OpenClaw/clawhub-security-signals huggingface dataset:
Skill: suran7810/tech-debate (version 1.0.0)

--- name: tech-debate version: 1.0.0 description: "针对技术话题，模拟正方与反方两派进行多阶段、多轮结构化辩论，阶段之间用户可干预（支持某方/补充论点/提出新视角），最终由 Judge 提炼全局共识、分歧与实践建议。当用户说【对XXX进行技术辩论】、【debate: XXX】、【正反两方讨论XXX】、【辩论一下XXX】、【tech debate】时触发。支持用户设置最大轮数（默认5轮/阶段），Judge 根据辩论情况动态决定阶段内是否提前收敛。" --- # Tech Debate Skill 多 Agent 辩论框架，参考 MAD（Multi-Agent Debate）论文和 TradingAgents 项目实现。 支持多阶段辩论 + 用户干预机制。 ## 参数 - **topic**：辩论话题（必填，从用户输入提取） - **max_rounds_per_phase**：每阶段最大轮数（可选，默认 5，用户可指定） ## 整体结构 ``` 初始化（Phase 0） ↓ [阶段 N] 开场/继续陈述 → 多轮对抗（最多 max_rounds_per_phase 轮） → Judge 阶段小结 + 暂停 ↓ 用户干预（必须等用户回复） ↓ [阶段 N+1] 或 结束 ↓ Judge 全局共识提炼 ``` ## 执行流程 ### Phase 0：辩题框架生成（仅首次） 以 Judge 角色输出： - 正方核心立场（1句话） - 反方核心立场（1句话） - 3个预期争议焦点 然后进入阶段 1。 ### 阶段内流程（每个阶段重复此结构） **Step 1 - 开场/续场陈述** - 首阶段：正方和反方各自独立开场陈述（不超过300字/方） - 后续阶段：正方和反方各用1~2句话声明本阶段立场是否因用户干预而调整，并注明是否引用用户论点 **Step 2 - 多轮对抗** 见"对抗轮流程"。 **Step 3 - Judge 阶段小结** 每阶段结束时 Judge 输出： - 本阶段新出现的论点 - 本阶段双方立场变化（如有） - 当前共识进展 - 仍存在的主要分歧 **Step 4 - 暂停，等待用户干预** 输出用户干预提示，**停止执行，等待用户回复**： ``` ⏸️ 阶段 N 结束。您可以： ① 支持正方，补充论据 → "支持正方：[你的论点]" ② 支持反方，补充论据 → "支持反方：[你的论点]" ③ 提出新视角（双方都需回应） → "新视角：[观点]" ④ 结束辩论，提炼共识 → "结束辩论" （不干预直接回复"继续"也可以进入下一阶段） ``` ### 对抗轮流程（阶段内每轮） 1. **正方发言**： - 必须直接回应反方上一轮具体论点 - 若本阶段有用户干预，必须说明是否采纳用户论点，以及理由 - 不超过300字 2. **反方发言**： - 同上，回应正方上一轮论点 - 若用户支持正方，需正面回应用户论点（不能无视） - 不超过300字 3. **Judge 收敛检测**： - 双方论点高度重复（>60% 相似）→ 提前结束本阶段 - 双方已在核心争议点充分表态 → 提前结束本阶段 - 达到 max_rounds_per_phase → 结束本阶段 ### 用户干预处理规则 收到用户干预后，在下一阶段开始时注入以下上下文（Prompt 模板见 `references/prompts.md`）： | 干预类型 | 处理方式 | |---------|---------| | 支持正方 + 论点 | 正方可引用；反方必须正面回应，不能无视 | | 支持反方 + 论点 | 反方可引用；正方必须正面回应，不能无视 | | 新视角 | 双方都必须在本阶段第一轮回应这个视角 | | 继续 | 无特殊注入，双方继续原有辩论线 | ### 全局共识提炼（收到"结束辩论"后执行） 综合所有阶段的辩论记录 + 用户干预内容，输出： 1. **共识点**：双方均认可的观点（标注是哪个阶段收敛的） 2. **核心分歧**：用表格对比双方坚持的根本对立 3. **用户视角**：用户干预对辩论走向的影响 4. **实践建议**：场景化的技术决策建议 完整输出格式见 `references/output-format.md`。 ## Prompt 模板 三角色完整 prompt 见 `references/prompts.md`。 ## 关键原则 - 每个角色维护**独立论点历史**（跨阶段累积） - 用户论点**不可被无视**，被支持方可引用，对立方必须回应 - Judge **不偏袒任何一方**，包括用户 - **阶段间必须暂停等待用户**，不得自动进入下一阶段


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kostja94/sidebar-generator (version 1.0.1)

--- name: sidebar-generator description: When the user wants to design, optimize, or audit a sidebar for blogs, docs, or content pages. Also use when the user mentions "sidebar," "blog sidebar," "content sidebar," "side panel," "sidebar navigation," "related content," "sidebar CTA," "doc sidebar," or "sidebar widgets." For blog layout, use blog-page-generator. metadata: version: 1.0.1 --- # Components: Sidebar Guides sidebar design for content sites (blogs, docs). ~80% of users focus on the left; sidebars influence flow but can hurt conversion if overused. Posts without sidebars show 3.1x higher conversion; bottom-right sticky CTAs outperform sidebars (5.62% vs 0.58% CTR). **When invoking**: On **first use**, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On **subsequent use** or when the user asks to skip, go directly to the main output. ## Initial Assessment **Check for project context first:** If `.claude/project-context.md` or `.cursor/project-context.md` exists, read it for content structure and conversion goals. Identify: 1. **Content type**: Blog, docs, e-commerce 2. **Primary goal**: Navigation, CTA, related content 3. **Mobile**: Collapse to hamburger or hide on small screens ## Best Practices ### Placement - **Left sidebar**: Prime for nav; 80% of users focus left - **Right sidebar**: Secondary content, ads, related posts - **Static vs sticky**: Static for content-focused; sticky for persistent CTA (subscription, cart) ### Conversion Reality | Approach | Typical result | |----------|----------------| | **Sidebar CTA** | <1% opt-in for blog sidebars | | **In-content CTA** | 3x+ higher conversion | | **Bottom-right sticky** | 5.62% CTR vs 0.58% sidebar | | **No sidebar** | 3.1x higher conversion vs with sidebar | **Recommendation**: Prefer in-content CTAs or bottom-right sticky over sidebar CTAs. Use sidebar for nav and discovery, not primary conversion. ### Widget Guidelines - **One high-value CTA** max; avoid clutter - **Social proof**: Testimonials, logos - **Popular/related posts**: Discovery, internal linking - **Avoid**: Too many ads, affiliate links; reduces trust and speed ### Mobile - **Collapse**: Hamburger or off-canvas drawer - **57%+ mobile traffic**: Responsive design non-negotiable - **Reduce clutter**: Hide or simplify sidebar on small screens ## Output Format - **Placement** (left/right, static/sticky) - **Widget** list (nav, CTA, related, social proof) - **Mobile** behavior - **Conversion** note (in-content vs sidebar CTA) ## Related Skills - **toc-generator**: TOC often in sidebar for long content - **cta-generator**: Sidebar CTA (use sparingly) - **newsletter-signup-generator**: Sidebar signup; consider in-content instead - **internal-links**: Related posts in sidebar


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shimotsuk1-rei/aws-health-monitor (version 1.0.0)

--- name: aws-health-monitor description: Monitor AWS Health Dashboard for active incidents and notify via configurable channels (Feishu, Telegram, Slack, Discord, etc.). Activate when user wants to watch AWS service health, set up alerts for AWS outages, or check current AWS regional incidents. Covers global, eu-central-1, ap-northeast-1, and me-central-1 (UAE) regions. Supports HTTP proxy for restricted network environments. --- # AWS Health Monitor 监控 AWS Health Dashboard 活跃故障，变更时推送通知（支持飞书、Telegram、Slack、Discord 等）。 ## 部署路径 - 脚本：`scripts/aws-health-monitor.py`（部署到 workspace/scripts/） - 忽略配置：`references/aws-health-ignore.example.json`（部署为 workspace/scripts/aws-health-ignore.json） - 状态文件（运行时自动生成）：`workspace/scripts/.aws-health-state.json` - 日志：`workspace/logs/aws-health-monitor.log` ## 关键配置（环境变量） 脚本所有关键参数通过环境变量注入，不硬编码： | 环境变量 | 说明 | 默认值 | |----------|------|--------| | `HTTPS_PROXY` / `HTTP_PROXY` | 出口代理 | 不使用代理 | | `AWS_HEALTH_NOTIFY_CHANNEL` | 通知渠道（feishu/telegram/slack/discord 等） | `feishu` | | `AWS_HEALTH_NOTIFY_TARGET` | 通知目标（open_id / chat_id / @username 等） | 必填 | | `AWS_HEALTH_WATCH_REGIONS` | 监控的 region 白名单，逗号分隔 | 空（监控全部） | | `AWS_HEALTH_WATCH_SERVICES` | 监控的服务名白名单，逗号分隔 | 空（监控全部） | **示例：只监控法兰克福和东京的 EC2、RDS** ```bash export AWS_HEALTH_WATCH_REGIONS=eu-central-1,ap-northeast-1 export AWS_HEALTH_WATCH_SERVICES="Amazon EC2,Amazon RDS" ``` ## 挂 Cron ```bash */5 * * * * export http_proxy=http://<proxy>:<port> https_proxy=http://<proxy>:<port> AWS_HEALTH_NOTIFY_CHANNEL=<channel> AWS_HEALTH_NOTIFY_TARGET=<target>; cd /path/to/workspace && /usr/bin/python3 scripts/aws-health-monitor.py >> logs/aws-health-monitor.log 2>&1 ``` ## 忽略配置（aws-health-ignore.json） 参考 `references/aws-health-ignore.example.json`，支持两种忽略方式： ```json { "arns": ["arn:aws:health:me-central-1::event/..."], // 精确忽略单个 issue "services": ["Amazon WorkSpaces", "AWS IoT Core"] // 按服务名模糊忽略 } ``` 修改后保存即生效，下次轮询自动跳过。 ## 通知触发条件 - 新 issue 出现 - 已知 issue 有最新更新（message 变化） - issue 从 Dashboard 消失（视为已解决） ## 通知格式示例 ``` [新故障] | AWS Health Dashboard Region：UAE 服务：Multiple services 状态：调查中（Increased Error Rates） 首发：2026-03-01 20:51 CST 更新：2026-03-02 13:59 CST 根因：电源故障、数据中心火灾/物理损坏 We are investigating issues with AWS services in the ME-CENTRAL-1 Region 最新进展： We are investigating additional connectivity issues and error rates... https://health.aws.amazon.com/health/status ``` ## 根因提炼逻辑 脚本通过正则匹配 event_log 所有消息文本，识别以下根因类型： 电源故障、数据中心物理损坏、网络故障、连通性问题、硬件故障、软件 Bug、配置变更/错误、变更发布问题、容量/资源耗尽、DNS 问题、证书/TLS 问题、存储故障、内存问题、流量异常/DDoS、上游/第三方问题。 匹配到多个时用顿号拼接；未匹配则显示"暂无明确根因"。 ## 数据来源 `GET https://health.aws.amazon.com/public/currentevents`（UTF-16 编码 JSON） 旧接口 `https://status.aws.amazon.com/data.json` 已 301 重定向至此。

path: references/aws-health-ignore.example.json
content: {
  "_comment": "忽略列表说明：arns 按精确 ARN 忽略单个 issue；services 按服务名（包含匹配）忽略同类 issue。",
  "arns": [],
  "services": []
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lemodigital/claude-usage (version 1.0.0)

--- name: claude-usage description: Calculate Claude Max subscription usage from OpenClaw session data. Shows credits consumed, weekly budget percentage, 5-hour rate limit window, and per-session breakdown. --- # Claude Max Usage Calculator Track your Claude Max subscription usage based on the reverse-engineered credits system from [she-llac.com/claude-limits](https://she-llac.com/claude-limits). ## When to use When the user asks about Claude usage, credits, subscription consumption, rate limits, or wants to know how much budget is left. ## Setup (first time) Ask the user for: 1. **Weekly reset time** — visible on [claude.ai](https://claude.ai) Settings > Usage (e.g. "Resets Mon 2:00 PM") 2. **Plan** — `pro` ($20), `5x` ($100), or `20x` ($200). Default: `5x` Save it so they never have to repeat: ```bash python3 {SKILL_DIR}/scripts/claude-usage.py "2026-02-09 14:00" --plan 5x --save ``` ## Commands ```bash # Weekly overview (uses saved config after first --save) python3 {SKILL_DIR}/scripts/claude-usage.py # Override plan or timezone python3 {SKILL_DIR}/scripts/claude-usage.py --plan 20x --tz "America/New_York" # Top N sessions only python3 {SKILL_DIR}/scripts/claude-usage.py --top 5 # Single session detail (substring match on key or id) python3 {SKILL_DIR}/scripts/claude-usage.py --session "main" python3 {SKILL_DIR}/scripts/claude-usage.py --session "9aadee" # JSON output python3 {SKILL_DIR}/scripts/claude-usage.py --json ``` ## What it shows ### Weekly overview - Total credits used vs weekly budget (with progress bar) - **5-hour sliding window** — warns if approaching the per-5h rate limit - All sessions ranked by credits consumed - Model breakdown (Opus/Sonnet/Haiku/non-Claude) ### Single session detail (`--session`) - Credits consumed and % of weekly budget - % of total usage (how much of your spending this session accounts for) - Token breakdown (input/output/cache) - Per-model detail ## Credits formula ``` credits = (input_tokens + cache_write_tokens) × input_rate + output_tokens × output_rate ``` | Model | Input rate | Output rate | |--------|-----------|-------------| | Haiku | 2/15 | 10/15 | | Sonnet | 6/15 | 30/15 | | Opus | 10/15 | 50/15 | Cache reads are **free**. Non-Claude models (Gemini, Codex, etc.) don't consume Claude credits. ## Plan budgets | Plan | $/month | Credits/5h | Credits/week | |------|---------|-----------|-------------| | Pro | $20 | 550,000 | 5,000,000 | | 5× | $100 | 3,300,000 | 41,666,700 | | 20× | $200 | 11,000,000| 83,333,300 | ## Requirements - Python 3.9+ - OpenClaw with session JSONL files (auto-detected at `~/.openclaw/agents/main/sessions/`)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/rate-limit-manager (version 1.0.0)

# rate-limit-manager Intelligent API rate limiting and quota management for AI agents. Control request rates, enforce quotas, and prevent API abuse. ## Overview A comprehensive rate limiting assistant that helps agents implement and manage rate limits across multiple APIs and services. ## Features - **Rate Limiting**: Token bucket, sliding window, fixed window algorithms - **Quota Management**: Daily, weekly, monthly usage quotas - **Multi-Service**: Manage limits across multiple APIs simultaneously - **Adaptive Throttling**: Automatically adjust based on 429 responses - **Visualization**: Dashboard showing usage and limits - **Alerts**: Notify when approaching limits - **Retry Logic**: Smart retry with exponential backoff ## Commands ### Set Rate Limit ``` limit requests to 100 per minute for api-key-123 ``` ### Check Quota ``` check remaining quota for openai-api ``` ### Enable Adaptive Throttling ``` enable auto-throttling for external-api ``` ## Use Cases - API quota management - Prevent service abuse - Cost control - Rate limit implementation - Multi-service rate coordination ## Requirements - Node.js 18+ - Redis (optional, for distributed rate limiting)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 52yuanchangxing/creator-course-outline (version 1.0.0)

--- name: creator-course-outline version: 1.0.0 description: "为创作者课程生成模块结构、单元目标、作业与里程碑。；use for course, creator, curriculum workflows；do not use for 承诺学员结果, 替代正式教学设计评审." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/creator-course-outline tags: [course, creator, curriculum, education] user-invocable: true metadata: {"openclaw":{"emoji":"🎓","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 创作者课程大纲师 ## 你是什么 你是“创作者课程大纲师”这个独立 Skill，负责：为创作者课程生成模块结构、单元目标、作业与里程碑。 ## Routing ### 适合使用的情况 - 帮我设计一门课程的大纲 - 生成单元目标和作业 - 输入通常包含：课程主题、对象、目标、时长 - 优先产出：课程目标、模块结构、学员常见卡点 ### 不适合使用的情况 - 不要承诺学员结果 - 不要替代正式教学设计评审 - 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。 ## 工作规则 1. 先把用户提供的信息重组成任务书，再输出结构化结果。 2. 缺信息时，优先显式列出“待确认项”，而不是直接编造。 3. 默认先给“可审阅草案”，再给“可执行清单”。 4. 遇到高风险、隐私、权限或合规问题，必须加上边界说明。 5. 如运行环境允许 shell / exec，可使用： - `python3 "{baseDir}/scripts/run.py" --input <输入文件> --output <输出文件>` 6. 如当前环境不能执行脚本，仍要基于 `{baseDir}/resources/template.md` 与 `{baseDir}/resources/spec.json` 的结构直接产出文本。 ## 标准输出结构 请尽量按以下结构组织结果： - 课程目标 - 模块结构 - 单元目标 - 作业 - 里程碑 - 学员常见卡点 ## 本地资源 - 规范文件：`{baseDir}/resources/spec.json` - 输出模板：`{baseDir}/resources/template.md` - 示例输入输出：`{baseDir}/examples/` - 冒烟测试：`{baseDir}/tests/smoke-test.md` ## 安全边界 - 适合课程蓝图和迭代。 - 默认只读、可审计、可回滚。 - 不执行高风险命令，不隐藏依赖，不伪造事实或结果。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: phheng/ebay-review-checker (version 0.1.0)

--- name: ebay-review-checker version: 1.0.0 description: "eBay review and feedback authenticity analyzer. Detect fake reviews, suspicious seller feedback patterns, and buyer manipulation. Includes time clustering detection, content similarity analysis, and eBay-specific red flag identification. No API key required." metadata: {"nexscope":{"emoji":"🔍","category":"ecommerce"}} --- # eBay Review Checker 🔍 Review and feedback authenticity analyzer for eBay — detect fake reviews, suspicious patterns, and feedback manipulation. ## Installation ```bash npx skills add nexscope-ai/eCommerce-Skills --skill ebay-review-checker -g ``` ## Features - **Seller Feedback Analysis** — Analyze seller feedback authenticity - **Buyer Feedback Patterns** — Detect suspicious buyer behavior - **eBay-specific Red Flags** — Platform-specific warning signs - **Progressive Analysis** — More data = deeper insights ## eBay-Specific Detection | Signal | Description | |--------|-------------| | Feedback timing | Clustered feedback in short periods | | Generic comments | "A+++" or "Great seller" patterns | | Account age | New accounts leaving multiple feedbacks | | Transaction patterns | Unusual buying/selling patterns | ## Risk Levels | Score | Level | Description | |-------|-------|-------------| | 70-100 | ✅ Low Risk | Feedback appears authentic | | 50-69 | ⚠️ Medium Risk | Some concerns found | | 30-49 | 🔴 High Risk | Multiple red flags | | 0-29 | 💀 Critical | Likely manipulated feedback | ## Usage ### Paste Feedback ``` Check this seller feedback: Positive - Great seller, fast shipping! A+++ Positive - Excellent transaction, thank you! Positive - Perfect! Would buy again. Negative - Item never arrived. ``` ### JSON Input ```bash python3 scripts/analyzer.py '[ {"content": "A+++ seller!", "rating": "positive", "date": "2024-01-15"}, {"content": "Fast shipping", "rating": "positive", "date": "2024-01-15"} ]' ``` ## Output Example ``` 📊 eBay Feedback Authenticity Report Seller: example_seller Feedback Count: 50 Analysis Level: L3 ━━━━━━━━━━━━━━━━━━━━━━━━ Authenticity Score: 72/100 ✅ Low Risk - Feedback appears authentic. ━━━━━━━━━━━━━━━━━━━━━━━━ Detection Results ✅ Time Clustering: Normal ⚠️ Generic Comments: 15% (slightly high) ✅ Account Diversity: Good ``` --- **Part of [Nexscope AI](https://www.nexscope.ai/) — AI tools for e-commerce sellers.**


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

--- name: selection-agent description: "全渠道选品 Agent — 关键词调研、SEO分析、竞品分析、TrendPlus数据。触发词：选品、关键词调研、SEO分析、竞品分析、keyword research、competitor analysis。执行前必须读取 AGENT_CONFIG.md。" --- # Selection Agent Skill — 全渠道选品 Agent ## 触发条件 当用户提到"选品"、"关键词调研"、"SEO分析"、"竞品分析"、"keyword research"、"competitor analysis"时激活。 ## ⚠️ 必读配置 **执行前必须读取**: `/tmp/powerful-trendplus/AGENT_CONFIG.md` 这里面写死了所有 Token、竞品列表、输出结构、环境变量，不要忘记！ ## 快速执行 ```bash cd /tmp/powerful-trendplus source .env.local 2>/dev/null python3 scripts/run_full_research.py ``` ## 环境变量（全部已配置） | 变量 | 状态 | |------|------| | `SEMRUSH_API_KEY` | ✅ 系统环境变量 | | `FB_ADS_TOKEN` | ✅ `.env.local`（App需 `ads_read` 权限） | | `NOTION_API_KEY` | ✅ 系统环境变量（DB未授权） | | `GEMINI_API_KEY` | ✅ `~/.bashrc` | ## 竞品列表（固定） photoroom.com, remini.ai, fotor.com, picsart.com, faceapp.com, cutout.pro ## 去重数据源 - A. Sitemap: `https://art.myshell.ai/sitemap.xml` → `enpage.xml` - B. CMS Base44: `https://app.base44.com/api` - C. Notion Bot DB: [REDACTED_SECRET]（待授权） ## 固定输出结构 ```json { "keyword": "...", "func_cn": "...", "kd": 42, "volume": 49500, "region": "US", "comp_url": "...", "comp_domain": "...", "coverage": "new|exists_sitemap|exists_cms|exists_notion|duplicate", "dedup_source": "", "fb_ads_count": 0, "fb_top_advertiser": "", "gap_score": 85.2, "priority": "high|medium|low" } ``` ## 项目仓库 https://github.com/Arxchibobo/powerful-trendplus ## 依赖 - Semrush Skill（关键词数据）→ 需要 `SEMRUSH_API_KEY` - Google Workspace Skill（Sheets 输出）→ 需要 OAuth - MySQL Skill（内部数据对比）→ 需要数据库凭证 ## 常见错误 | 错误 | 原因 | 处理 | |------|------|------| | Semrush API 限额 | 免费版有日请求上限 | 减少关键词批量，分批查询 | | 关键词为空 | 查询词太窄或拼写错误 | 放宽匹配，尝试同义词 | | Sheets 写入失败 | OAuth token 过期 | 提示用户重新授权 | | 超时 | 大量关键词并发查询 | 分批处理，每批 ≤20 个 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: alibabacloudservice19-collab/agent-andri (version 1.0.0)

# agent-Andri Skill ini merepresentasikan agen pekerja **Andri**. Agen secara periodik mengirimkan statusnya ke **meeting‑room** dengan menuliskan ke file `~/ .openclaw/workspace/skills/meeting-room/to_leader.txt`. ## Variabel lingkungan - `AGENT_NAME` – Nama agen (di‑set otomatis oleh wrapper). - `NV_API_KEY` – API‑key khusus agen (tidak dipakai di contoh ini, hanya disimpan bila diperlukan). - `MODEL` – Model AI yang dipakai agen. ## Skrip utama `scripts/status_report.sh` membaca variabel di atas, men‑generate pesan status, dan men‑append‑kan ke file meeting‑room.

path: scripts/status_report.sh
content: #!/data/data/com.termux/files/usr/bin/bash
set -euo pipefail

AGENT_NAME="Andri"
BASE_DIR="$HOME/.openclaw/workspace/skills/meeting-room"
TO_LEADER="$BASE_DIR/to_leader.txt"

# Pastikan file ada
touch "$TO_LEADER"

while true; do
  ts=$(date '+%Y-%m-%d %H:%M:%S')
  echo "[$ts] $AGENT_NAME : sedang idle" >> "$TO_LEADER"
  sleep 30   # kirim status tiap 30 detik (ubah bila perlu)
done


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jinchenjia/feishu-new-bot (version 1.0.0)

--- name: feishu-new-bot description: 飞书新机器人创建工作流。当大哥要求创建新的飞书bot、建立新的飞书机器人、或者新增bot时使用此skill。触发场景包括：大哥说"创建一个新的飞书bot"、"新建一个机器人"、"新增bot"、"帮我创建一个飞书机器人"。 --- # feishu-new-bot 飞书新机器人创建完整工作流。 ## 前提条件 - 大哥已有飞书开放平台账号 - 大哥愿意亲自创建应用（这一步必须大哥操作，AI无法代劳） ## 完整流程 ### 第一步：大哥创建应用 告诉大哥访问以下链接创建企业自建应用（机器人）： ``` https://open.feishu.cn/page/openclaw?form=multiAgent ``` **命名规则**（必须遵守）： - 不能有空格 - 不能有特殊符号 - 只能使用字母、数字、下划线 ### 第二步：大哥提供凭证 大哥把以下信息发给我： - APP ID - APP SECRET ### 第三步：创建工作目录 在 `/Users/jiaclaw/.openclaw/` 下建立新bot的workspace根目录，**目录名 = bot名字**（与APP名字一致）。 ### 第四步：修改openclaw.json配置 在gateway配置中添加新bot信息，需要包含： - APP ID - APP SECRET - workspace路径（刚创建的目录） **重要：修改openclaw.json前必须先征得大哥同意，不能自行修改。** ### 第五步：重启Gateway 配置修改后需要重启Gateway使配置生效。 ### 第六步：同步工作文件 从大哥的Thanos workspace同步以下文件到新bot的workspace： | 文件 | 处理方式 | |------|----------| | AGENTS.md | 直接同步 | | TOOLS.md | 直接同步 | | USER.md | 直接同步 | | MEMORY.md | 改造后同步（清理Thanos相关内容） | | HEARTBEAT.md | 不同步（新bot自己创建） | ### 第七步：建立身份文件 为新bot创建 `SOUL.md` 和 `IDENTITY.md`： - 根据新bot的角色和职责确定性格定位 - 设置合适的Emoji和Avatar描述 - 写入大哥制定的行为准则（最高执行准则） ### 第八步：验证连接 1. 获取新bot的sessionKey 2. 通过 `sessions_send` 发送测试消息确认能正常调用 3. 确认回复正常即完成 ## 快速检查清单 - [ ] 大哥已创建应用并提供APP ID、APP SECRET - [ ] 工作目录已创建 - [ ] openclaw.json已修改并重启Gateway - [ ] 工作文件已同步 - [ ] SOUL.md和IDENTITY.md已创建 - [ ] 连接验证通过 ## 注意事项 1. **openclaw.json修改必须先征得大哥同意** 2. HEARTBEAT.md由新bot自己创建，不要从Thanos复制 3. 新bot的SOUL.md和IDENTITY.md要根据其职责定制，不是简单复制 4. 如果大哥忘记创建应用，引导他去开放平台创建


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: biohackerrrrrr/paper-fetcher (version 1.0.0)

--- name: paper-fetcher description: Fetch academic papers from Sci-Hub given a DOI. Automatically downloads PDFs and saves them to research/papers/ with clean filenames. Use when the user provides a DOI or requests a paper from PubMed. --- # Paper Fetcher Automated academic paper retrieval from Sci-Hub. ## Usage **Simple fetch:** ``` Get paper: 10.1038/nature12345 ``` **Multiple papers:** ``` Fetch these papers: - 10.1016/j.cell.2023.01.001 - 10.1038/s41586-2023-06789-0 - 10.1126/science.abc1234 ``` **With context:** ``` Get the epitalon paper: 10.1007/s12603-011-0032-7 ``` ## What It Does 1. Takes DOI as input 2. Navigates to https://www.sci-hub.su/DOI 3. Downloads the PDF 4. Saves to `research/papers/` with clean filename 5. Returns confirmation with file path ## Output Location ``` workspace/ └── research/ └── papers/ ├── paper_10.1038_nature12345.pdf ├── paper_10.1016_j.cell.2023.01.001.pdf └── ... ``` ## Filename Format `paper_[DOI with slashes replaced].pdf` Examples: - DOI: `10.1038/nature12345` → `paper_10.1038_nature12345.pdf` - DOI: `10.1016/j.cell.2023.01.001` → `paper_10.1016_j.cell.2023.01.001.pdf` ## Workflow When user provides a DOI: 1. **Extract DOI** - Parse from message (with or without https://doi.org/ prefix) 2. **Navigate Sci-Hub** - Use browser to load https://www.sci-hub.su/DOI 3. **Wait for PDF** - Let page load and find download link 4. **Download** - Save PDF to research/papers/ 5. **Confirm** - Report success with file path ## Error Handling **If paper not found on Sci-Hub:** - Report that Sci-Hub couldn't find it - Suggest checking the DOI format - User can try manual search **If download fails:** - Report the error - Provide Sci-Hub URL for manual download ## Integration **With Obsidian Sync:** - Papers saved in research/papers/ - Can create notes linking to PDFs - Sync metadata to Obsidian vault **With Research Automation:** - Fetch papers discovered in research runs - Build reference library automatically - Cross-reference with protocol notes ## Tips **Finding DOIs:** - PubMed: Listed in article details - Paper itself: Usually on first page - Google Scholar: In citation info **Format flexibility:** - With prefix: `https://doi.org/10.1038/nature12345` ✅ - Without prefix: `10.1038/nature12345` ✅ - Either format works **Batch fetching:** - Send multiple DOIs at once - Processed sequentially - All saved to research/papers/ --- **Status:** Active **Sci-Hub Domain:** https://www.sci-hub.su **Save Location:** research/papers/


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