OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mpbshhx/cron-watchdog (version 1.0.0)

--- name: watchdog description: Monitor all OpenClaw cron jobs for failures and auto-fix common errors (model-not-allowed, timeouts). Posts to Slack only when issues are found. Runs every 6 hours. Use when you need automated cron health monitoring and self-healing. metadata: openclaw: emoji: "🐺" requires: tools: - cron - message --- # Watchdog — Cron Health Monitor Monitors all cron jobs for failures and auto-fixes them. Posts to Slack only when issues are found or unfixable errors exist. ## CRITICAL: Slack Routing When sending messages to Slack, you MUST specify `channel: "slack"` in every message tool call: ``` message(action: "send", channel: "slack", target: "C0AHYTV5WP7", message: "...") ``` Without `channel: "slack"`, messages will fail silently. ## Schedule Every 6 hours: 5, 11, 17, 23 CT ## Steps 1. `cron(action: "list")` — get all jobs and their current status 2. For each job, check: `lastStatus` error? `consecutiveErrors > 0`? What was `lastError`? 3. For **model not allowed** errors: use `cron(action: "update", jobId: "...", patch: { payload: { model: "anthropic/claude-sonnet-4-6" } })`, then force-run, log change 4. For **timeout errors**: use `cron(action: "update", jobId: "...", patch: { payload: { timeoutSeconds: <current + 60> } })` — NEVER edit cron JSON files directly 5. For **other errors**: analyze, attempt fix if possible, or flag as unresolved 6. Post to Slack `C0AHYTV5WP7` (#morning-briefs) **ONLY if issues were found/fixed or unfixable errors exist** 7. If everything is healthy: no Slack message (silent pass) ## CRITICAL: Never Edit cron/jobs.json Directly Always use the `cron` tool with `action="update"` to modify job settings. Direct file edits break the cron system. ## Slack Alert Format ``` 🐺 Watchdog Report — <timestamp> ✅ Fixed: <job-name> — <what was fixed> ❌ Unfixable: <job-name> — <error summary> ⚠️ Flagged: <job-name> — <issue description> ``` Only send if at least one issue exists.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shawcc/memo-teller (version 1.0.0)

# 生产队往事挖掘机 (Legacy Story Agent) 这是一个专门为老一辈设计的口述历史挖掘工具。 ### 功能特点： - **诱导提问**：提供专业的采访逻辑，引导老人回忆细节。 - **故事记录**：结构化保存口述素材。 - **剧本转化**：自动生成短视频/短剧脚本。 ### 如何使用： 直接对 Claw 说：“帮我采访我爸关于生产队分粮的故事”。

path: production_team_storyteller.py
content: import os
import json
from datetime import datetime

# 技能元数据定义
SKILL_NAME = "LegacyStoryAgent"
DESCRIPTION = "专门用于协助老一辈回忆生产队往事，记录口述历史，并将其转化为自媒体/短剧剧本。"

class LegacyStoryAgent:
    def __init__(self, data_dir="./father_stories"):
        self.data_dir = data_dir
        if not os.path.exists(data_dir):
            os.makedirs(data_dir)

    def get_interview_hooks(self, theme="通用"):
        """
        根据不同主题，提供能够诱导老人开口的‘钩子’问题。
        """
        hooks = {
            "工分与劳动": ["爸，那时候干一天活能挣几个工分？", "队里力气最大的人是谁？他有什么出名的事吗？"],
            "饮食与生活": ["那时候最馋什么？过年才能吃到什么？", "您还记得第一件的确良衣服是怎么来的吗？"],
            "情感与社交": ["您和妈结婚的时候，队里是怎么闹洞房的？", "那时候看露天电影要跑多少里地？"],
            "突发事件": ["那时候遇到大旱或者洪水，大家是怎么自救的？", "有没有哪个知青让您印象特别深？"]
        }
        return hooks.get(theme, hooks["通用"])

    def save_raw_memory(self, content, tags):
        """
        保存原始口述记录，带上时间戳和标签，方便后续翻查。
        """
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"{self.data_dir}/memory_{timestamp}.json"
        data = {
            "timestamp": timestamp,
            "content": content,
            "tags": tags,
            "status": "raw"
        }
        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(data, f, ensure_ascii=False, indent=4)
        return f"已存入素材库，编号：{timestamp}"

    def draft_short_drama_script(self, story_content):
        """
        将杂乱的口述转化为短剧脚本逻辑（包含：视觉、对白、冲突点）。
        """
        # 这里模拟 AI 转换逻辑
        script_template = f"""
        【短剧脚本建议】
        场景：生产队大晒场 / 破旧祖屋内部
        视觉重点：阳光穿过烟雾、粗糙的手、旧式农具
        核心冲突点：{story_content[:20]}... 
        剧本梗概：
        1. 开场（Hook）：展示一个现在见不到的细节（如：摇动的老式风车）。
        2. 发展：老人（主角）面临的一个困境（如：丢了公分票）。
        3. 反转/情感：在那段艰苦日子里体现的人性光辉。
        4. 结尾：画面拉回现实，老人在阳光下微笑。
        """
        return script_template

# 注册给 MoltClaw 的接口
def get_tools():
    return [
        {
            "name": "suggest_interview_questions",
            "description": "当不知道该问爸爸什么时，调用此工具获取诱导式问题。",
            "parameters": {"theme": "string"}
        },
        {
            "name": "record_story_segment",
            "description": "记录爸爸讲的一段具体故事。",
            "parameters": {"content": "string", "tags": "list"}
        },
        {
            "name": "transform_to_script",
            "description": "将选中的故事素材转化为适合自媒体发布的短剧脚本。",
            "parameters": {"story_content": "string"}
        }
    ]

Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bytesagain1/disk-analyzer (version 1.0.0)

--- name: disk-analyzer description: "Duf — duf — Disk Usage/Free analyzer. Automated tool for duf tasks. Use when you need Duf capabilities." runtime: python3 license: MIT --- # Duf duf — Disk Usage/Free analyzer ## Why This Skill? - Inspired by popular open-source projects (thousands of GitHub stars) - No installation required — works with standard system tools - Real functionality — runs actual commands, produces real output ## Commands Run `scripts/duf.sh <command>` to use. - `overview` — Disk usage overview (all mounts) - `usage` — [path] Directory usage breakdown - `top` — [n] [path] Top n largest files - `find-big` — [size] Find files larger than size (e.g. 100M) - `duplicates` — [path] Find duplicate files - `clean` — [path] Suggest cleanup targets - `watch` — [path] Monitor disk usage changes - `export` — [format] Export report (md/json) - `info` — Version info ## Quick Start ```bash duf.sh help ``` --- > **Disclaimer**: This skill is an independent, original implementation. It is not affiliated with, endorsed by, or derived from the referenced open-source project. No code was copied. The reference is for context only. Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lemons-niit/resume-analyzer (version 1.0.0)

--- name: "resume-analyzer" description: "分析简历内容并提供深度审计报告，包括评分、优缺点分析和改进建议。当用户需要分析简历、获取简历优化建议或进行简历评估时调用。" --- # 简历分析器 ## 功能说明 本技能提供专业的简历分析服务，基于AI模型对简历内容进行深度审计，生成详细的分析报告。 ## 分析维度 - **项目经验评分** (0-40分)：评估项目深度、技术复杂度和业务价值 - **技能匹配度** (0-20分)：评估技术栈专业度和与岗位的匹配程度 - **内容完整性** (0-15分)：评估简历信息的完整性和模块顺序合理性 - **结构清晰度** (0-15分)：评估简历结构和格式的清晰度 - **表达专业性** (0-10分)：评估语言表达的专业性和简洁性 ## 输入格式 ```json { "resumeText": "简历文本内容" } ``` ## 输出格式 ```json { "overallScore": 85, "scoreDetail": { "projectScore": 35, "skillMatchScore": 18, "contentScore": 12, "structureScore": 13, "expressionScore": 7 }, "summary": "整体简历质量良好，项目经验丰富，但技能描述可进一步优化", "strengths": [ "项目经验丰富，包含多个大型系统开发", "技术栈全面，涵盖主流后端技术" ], "suggestions": [ { "category": "技能", "priority": "高", "issue": "技能描述过于笼统，缺乏具体技术深度", "recommendation": "建议具体描述使用过的技术栈版本和应用场景" }, { "category": "项目", "priority": "中", "issue": "项目描述缺乏量化成果", "recommendation": "添加具体的性能优化数据或业务成果指标" } ] } ``` ## 技术优化基准 在分析简历时，会参考以下高标准场景： ### 高并发与缓存优化 - **多级缓存**：Redis + Caffeine 两级缓存架构，解决击穿/穿透/雪崩，支撑 30w+ QPS - **原子操作**：Redis Lua 脚本实现分布式令牌桶限流或原子库存扣减 ### 异步与性能调优 - **异步编排**：`CompletableFuture` 对多源 RPC 调用编排，RT 从秒级到百毫秒级 - **线程治理**：动态线程池参数监控与调整，解决父子任务线程池隔离导致的死锁问题 ### 微服务架构与数据一致性 - **数据同步**：Canal + RabbitMQ/RocketMQ 实现 MySQL 增量数据实时同步至 Elasticsearch - **分布式事务**：基于消息队列（延时消息）实现订单超时关闭或数据最终一致性 - **网关与安全**：Spring Cloud Gateway + Spring Security OAuth2 + JWT + RBAC 动态权限控制 ### 复杂业务建模与设计模式 - **DDD 领域驱动**：抽象领域模型，运用工厂、策略、模板方法模式构建业务链路 - **规则引擎**：责任链模式处理前置校验，组合模式+决策树支撑复杂业务逻辑 ## 分析流程 1. **名词纠错**：扫描全文，列出所有不规范的技术名词 2. **深度重写**：从简历中挑选 2-3 条核心项目描述，基于 STAR 法则进行优化重写 3. **方案优化建议**：针对简历中平庸的技术方案，给出更具竞争力的替代方案 ## 使用示例 ### 输入示例 ```json { "resumeText": "张三，Java开发工程师，5年经验，熟悉Spring Boot、MySQL、Redis等技术栈。曾参与电商系统开发，负责订单模块。" } ``` ### 输出示例 ```json { "overallScore": 65, "scoreDetail": { "projectScore": 25, "skillMatchScore": 15, "contentScore": 10, "structureScore": 10, "expressionScore": 5 }, "summary": "简历基础信息完整，但项目描述过于简单，缺乏技术深度和量化成果", "strengths": [ "具有5年Java开发经验，技术栈基础扎实" ], "suggestions": [ { "category": "项目", "priority": "高", "issue": "项目描述过于简单，缺乏具体职责和成果", "recommendation": "使用STAR法则描述项目：情境(Situation)、任务(Task)、行动(Action)、结果(Result)，添加具体的技术实现和量化成果" }, { "category": "技能", "priority": "中", "issue": "技能描述过于笼统，缺乏具体版本和应用场景", "recommendation": "具体说明使用的Spring Boot版本、Redis应用场景（如缓存策略）等技术细节" } ] } ``` ## 注意事项 1. 输入的简历文本应尽量完整，包含个人信息、教育背景、工作经验、项目经验和技能等内容 2. 分析结果基于AI模型，仅供参考，最终决策需结合实际情况 3. 对于特别简短的简历，分析深度可能会受到限制


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: toannhu96/nudgen (version 1.0.0)

--- name: nudgen description: > The primary skill for all things Nudgen. Use this for AI-powered email retention, automation, and marketing strategy. It provides access to API workflows, CLI commands, and email-sending best practices. Trigger on "Nudgen", "retention strategy", "email automation", or general marketing automation tasks. metadata: version: 1.0.0 --- # Nudgen: AI-Powered Email Retention & Automation This is the core Nudgen skill package. It equips agents with the knowledge and tools needed to build, manage, and optimize email-driven retention and automation workflows. ## Capabilities This package includes specialized sub-skills for: - **API Integration**: Server-side request design and implementation. - **CLI Usage**: Terminal-based automation and data management. - **Email Best Practices**: Strategic guidance on deliverability, reputation, and lifecycle flows. ## When To Use Activate this skill when the user is: - Setting up a new Nudgen integration. - Troubleshooting existing email campaigns or deliverability issues. - Looking for advice on retention strategies or automation logic. - Using the `nudgen` CLI for team management or data exports. ## Getting Started If you are new to Nudgen, start by exploring the `api` or `cli` skills for technical implementation, or the `email-sending-best-practices` skill for high-level strategy.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: alamby/system-uptime (version 1.0.0)

--- name: system-uptime description: "Get the current system uptime using the native 'uptime' command. Use when: user asks about system uptime, system status, or how long the system has been running." metadata: { "openclaw": { "emoji": "⏱️", "requires": { "bins": ["uptime"] } } } --- # System Uptime Skill Get the current system uptime using the built-in `uptime` command. ## When to Use ✅ **USE this skill when:** - "What's the system uptime?" - "How long has the system been running?" - "Show system status" - "When was the last reboot?" ## When NOT to Use ❌ **DON'T use this skill when:** - Need detailed system metrics → use monitoring tools - Remote system uptime → use SSH or remote monitoring - Historical uptime data → check system logs ## Commands ### Get System Uptime ```bash # Basic uptime uptime # Using the skill CLI node uptime-cli.js ``` ## Example Output ``` 11:30:45 up 2 days, 4:23, 2 users, load average: 1.23, 1.15, 1.08 ``` ## Notes - Uses the standard Unix `uptime` command - Works on macOS, Linux, and other Unix-like systems - No additional dependencies required


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: changeworldy-cyber/simple-pdf-toolkit (version 1.0.0)

--- name: pdf-toolkit description: PDF 工具箱技能。支持 PDF 合并、拆分、旋转、压缩、格式转换、文字提取、水印添加等常用操作。使用场景：(1) 合并多个 PDF，(2) 拆分 PDF 页面，(3) 旋转/调整方向，(4) 压缩 PDF 大小，(5) PDF 转图片/Word，(6) 提取 PDF 文字，(7) 添加/移除水印。 --- # PDF 工具箱 ## 核心功能 提供一站式 PDF 处理解决方案，满足日常办公、学习中的各种 PDF 处理需求。 ### 支持的操作 - 📎 **合并** - 多个 PDF 合并成一个 - ✂️ **拆分** - 按页码拆分 PDF - 🔄 **旋转** - 调整页面方向 - 📦 **压缩** - 减小 PDF 文件大小 - 📝 **转换** - PDF 转 Word/图片/文本 - 📄 **提取** - 提取 PDF 中的文字/图片 - 💧 **水印** - 添加或移除水印 - 🔒 **加密** - 设置密码保护 --- ## 使用方式 ### 1. 合并 PDF 用户请求示例： - "把这几个 PDF 合并成一个" - "合并 report1.pdf, report2.pdf, report3.pdf" **命令：** ```bash python scripts/merge_pdf.py --output merged.pdf file1.pdf file2.pdf file3.pdf ``` ### 2. 拆分 PDF 用户请求示例： - "把这个 PDF 拆分成单独页面" - "提取第 1-5 页" **命令：** ```bash python scripts/split_pdf.py --input input.pdf --pages 1-5 --output output.pdf ``` ### 3. 旋转 PDF 用户请求示例： - "把这个 PDF 顺时针旋转 90 度" - "所有页面旋转 180 度" **命令：** ```bash python scripts/rotate_pdf.py --input input.pdf --angle 90 --output output.pdf ``` ### 4. 压缩 PDF 用户请求示例： - "压缩这个 PDF，太大了" - "把 PDF 压缩到 5MB 以内" **命令：** ```bash python scripts/compress_pdf.py --input input.pdf --output output.pdf --quality medium ``` ### 5. PDF 转换 用户请求示例： - "把 PDF 转成 Word" - "PDF 转图片" **命令：** ```bash python scripts/convert_pdf.py --input input.pdf --format word --output output.docx python scripts/convert_pdf.py --input input.pdf --format image --output-dir images/ ``` ### 6. 提取文字 用户请求示例： - "提取这个 PDF 的文字内容" - "把 PDF 转成文本" **命令：** ```bash python scripts/extract_text.py --input input.pdf --output output.txt ``` ### 7. 添加水印 用户请求示例： - "给 PDF 添加水印" - "加上'机密'两个字" **命令：** ```bash python scripts/watermark.py --input input.pdf --text "机密" --output output.pdf ``` --- ## 质量选项 ### 压缩质量 | 选项 | 说明 | 适用场景 | |------|------|---------| | low | 高压缩，质量较低 | 邮件发送、网页展示 | | medium | 平衡压缩和质量 | 日常使用 | | high | 低压缩，保持质量 | 打印、存档 | ### 转换格式 | 格式 | 说明 | 保留内容 | |------|------|---------| | word | 转 Word 文档 | 文字、格式、图片 | | image | 转图片（PNG） | 每页一张图片 | | text | 纯文本 | 仅文字内容 | | html | 网页格式 | 文字、基本格式 | --- ## 常见问题 ### Q: 合并后顺序不对怎么办？ A: 按文件名排序，确保文件名有序（如 01.pdf, 02.pdf） ### Q: 压缩后文字模糊？ A: 使用 high 质量选项，或降低压缩比例 ### Q: 提取的文字乱码？ A: 可能是扫描版 PDF，需要 OCR 功能（需额外安装） ### Q: 加密的 PDF 怎么处理？ A: 先提供密码解密，处理后再加密 --- ## 批量处理 支持批量操作多个文件： ```bash # 批量旋转当前目录所有 PDF for f in *.pdf; do python scripts/rotate_pdf.py --input "$f" --angle 90 --output "rotated_$f" done # 批量压缩 for f in *.pdf; do python scripts/compress_pdf.py --input "$f" --output "compressed_$f" --quality medium done ``` --- ## 参考资源 - 脚本使用说明：见 `references/scripts.md` - 常见问题解答：见 `references/faq.md` - 批量处理示例：见 `references/batch-examples.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/catalan (version 1.0.0)

--- name: Catalan description: Write Catalan that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Catalan is technically correct but sounds off. Too formal. Too literary. Natives write more casually, with warmth and local flavor. Match that. ## Formality Default Default register is too high. Casual Catalan is warm and direct. Unless explicitly formal: lean casual. "Hola" or "Ei" not "Bon dia". "Vale" not "D'acord". ## Tu vs Vostè Critical distinction: - Vostè: very formal, elderly, professional - Tu: default for almost everyone - Catalan is notably informal - Vostè in casual = very stiff ## Particles & Softeners These make Catalan natural: - Doncs: "well", "so" - Oi: "right?", seeking agreement (very Catalan!) - Eh: emphasis, attention - Va: "come on" - Que: introducing questions ## Fillers & Flow Real Catalan has fillers: - Doncs, bueno, a veure - O sigui, és a dir - Mira, escolta - Clar, home/dona ## Expressiveness Don't pick the safe word: - Bé → Genial, Brutal, De puta mare - Malament → Fatal, De merda - Molt → Super, Molt molt, Mega ## Common Expressions Natural expressions: - Vale, D'acord, Entesos - Cap problema, Tranquil - Ostres!, Caram!, Collons! - Què fort!, Al·lucinant! ## Reactions React naturally: - De veritat?, Serios?, Què dius? - Ostres!, Mare meva!, Déu n'hi do! - Genial!, Brutal!, Què guai! - Haha, jaja in text ## Regional Variation Central vs Valencia vs Balearic: - Vocabulary differences exist - Valencia: some different words - Balearic: distinctive features - Stay consistent if region known ## The "Native Test" Before sending: would a Catalan speaker screenshot this as "AI-generated"? If yes—too formal, no "oi", too stiff. Add colloquial warmth.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fmouseboy/gold-market (version 1.0.8)

--- AIGC: ContentProducer: Minimax Agent AI ContentPropagator: Minimax Agent AI Label: AIGC ProduceID: 9bcfdfd49c107372a83e26df34fe442b PropagateID: 9bcfdfd49c107372a83e26df34fe442b ReservedCode1: 3046022100dea4dce72590e3fc45773029886c78fb96047c37dfb537f9625cff8646eb0ca60221009ff704d6fc8765d1907c8627e85499d70ff9adad91486d252aa3fa321dda7ac6 ReservedCode2: 3046022100f641fd1c94f94e45aaf49e31b944f12aa824fd5839b55101faeb47a31d4eb04402210083ae3ca3a5935569ac50513f276d1f6014ffa7104c402f1fc60285af83456c63 description: 国内金价查询工具。用于查询黄金实时价格、银行金条价格、金店零售价、国际金价和黄金未来趋势分析。当用户询问金价、黄金价格、今日金价、金店价格、银行金条、国际金价、伦敦金、黄金走势或未来趋势时触发此技能。 name: gold-market --- # 金价查询工具 ## 数据来源 使用 `extract_content_from_websites` 工具从 **金价网 (jinjia.com.cn)** 获取数据。 ## 功能范围 1. **国内金价**：上海黄金交易所行情（AU9999、AU9995 等） 2. **国际金价**：伦敦金、伦敦银实时行情 3. **银行金条**：各大银行投资金条价格 4. **金店零售价**：周大福、周生生、老凤祥等品牌金店 5. **黄金回收价**：当日黄金回收参考价格 ## 使用方式 ### 查询实时金价 使用 `extract_content_from_websites` 提取 jinjia.com.cn 的黄金价格数据。 ```python # 提取黄金价格数据 extract_content_from_websites([{ "url": "https://www.jinjia.com.cn/", "prompt": "提取所有黄金价格数据，包括国内金价、国际金价、金店价格、银行金条价格" }]) ``` ### 数据解读 提取后，按以下格式整理输出： ``` 📈 今日金价（人民币/克） - AU9999: ¥1149.00 (+0.85%) - 伦敦金: $5171.06 (+1.74%) 🏦 银行金条价格 - 农行传世之宝金条: ¥805.27/克 - 浦发银行投资金条: ¥xxx 🏪 金店零售价（饰品金） - 周大福: ¥1590/克 - 周生生: ¥1595/克 - 老凤祥: ¥1570/克 ``` ### 未来趋势分析 金价趋势分析要点： - 关注国际金价走势（伦敦金） - 参考 AU9999 收盘价变化 - 结合美元指数、避险情绪等因素 - 参考专业机构分析（可搜索相关新闻） 趋势查询示例： ```python # 搜索金价趋势分析 batch_web_search([{ "query": "黄金价格走势预测 2025 2026" }]) ``` ## 注意事项 - 数据仅供参考，投资需谨慎 - 金店价格通常含工费，需以实际为准 - 银行金条价格可能有买卖价差


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: feiernk/video-download-archive (version 1.0.0)

--- name: 视频下载归档助手 slug: video-download-archive version: 0.2.0 description: Validate single video URLs, download highest-quality files with yt-dlp, and archive results into a Feishu Bitable using platform tools. --- ## When to Use - 用户发来单个视频链接，希望自动下载并归档 - 需要复用既有 yt-dlp 下载规则与元数据补全规则 - 需要把下载结果写入指定飞书多维表格 - 需要对已存在文件做补全、核对或补记录，而不是盲目重下 ## Quick Reference | Topic | File | |------|------| | 主流程 | `workflow.md` | | 飞书表字段映射 | `bitable-mapping.md` | | 错误与终止规则 | `error-handling.md` | | 执行脚本 | `scripts/run_video_archive.py` | | 使用说明 | `usage.md` | ## Core Rules 1. 只处理**单个视频链接**；不处理播放列表、频道页、多链接批量任务。 2. 必须先调用 `scripts/run_video_archive.py <url>` 完成预检、主键生成、本地查重、下载/补全和字段组装。 3. 主键固定为 `站点-视频ID`，同时写入“视频主键”和“视频唯一ID”。 4. 本地归档目录固定为 `~/Downloads/yt-dlp/<上传者目录>/`；优先用 `@handle` 作为上传者目录名。 5. 默认下载最高质量版本、优先 mkv、保留 `.info.json`，并沿用现有元数据补全规则。 6. 下载脚本只负责本地执行与生成 `record_fields`；飞书写表必须使用平台提供的 `feishu_bitable_app_table_record` 工具完成。 7. 写入飞书前若发现字段缺失、字段类型不匹配，或需要新增字段，必须暂停并先询问用户。 8. 已存在文件时优先补全与补记录，不要因为路径变化就直接重下。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nansen-devops/nansen-dca-tracker (version 0.1.0)

--- name: nansen-dca-tracker description: "What tokens are whales dollar-cost averaging into? Jupiter DCA strategies by smart money and target token fundamentals." metadata: openclaw: requires: env: - NANSEN_API_KEY bins: - nansen primaryEnv: NANSEN_API_KEY install: - kind: node package: nansen-cli bins: [nansen] allowed-tools: Bash(nansen:*) --- # DCA Watch **Answers:** "What tokens are whales dollar-cost averaging into on Solana?" ```bash nansen research smart-money dcas --limit 20 # → trader_address, trader_address_label, input/output_token_symbol, deposit_value_usd, dca_status, dca_created_at # For each top DCA target, check token fundamentals TARGET=<output_token_address> nansen research token info --token $TARGET --chain solana # → name, symbol, price, market_cap, token_details nansen research token flow-intelligence --token $TARGET --chain solana # → net_flow_usd per label: smart_trader, whale, exchange, fresh_wallets ``` To see DCA strategies targeting a specific token: `nansen research token jup-dca --token $TARGET` DCAs show long-term conviction — SM DCA targets with positive smart_trader_net_flow = high-confidence accumulation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yuh-yang/cli-anything-hub (version 1.0.1)

--- name: cli-hub-meta-skill description: >- Discover agent-native CLIs for professional software. Access the live catalog to find tools for creative workflows, productivity, AI, and more. --- # CLI-Hub Meta-Skill CLI-Hub is a marketplace of agent-native command-line interfaces that make professional software accessible to AI agents. ## Quick Start ```bash # Install the CLI Hub package manager pip install cli-anything-hub # Browse all available CLIs cli-hub list # Search by category or keyword cli-hub search image cli-hub search "3d modeling" # Install a CLI cli-hub install gimp # Show details for a CLI cli-hub info gimp ``` ## Live Catalog **URL**: [`https://clianything.cc/SKILL.txt`](https://clianything.cc/SKILL.txt) The catalog is auto-updated and provides: - Full list of available CLIs organized by category - One-line `cli-hub install` commands for each tool - Complete descriptions and usage patterns **Note**: The file is served as `.txt` but contains markdown formatting for easy parsing. ## What Can You Do? CLI-Hub covers a broad range of software and codebases, empowering agents to conduct complex workflows via CLI: - **Creative workflows**: Image editing, 3D modeling, video production, audio processing, music notation - **Productivity tools**: Office suites, knowledge management, live streaming - **AI platforms**: Local LLMs, image generation, AI APIs, research assistants - **Communication**: Video conferencing and collaboration - **Development**: Diagramming, browser automation, network management - **Content generation**: AI-powered document and media creation Each CLI provides stateful operations, JSON output for agents, REPL mode, and integrates with real software backends. ## How It Works `cli-hub` is a lightweight wrapper around `pip`. When you run `cli-hub install gimp`, it installs a separate Python package (`cli-anything-gimp`) with its own CLI entry point (`cli-anything-gimp`). Each CLI is an independent pip package — `cli-hub` simply resolves names from the registry and tracks installs. ## How to Use 1. **Install cli-hub**: `pip install cli-anything-hub` 2. **Find your tool**: `cli-hub search <keyword>` or `cli-hub list -c <category>` 3. **Install**: `cli-hub install <name>` (installs the `cli-anything-<name>` pip package) 4. **Run**: `cli-anything-<name>` for REPL, or `cli-anything-<name> <command>` for one-shot 5. **JSON output**: All CLIs support `--json` flag for machine-readable output ## Example Workflow ```bash # Install the hub pip install cli-anything-hub # Find what you need cli-hub search video # Install it cli-hub install kdenlive # Use it with JSON output cli-anything-kdenlive --json project create --name my-project ``` ## More Info - Live Catalog: https://clianything.cc/SKILL.txt - Web Hub: https://clianything.cc - Repository: https://github.com/HKUDS/CLI-Anything


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