OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xyc5872/paperdaily (version 1.0.0)

--- name: paperdaily description: 每日从 arXiv 计算机科学论文中筛选高价值文献推荐 --- # PaperDaily Skill 每日从 arXiv 计算机科学论文中筛选高价值文献推荐。 ## 安装 ```bash cd ~/.openclaw/skills/paperdaily npm install ``` 安装后创建配置文件 `node_modules/openclaw-paperdaily/.env`： ```env # 飞书应用配置（必填） FEISHU_APP_ID=your_app_id FEISHU_APP_SECRET=your_app_secret FEISHU_CHAT_ID=your_chat_id # 可选配置 DEFAULT_TIMEZONE=Asia/Shanghai CACHE_HOURS=24 KEYWORDS=machine learning,deep learning,transformer,LLM ``` ## 触发词 - `今日文献` - 获取今日推荐的计算机论文（使用缓存） - `刷新文献` - 强制刷新并获取新的今日推荐论文 ## 使用方式 在聊天中发送 "今日文献" 或 "刷新文献" 即可触发。 ## 功能说明 - 从 arXiv CS 类近 7 天论文中筛选 - 基于时间新鲜度 (30%)、关键词匹配 (40%)、信息量 (30%) 评分 - 同一天多次请求返回缓存结果 - 支持强制刷新 ## 配置说明 | 变量 | 说明 | 必填 | |------|------|------| | `FEISHU_APP_ID` | 飞书应用 ID | ✅ | | `FEISHU_APP_SECRET` | 飞书应用 Secret | ✅ | | `FEISHU_CHAT_ID` | 飞书群聊 ID | ✅ | | `KEYWORDS` | 论文筛选关键词 | ❌ | | `DEFAULT_TIMEZONE` | 默认时区 | ❌ | | `CACHE_HOURS` | 缓存时长（小时） | ❌ | ## 获取飞书配置 1. 访问 [飞书开放平台](https://open.feishu.cn/) 2. 创建企业自建应用 3. 获取 App ID 和 App Secret 4. 配置应用权限并发布 5. 将机器人添加到目标群聊，获取 Chat ID


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bobowg163/firecrawl-local-search (version 1.0.0)

--- name: firecrawl-local-search description: 抓取网站数据的本地 Firecrawl 服务。使用本地 API (http://192.168.1.2:3002/) 进行网页抓取、数据提取和站内搜索。 --- # Firecrawl 本地搜索技能 ## 快速开始 使用本地 Firecrawl 服务抓取网站数据。 ### API 配置 - **基础 URL**: `http://192.168.1.2:3002/` - **协议**: HTTP (本地服务) - **超时**: 30 秒 ## 主要功能 ### 1. 网页抓取 (Scrape) ```bash curl -X POST "http://192.168.1.2:3002/v1/scrape" \ -H "Content-Type: application/json" \ -d '{"url": "https://example.com", "formats": ["markdown"]}' ``` ### 2. 网站地图 (Map) ```bash curl -X POST "http://192.168.1.2:3002/v1/map" \ -H "Content-Type: application/json" \ -d '{"url": "https://example.com"}' ``` ### 3. 内容搜索 (Search) ```bash python3 scripts/firecrawl_search.py "关键词" --site https://example.com ``` ## 使用脚本 - `scripts/firecrawl_scrape.py` - 网页抓取（无需外部依赖） - `scripts/firecrawl_map.py` - 网站地图 - `scripts/firecrawl_search.py` - 内容搜索 ## 依赖说明 ✅ **无需安装任何依赖** - 使用 Python 标准库 `urllib.request` ## 注意事项 1. **本地服务**: API 运行在本地网络，确保网络连接正常 2. **超时设置**: 默认 30 秒超时 3. **速率限制**: 根据服务器配置调整请求频率 4. **网络要求**: 需要访问 `http://192.168.1.2:3002/`

path: scripts/firecrawl_scrape.py
content: #!/usr/bin/env python3
"""Firecrawl 本地搜索 - 网页抓取脚本（不依赖 requests）"""
import sys, json, urllib.request, urllib.parse, urllib.error
from urllib.parse import urlparse

API_BASE_URL = "http://192.168.1.2:3002"

def scrape_url(url, formats=["markdown"]):
    """抓取网页内容"""
    try:
        data = json.dumps({"url": url, "formats": formats}).encode('utf-8')
        req = urllib.request.Request(
            f"{API_BASE_URL}/v1/scrape",
            data=data,
            headers={'Content-Type': 'application/json'},
            method='POST'
        )
        with urllib.request.urlopen(req, timeout=30) as response:
            return json.loads(response.read().decode('utf-8'))
    except urllib.error.URLError as e:
        return {"error": f"连接失败: {e.reason}"}
    except Exception as e:
        return {"error": f"抓取失败: {str(e)}"}

if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("用法：python3 firecrawl_scrape.py <URL>")
        print("示例：python3 firecrawl_scrape.py https://example.com")
        sys.exit(1)

    result = scrape_url(sys.argv[1])
    print(json.dumps(result, indent=2, ensure_ascii=False))


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ncepuee/pll-designer (version 1.0.0)

--- name: pll-bandwidth-calc description: 锁相环（PLL）PI 控制器参数计算工具。用于电力电子/新能源领域的锁相环设计与调试。支持两种计算方向：(1) 根据目标带宽和阻尼比正向计算 PI 参数（KPLLp/KPLLi）；(2) 根据已知 PI 标幺值参数反推闭环带宽和阻尼比。当用户提到"锁相环"、"PLL"、"PLL带宽"、"PI参数"、"KPLLp"、"KPLLi"、"阻尼比"、"自然频率"等关键词时触发。 --- # PLL 带宽计算工具 基于二阶系统理论的锁相环 PI 控制器参数设计与分析工具。 ## 系统模型说明 本工具基于以下 SRF-PLL（同步旋转坐标系锁相环）线性化模型： - 开环传递函数：`G(s) = U_mag * (Kp*s + Ki) / s^2` - 闭环特征方程：`s^2 + U_mag*Kp*s + U_mag*Ki = 0` - 自然角频率：`wn = sqrt(U_mag * Ki)` - 阻尼比：`zeta = U_mag*Kp / (2*wn)` - 带宽扩展系数：`C = sqrt(1 + 2*zeta^2 + sqrt(2 + 4*zeta^2 + 4*zeta^4))` - 闭环带宽：`f_BW = wn * C / (2*pi)` 其中 `U_mag = Unom/sqrt(3)*sqrt(2)` 为相电压峰值（前馈解耦项）。 ## 标幺值换算 物理值与标幺值的换算关系（基准值 = 额定角频率 / 相电压峰值）： ``` KPLLp_pu = KPLLp * U_mag / w0 KPLLi_pu = KPLLi * U_mag / w0 ``` 其中 `w0 = 2*pi*f0`。 ## ⚠️ 交互规则（必须遵守） **在执行任何计算之前，若用户未明确提供以下参数，必须主动询问，不得使用默认值静默计算：** 1. **`Unom`（线电压有效值，V）** — 必问，不同电压等级结果差异显著 - 常见值：690V（风电变流器）、380V（工业低压）、10kV/35kV（中压）等 - 询问示例："请问您的系统线电压有效值是多少 V？（如 690V、380V 等）" 2. **`f_BW_target`（目标带宽，Hz）** — 正向设计时必问（若未提供） 3. **`KPLLp_pu` / `KPLLi_pu`** — 反向分析时必问（若未提供） 以下参数有合理默认值，**用户未提供时可直接使用，但需在结果中注明**： - `f0 = 50 Hz`（电网频率，中国/欧洲标准） - `zeta = 0.707`（最优阻尼比） ## 使用方式 ### 方向一：正向设计（目标带宽 → PI 参数） 用户提供目标带宽，可选提供电压、频率、阻尼比，计算 PI 参数。 **调用 MATLAB 函数：** ```matlab [KPLLp_pu, KPLLi_pu, KPLLp, KPLLi] = get_pll_pi_parameters(f_BW_target) [KPLLp_pu, KPLLi_pu, KPLLp, KPLLi] = get_pll_pi_parameters(f_BW_target, Unom, f0, zeta) ``` **典型示例：** ```matlab % 目标带宽 20Hz，默认 690V/50Hz/zeta=0.707 [Kp_pu, Ki_pu, Kp, Ki] = get_pll_pi_parameters(20) % 自定义参数 [Kp_pu, Ki_pu, Kp, Ki] = get_pll_pi_parameters(30, 380, 50, 0.707) ``` ### 方向二：反向分析（PI 参数 → 带宽/阻尼比） 用户提供已知的 PI 标幺值参数，反推带宽和阻尼比。 **调用 MATLAB 函数：** ```matlab [f_BW, zeta, wn, KPLLp, KPLLi] = calc_pll_bandwidth(KPLLp_pu, KPLLi_pu) [f_BW, zeta, wn, KPLLp, KPLLi] = calc_pll_bandwidth(KPLLp_pu, KPLLi_pu, Unom, f0) ``` **典型示例：** ```matlab % 已知标幺值参数，默认 690V/50Hz [f_BW, zeta, wn, Kp, Ki] = calc_pll_bandwidth(0.05, 0.8) ``` ## MATLAB 脚本文件 - `scripts/get_pll_pi_parameters.m` — 正向设计函数 - `scripts/calc_pll_bandwidth.m` — 反向分析函数 ## 默认参数 | 参数 | 默认值 | 说明 | |------|--------|------| | Unom | 690 V | 电网线电压有效值（风电变流器典型值） | | f0 | 50 Hz | 电网额定基波频率 | | zeta | 0.707 | 阻尼比（临界阻尼，最优响应） | ## 直接计算（无需 MATLAB） 当用户只需要数值结果时，可直接用以下公式计算，无需运行 MATLAB： **正向（已知 f_BW_target, zeta）：** 1. `C = sqrt(1 + 2*zeta^2 + sqrt(2 + 4*zeta^2 + 4*zeta^4))` 2. `wn = 2*pi*f_BW_target / C` 3. `U_mag = Unom/sqrt(3)*sqrt(2)` 4. `KPLLp = 2*zeta*wn / U_mag` 5. `KPLLi = wn^2 / U_mag` 6. `KPLLp_pu = KPLLp * U_mag / (2*pi*f0)` 7. `KPLLi_pu = KPLLi * U_mag / (2*pi*f0)` **反向（已知 KPLLp_pu, KPLLi_pu）：** 1. `U_mag = Unom/sqrt(3)*sqrt(2)`, `w0 = 2*pi*f0` 2. `KPLLp = KPLLp_pu * w0 / U_mag` 3. `KPLLi = KPLLi_pu * w0 / U_mag` 4. `wn = sqrt(U_mag * KPLLi)` 5. `zeta = U_mag*KPLLp / (2*wn)` 6. `C = sqrt(1 + 2*zeta^2 + sqrt(2 + 4*zeta^2 + 4*zeta^4))` 7. `f_BW = wn*C / (2*pi)`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: emergenceronearth/agentic-stats-analyzer (version 0.1.0)

--- name: stats_analyzer description: 对候选队列执行描述性统计与组间比较，生成感染率对比、描述统计表、结果摘要与论文框架草案。当用户需要做统计分析或生成论文框架时触发。 --- # 统计分析与论文框架 Skill ## 何时使用 当用户需要**单独执行**统计分析或论文框架生成时使用，例如： - 「做一下初步统计分析」 - 「比较不同方案的感染率」 - 「帮我生成论文框架」 如果是完整任务流程的一部分，则由 task-planner 调度。 ## 执行步骤 ### 1. 上报开始 ```bash curl -s -X POST http://localhost:5001/api/report \ -H "Content-Type: application/json" \ -d '{"skill":"stats-analyzer","display_name":"统计分析与论文框架","status":"running","message":"正在进行描述性统计、组间比较与论文框架生成..."}' ``` ### 2. 读取数据并输出 读取 `/home/ubuntu/workspace/demo/mock_data/analysis.json`，向用户展示： - **分析标题**与总体样本量、主要终点、对比分组 - **感染率对比**（表格）：各方案的样本量和感染率 - **描述统计表**：各方案的平均年龄、男性占比、平均手术时长、平均住院天数 - **高风险亚组预览**：如果数据中有相关字段 - **关键发现摘要**：逐条列出 - **注意事项** - **论文框架**： - 建议论文标题 - 章节列表 - 摘要草稿（如果数据中有） ### 3. 上报完成 ```bash curl -s -X POST http://localhost:5001/api/report \ -H "Content-Type: application/json" \ -d '{"skill":"stats-analyzer","display_name":"统计分析与论文框架","status":"completed","message":"统计分析完成，已生成论文框架草案"}' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: solarspiker/appian-deploy (version 1.2.3)

--- name: appian-deploy description: Deploy (import) an Appian package ZIP into an Appian environment. Use when the user wants to push, import, or deploy a package file to an Appian environment. metadata: clawdbot: emoji: "🚀" requires: env: - APPIAN_BASE_URL - APPIAN_API_KEY primaryEnv: APPIAN_BASE_URL --- # Appian Deploy Imports a package ZIP into the Appian environment using the [v2 Deployment Management API](https://docs.appian.com/suite/help/25.4/Deploy_Package_API.html). Polls until a terminal status and prints a full object summary. ## Usage ```bash node {baseDir}/scripts/index.js <zipPath> <deploymentName> [description] [customizationFilePath] ``` ## Example ```bash node {baseDir}/scripts/index.js "./appian-exports/MyPackage.zip" "Prod Release 1.2" ``` Exported packages are saved to `./appian-exports/` by `appian-export`. Run `appian-inspectpkg` first to validate before deploying. If the environment requires approval, the deployment will return `PENDING_REVIEW` — check Appian Designer's Deploy view to approve. ## External endpoints - `POST ${APPIAN_BASE_URL}/deployments` — triggers the import - `GET ${APPIAN_BASE_URL}/deployments/{uuid}` — polls for completion ## Security - Credentials (`APPIAN_BASE_URL`, `APPIAN_API_KEY`) are read from environment variables (injected by OpenClaw at runtime). If not injected, the script falls back to a `appian.json` file in the current working directory. - The package ZIP is uploaded only to your configured Appian environment. - No data is sent to any third-party service. - No shell commands are executed; all operations use Node.js built-in APIs.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zlszhonglongshen/github-trending-blog (version 1.0.0)

--- name: github-trending-blog description: GitHub Trending 监控 → AI摘要分析 → 知识卡片生成 → 公众号技术博客一键创作，自动化技术内容生产工作流 category: AI triggers: GitHub热门, 技术博客, 趋势追踪, 开发者内容, GitHub日报, 技术周报, 追踪开源趋势 --- # GitHub Trending Blog Pipeline 自动化技术内容生产工作流：从 GitHub Trending 监控到公众号技术博客发布的完整流水线。 ## 业务场景 技术博主、内容创作者每天花 2-3 小时手动搜索 GitHub Trending、整理项目信息、撰写技术博客。本工作流将这个过程压缩到 10 分钟以内。 **典型场景：** - "帮我写一篇关于今天 GitHub Trending Python 项目的技术博客" - "追踪本周 Rust 热门项目，生成周报" - "分析某个 GitHub 仓库，生成配图卡片" ## 工作流步骤 ### Step 1: GitHub Trending 数据采集 (`github`) 使用 `gh api` 采集 GitHub Trending 页面的热门项目列表。 ```bash gh api graphql -f query=' { search(query: "stars:>1000 pushed:>2026-05-01", type: REPOSITORY, first: 20) { nodes { ... on Repository { nameWithOwner description stargazerCount primaryLanguage url } } } }' ``` ### Step 2: 项目详情摘要 (`summarize`) 对每个目标仓库的 README 和关键文件使用 `summarize` 提取核心信息。 ```bash summarize "https://github.com/owner/repo" --extract-only ``` ### Step 3: 开发者知识卡片生成 (`card-renderer`) 将分析结果渲染为精美的开发者风格知识卡片（支持 Mac Pro / VS Code / 赛博朋克等多种风格）。 ```bash python3 /root/.openclaw/workspace/skills/card-renderer/scripts/render_vscode_card.py \ "项目名称" "一行简介" "/tmp/readme_summary.md" "/tmp/output/" ``` ### Step 4: 技术博客撰写 (`wechat-article-pro`) 生成 3000-5000 字刘润风格技术博客，包含开篇洞察 + 案例分析 + 结论建议。 ### Step 5: 保存草稿 将文章保存为 Markdown 文件，完成整个工作流。 ## 输出产物 ```json { "repos_discovered": 20, "selected_repo": "owner/repo", "summary": "项目核心价值描述", "card_path": "/tmp/output/cover.png", "blog_path": "/tmp/blog_article.md", "language": "Python", "stars": "5000+", "key_insights": ["洞察1", "洞察2", "洞察3"] } ``` ## 技能编排图谱 ``` [GitHub Trending] ↓ github (数据采集) [项目列表 + Stars + 语言] ↓ summarize (AI摘要) [README核心内容 + 关键特性] ↓ card-renderer (可视化) [开发者风格知识卡片] ↓ wechat-article-pro (文章创作) [3000-5000字技术博客] ↓ 文件保存 [完整产出物] ``` ## 依赖技能 - **github** — GitHub API 采集 Trending 数据 - **summarize** — URL/文档 AI 摘要 - **card-renderer** — 开发者风格知识卡片生成 - **wechat-article-pro** — 公众号技术博客撰写 ## 注意事项 - GitHub API 有速率限制，建议每次采集间隔 >1 分钟 - 公众号草稿需用户手动在 mp.weixin.qq.com 发布 - 卡片渲染建议使用 `render_vscode_card.py` 风格（开发者最友好）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: knifean/golang-coding (version 1.0.0)

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Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/review-monitoring (version 1.0.0)

--- name: review-monitoring description: "Set up systematic review monitoring across e-commerce platforms. Track new reviews, detect negative review spikes, monitor competitor reviews, and automate review response workflows." metadata: nexscope: emoji: "👁️" category: ecommerce --- # Review Monitoring 👁️ Set up systematic review monitoring across e-commerce platforms. Track new reviews, detect negative review spikes, monitor competitor reviews, and automate review response workflows. **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 review-monitoring -g ``` ## Usage ``` Set up a review monitoring system for my products. I sell on Amazon (3 ASINs) and Etsy (15 listings). I want to catch negative reviews within 24 hours. ``` ## Capabilities - Multi-platform review monitoring setup (Amazon, Etsy, Walmart, Shopify) - Negative review alert framework and response templates - Review velocity tracking and anomaly detection - Competitor review monitoring strategy - Review response best practices by platform - Review solicitation compliance guide (per platform rules) ## 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: pxyyqm626529-afk/pdf-helper (version 1.0.0)

# PDF助手 - 文件处理工具 ## 简介 **PDF助手** - 专业的PDF文件处理工具，支持PDF创建、转换、合并、拆分等功能。 ## 功能 1. **PDF转换** - PDF转Word、Excel、PPT、图片等 2. **文件转PDF** - Word/Excel/PPT/图片转PDF 3. **PDF合并** - 多个PDF文件合并为一个 4. **PDF拆分** - 将PDF拆分为多个文件 5. **PDF压缩** - 压缩PDF文件大小 6. **PDF编辑** - PDF旋转、裁剪、提取页面等 ## 定价 - **费用**: 0.001 USDT / 每次调用 - **支付方式**: SkillPay.me - **API Key**: [REDACTED_SECRET] ## 使用方法 ### 转换功能 ``` 把 这个PDF转成Word 把这个Word文件转成PDF 把图片转成PDF ``` ### 合并功能 ``` 合并这几个PDF文件 把file1.pdf和file2.pdf合并 ``` ### 拆分功能 ``` 拆分这个PDF 把PDF每页拆分成单独文件 ``` ### 其他 ``` 压缩这个PDF 旋转PDF的页面 提取PDF的指定页面 ``` ## 支持的格式 **输入格式**: - PDF, DOC, DOCX, XLS, XLSX, PPT, PPTX - JPG, JPEG, PNG, GIF, BMP, WEBP - TXT, HTML **输出格式**: - PDF, DOC, DOCX, XLS, XLSX, PPT, PPTX - JPG, PNG, WEBP - TXT, HTML ## 处理流程 1. 用户上传需要处理的文件 2. 系统将文件提交至处理平台 3. 完成处理后返回结果文件给用户 4. 所有文件处理完成后自动清理，保护隐私 ## 注意事项 - 单个文件大小限制：10MB - 每次最多处理5个文件 - 处理时间根据文件大小和网络情况可能需要等待 - 所有上传文件会在24小时后自动删除 ## 数据来源 使用 [TinyWow](https://tinywow.com/) 进行文件处理 - 免费在线文件处理工具。 ## 版本 - v1.0.0 - 初始版本


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: paul-phan/crisp (version 1.0.0)

--- name: crisp description: Customer support via Crisp API. Use when the user asks to check, read, search, or respond to Crisp inbox messages. Requires Crisp website ID and plugin token (authenticated via environment variables CRISP_WEBSITE_ID, CRISP_TOKEN_ID, and CRISP_TOKEN_KEY). --- # Crisp Customer Support Crisp is a customer support platform. Use this skill when the user needs to: - Check for new messages in the inbox - Read conversation history - Search conversations - Send replies to customers - Check conversation status ## Credentials Crisp requires authentication via HTTP headers with a token identifier and key (Basic Auth), plus the website ID for the API URL. Set these as environment variables (stored securely, never logged): - `CRISP_WEBSITE_ID` - Your website identifier (e.g., `0f4c...`) - `CRISP_TOKEN_ID` - Your Plugin Token Identifier (e.g., `e47d...`) - `CRISP_TOKEN_KEY` - Your Plugin Token Key (e.g., `a7d7...`) ## Common Workflows ### Check Inbox Status ```bash scripts/crisp.py inbox list --page 1 ``` ### Read Conversation ```bash scripts/crisp.py conversation get <session_id> ``` ### Get Messages in Conversation ```bash scripts/crisp.py messages get <session_id> ``` ### Send a Reply ```bash scripts/crisp.py message send <session_id> "Your reply text here" ``` ### Search Conversations ```bash scripts/crisp.py conversations search "query terms" --filter unresolved --max 10 ``` ### Mark as Read ```bash scripts/crisp.py conversation read <session_id> ``` ### Resolve Conversation ```bash scripts/crisp.py conversation resolve <session_id> ``` ## API Reference Key endpoints used: - `GET /v1/website/{website_id}/conversations/{page}` - List conversations - `GET /v1/website/{website_id}/conversation/{session_id}` - Get conversation details - `GET /v1/website/{website_id}/conversation/{session_id}/messages` - Get messages - `POST /v1/website/{website_id}/conversation/{session_id}/message` - Send message - `PATCH /v1/website/{website_id}/conversation/{session_id}/read` - Mark as read - `PATCH /v1/website/{website_id}/conversation/{session_id}` - Update/resolve Base URL: `https://api.crisp.chat` ## Notes - Always ask before sending customer replies to confirm tone/content - Check for `meta.email` in conversation for customer email - Verify `CRISP_WEBSITE_ID`, `CRISP_TOKEN_ID`, and `CRISP_TOKEN_KEY` are set before running commands - Use `--json` flag for script output when parsing programmatically


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wimi321/remote-agent-scheduler (version 1.0.0)

--- name: "remote_agent_scheduler" description: "Use when the user wants to create, inspect, update, or run scheduled remote agents in the cloud rather than local cron jobs." --- # Remote Agent Scheduler Use this skill for recurring remote agent jobs that run in isolated cloud environments. ## Workflow 1. Determine whether the user wants to create, list, update, or run a trigger. 2. Collect repo source, environment, connectors, schedule, and prompt payload. 3. Build or inspect the trigger definition. 4. Confirm authentication, environment, and connector assumptions. 5. Return the trigger details and next management steps. ## Guardrails - Distinguish remote cloud runs from local cron jobs. - Keep repo URL, environment ID, and schedule explicit. - Surface setup blockers like missing auth or repo access early. ## Example Requests - Create a nightly remote agent to summarize repo health. - Update this cloud trigger to use a different environment and cron. ## Inputs - Desired action - Repo source - Environment and connector context - Cron schedule ## Outputs - Trigger definition - Run or update confirmation - Operational notes ## Success Criteria - The action type is clear. - Remote environment assumptions are explicit. - Auth and repo blockers are surfaced. ## Non-Goals - Local cron setup - Ambiguous cloud-vs-local scheduling ## Source Provenance Derived from `src/skills/bundled/scheduleRemoteAgents.ts`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: impkind/basal-ganglia-memory (version 0.1.1)

--- name: basal-ganglia-memory description: "Habit formation and procedural learning for AI agents. Develop preferences and shortcuts through repetition. Part of the AI Brain series." metadata: openclaw: emoji: "🎯" version: "0.1.1" author: "ImpKind" requires: os: ["darwin", "linux"] tags: ["memory", "habits", "ai-brain"] --- # Basal Ganglia Memory 🎯 **Habit formation for AI agents.** Part of the AI Brain series. ## Status: 🚧 Under Development This skill is being developed. Star/watch for updates! ## Concept The basal ganglia is responsible for habit formation and procedural learning. This skill will give AI agents: - **Habit tracking** — actions repeated become automatic preferences - **Procedural memory** — "muscle memory" for common workflows - **Reward-based learning** — reinforce patterns that work - **Preference development** — "I always do X this way because it works" ## AI Brain Series | Part | Function | Status | |------|----------|--------| | [hippocampus](https://www.clawhub.ai/skills/hippocampus) | Memory formation, decay, reinforcement | ✅ Live | | [amygdala-memory](https://www.clawhub.ai/skills/amygdala-memory) | Emotional processing | ✅ Live | | **basal-ganglia-memory** | Habit formation | 🚧 Development | | [anterior-cingulate-memory](https://www.clawhub.ai/skills/anterior-cingulate-memory) | Conflict detection | 🚧 Development | | [insula-memory](https://www.clawhub.ai/skills/insula-memory) | Internal state awareness | 🚧 Development | ## Coming Soon Based on neuroscience research on the basal ganglia's role in procedural learning and habit formation. --- *Built with ❤️ by the OpenClaw community*


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