | 参数 | 必填 | 说明 | |------|------|------| | name | 是 | 姓名，如"张宁" | | username | 是 | 登录账号 | | password | 是 | 登录密码 | | content | 是 | 日报内容（Markdown 格式） | | date | 否 | 日期，默认昨天，格式：today / yesterday / YYYY-MM-DD | ## 使用示例 **填写昨天的日报：** ``` 填写日报，姓名：张宁，账号：your_username，密码：your_password，内容： ### 1. 完善模糊测试工具 ### 2. 完善TCS接口文档 ### 3. 参加OpenClaw培训会议 ``` **指定具体日期：** ``` 填写2026-03-24的日报，姓名：张宁，内容：完成XX功能开发 ``` ## 自动化流程 1. 打开浏览器访问 iHub 登录页 2. 输入账号密码登录 3. 点击左侧菜单「项目」 4. 点击「万花筒工具平台」 5. 找到并点击「查看工作项」（鼠标悬停弹出） 6. 找到「工具小组的日报」 7. 根据当前日期计算周文件夹（如"第四周(3.23-3.29)"） 8. 在周文件夹中找到对应用户的姓名记录 9. 点击姓名进入详情 10. 根据日期判断是周几，找到对应的日报行（周一到周日） 11. 点击该行的 markdown 编辑按钮 12. 使用 JavaScript 填入内容（触发 Vue 双向绑定） 13. 点击「已完成」保存内容 14. 点击「提交」按钮 15. 填写变更理由并确认提交 ## 技术要点 - 日期自动计算：根据日期找到对应的周文件夹 - 使用 evaluate API 注入 JavaScript 填入内容 - 自动判断星期几，对应填写周一到周日的日报 - 支持填写任意日期的日报（默认昨天） ## 注意事项 - 首次使用需要提供账号密码 - 日期会自动计算当周的文件夹路径 - 内容支持 Markdown 格式 - 任务完成后浏览器保持打开状态，方便确认


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: nexscope/ecommerce-social-media-marketing (version 1.0.0)

--- name: ecommerce-social-media-marketing description: "Plan social media marketing strategy for e-commerce brands. Content calendars, platform selection, posting schedules, engagement tactics, and social commerce integration for Instagram, TikTok, Facebook, Pinterest, and YouTube." metadata: nexscope: emoji: "📲" category: ecommerce --- # E-Commerce Social Media Marketing 📲 Plan social media marketing strategy for e-commerce brands. Content calendars, platform selection, posting schedules, engagement tactics, and social commerce integration for Instagram, TikTok, Facebook, Pinterest, and YouTube. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill ecommerce-social-media-marketing -g ``` ## Usage ``` Create a social media strategy for my jewelry brand. I sell on Etsy and my own website. Target audience: women 25-45. I can post 4-5 times per week. ``` ## Capabilities - Platform selection based on product type and target audience - Content pillar development (educational, entertaining, promotional, UGC) - Content calendar creation (weekly/monthly planning) - Platform-specific best practices (TikTok, Instagram Reels, Pinterest, YouTube Shorts) - Social commerce integration (Instagram Shop, TikTok Shop, Pinterest Shopping) - Engagement and community building tactics - Influencer collaboration framework ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


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