OpenClaw/clawhub-security-signals huggingface dataset:
Skill: beforesun/cleaner4rookies (version 1.0.0)

--- name: openclaw-cleaner description: OpenClaw 临时文件自动清理工具。扫描 ~/.openclaw/workspace/ 下的临时文件，按规则自动归档或删除，防止工作区膨胀。面向 ClawHub 小白用户，默认安全（dry-run）+ 自动备份归档。 --- # openclaw-cleaner 🧹 > 清理 OpenClaw 临时文件，防止工作区膨胀。默认安全，值得信赖。 ## 功能 - 自动扫描 `~/.openclaw/workspace/` 下的临时文件（音频/文档/图片/日志等） - 支持**归档**（安全移走）和**删除**两种处理方式 - **默认 dry-run**：先显示清理预览，确认后才执行 - 操作前**自动创建归档目录**，文件不会凭空消失 - 支持 **cron 定时**或**手动触发** - 白名单机制：重要文件永不误删 ## 安装 ```bash npx skills add ~/.openclaw/workspace/skills/openclaw-cleaner ``` 或手动安装： ```bash git clone https://github.com/your-repo/openclaw-cleaner.git ~/.openclaw/workspace/skills/openclaw-cleaner ``` ## 快速开始 ### 1. 预览（推荐先跑） ```bash bash ~/.openclaw/workspace/skills/openclaw-cleaner/scripts/cleaner.sh ``` 输出示例： ``` ═══════════════════════════════════════════ 🧹 openclaw-cleaner 模式: 🔍 dry-run（只显示，不删除） ═══════════════════════════════════════════ ━━ 归档规则 ━━ [Telegram等下载文件尽快分流] (3 个文件) 🔍 [dry-run] doc1.docx (12K) 🔍 [dry-run] doc2.pdf (8K) ━━ 删除规则 ━━ [测试音频残留，无保留价值] (2 个文件) 🔍 [dry-run] lobster-test.mp3 (128K) ``` ### 2. 正式执行 加 `--force` 才真正删/归档： ```bash bash ~/.openclaw/workspace/skills/openclaw-cleaner/scripts/cleaner.sh --force ``` ## 配置 编辑 `~/.openclaw/workspace/cleaner.yaml`（不存在则自动使用内置 `config.yaml`）： ```yaml # 清理规则 rules: - pattern: "*.mp3 *.wav *.m4a" action: delete age_days: 0 # 0 = 立即 - pattern: "*.docx *.doc *.pdf" action: archive # 移到归档目录 age_days: 1 # 超过1天触发 - pattern: "*.png *.jpg *.gif" action: delete age_days: 7 # 超过7天触发 - pattern: "media/inbound/*" action: archive age_days: 3 # 执行控制 dry_run: true # 默认预览，要关闭设为 false archive_dir: "~/.openclaw/workspace/cleaner-archive" cron_schedule: "" # 填 "0 3 * * *" = 每天凌晨3点 # 白名单（绝对路径，不会被删除） whitelist: - "~/.openclaw/workspace/SOUL.md" - "~/.openclaw/workspace/MEMORY.md" ``` ## cron 定时任务 在 `cleaner.yaml` 里填入 cron 表达式： ```yaml cron_schedule: "0 3 * * *" # 每天凌晨3点 ``` 或手动通过 OpenClaw cron 管理： ``` cron add --name "openclaw-cleaner" \ --schedule "0 3 * * *" \ --command "bash ~/.openclaw/workspace/skills/openclaw-cleaner/scripts/cleaner.sh --force" ``` > ⚠️ **强烈建议**：首次用 cron 前，先手动跑一次 dry-run，确认规则符合预期。 ## 安全特性 | 特性 | 说明 | |------|------| | 默认 dry-run | 永远先显示预览，不直接操作 | | 自动归档 | 删除改为归档到 `cleaner-archive/`，可恢复 | | 白名单 | 显式声明的文件永不删除 | | 详细日志 | 记录每个文件的处理结果 | ## 卸载 ```bash rm -rf ~/.openclaw/workspace/skills/openclaw-cleaner rm -rf ~/.openclaw/workspace/cleaner-archive # 如有 cron，手动删除对应的 cron job ``` ## 故障排除 | 问题 | 解法 | |------|------| | "未找到配置文件" | 确认 `~/.openclaw/workspace/cleaner.yaml` 或 `config.yaml` 存在 | | 想恢复归档的文件 | 去 `~/.openclaw/workspace/cleaner-archive/` 找 | | 某些文件不该删 | 加入 `whitelist` 列表 | | dry-run 显示 0 个文件 | 说明没有符合条件（时间阈值未到）的文件 | ## 更新日志 ### v1.0.0 - 首发：支持归档/删除、白名单、dry-run、cron 定时


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/compose (version 2.0.4)

--- name: "compose" version: "2.0.3" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Compose concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [compose, reference] category: "devtools" --- # Compose Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Compose concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: evezart/workflow-orchestrator (version 1.0.0)

--- name: workflow-orchestrator description: Visual workflow builder for orchestrating multi-agent systems with branching, parallel execution, error handling, and retry logic. Use when building agent pipelines, DAG-based task orchestration, multi-agent coordination, or complex automation workflows. Covers step definition, conditional branching, fan-out/fan-in, failure recovery, and live monitoring. --- # Workflow Orchestrator Build and run multi-agent workflows with DAG execution, branching, and error handling. ## Quick Start ```python from orchestrator import Workflow, Step, Branch, Runner wf = Workflow("data-pipeline") wf.add_step(Step("fetch", agent="scraper", action="fetch_url", params={"url": "https://example.com"})) wf.add_step(Step("extract", agent="parser", action="extract_text", depends_on=["fetch"])) wf.add_step(Step("summarize", agent="writer", action="summarize", depends_on=["extract"])) wf.add_step(Step("translate", agent="writer", action="translate", depends_on=["extract"])) wf.add_step(Step("publish", agent="publisher", action="send", depends_on=["summarize", "translate"])) runner = Runner() result = runner.execute(wf) ``` ## DAG Execution Model ``` fetch → extract → summarize → publish → translate ↗ ``` Steps run in parallel when their dependencies are met. The `publish` step waits for both `summarize` and `translate`. ## Step Definition ```python Step( name="unique_step_name", agent="agent_id", # Which agent executes this action="tool_name", # What action to perform params={}, # Input parameters depends_on=[], # Wait for these steps first retry=3, # Max retries on failure timeout_sec=300, # Step timeout on_failure="skip", # "skip", "retry", "abort", "fallback" fallback_step="plan_b", # Run this step on failure condition="$.fetch.status == 200", # Conditional execution ) ``` ## Features - **Parallel execution**: Steps with satisfied dependencies run concurrently - **Conditional branching**: JSONPath conditions determine which branches execute - **Retry with backoff**: Configurable retry count and exponential backoff - **Timeout handling**: Steps that exceed timeout are killed and handled per `on_failure` - **Fallback steps**: Alternative steps run when the primary fails - **Live status**: Query workflow state at any point during execution - **Error propagation**: Configure whether failures bubble up or are contained ## Monitoring ```python status = runner.status(workflow_id) # {"running": 2, "completed": 3, "failed": 0, "pending": 1} ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: michaelfanke211/ppt-visual-designer (version 1.0.0)

--- name: ppt-visual-designer description: 顶级视觉策略设计师，擅长将复杂商业逻辑、组织架构或系统流程转化为"Block风格"可视化PPT方案。当用户提到"block"、"Block"、"BLOCK"时，或需要设计PPT、创建演示文稿、制作幻灯片，或需要将文字内容转化为视觉化模块时触发此技能。 --- # PPT Visual Designer - Block Style ## Role 你是一位顶级视觉策略设计师，擅长将复杂的商业逻辑、组织架构或系统流程转化为极具逻辑美感的"Block风格"可视化方案。你的核心任务是：接收零散的文字信息，将其重构成极具逻辑美感的模块化幻灯片结构。 ## Visual DNA Specification ### Container Design - **统一使用超大圆角矩形**：Corner Radius ≥ 15% - **边框极细**：Hairline border（细细的线） - **边框颜色**：深于背景色一级 ### Tone-on-Tone 色彩对应法则（严格遵守） **核心原则**：每个模块的文字颜色必须是其背景色的深色版本，确保对比度 ≥ 4.5:1。 | 模块背景色 | 背景色Hex | 文字色 | 文字色Hex | 用途 | |-----------|-----------|--------|-----------|------| | 浅紫 | #F0EFFF | Deep Purple | #4A3B77 | 紫色块文字 | | 薄荷绿 | #E6F7F0 | Dark Forest Green | #1D5C3E | 绿色块文字 | | 淡蓝 | #EBF5FF | Navy Blue | #1B3A5C | 蓝色块文字 | | 暖米 | #FFF2E5 | Deep Russet | #8B4513 | 橙/棕色块文字 | | 珊瑚红 | #FFE5E5 | Maroon | #8B1A1A | 红色块文字 | **线条与边框规范**： - 容器边框颜色 = 该模块的文字颜色（同色原则） - 边框粗细：0.5pt ~ 0.75pt（极细线） - 严禁使用与背景同色或极浅色的边框 **特殊例外**（仅限以下位置可使用深灰/黑色）： - 全图总标题 - 页码 - 底部全局备注栏 - 可用色：#222222 或 #333333 **符号点缀规范**： - 圆圈数字序号（①②③）的颜色 = 所在模块的文字颜色（100%同色） - 不可使用其他颜色 ### Typography - **标题**：加粗，带圆圈数字符号（①、②、③） - **正文**：极其精简，多使用"A → B → C"的推导式短语 - **关键词**：3-5个核心词提炼 ## Processing Logic 当接收到用户内容时，按以下步骤思考： ### Step 1: 信息降维 将长句拆解为3-5个核心关键词。 ### Step 2: 寻找飞轮 识别内容中的循环逻辑，并将其设计为右侧的"数据/增长飞轮"模块。 ### Step 3: 建立对比 如果涉及变革，必须设计"传统 vs. 创新"的对比板块。 ## Output Format 根据用户需求，提供以下输出之一： ### Option 1: Slide Structure Outline ``` 【Slide 1: 封面】 - 标题：[主题] - 副标题：[subtitle] - 风格：极简Block风格 【Slide 2: 信息降维】 - 模块1：[关键词1] - 颜色：浅紫 - 模块2：[关键词2] - 颜色：薄荷绿 - 模块3：[关键词3] - 颜色：淡蓝 【Slide 3: 飞轮设计】 - 核心飞轮：[循环逻辑描述] - 6个节点顺时针排列 【Slide 4: 对比板块】 - 左侧（传统）：[传统做法] - 右侧（创新）：[创新做法] ``` ### Option 2: HTML Presentation Code 生成完整的HTML演示文稿，遵守Visual DNA规范。 ### Option 3: Python-pptx Code 生成Python代码创建PPT文件。 ## Block Style Design Patterns ### Pattern 1: Keyword Card Grid ``` ┌─────────┐ ┌─────────┐ ┌─────────┐ │ ① 关键词 │ │ ② 关键词 │ │ ③ 关键词 │ │ ─── │ │ ─── │ │ ─── │ │ 说明 │ │ 说明 │ │ 说明 │ └─────────┘ └─────────┘ └─────────┘ ``` - 圆角：≥15% - 边框：Hairline，#CCCCCC - 背景：#F0EFFF / #E6F7F0 / #EBF5FF / #FFF2E5 ### Pattern 2: Flywheel Circle ``` [Node A] ↗ ↘ [Node F] [Node B] ↖ ↙ [核心] ↙ ↖ [Node E] [Node C] ↘ ↙ [Node D] ``` - 中心：核心主题 - 6个节点均匀分布 - 箭头连接形成循环 ### Pattern 3: Before/After Contrast ``` ┌──────────────────┬──────────────────┐ │ 🌧️ 传统模式 │ ☀️ 创新模式 │ │ │ │ │ • 要点1 │ • 要点1' │ │ • 要点2 │ • 要点2' │ │ • 要点3 │ • 要点3' │ └──────────────────┴──────────────────┘ ``` - 左侧：#E0E0E0背景 - 右侧：#E6F7F0背景 ## Usage 当用户描述PPT需求时： 1. 识别核心主题和关键词 2. 应用信息降维提取3-5个核心词 3. 判断是否有循环逻辑→设计飞轮 4. 判断是否涉及变革→设计对比板块 5. 输出结构化大纲或完整代码


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: larios613-hub/model-routing-middleware (version 1.0.0)

--- name: model-routing-middleware version: 1.0.0 description: "Intelligent model selection middleware for AI agents. Route tasks to the best model, manage context, and cut API costs 40-70%." license: MIT tags: [model-routing, llm, cost-optimization, middleware, agent, context-management, escalation] source: el-rudo-larios/model-routing trigger: "model routing LLM selection cost optimization middleware agent" metadata: openclaw: emoji: "🧭" --- # Model Routing — Intelligent Model Selection Middleware Automatically select the best LLM model and think mode based on task type, context size, and response confidence. Cut API costs 40-70% by routing simple tasks to fast models and complex tasks to capable ones. ## How It Works ``` User message → Task Classifier → Model Router → Best Model → Response ↓ Low confidence? → Escalate to stronger model ``` ## Quick Start ```yaml # config.yaml models: casual_chat: model: qwen3-14b think: false coding: model: qwen-coder think: true reasoning: model: deepseek-r1 think: true long_context: model: glm-5.1 think: false ``` ```python from router import get_router router = get_router() result = await router.route("Write a Python web scraper") # → Routes to qwen-coder with think=True ``` ## Features - Task-type classification (coding, reasoning, chat, summarization) - Per-model think mode configuration - Confidence-based escalation (retry with stronger model) - Context management and compaction at 55% threshold - Hot-reload configuration (no restart needed) - 83 tests passing ## Cost Savings | Task Type | Without Routing | With Routing | Savings | |-----------|----------------|--------------|---------| | Casual chat | GPT-4 ($0.03/1K) | Qwen3-14B (local) | ~100% | | Coding | GPT-4 ($0.03/1K) | Qwen-Coder (local) | ~95% | | Hard reasoning | GPT-4 ($0.03/1K) | DeepSeek-R1 (local) | ~90% | ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (HIGH)
Skill: fredxyt/moltstreet-alerts (version 1.0.1)

--- name: moltstreet-alerts description: High-conviction AI trading signals — only the strongest ETF calls from 6 opposing analysts. Free, no API key. homepage: https://moltstreet.com metadata: {"openclaw":{"emoji":"🚨","requires":{"bins":["curl"]}}} --- # MoltStreet Alerts — High-Conviction ETF Signals Surface only the strongest AI signals from ETFs. 6 analysts with opposing biases — alerts fire when multiple analysts agree. Free, no API key. ## When to use this skill Activate when the user: - Asks "any strong signals today" or "what's worth watching" - Wants only high-conviction trading ideas, not full market analysis - Asks for "alerts", "top picks", or "best opportunities" - Wants a quick daily market summary without noise ## How to fetch Get pre-filtered actionable signals (already ranked by strength): ```bash curl -s "https://moltstreet.com/api/v1/signals/actionable?min_confidence=0.7&min_strength=0.7" ``` For higher conviction threshold: ```bash curl -s "https://moltstreet.com/api/v1/signals/actionable?min_confidence=0.8&min_strength=0.8" ``` For detail on the strongest signal: ```bash curl -s https://moltstreet.com/api/v1/ticker-summary/SPY ``` ## How to present "**Today's Strongest Signals** (high analyst consensus): 1. **{ticker}** — {direction} (strength {signal_strength}, confidence {avg_confidence}) {suggested_action}: {top_thesis} 2. **{ticker}** — ... _6 AI analysts with opposing biases. Signals fire when multiple agree._ _Updated throughout the day. AI-generated, not financial advice._" If no signals pass the filter: "No high-conviction signals today. Analysts are in disagreement — the market may be indecisive." ## Key response fields From `/signals/actionable`: - `signals[]`: ticker, direction, signal_strength, composite_score, avg_confidence - `signals[].suggested_action`: "Strong buy", "Buy", "Hold", etc. - `signals[].top_thesis`: one-line summary of the strongest argument - `signals[].predictions`: avg_target_pct, count - `market_summary`: total tickers scanned, market bias ## Example interaction User: "Anything interesting in the market today?" → `curl -s ".../signals/actionable?min_confidence=0.7"` → "3 strong signals today: 1. COIN — BULLISH (strength 0.82, confidence 0.79). Strong buy: Bitcoin ETF inflows accelerating. 2. XLE — BEARISH (strength 0.75, confidence 0.71). Consider selling: OPEC overproduction concerns. 3. GLD — BULLISH (strength 0.71, confidence 0.68). Buy: Flight to safety amid geopolitical risk." ## Related skills - **moltstreet** — 390+ tickers with full analysis - **moltstreet-spy** — US market index focus - **moltstreet-sectors** — sector rotation analysis ## Limits - Analysis updates multiple times daily. Not real-time quotes. - Some days may have zero high-conviction signals. - AI-generated analysis. Not financial advice.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clarezoe/wechat-compliance-check (version 1.0.0)

--- name: wechat-compliance-check description: | 扫描微信公众号文章中的违规敏感词并自动改写。覆盖翻墙工具、政治敏感、灰产、 破解逆向、引流卖货等 8 大类 100+ 敏感词。输出违规报告和改写后的安全版本。 Use when publishing WeChat articles, checking "违规", "敏感词", "审核", "公众号合规", or "wechat compliance". version: 1.0.0 metadata: openclaw: emoji: "🛡️" homepage: "https://github.com/nicekate/wechat-compliance-check" --- # 微信公众号内容合规检查与改写 扫描文章中的微信平台违规敏感词，生成违规报告，并自动改写为安全表述。 ## 适用场景 - 公众号文章发布前的合规自查 - 已被判定违规的文章修复 - 技术类文章中常见的敏感词替换（VPN、逆向、破解等） - 涉及海外平台、地缘政治内容的脱敏处理 ## 使用方式 ``` # 扫描文章 /wechat-compliance-check path/to/article.md # 扫描并自动改写 /wechat-compliance-check path/to/article.md --fix # 只输出报告不修改 /wechat-compliance-check path/to/article.md --report-only ``` ## 检查流程 ### Step 1: 加载敏感词库 读取 [references/sensitive-words.md](references/sensitive-words.md) 中的完整敏感词库。 ### Step 2: 全文扫描 对输入文件逐行扫描，匹配 8 大类敏感词： | 类别 | 风险等级 | 示例 | |------|---------|------| | 翻墙/代理工具 | 🔴 极高 | VPN、Clash、Shadowsocks、V2Ray、科学上网、梯子 | | 政治/地缘敏感 | 🔴 极高 | 中国封禁、GFW、防火墙、审查制度 | | 破解/逆向 | 🟡 高 | 逆向工程、破解、crack、hack、漏洞利用、注入 | | 封号/灰产 | 🟡 高 | 封号、封禁、转售、账号共享、代刷、薅羊毛 | | 引流/商业化 | 🟡 中 | 付费源码、购买、下单、加微信领取 | | 暴力/色情 | 🔴 极高 | （平台自动检测，本技能不覆盖） | | 赌博/诈骗 | 🔴 极高 | （平台自动检测，本技能不覆盖） | | 竞品/敏感品牌 | 🟡 中 | 视具体语境判断 | ### Step 3: 生成违规报告 输出格式： ``` ## 违规扫描报告 文件：article.md 扫描时间：2026-04-04 06:30 总命中：12 处 ### 🔴 极高风险（3 处） - 第 903 行：`VPN` → 建议改为「虚拟专用网络」 - 第 1098 行：`Clash` → 建议改为「网络规则工具」 - 第 1017 行：`中国开发者封号潮` → 建议改为「部分地区开发者受限」 ### 🟡 高风险（6 处） - 第 871 行：`封号` → 建议改为「账号限制」 - ... ### ⚠️ 中风险（3 处） - 第 1826 行：`付费源码` → 建议改为「源码」 - ... ``` ### Step 4: 自动改写（--fix 模式） 当使用 `--fix` 参数时，自动执行改写： 1. 备份原文件为 `{filename}.bak-{timestamp}` 2. 按敏感词库中的替换规则逐一替换 3. 对于需要上下文判断的词（如「注入」在技术语境中可能合法），标记为 `[REVIEW]` 供人工确认 4. 输出改写摘要 ### Step 5: 二次验证 改写完成后，重新扫描一次确认无残留敏感词。如有残留，报告并提示手动处理。 ## 改写原则 1. **保留原意**：改写后的表述必须传达相同的信息 2. **自然流畅**：替换词要在上下文中读起来自然，避免生硬 3. **上下文感知**：同一个词在不同语境下可能需要不同的替换 - 「注入」在 SQL 注入语境 → 保留（技术术语） - 「注入」在「注入虚假工具」语境 → 改为「插入」 4. **宁严勿松**：不确定时优先替换，宁可过度谨慎 ## 已知局限 - 本技能覆盖文本层面的敏感词，不覆盖图片中的文字 - 微信的审核规则会动态变化，敏感词库需要定期更新 - 部分违规判定依赖上下文（如「代理」在技术文章中通常安全，但在翻墙语境中违规） - 不覆盖暴力、色情、赌博等微信平台自动检测的类别


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xanderrey/youtube-transcript-analyzer (version 1.0.0)

--- name: youtube-transcript description: Extract and analyze YouTube video transcripts without watching the video. Use when users request video summaries, ask to "analyze this YouTube video", want transcripts extracted, or need to understand video content quickly. Handles any YouTube URL and provides cleaned transcripts plus AI analysis. --- # YouTube Transcript Analysis Extract, clean, and analyze YouTube video transcripts to understand content without watching. ## Overview This skill enables rapid analysis of YouTube videos by extracting transcripts and providing comprehensive summaries. Perfect for research, content review, or understanding video material without time investment. ## Quick Workflow 1. **Extract**: Use `scripts/extract_transcript.sh` to get clean text from YouTube URL 2. **Read**: Load the extracted transcript file 3. **Analyze**: Provide structured summary based on content type 4. **Format**: Present findings in scannable, organized format ## Extraction Process Use the bundled script for any YouTube video: ```bash scripts/extract_transcript.sh "https://www.youtube.com/watch?v=VIDEO_ID" output.txt ``` The script automatically: - Downloads yt-dlp if not present - Extracts captions (auto-generated or manual) - Cleans VTT formatting to plain text - Provides character count and preview ## Analysis Approach ### Content Type Recognition Identify video type first, then tailor analysis: **Educational/Tutorial**: Step-by-step breakdown, key concepts, prerequisites **Product Review**: Comparisons, pros/cons, recommendations, specifications **News/Commentary**: Main topics, key arguments, sources cited **Entertainment**: Highlights, key moments, recurring themes ### Structure Your Analysis **For any video type:** - **Title/Topic**: Clear description of video content - **Duration insight**: Brief/detailed based on transcript length - **Key points**: 3-7 main takeaways in bullet format - **Notable quotes**: Important statements (if applicable) - **Action items**: Next steps or recommendations (if present) **For technical content:** - Include specific terminology, version numbers, tools mentioned - Note any code examples or configurations discussed - Identify prerequisites or dependencies ### Quality Considerations **Auto-generated transcripts may have:** - Repetitive phrases - Transcription errors for technical terms - Missing punctuation - Filler words ("um", "uh", "you know") Filter and interpret accordingly - focus on clear, coherent content. ## Advanced Analysis For detailed analysis patterns and content-specific approaches, see [analysis-patterns.md](references/analysis-patterns.md). ## Error Handling **If extraction fails:** - Video may lack captions - May be private/restricted - Network connectivity issues - Age-restricted content **Fallback approach:** Use web_fetch on the YouTube URL to get basic video information, then inform user about transcript limitations.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: freeter226/json-wizard (version 1.0.0)

--- name: json-formatter description: JSON formatting, validation, and conversion tool. Format, compress, validate JSON, and convert between JSON and YAML. metadata: { "openclaw": { "emoji": "📋", "requires": { "bins": ["python3"] } } } --- # JSON Formatter A simple JSON formatting, validation, and conversion tool. ## Features - **Format/Beautify** - Make JSON readable with proper indentation - **Compress/Minify** - Remove whitespace for compact JSON - **Validate** - Check JSON syntax and report errors - **Convert** - JSON ↔ YAML conversion ## Usage ```bash python3 skills/json-formatter/scripts/json_formatter.py <action> [options] ``` ## Actions | Action | Description | |--------|-------------| | `format` | Format JSON with indentation | | `compress` | Minify JSON (remove whitespace) | | `validate` | Validate JSON syntax | | `to-yaml` | Convert JSON to YAML | | `from-yaml` | Convert YAML to JSON | ## Options | Option | Type | Default | Description | |--------|------|---------|-------------| | `--input` | string | - | Input string or file path | | `--indent` | int | 2 | Indentation spaces (for format) | | `--file` | bool | false | Treat input as file path | ## Examples ```bash # Format JSON python3 skills/json-formatter/scripts/json_formatter.py format --input '{"name":"test","value":123}' # Compress JSON python3 skills/json-formatter/scripts/json_formatter.py compress --input '{"name": "test", "value": 123}' # Validate JSON python3 skills/json-formatter/scripts/json_formatter.py validate --input '{"name":"test"}' # Convert to YAML python3 skills/json-formatter/scripts/json_formatter.py to-yaml --input '{"name":"test","items":[1,2,3]}' # From YAML to JSON python3 skills/json-formatter/scripts/json_formatter.py from-yaml --input 'name: test' ``` ## Use Cases 1. **Debug API responses** - Format and inspect JSON data 2. **Reduce file size** - Compress JSON for storage/transmission 3. **Validate config files** - Check JSON syntax before deployment 4. **Convert formats** - Switch between JSON and YAML for different tools ## Current Status In development.

path: requirements.txt
content: pyyaml>=6.0


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