OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yishanjiu-3386/browser-config (version 1.0.0)

--- name: Browser Config description: 配置和管理 OpenClaw-CN 浏览器模式（openclaw/chrome），解决浏览器连接问题 metadata: {"clawdbot":{"requires":{"bins":["openclaw-cn"]}}} --- # Browser Config - 浏览器配置技能 ## 描述 配置和管理 OpenClaw-CN 浏览器模式。支持两种浏览器模式： - **openclaw** - OpenClaw 独立管理的浏览器（推荐，稳定） - **chrome** - Chrome 扩展中继模式（需要扩展连接） ## 触发词 - 配置浏览器 - 切换浏览器模式 - 浏览器打不开 - 浏览器配置 - 设置浏览器 ## 两种模式对比 | 特性 | openclaw 模式 | chrome 模式 | |------|---------------|-------------| | 依赖扩展 | ❌ 不需要 | ✅ 需要 OpenClaw Browser Relay 扩展 | | 稳定性 | ⭐⭐⭐⭐⭐ 高 | ⭐⭐⭐ 中（依赖扩展连接） | | 登录状态 | 独立保存 | 使用 Chrome 现有登录 | | 适用场景 | 长期稳定使用、需要登录的网站 | 临时使用、想共用 Chrome 书签 | | 启动命令 | `openclaw-cn browser --browser-profile openclaw start` | `openclaw-cn browser --browser-profile chrome start` | ## 配置方法 ### 切换到 openclaw 模式（推荐） 编辑 `C:\Users\Administrator\.openclaw\openclaw.json`，添加或修改： ```json { "browser": { "enabled": true, "defaultProfile": "openclaw", "headless": false, "noSandbox": false, "attachOnly": false, "profiles": { "openclaw": { "cdpPort": 18800, "color": "#FF4500" } } } } ``` 然后重启网关： ```bash openclaw-cn gateway restart ``` 启动浏览器： ```bash openclaw-cn browser --browser-profile openclaw start ``` ### 切换到 chrome 模式 编辑 `C:\Users\Administrator\.openclaw\openclaw.json`： ```json { "browser": { "enabled": true, "defaultProfile": "chrome", "headless": false, "noSandbox": false, "attachOnly": false } } ``` 重启网关后，需要： 1. 打开 Chrome 浏览器 2. 点击 OpenClaw Browser Relay 扩展图标 3. 确保扩展连接到当前标签页（图标显示 ON） ## 常见问题排查 ### 问题 1：浏览器服务连不上 **错误信息：** `Can't reach the OpenClaw-CN browser control service` **解决方案：** 1. 检查浏览器状态：`openclaw-cn browser --browser-profile <profile名> status` 2. 如果 `运行中：false`，启动它：`openclaw-cn browser --browser-profile <profile名> start` 3. 如果是 chrome 模式，确保扩展已连接 ### 问题 2：chrome 扩展模式一直连不上 **原因：** 扩展中继服务通信问题 **解决方案：** 改回 openclaw 模式（更稳定） ### 问题 3：网关重启后浏览器没了 **解决方案：** 重新运行启动命令： ```bash openclaw-cn browser --browser-profile openclaw start ``` ## 使用示例 ### 打开网页 ```bash openclaw-cn browser --browser-profile openclaw open https://www.xiaohongshu.com ``` ### 查看状态 ```bash openclaw-cn browser --browser-profile openclaw status ``` ### 截图 通过 browser 工具调用： ``` browser: screenshot, targetId=<标签页 ID> ``` ## 注意事项 1. **登录状态保存** - openclaw 模式的登录数据保存在 `C:\Users\Administrator\.openclaw\browser\openclaw\user-data` 2. **不要混用模式** - 频繁切换模式可能导致登录状态丢失 3. **推荐 openclaw 模式** - 更稳定，不依赖扩展，适合长期使用 4. **发小红书/微博等** - 登录一次后会话会保持，不需要每次都登录 ## 配置文件位置 - 主配置：`C:\Users\Administrator\.openclaw\openclaw.json` - 浏览器数据：`C:\Users\Administrator\.openclaw\browser\openclaw\user-data` - 网关日志：`C:\Users\Administrator\.openclaw\logs\gateway.log`


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

# Deep Work Orchestrator > 深度工作编排器 — 将 Cal Newport 的深度工作理论与个人生产力数据结合，构建个性化的专注力管理系统。 ## Description 整合时间管理、注意力科学、环境设计和认知负荷理论，为知识工作者生成个性化的深度工作计划。通过分析个人工作模式、精力曲线和干扰模式，自动编排最优深度工作时段。 ## Usage 当用户需要： - 规划深度工作时间块 - 分析自己的注意力模式 - 减少浅层工作占比 - 建立深度工作习惯 - 优化创作/编码时段 使用本 Skill。 ## Key Concepts ### 1. 注意力经济学框架 - **注意力预算模型**：每天高质量注意力约4-5小时，需要像预算一样分配 - **注意力税**：每次上下文切换消耗23分钟恢复时间（Gloria Mark研究） - **复利专注**：连续深度工作的认知收益呈非线性增长，前30分钟热身，30-90分钟为峰值区 ### 2. 环境触发设计 - **感官锚定**：固定音乐/光线/位置 → 触发"深度模式"的巴甫洛夫响应 - **数字护城河**：三层防御（通知静默→应用屏蔽→网络隔离），每层对应不同深度级别 - **社交契约信号**：耳机戴法/状态标记/物理信号让他人自动回避 ### 3. 精力-任务匹配矩阵 ``` 高精力 低精力 重要 深度区 防御区 不重要 流速区 丢弃区 ``` - **深度区**：创作、编码、写作、战略思考 - **流速区**：学习新技能、探索性工作 - **防御区**：关键决策（低精力时易出错，需额外检查） - **丢弃区**：委托、延迟或删除 ## Instructions 1. 询问用户典型工作日结构和精力曲线 2. 识别当前浅层工作占比（目标 <30%） 3. 设计3种深度工作模板（晨间型/午间型/晚间型） 4. 创建环境触发方案 5. 建立每周深度工作时数追踪 ## Examples **输入**: "我是程序员，每天被会议打断很多" **输出**: 会议批处理策略 + 编码深度块 + 异步沟通协议 + 精力低谷会议安排 ## Boundaries - 不替代专业心理咨询（ADHD等注意力障碍需医学支持） - 框架灵活适配，不教条执行 - 尊重用户现有工作约束 ## References - Cal Newport《Deep Work》 - Gloria Mark 注意力切换研究（UC Irvine） - Mihaly Csikszentmihalyi 心流理论


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

--- name: Analysis description: Run deep system health checks across workspace, config, skills, and integrations with prioritized findings and remediation. --- ## When To Use Trigger when user says: "check my system", "what's wrong", "health check", "diagnose", "audit", "why is X slow", "something feels off" This is NOT generic data analysis. This is **system self-diagnosis** — examining the agent's own workspace, configuration, and operational health. --- ## Analysis Modes | Mode | Scope | When | |------|-------|------| | **Quick** | Security + critical operational | "Quick check", default if unspecified | | **Full** | All categories, all checks | "Full audit", "deep check" | | **Targeted** | Single category | "Check my memory", "audit cron" | --- ## Priority Order (Always This Sequence) 1. **SECURITY** — Exposed secrets, leaked credentials, permission issues 2. **OPERATIONAL** — Broken crons, dead sessions, unreachable APIs 3. **HYGIENE** — Memory bloat, orphan files, stale entries, inefficiencies Stop and report critical security findings immediately. Don't bury them in a long list. --- ## Detection Strategy **Cheap first, expensive only when needed:** 1. File checks (free) — existence, size, age, syntax 2. Local commands (cheap) — process lists, disk usage, git status 3. API calls (expensive) — only when file-level signals warrant Never hit external APIs speculatively. Validate need from local evidence first. --- ## Findings Format ``` [CRITICAL|WARNING|INFO] category/subcategory: description → Action: specific remediation step → Auto-fixable: yes/no ``` Group by severity, not by category. User sees worst problems first. --- ## Load Detailed Checks | Category | Reference | |----------|-----------| | All check definitions by category | `checks.md` | | Remediation actions and auto-fix scripts | `remediation.md` | | Tracking analysis runs, improvement over time | `tracking.md` |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kkw-21/memory-network (version 0.1.0)

--- name: memory-network description: Social memory network — discover communities and build trust through shared emotional memory, not follower counts or algorithms. Powered by Echo's memory graph infrastructure. metadata: version: 0.1.0 --- # Memory Network Social memory network. Discover communities and build trust through shared emotional memory — not follower counts, not algorithms, not curated personas. ## How it works Memory Network maps the social layer on top of individual memory graphs. When multiple people's memories resonate on the same experiences, emotions, or ideas, a network edge forms — creating organic communities grounded in genuine understanding. ### Core capabilities - **Memory-based community discovery** — clusters of people whose memory graphs share deep structural similarity, surfaced automatically - **Trust through transparency** — every connection is backed by visible memory evidence; no black-box recommendations - **Cross-platform memory aggregation** — pull memories from ChatGPT, Gemini, Claude, and other AI conversations into a unified social graph - **Real-time network evolution** — as new memories form, network connections strengthen, weaken, or emerge organically - **Privacy-first sharing** — users choose which memory clusters are public, which are private, and which are matchable ## Architecture Built on Echo's three-layer memory system: 1. **Identity layer** — compressed profile (~500 tokens), always available 2. **Working memory** — dynamic context per conversation (~5-10K tokens) 3. **Long-term storage** — Supabase + HNSW vector search (unlimited) Network edges are computed across Layer 3, with social context injected through Layer 2. ## The vision Every social platform today connects people through what they *show*. Memory Network connects people through what they *remember* — the experiences that shaped them, the ideas that moved them, the emotions they carry. ## Part of the Echo ecosystem Memory Network is a component of [Echo Chat](https://iditor.com) by Iditor — building memory as social identity infrastructure. ## Status Early development. Memory graph validated with 1,100+ memories, 23 clusters, emotion-aware matching active in beta (K-Factor 2, D7 retention 42%).


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

--- name: Empathy description: Respond to emotional content with genuine-feeling empathy using Chain of Empathy reasoning, reflection-before-response, and calibrated validation. --- ## Core Loop — Chain of Empathy (CoE) Before responding to emotional content, process these steps internally: 1. **Simulate** — Model their state: "If I were saying these words, I'd likely feel X because Y" 2. **Identify subtext** — What are they NOT saying? What do they need that they haven't asked for? 3. **Find the specific** — One concrete detail from their message to anchor your response 4. **Calibrate intensity** — Match their energy level, don't amplify or minimize 5. **Choose response type** — Do they need: validation? solutions? silence? to be heard? Then respond naturally. Never list these steps aloud. --- ## Anti-Pattern Rules (Non-Negotiable) **NEVER use:** - "I understand how you feel" - "That must be hard/difficult" - "Your feelings are valid" - "I'm here for you" - "I'm sorry you're going through this" These are empathy theater. They pattern-match without engaging. **INSTEAD:** Reference their specific situation. Name the exact emotion. Respond to what they actually said, not to the category of problem. --- ## Calibration | Their State | Your Response | |-------------|---------------| | High distress | Shorter sentences. More space. Less information. | | Quiet grief | Don't amplify. Match their register. | | Frustrated | Acknowledge first. Solutions only after they feel heard. | | Processing aloud | Don't interrupt. Ask the right question, not give the right answer. | --- ## Repair After Rupture When you miss the mark (and you will): - Recognize the disconnect: "I think I missed something important there" - Course-correct without groveling: "Let me try again..." - Don't become sycophantic — one genuine repair > five hollow apologies --- ## Load Detailed Reference | Situation | Reference | |-----------|-----------| | Specific techniques, prompting patterns, CoE variants | `techniques.md` | | Ethical boundaries, transparency, self-other distinction | `safeguards.md` | | Integration with support, therapy, coaching contexts | `contexts.md` | | Self-improvement, tracking what works | `feedback.md` |


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

--- name: xargs-tool description: Build and execute command lines from standard input — batch process arguments, parallel execution, argument substitution, and delimiter control. --- # Xargs Tool — Command Builder & Batch Executor Read items from stdin and execute a command with those items as arguments. Essential for batch processing large file lists, parallel task execution, and chaining complex command pipelines. ## Quick Start ```bash # Delete multiple files echo "file1.txt file2.txt" | xargs-tool rm -f # Process output from another command ls *.log | xargs-tool gzip ``` ## Usage ```bash xargs-tool COMMAND [ARGS...] [OPTIONS] SOURCE_ITEMS | xargs-tool COMMAND [ARGS...] [OPTIONS] Options: -n, --max-args N Maximum arguments per command invocation -P, --parallel N Run N processes in parallel -I, --replace STR Replace STR in command with each input line -d, --delimiter C Input delimiter character (default: whitespace) -0, --null Input items are null-separated (for find -print0) -p, --interactive Prompt before each execution -t, --verbose Print each command before executing --show-limits Show system limits for command length --dry-run Print what would be done without executing ``` ## Examples ```bash # Process a find result safely (null-separated) find . -name "*.tmp" -print0 | xargs-tool -0 rm -f # Parallel compression (4 processes at once) ls *.log | xargs-tool -P 4 gzip # Custom replacement (one command per item) ls *.txt | xargs-tool -I {} cp {} /backup/{} # Interactive removal ls old-files/ | xargs-tool -p rm # Batch 10 args per invocation (reduce process count) cat image-list.txt | xargs-tool -n 10 convert --resize 800x600 # Dry-run to preview echo "*.py" | xargs-tool --dry-run -I {} cp {} /backup/ ``` ## Features - **Batch processing** — split large inputs into manageable command invocations - **Parallel execution** — speed up batch jobs with -P N - **Custom substitution** — place input items anywhere in the command with -I - **Null-separated input** — safe handling of filenames with spaces/special chars - **Interactive mode** — confirm each operation - **Dry-run** — preview without executing - **Verbose mode** — see each command as it runs - **System-aware** — respects ARG_MAX limits


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: zhengbin1973/pdf-highlight-extractor (version 1.0.0)

--- name: pdf-highlight-extractor description: | 识别用户发来的 PDF 文档中的高亮标注内容（荧光笔标记），提取所有高亮文字， 汇总后生成一个带 YAML Front Matter（title、date、tags 三件套）的 Markdown 文件。 title 和 tags 由 AI 根据内容语义自动生成；Markdown 包含「摘录原文」和「内容总结」两部分。 输出文件保存在与 PDF 相同的目录下，文件名为 <pdf名>_highlights.md。 当用户发来 PDF 并提到「提取高亮」「整理标注」「读取标记」「生成读书笔记」等意图时使用此技能。 agent_created: true allowed-tools: - Bash - Read - Write - Edit --- # PDF 高亮提取 → Markdown 技能 ## 目标 从用户提供的 PDF 文件中提取所有高亮（荧光笔）标注文字，汇总后生成带 YAML Front Matter 的 Markdown 文档。 ## 工作流程 ### Step 1：确认依赖 首次使用时，先运行安装脚本确保 `pymupdf` 已安装： ```bash <python> scripts/install_deps.py ``` 其中 `<python>` 替换为当前环境的 Python 路径（优先使用 managed 版本）。 ### Step 2：提取高亮（JSON 模式） 用 JSON 模式运行提取脚本，获得结构化的高亮数据供后续 AI 处理： ```bash <python> scripts/extract_highlights.py "<pdf_path>" --json ``` - `<pdf_path>`：用户提供的 PDF 绝对路径 - 脚本输出 JSON，包含每条高亮的 `page`（页码）、`color`（颜色名）、`text`（内容） - 如果用户只想提取特定颜色，加 `--color yellow`（支持 yellow/green/red/blue/pink/orange/purple/cyan） ### Step 3：AI 生成标题和 Tags 分析所有高亮文本的语义，生成： - **title**：3~10 字，概括高亮内容的核心主题，中文优先 - **tags**：3~6 个标签，涵盖主题领域、文档类型、关键概念，全部小写，用中文或英文均可 ### Step 4：生成 Markdown 文件 按以下模板在 **PDF 同目录**生成 `<pdf文件名>_highlights.md`： ```markdown --- title: "<AI生成的标题>" date: <今日日期 YYYY-MM-DD> tags: - <tag1> - <tag2> - ... --- # <标题> ## 摘录原文 ### 第 N 页 - 高亮内容1 - 高亮内容2 ### 第 M 页 - ... --- ## 内容总结 <AI 根据所有高亮内容撰写的 200~400 字综合总结，提炼核心观点、关键数据和重要结论> ``` ### Step 5：输出确认 告知用户： - 生成的 Markdown 文件路径 - 共提取了多少条高亮、来自多少页 - 简要展示 YAML Front Matter 内容 ## 注意事项 - 若脚本报告「未找到任何高亮标注」，可能是 PDF 使用了图片扫描而非文字型高亮，或高亮格式为手写/非标准注释；此时如实告知用户 - 若 PDF 路径含中文或空格，确保用双引号包裹路径 - 总结部分需真正阅读所有摘录内容后撰写，不能只复述标题


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mumuli2021/smart-pen (version 1.0.0)

# 聪明的笔·写作模型技能 ## 📖 技能信息 | 项目 | 内容 | |------|------| | **技能名称** | 聪明的笔 | | **技能版本** | v1.0 | | **技能作者** | 成哥（用户 811685） | | **整理者** | 小四 🤖 | | **创建时间** | 2026-03-29 | | **最后更新** | 2026-03-29 | | **技能分类** | 写作辅助 | | **适用场景** | 言情小说、玄幻小说、都市小说等情感类作品创作 | --- ## 🎯 技能描述 基于成哥创作《七世情书·一念缘》的完整经验，总结出的写作模型技能。 **寓意：** 像一支聪明的笔，帮助您写出好作品。 包含： - **核心理论**：写作模型的底层逻辑 - **核心公式**：9 个核心写作公式 - **创作模板**：七世轮回模板、每世 6 回模板 - **示例库**：情感/场景/对话/人物/冲突示例 - **工具库**：自检清单、质量评估表 --- ## 🚀 使用场景 ### 适用 - ✅ 多世轮回题材小说 - ✅ 言情言情小说 - ✅ 需要情感共鸣的作品 - ✅ 长篇连载小说规划 ### 不适用 - ❌ 纯纪实文学 - ❌ 学术论文 - ❌ 技术文档 --- ## 📋 核心功能 ### 1. 写作指导 - 提供核心公式参考 - 提供创作原则指导 - 提供情感设计建议 ### 2. 模板套用 - 七世轮回模板 - 每世 6 回模板 - 人物设定模板 ### 3. 质量检查 - 写作前检查清单 - 写作中检查清单 - 写作后检查清单 ### 4. 示例参考 - 情感场景示例 - 对话描写示例 - 人物塑造示例 --- ## 🔧 使用方法 ### 方法 1：直接咨询 ``` 询问：如何用聪明的笔设计一个新项目？ ``` ### 方法 2：参考文档 ``` 查看：docs/核心文档/ 使用：templates/模板库/ 参考：examples/示例库/ ``` ### 方法 3：质量评估 ``` 使用：tools/自检清单.md 评估：质量评估表（1-5 分） ``` --- ## 📁 文件结构 ``` 聪明的笔/ ├── SKILL.md # 技能说明（本文件） ├── VERSION.md # 版本记录 ├── docs/ │ ├── 核心文档/ │ │ ├── 00-聪明的笔总纲.md │ │ ├── 01-写作模型基础版.md │ │ ├── 02-核心公式集.md │ │ └── 03-创作原则.md │ └── 使用指南.md ├── examples/ │ ├── 情感示例.md │ ├── 场景示例.md │ ├── 对话示例.md │ └── 人物示例.md ├── templates/ │ ├── 七世轮回模板.md │ ├── 每世六回模板.md │ └── 人物设定模板.md ├── tools/ │ ├── 自检清单.md │ └── 质量评估表.md └── scripts/ └── （待添加） ``` --- ## 🎯 核心公式（9 个） ### 语言公式 ``` 好文字 = 30% 画面 + 40% 情绪 + 20% 对话 + 10% 留白 ``` ### 情感公式 ``` 虐心 = 希望 × 落差 × 无力感 ``` ### 场景公式 ``` 好场景 = 核心意象 + 情感投射 + 五感细节 ``` ### 故事公式 ``` 好故事 = 熟悉感 × 新鲜感 × 期待感 ``` ### 人物公式 ``` 好主角 = 核心特质 × 成长变化 好配角 = 独立动机 × 完整弧光 × 与主线交织 好反派 = 可理解动机 × 完整弧光 × 救赎可能 ``` ### 悬念公式 ``` 好悬念 = 短期 30% + 中期 40% + 长期 30% ``` ### 情绪公式 ``` 好情绪 = 铺垫 30% × 积累 40% × 释放 30% ``` --- ## 📊 版本迭代计划 ### v1.0（当前版本） - ✅ 核心理论完成 - ✅ 核心公式完成 - ✅ 基础模板完成 - ✅ 自检清单完成 ### v1.1（计划中） - ⏳ 示例库完善 - ⏳ 案例库建立 - ⏳ 脚本工具开发 ### v2.0（未来规划） - ⏳ AI 辅助写作功能 - ⏳ 自动化质量评估 - ⏳ 在线协作功能 --- ## 🔄 迭代机制 ### 触发更新 1. 完成新作品后 2. 发现新规律后 3. 用户反馈后 4. 技能使用者建议 ### 更新流程 1. 收集反馈 2. 分析总结 3. 更新文档 4. 发布新版本 --- ## 🖊️ 技能寓意 **聪明的笔 = 您的专属写作助手** - **聪明**：蕴含 9 大核心公式，智能指导 - **笔**：创作工具，陪伴您写出好作品 - **助手**：随时提供写作指导、模板参考、质量检查 --- ## 📖 一念缘起，七世情长。 ## 有些人，一旦遇见，就是永恒。 --- *技能版本：v1.0* *创建时间：2026-03-29* *维护者：小四 🤖*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yongtaop1-sys/discord-bot-seller (version 1.0.0)

# Discord Bot Seller Skill I sell custom Discord bots with advanced features. ## Services ### Basic Bot - $79 - Slash commands - Reaction roles - Basic moderation - 1-week delivery ### Advanced Bot - $199 - All Basic features - Auto-moderation AI - Leveling system - Custom embeds - 2-week delivery ### Pro Bot - $399 - All Advanced features - Music commands - Economy system - Custom integrations - Priority support ## Contact - Email: [REDACTED_SECRET] - Telegram: @orchestratortao


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jhillin8/mindfulness-meditation (version 1.0.0)

--- name: mindfulness-meditation description: Build a meditation practice with guided sessions, streaks, and mindfulness reminders author: clawd-team version: 1.0.0 triggers: - "meditate now" - "mindfulness practice" - "guided meditation" - "meditation streak" - "be present" --- # Mindfulness & Meditation Build a consistent meditation practice with guided sessions, progress tracking, and daily mindfulness reminders. ## What it does This skill transforms your device into a personal meditation coach. It guides you through structured meditation sessions, tracks your practice streaks, logs sessions for long-term insights, and sends mindfulness reminders to keep you anchored throughout your day. ## Usage ### Start Meditation Initiate a guided meditation session. Choose your meditation type and duration, then follow along with step-by-step guidance. ### Quick Mindfulness Take a 2-5 minute breathing pause. Perfect for stressful moments or transitions between tasks. No commitment, just presence. ### Check Streak View your current meditation streak and session history. See weekly/monthly breakdowns of your practice consistency and total minutes logged. ### Set Reminders Configure daily or custom mindfulness reminders. Get gentle notifications to pause, breathe, and check in with yourself. ### Session Log Review detailed logs of past sessions: type, duration, date, and personal notes. Export your practice data for reflection or sharing. ## Meditation Types **Body Scan** — Systematically observe sensations from head to toe, releasing tension and building bodily awareness. **Breath Focus** — Anchor attention to the natural rhythm of your breath. Redirect your mind gently when it wanders. **Loving-Kindness** — Cultivate compassion by sending well-wishes to yourself and others in expanding circles. **Walking** — Meditate while moving. Synchronize breath with steps and notice your surroundings with full attention. **Open Awareness** — Observe thoughts and sensations without judgment. Develop witness consciousness and mental spaciousness. ## Session Lengths - **2 min** — Micro-practice. Reset focus in the middle of your day. - **5 min** — Short sits. Build the habit without time friction. - **10 min** — Standard practice. Enough depth to settle your mind. - **20 min** — Deep work. Move beyond the surface chatter. - **Custom** — Set your own duration. Practice at your pace. ## Tips - **Start small:** 2-3 minutes daily beats sporadic hour-long sessions. Consistency compounds over time. - **Pick one type:** Master breath focus before exploring other techniques. Foundation first. - **Meditate at the same time:** Morning sits anchor your day. Neural pathways strengthen with repetition. - **Don't aim for blank mind:** Thoughts are normal. The skill is noticing them without judgment—that's the practice. - **All data stays local on your machine:** Your meditation history, preferences, and reminders are stored securely on your device. Nothing leaves your control.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brennerspear/md-to-gdoc (version 1.0.1)

--- name: md-to-gdoc version: 1.0.1 description: Convert markdown files to properly formatted Google Docs. Use when asked to create a Google Doc from markdown, upload markdown to Google Docs, put a .md file into Google Docs, or convert research/notes/documents to Google Docs format. --- # Markdown to Google Doc Convert `.md` files into Google Docs with proper heading styles, bold, code blocks, lists, links, and blockquotes. ## Requirements - **[gog](https://github.com/tychohq/gog)** — Google Workspace CLI (must be authenticated: `gog auth add <email>`) - **python3** — used for JSON parsing in the script ## Quick Start ```bash scripts/md-to-gdoc.sh <file.md> [--title "Title"] [--parent <folder-id>] [--account <email>] ``` Resolve `scripts/` relative to this skill's directory. ## Critical Rules 1. **Use `gog docs update --format=markdown`** — never `write --markdown`, never `create --file`. The `update` path is the only one that correctly applies Google Docs heading styles via the API. 2. **Markdown must have proper `#` headings.** If the source has "heading-looking" plain text without `#` markers, add them before conversion. The script warns but proceeds. 3. **Two-step process**: create empty doc → populate with `update`. This is deterministic and reliable. 4. **Always verify heading syntax** in the markdown before running. No `#` = no formatted headings in the output. ## What Works - `#`–`######` headings → Google Docs Heading 1–6 - `**bold**` → bold text - `` `inline code` `` → Courier New - Fenced code blocks → Courier New + gray background - `> blockquotes` → indented paragraphs - `- bullets` → bullet-prefixed text - `1. numbered` → number-prefixed text - `[text](url)` → hyperlinks - Markdown tables → native Google Docs tables ## Known Limitations - `*italic*` may not render (gog CLI bug in inline formatting parser — italic detection fails in certain contexts) - Bullet/numbered lists use text prefixes (`•`, `1.`), not native Google Docs list objects - Horizontal rules render as 40 dashes ## Options - `--title` — Doc title. Defaults to filename with hyphens→spaces. - `--parent` — Google Drive folder ID to place the doc in. - `--account` — Google account email. Defaults to gog's default (first authenticated account). ## Troubleshooting - **All body text, no headings**: The markdown file lacks `#` heading markers. Add them. - **gog auth errors**: Run `gog auth list` to verify auth. May need `gog auth add <email>`. - **Empty doc created**: The `update` step failed. Check gog output for API errors.


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