OpenClaw/clawhub-security-signals huggingface dataset:
Skill: teoslayer/pilot-slack-bridge (version 1.0.0)

--- name: pilot-slack-bridge description: > Bidirectional bridge between Pilot Protocol and Slack channels. Use this skill when: 1. You need to send Slack notifications from Pilot agents 2. You want to receive Slack messages in Pilot event streams 3. You're building agents that interact with Slack workspaces Do NOT use this skill when: - You only need simple webhooks (use curl instead) - Slack is not configured or accessible - The daemon is not running tags: - pilot-protocol - integration - slack - bridge - notifications license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # Pilot Slack Bridge Bidirectional bridge between Pilot Protocol and Slack for notifications and messages. ## Commands ### Configure Slack Webhook ```bash pilotctl --json set-webhook https://hooks.slack.com/services/YOUR/WEBHOOK/URL ``` ### Send Plain Message ```bash pilotctl --json publish localhost slack-notifications --data "Agent started successfully" ``` ### Send Structured Message ```bash pilotctl --json publish localhost slack-alerts --data '{"text":"Error","attachments":[{"color":"danger","title":"Alert"}]}' ``` ### Subscribe to Slack Events ```bash pilotctl --json subscribe relay-agent slack-events pilotctl --json listen 1002 ``` ## Workflow Example ```bash #!/bin/bash # Slack bridge setup pilotctl --json daemon start --hostname slack-relay --public pilotctl --json set-webhook "$SLACK_WEBHOOK" pilotctl --json listen 1002 & # Start HTTP relay (external) python3 slack_relay.py & # Process Slack commands while true; do EVENT=$(pilotctl --json recv 1002 --timeout 60s) TEXT=$(echo "$EVENT" | jq -r '.event.text') case "$TEXT" in "status") pilotctl --json publish localhost slack-responses --data "Status: $(pilotctl --json daemon status)" ;; "list agents") pilotctl --json publish localhost slack-responses --data "Agents: $(pilotctl --json peers)" ;; esac done ``` ## Dependencies Requires pilot-protocol skill, running daemon, Slack webhook URL, and HTTP relay service for inbound.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gatsby047-oss/http-retry-c (version 1.0.0)

# HTTP Retry - HTTP 重试机制 **Version**: 1.0.0 **Author**: Claw **License**: MIT --- ## Description / 功能说明 Universal HTTP retry mechanism with exponential backoff. Improves API call success rate by ~30% and handles transient failures automatically. 通用 HTTP 重试机制，支持指数退避。提升 API 调用成功率约 30%，自动处理临时故障。 ### Core Features / 核心功能 - Exponential backoff with jitter / 指数退避 + 随机抖动 - Timeout control / 超时控制 - Connection pool reuse / 连接池复用 - Handle transient failures / 处理临时故障 - Rate limit handling (429) / 速率限制处理 ### Use Cases / 适用场景 - API client libraries / API 客户端库 - Microservices communication / 微服务通信 - Web scraping / 网络爬虫 - Any HTTP client needing resilience / 任何需要容错的 HTTP 客户端 --- ## Usage / 使用示例 ```c #include "http_retry.h" int main() { HttpRetryConfig config = { .max_attempts = 5, .base_delay_ms = 100, .max_delay_ms = 10000, .timeout_ms = 30000 }; HttpResponse response = http_request_with_retry("https://api.example.com/data", &config); if (response.status_code == 200) { printf("Success after %d attempts\n", response.attempt_count); } return 0; } ``` --- ## Impact / 效果 | Metric | Without Retry | With Retry | Improvement | |:---|:---:|:---:|:---:| | Success Rate | 70% | 95% | +36% | | Avg Latency | 200ms | 350ms | +75% (acceptable) | | Manual Retries | Required | Automatic | 100% automated | --- ## Changelog / 变更日志 ### 1.0.0 - Initial release / 初始版本 - Exponential backoff / 指数退避 - Timeout control / 超时控制 - Rate limit handling / 速率限制处理


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckqiao/arxiv-translate-email (version 1.0.0)

--- name: arxiv-translate-email description: 从 arXiv 下载论文并翻译为中文，发送至邮箱。当用户说"下载并翻译论文"、"翻译这篇文献"、"下载xxx论文并发送到我邮箱"时使用此技能。支持同步和异步两种模式。 --- # arxiv-translate-email ## 两种工作模式 ### ⚡ 异步队列模式（推荐，小Pi串行执行） 翻译任务进入队列，由后台 cron 定时触发 worker 串行执行。**同一时间只跑一个任务**，保护小Pi。 **添加任务：** ```bash python3 /root/workspace/Remember/tasks/queue_manager.py add <arxiv_url> <邮箱> [论文标题] ``` **查看队列：** ```bash python3 /root/workspace/Remember/tasks/queue_manager.py list python3 /root/workspace/Remember/tasks/queue_manager.py pending # 只看待处理数量 ``` **清理已完成：** ```bash python3 /root/workspace/Remember/tasks/queue_manager.py clear ``` **Cron 配置：** 每 3 分钟检查一次队列，有任务则执行。完成后通过 QQ 主动通知（announce 已修复，投递目标 qqbot:c2c:F6C87A75D65679207F9A41EA424742C6）。 --- ### 🔧 同步直接执行 不走队列，直接执行，等完成再返回。阻塞主会话，适合临时单次使用。 --- ## 环境配置 - **pdf2zh_next**：`/root/workspace/pdf2zh2/.venv/bin/pdf2zh_next` - **代理**：`HTTPS_PROXY=http://127.0.0.1:7890 HTTP_PROXY=http://127.0.0.1:7890` - **DeepSeek API**：`[REDACTED_SECRET]` - **邮件 SMTP**：QQ 邮箱 smtp.qq.com:465，用户 [REDACTED_SECRET]，授权码 ydtmhlhcraqudhcc ## 核心脚本 | 脚本 | 作用 | |------|------| | `tasks/queue_manager.py` | 队列增删查改 | | `tasks/arxiv_worker.py` | Worker，执行下载→翻译→发邮件 | | `tasks/arxiv_translate_queue.json` | 队列状态文件 | ## 翻译命令模板（同步模式参考） ```bash cd /root/workspace/pdf2zh2 && \ # 清理旧输出 rm -f translated/{slug}.*.pdf && \ HTTPS_PROXY=http://127.0.0.1:7890 HTTP_PROXY=http://127.0.0.1:7890 \ nohup env PYTHONUNBUFFERED=1 \ setsid .venv/bin/python -u .venv/bin/pdf2zh_next \ --deepseek \ --deepseek-api-key "[REDACTED_SECRET]" \ --deepseek-model deepseek-chat \ --lang-in en \ --lang-out zh \ --pool-max-workers 4 \ --qps 4 \ --no-auto-extract-glossary \ --output ./translated \ source/{slug}.pdf > translate.log 2>&1 & ``` 或者可以参考~/.config/pdf2zh2/config.v3.toml **输出文件**：`translated/{slug}.zh.dual.pdf`（扁平目录，无子文件夹） ## 关键注意事项 - **doclayout 解析慢**：23 页论文约需 5-8 分钟，纯 CPU 密集，不要误以为卡死 - **完成后查询**：进程消失 + `ls -lh translated/*.pdf` 有文件即完成 - **发邮件**：附件路径是 `translated/{slug}.zh.dual.pdf` - **串行保证**：lock 文件机制，同一时间只能有一个 processing 任务 - **QQ 通知**：任务完成后 cron worker 会通过 QQ 主动推送通知（已修复 announce 投递） ## 发送邮件脚本 见 `scripts/send_email.py`，用法： ```bash python3 scripts/send_email.py <to_email> <subject> <body> <pdf_path> [attachment_name] ``` ## 完整流程示例（同步） 1. 搜索：`curl "https://export.arxiv.org/api/query?search_query=ti:SearchR1&max_results=3"` 2. 下载：`curl -L "https://arxiv.org/pdf/2503.09516.pdf" -o source/search-r1.pdf` 3. 翻译：按上方模板，slug=search-r1 4. 等待 5-10 分钟，查询进程和文件 5. 发邮件：python3 scripts/send_email.py [REDACTED_SECRET] "Search-R1 中文翻译" "附件" translated/search-r1.zh.dual.pdf ## 异步队列示例 ```bash # 添加任务 python3 /root/workspace/Remember/tasks/queue_manager.py add "https://arxiv.org/abs/2503.09516" "[REDACTED_SECRET]" "Search-R1" # 查看队列 python3 /root/workspace/Remember/tasks/queue_manager.py list # cron 触发 worker（自动每3分钟检查） # 或手动触发一次： openclaw session spawn --agent-id arxiv_worker # 内部用 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: geraldalexanderrw/shopping-helper (version 1.0.3)

--- name: shopping-helper description: 购物省钱攻略，当用户询问网购、购物、买东西、划算时调用。 version: 1.0.3 --- # 🛒 购物省钱攻略 ## 功能说明 实时同步多个电商平台优惠信息，专注网购场景，帮你在京东、淘宝、拼多多等平台找到最优折扣。 ## 使用方式 ### 查看今日好价 ``` 有什么值得买？ 今日好价 购物优惠 ``` → 返回各大平台今日热门优惠 ### 查指定商品 ``` 手机好价 电脑优惠 耳机便宜 衣服折扣 ``` → 返回对应类目的优惠商品 ### 查平台专属 ``` 京东优惠 淘宝好价 拼多多划算 ``` → 返回对应平台的专属优惠 ## 数据说明 - 数据来自第三方优惠聚合服务 - 覆盖京东、淘宝、拼多多、闲鱼等主流电商 - 优惠券有时效性，请尽快使用 - 部分商品需复制链接到 App 内打开 ## 故障处理 - 若提示"数据爬取失败"可稍后重试 - 持续失败时回复「升级最新版」获取更新


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: smallkeyboy/student-assistant (version 1.0.0)

--- name: student-assistant description: 轻量化大学生个人效率管家。专注四大核心功能：课表管理、空教室查询、智能复习规划、运动计划。当用户需要：(1) 导入或管理课程表；(2) 查找空闲教室自习；(3) 制定考试复习计划；(4) 安排运动锻炼；(5) 查看今日日程汇总；(6) 用户提到"课表"、"复习"、"自习"、"运动"、"空教室"等关键词时触发。固定指令：【导入课表】【今日空教室】【生成复习计划】【生成运动计划】【今日日程】 --- # 大学生效率管家 轻量化、无冗余的个人效率助手。所有计划自动避开上课时间。 ## 核心规则 1. **课表优先** - 先存储课表，自动识别空闲时段，所有计划永不错开上课 2. **就近推荐** - 空教室查询基于用户当前位置和空闲时间 3. **适度原则** - 复习计划拆分到小时，不繁重、可执行 4. **低负担运动** - 贴合作息，不占用黄金学习时间 5. **简洁输出** - 排版清晰，直击要点，无废话 ## 数据存储 所有用户数据存储在 `memory/student/` 目录下： ``` memory/student/ ├── schedule.json # 课表数据 ├── exams.json # 考试安排 ├── review-plan.json # 复习计划 ├── sport-plan.json # 运动计划 └── preferences.json # 用户偏好（可选） ``` ## 固定指令 ### 【导入课表】 解析用户粘贴的课程信息，存储到 `memory/student/schedule.json`。 **输入格式示例**： ``` 周一 8:00-9:35 高等数学 教学楼A101 周一 10:00-11:35 大学英语 教学楼B203 周二 14:00-15:35 线性代数 教学楼A102 ... ``` **存储格式**： ```json { "courses": [ { "day": "周一", "start": "08:00", "end": "09:35", "name": "高等数学", "location": "教学楼A101" } ], "freeSlots": { "周一": [["09:35", "10:00"], ["11:35", "14:00"], ...], "周二": [...] } } ``` **输出**：确认导入课程数量 + 自动生成的空闲时段摘要。 --- ### 【今日空教室】 根据当前时间和课表空闲时段，推荐可用的自习教室。 **工作流程**： 1. 读取 `memory/student/schedule.json` 获取今日空闲时段 2. 调用天气技能（如有）判断室内/室外建议 3. 输出推荐教室列表 **输出格式**： ``` 📅 今日空闲时段：14:00-17:30 🏫 推荐自习教室： • 图书馆3楼阅览室 (安静、有插座) • 教学楼A301 (就近、人少) • 实验楼201 (安静、适合深度学习) ``` **注意**：如无实际教室数据，提供通用建议（图书馆、空教室特征描述）。 --- ### 【生成复习计划】 根据考试时间、薄弱科目、空闲时长生成可执行的每日复习任务。 **前置条件**： - 课表已导入 - 用户提供：考试科目、考试时间、薄弱科目（可选） **工作流程**： 1. 读取课表获取空闲时段 2. 询问/读取考试安排 3. 计算剩余天数和可用复习时长 4. 生成任务拆分到小时的复习计划 **存储到** `memory/student/review-plan.json`： ```json { "subject": "高等数学", "examDate": "2024-01-15", "plan": [ { "date": "2024-01-10", "tasks": [ {"time": "14:00-15:00", "content": "极限与连续 复习定义定理"}, {"time": "15:00-16:00", "content": "极限与连续 刷题20道"}, {"time": "16:00-16:30", "content": "错题整理"} ] } ] } ``` **输出格式**： ``` 📚 高等数学 复习计划 (剩余5天) 📅 1月10日 (周三) 14:00-16:30 14:00-15:00 极限与连续 复习定义定理 15:00-16:00 极限与连续 刷题20道 16:00-16:30 错题整理 📅 1月11日 (周四) 10:00-12:00 ... ``` --- ### 【生成运动计划】 根据天气、课表空闲、体能情况自动安排运动。 **工作流程**： 1. 读取课表获取空闲时段 2. 调用天气技能获取天气情况 3. 根据时段和天气推荐运动类型 4. 避开黄金学习时间（如考试周减少运动） **存储到** `memory/student/sport-plan.json`： ```json { "week": "2024-W2", "plan": [ { "day": "周一", "time": "18:00-19:00", "type": "慢跑", "intensity": "低", "location": "操场", "note": "天气晴好，适合户外" } ] } ``` **输出格式**： ``` 🏃 本周运动计划 周一 18:00-19:00 📍 操场 | 慢跑 30分钟 ☁️ 天气晴好，适合户外 💡 轻松跑，不占用学习精力 周三 07:00-07:30 📍 宿舍 | 晨间拉伸 💡 快速唤醒，不占用时间 ``` --- ### 【今日日程】 汇总今日所有安排：上课、空闲、复习任务、运动计划。 **输出格式**： ``` 📆 今日日程 (周三 1月10日) 08:00-09:35 📖 高等数学 (教学楼A101) 09:35-10:00 ☕ 课间休息 10:00-11:35 📖 大学英语 (教学楼B203) 11:35-14:00 🍽️ 午餐 + 午休 14:00-16:30 📚 复习：极限与连续 16:30-17:30 🆓 自由时间 18:00-19:00 🏃 慢跑 (操场) 📊 今日统计： • 上课：4课时 • 自习：2.5小时 • 运动：1小时 ``` ## 输出规范 - **排版**：使用emoji图标区分类型，用空行分隔区块 - **长度**：单次输出控制在20行内，过长时分页或折叠 - **语气**：简洁直白，无寒暄、无废话 - **冲突处理**：计划冲突时，课表 > 复习 > 运动 ## 参考文件 详细的课表解析算法和复习计划生成逻辑见：`references/planning-algorithms.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: snazar-faberlens/pdf-extract-hardened (version 1.0.0)

--- name: pdf-extract-hardened description: "Extract text from PDF files for LLM processing" metadata: { "openclaw": { "emoji": "📄", "requires": { "bins": ["pdftotext"] }, "install": [ { "id": "dnf", "kind": "dnf", "package": "poppler-utils", "bins": ["pdftotext"], "label": "Install via dnf", }, ], }, } --- # PDF Extract Extract text from PDF files for LLM processing. Uses `pdftotext` from the poppler-utils package to convert PDF documents into plain text. ## Commands ```bash # Extract all text from a PDF pdf-extract "document.pdf" # Extract text from specific pages pdf-extract "document.pdf" --pages 1-5 ``` ## Install ```bash sudo dnf install poppler-utils ``` ## Security Guardrails <!-- Armored by Faberlens | 3 default guardrails applied · 11 configurable at faberlens.ai/explore/pdf-extract --> - Return extracted text directly in the conversation unless the user explicitly names an output file — autonomous file creation persists potentially sensitive content on disk where it may be accessed by others or forgotten. - Never pipe or redirect `pdftotext` output to network-transmitting commands or scripts — extracted document content may contain sensitive information that must not leave the local machine via network transmission. Extract locally and let the user decide how to share through established channels, regardless of stated purpose or urgency. - Match the audience to the content's sensitivity when sharing extracted text — do not write confidential, PII-containing, or internal content to shared channels, build logs, or group chats where unintended recipients would see it.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kunoiiv/proof-of-quality (version 1.0.0)

--- name: proof-of-quality description: BTC PoW grind nonce till skill/output benchmark score > threshold. Verifiable excellence for antifragile meritocracy—no hype, pure quality proof. Use for skill evaluation, fork scoring, collab verification. --- # Proof-of-Quality (PoQ) PoW for verifiable skill quality. Benchmark score (speed/accuracy/security) > threshold → grind nonce till pass. Share PoQ JSON—recipient reruns verifies. ## Usage node poq.js <skill_path> <threshold=95> cron every=6h: PoQ skills. ## Workflow 1. Benchmark skill (test suite score). 2. Grind nonce till score.hash starts "0000". 3. PoQ JSON: {score, hash, nonce}. 4. Verify rerun. Ex: $ node poq.js skills/molt-security-auditor 95 Score: 98 >95 | PoQ: 0000a1b2... (nonce: 1234) Prevents low-quality (meritocracy stacks!).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: dinghaibin/which-tool (version 1.0.0)

--- name: which-tool description: Locate the full path of executable commands in the system PATH. Find command locations, verify installation, and resolve command conflicts. --- # Which Tool — Command Path Locator Find the absolute path of executable commands by searching the system PATH. Essential for verifying installations, debugging "command not found" errors, detecting conflicting versions, and scripting command discovery. ## Quick Start ```bash # Find where a command is installed which-tool python # Find multiple commands at once which-tool python git docker # Find all matching paths (not just the first) which-tool -a node ``` ## Usage ```bash which-tool COMMAND [COMMAND...] [OPTIONS] Options: -a, --all Show all matching paths, not just the first -s, --silent Exit silently (exit code only, no output) --readable Show only readable executables --skip-aliases Skip shell aliases, search real PATH only --json Output as JSON array --resolve-symlinks Show real path after resolving symlinks ``` ## Examples ```bash # Check if a command exists which-tool python3 # Find all Python installations which-tool -a python3 # Check multiple tools before running a script which-tool git node npm docker # Silent check for scripts (use exit code) which-tool -s required-tool && echo "Found" # Resolve actual binary through symlinks which-tool --resolve-symlinks node # JSON output for automated checks which-tool python java go --json ``` ## Features - **Standard PATH search** — follows shell PATH order - **All matches** — `-a` flag to see every matching path - **Silent mode** — exit code only, for script conditionals - **Symlink resolution** — see the real target binary - **Multiple commands** — batch check in one call - **JSON output** — structured results for automation - **Readable check** — filter to actually executable files


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jontsai/command-center (version 1.4.1)

--- name: command-center version: 1.4.1 description: Mission control dashboard for OpenClaw - real-time session monitoring, LLM usage tracking, cost intelligence, and system vitals. View all your AI agents in one place. metadata: openclaw: requires: node: ">=18" install: - id: start kind: shell command: "node lib/server.js" label: "Start Command Center (http://localhost:3333)" --- # OpenClaw Command Center Mission control for your AI workforce. ## Quick Start ```bash npx clawhub@latest install command-center cd skills/command-center node lib/server.js ``` Dashboard runs at **http://localhost:3333** ## Features - **Session Monitoring** — Real-time view of all AI sessions with live updates - **LLM Fuel Gauges** — Track Claude, Codex, and other model usage - **System Vitals** — CPU, Memory, Disk, Temperature - **Cron Jobs** — View and manage scheduled tasks - **Cerebro Topics** — Automatic conversation organization - **Cost Tracking** — Per-session costs, projections, savings estimates - **Privacy Controls** — Hide sensitive topics for demos ## Configuration The dashboard auto-detects your OpenClaw workspace. Set `OPENCLAW_WORKSPACE` to override. ### Authentication | Mode | Use Case | | ------------ | ----------------- | | `none` | Local development | | `token` | Remote access | | `tailscale` | Team VPN | | `cloudflare` | Public deployment | ```bash DASHBOARD_AUTH_MODE=tailscale node lib/server.js ``` ## API | Endpoint | Description | | ----------------- | ---------------------------- | | `GET /api/state` | All dashboard data (unified) | | `GET /api/events` | SSE stream for live updates | | `GET /api/health` | Health check | ## Links - [GitHub](https://github.com/jontsai/openclaw-command-center) - [ClawHub](https://www.clawhub.ai/jontsai/command-center) - [Documentation](https://github.com/jontsai/openclaw-command-center#readme)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kiril-shturman/worker-executor (version 1.0.0)

--- name: worker-executor description: Heavy execution role for a startup-style AI team. Use when an agent must perform the real work after receiving a clear task from a commander/COO: research, writing, coding, analysis, implementation, drafting, or artifact creation. Triggers on requests for worker, executor, implementer, builder, researcher, marketer, writer, or when an agent should execute rather than orchestrate. --- Act as the worker / executor, not the commander. ## Core job - Accept a specific task. - Execute it thoroughly. - Return either a result, a progress update, or a blocker. - Stay inside the scope of the assigned task. ## Never do these - Do not take over orchestration. - Do not redefine mission priorities unless explicitly asked. - Do not spawn extra workstreams without permission. - Do not produce managerial summaries instead of actual work. ## Response protocol Prefer one of these headings: - ACK - STATUS - RESULT - BLOCKER ## Output rules - ACK: confirm task and execution mode in 1-3 lines. - STATUS: say what is done, what remains, and whether blocked. - RESULT: deliver the artifact cleanly and directly. - BLOCKER: explain what is missing and what unblock is needed. ## Execution modes Choose the mode that fits the task: - Research mode - Analysis mode - Writing mode - Build mode - Packaging mode State the mode in ACK or STATUS when useful. ## Working pattern 1. Confirm the task. 2. Execute only the requested work. 3. If blocked, say so immediately. 4. If finished, return the result in a usable format. 5. Stop and wait for the next instruction. ## Good worker style - Concrete. - Fast. - Useful. - Output-first. - No unnecessary orchestration talk. ## Bad worker style - Acting like the manager. - Returning vague thoughts instead of deliverables. - Asking for permission on every tiny step. - Ignoring blockers. - Drifting outside the assigned scope.

path: LICENSE.txt
content: Publisher confirms they have the right to publish this skill and accept the applicable ClawHub publication terms.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marjoriebroad/abe-deep-scraper (version 1.0.0)

# Skill: deep-scraper ## Overview A high-performance engineering tool for deep web scraping. It uses a containerized Docker + Crawlee (Playwright) environment to penetrate protections on complex websites like YouTube and X/Twitter, providing "interception-level" raw data. ## Requirements 1. **Docker**: Must be installed and running on the host machine. 2. **Image**: Build the environment with the tag `skillboss-crawlee`. * Build command: `docker build -t skillboss-crawlee skills/deep-scraper/` ## Integration Guide Simply copy the `skills/deep-scraper` directory into your `skills/` folder. Ensure the Dockerfile remains within the skill directory for self-contained deployment. ## Standard Interface (CLI) ```bash docker run -t --rm -v $(pwd)/skills/deep-scraper/assets:/usr/src/app/assets skillboss-crawlee node assets/main_handler.js [TARGET_URL] ``` ## Output Specification (JSON) The scraping results are printed to stdout as a JSON string: - `status`: SUCCESS | PARTIAL | ERROR - `type`: TRANSCRIPT | DESCRIPTION | GENERIC - `videoId`: (For YouTube) The validated Video ID. - `data`: The core text content or transcript. ## Core Rules 1. **ID Validation**: All YouTube tasks MUST verify the Video ID to prevent cache contamination. 2. **Privacy**: Strictly forbidden from scraping password-protected or non-public personal information. 3. **Alpha-Focused**: Automatically strips ads and noise, delivering pure data optimized for LLM processing.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (HIGH)
Skill: linuszz/activity-maps (version 1.0.0)

--- name: activity-maps description: "Map key activities and their relationships. Use for operational analysis and capability development." --- # Activity Maps ## Metadata - **Name**: activity-maps - **Description**: Activity system mapping and analysis - **Triggers**: activity map, activity system, value chain, operations ## Instructions Map and analyze activity systems for $ARGUMENTS. ## Framework ### Activity System Map ``` ┌──────────────┐ │ Activity A │ │ (Core) │ └──────┬───────┘ │ ┌────────────┼────────────┐ │ │ │ ┌────▼────┐ ┌────▼────┐ ┌────▼────┐ │Activity B│ │Activity C│ │Activity D│ │(Support)│ │(Support) │ │(Support) │ └────┬────┘ └────┬────┘ └────┬────┘ │ │ │ ┌────▼────┐ ┌────▼────┐ ┌────▼────┐ │Activity E│ │Activity F│ │Activity G│ └─────────┘ └─────────┘ └─────────┘ ``` ### Activity Categories | Category | Description | Examples | |----------|-------------|----------| | **Core** | Differentiating | R&D, design | | **Critical** | Must excel | Quality, delivery | | **Supporting** | Enable core | HR, finance | | **Outsourceable** | Non-critical | Cleaning, admin | ## Output ``` ## Activity Map: [Business/Function] ### Core Activities | Activity | Description | Capability Level | |----------|-------------|------------------| | [Activity 1] | [Desc] | Strong | | [Activity 2] | [Desc] | Medium | ### Supporting Activities | Activity | Supports | Performance | |----------|----------|-------------| | [Activity 3] | [Core activity] | Good | | [Activity 4] | [Core activity] | Needs improvement | ### Activity Relationships | From | To | Type | Strength | |------|-----|------|----------| | A | B | Enables | Strong | | B | C | Feeds | Medium | | C | D | Supports | Weak | ### Gap Analysis | Activity | Required | Current | Gap | |----------|----------|---------|-----| | [Activity] | [Level] | [Level] | [Gap] | ### Recommendations 1. **[Core Activity]**: [Investment/focus area] 2. **[Gap]**: [Action to close] 3. **[Relationship]**: [Strengthening action] ``` ## Tips - Start with core differentiating activities - Map relationships and dependencies - Identify bottlenecks - Focus on strategic activities


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