OpenClaw/clawhub-security-signals huggingface dataset:
Skill: pry520okgpt/douyin-super-agent (version 1.1.0)

--- name: douyin-super-agent version: 1.1.0 author: PRY520OKGPT license: MIT description: 免费抖音处理工具。视频解析、音频提取、语音识别、文案纠错，全链路自动化，零付费依赖。 homepage: https://github.com/openclaw/skills dependencies: - faster-whisper - ffmpeg - mcporter - qwen-asr (optional) - tencentcloud-asr (optional) - memory-manager (optional) runtime_dependencies: - uv (for qwen-asr) - douyin-mcp (via mcporter) tags: - douyin - 抖音 - video - asr - transcription - whisper - 音频提取 - 免费 --- # 🚀 douyin-super-agent **一句话：** 丢一个抖音链接，自动给你提取文案。纯免费，零付费依赖。 ## 安全声明 - ✅ **无恶意代码**：不上传数据、不执行敏感命令 - ✅ **所有外部调用均已声明**（见下方依赖表） - ✅ **跨技能调用均为可选降级**，缺失不影响核心功能 - ✅ **数据流透明**：链接 → 解析 → 下载 → 音频 → 识别 → 文案 → 桌面输出 ## 核心能力 | 功能 | 说明 | |------|------| | **抖音视频解析** | 标题、ID、下载链接（MCP） ✅ | | **音频转写** | 任意音频 → 语音识别 ✅ | | **三级 ASR 降级** | qwen-asr（远程优先）→ whisper-medium（本地降级）→ 腾讯云（备选） ✅ | | **文案自动纠错** | 擎天柱/铁疙瘩/Grok 等 10+ 常见错别字 ✅ | | **视频下载** | MCP 直链下载 ⚠️ 依赖 MCP 稳定性 | ## 完整依赖表 ### 必选 | 依赖 | 用途 | 网络 | 凭据 | |------|------|------|------| | Python 3.10+ | 运行环境 | 安装时 | 无 | | faster-whisper | 本地 ASR | 首次下载模型 | 无 | | ffmpeg | 音频提取 | 安装时 | 无 | ### 可选（自动检测，缺失则降级/跳过） | 依赖 | 用途 | 调用方式 | 降级处理 | |------|------|----------|----------| | mcporter | 抖音解析 | 子进程 | 提示安装 | | uv | qwen-asr 运行时 | 子进程 | 跳过远程，用本地 | | qwen-asr skill | 远程 ASR | 子进程脚本 | 本地 whisper | | tencentcloud-asr | 云备选 | 子进程脚本 | 报错提示 | | memory-manager | 记忆存储 | 子进程脚本 | 静默忽略 | ## 外部二进制 | 工具 | 调用方式 | 用途 | |------|----------|------| | `mcporter` | 子进程 | MCP 客户端 | | `curl` | Python subprocess | 视频直链下载 | | `ffmpeg` | Python subprocess | 音频提取 | | `uv` | Python subprocess | qwen-asr 环境 | ## 数据流 ``` 抖音短链接 ↓ mcporter parse 标题 + ID + 下载链接 ↓ curl / mcporter download 视频文件 (mp4) ↓ ffmpeg 音频文件 (mp3) ↓ qwen-asr / whisper / tencentcloud 识别文本 ↓ simplify_text 精简文案 → 保存桌面 ``` ## 文件写入 | 路径 | 内容 | |------|------| | `~/Desktop/douyin-super-agent/` | 视频/音频/文案 | | `~/.cache/whisper/` | whisper 模型 | ## 快速开始 ### 安装 ```bash chmod +x setup.sh && ./setup.sh ``` ### 日常使用 ```bash # 完整流程：链接 → 文案 python3 scripts/douyin.py video "https://v.douyin.com/xxx/" # 仅音频转写 python3 scripts/douyin.py audio audio.mp3 # 查看能力统计 python3 scripts/douyin.py stats ``` ## 输出文件 `~/Desktop/douyin-super-agent/` 自动保存： - `dy_<ID>.mp4` 视频 - `dy_<ID>.mp3` 音频 - `transcript_<ID>.txt` 精简文案 - `result_<ID>.json` 结构化结果 ## 自动纠错 已知 ASR 错误自动修正： - 晴天柱 → 擎天柱 - 铁哥 → 铁疙瘩 - 住进/注进 → 注入 - 这特曼 → 这特么 - AI减4 / AI加4 → AI-FSD - 零言池 → 零延迟 - Grogg → Grok - 几倍发凉 → 脊背发凉 ## 技术依赖 **必选：** Python 3.10+, faster-whisper, ffmpeg **可选：** uv (qwen-asr), mcporter/douyin-mcp (抖音解析), tencentcloud-asr, memory-manager **模型：** whisper-medium (~1.5GB，首次运行自动下载，无需手动干预) **费用：** 全部免费，无付费依赖 ## 文件结构 ``` douyin-super-agent/ ├── SKILL.md ← 技能文档（安全声明） ├── README.md ← 详细使用指南 ├── requirements.txt ← Python 依赖 ├── setup.sh ← 一键安装脚本 └── scripts/ └── douyin.py ← 主程序 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: p697/esp32-cam-eyes (version 1.0.0)

--- name: esp32-cam-eyes description: Set up ESP32-S3-CAM modules as visual sensors (eyes) for OpenClaw agents. Covers hardware identification, firmware flashing, WiFi configuration, and HTTP camera server deployment using PlatformIO + Arduino framework. Use when the user wants to give their agent physical vision, set up ESP32-CAM, connect a camera module, flash camera firmware, or troubleshoot ESP32-S3 camera issues. Supports Hiwonder/Freenove boards with GC2145, OV2640, and OV5640 sensors. --- # ESP32-CAM Eyes Give your OpenClaw agent physical eyes using ESP32-S3-CAM modules. ## Overview Each ESP32-CAM module runs a lightweight HTTP server exposing `/capture` (single JPEG snapshot) and `/stream` (MJPEG live stream). Once connected to WiFi, the agent can grab images via `curl` for vision analysis. ## Prerequisites - **Hardware**: ESP32-S3 development board with camera sensor (Hiwonder, Freenove, or similar) - **Software**: macOS or Linux with Python 3 installed - **Tools**: PlatformIO CLI (`pip3 install platformio`), pyserial (`pip3 install pyserial`) ## Quick Start 1. Plug in the ESP32-CAM via USB 2. Identify the serial port: `ls /dev/cu.usb*` (macOS) or `ls /dev/ttyUSB*` (Linux) 3. Identify the sensor model (critical — determines firmware config) 4. Create PlatformIO project, flash firmware 5. Connect to WiFi, test with `curl -o photo.jpg http://<IP>/capture` For the complete step-by-step guide with firmware code, pin definitions, performance benchmarks, and troubleshooting: read [references/setup-guide.md](references/setup-guide.md). ## Key Decision: Sensor Type The sensor model determines your firmware strategy: | Sensor | PID | Hardware JPEG | Recommended Format | |--------|-----|---------------|-------------------| | OV2640 | 0x2640 | ✅ Yes | `PIXFORMAT_JPEG` directly | | OV5640 | 0x5640 | ✅ Yes | `PIXFORMAT_JPEG` directly | | GC2145 | 0x2145 | ❌ No | `PIXFORMAT_RGB565` + software `frame2jpg()` | If buying new boards, prefer **OV2640** — hardware JPEG is significantly faster. ## API Endpoints Once flashed and connected: | Path | Function | |------|----------| | `/capture` | Single JPEG snapshot | | `/stream` | MJPEG live stream | | `/` | Web UI with stream viewer | ## Multi-Camera Deployment Multiple ESP32-CAMs can join the same WiFi network for multi-angle coverage. Bind fixed IPs via router DHCP reservation to avoid IP changes on reboot. ## Common Pitfalls - **Wrong sensor ID**: Always verify PID before choosing firmware config - **Upload speed**: Use 460800 baud, not 921600 (causes flash verification failures on many boards) - **WiFi band**: ESP32 only supports 2.4GHz — ensure your router has a 2.4GHz SSID available - **QQVGA is slower than VGA**: Counter-intuitive but true due to PSRAM DMA buffer efficiency; use XGA (1024×768) for best speed/quality balance


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: boboy-j/feishu-dingtalk-bridge (version 1.0.0)

--- name: feishu-dingtalk-bridge version: 1.0.0 author: AI-Workflows license: MIT description: 打通飞书/钉钉开放API，实现日程同步、审批追踪、文档解析与智能待办分发的企业协同中枢 tags: - 企业协同 - 办公自动化 - 飞书 - 钉钉 - 流程引擎 parameters: platform: type: string required: true description: 目标平台（feishu/dingtalk） action: type: string required: true description: 执行动作（sync_calendar/fetch_approval/parse_doc/distribute_task） auth_token: type: string required: true description: 平台应用访问凭证（App Token/Access Token） query_context: type: string required: false description: 查询条件或上下文（如：部门ID/时间范围/文档链接/待办关键词） output_format: markdown compatibility: - OpenClaw - Dify - Coze - SkillHub --- # 🌐 飞书钉钉协同中枢 ## 🎯 核心定位 将企业IM/审批/日历/文档API转化为结构化工作流，自动聚合碎片信息并生成可执行待办清单。 ## 🔄 工作流指令 1. 校验 `platform` 与 `auth_token` 格式，失败则终止并提示授权路径。 2. 根据 `action` 路由至对应API逻辑： - `sync_calendar`：拉取指定周期日程 → 冲突检测 → 输出时间轴 - `fetch_approval`：查询审批流状态 → 超时/驳回预警 → 输出处理建议 - `parse_doc`：提取文档关键段落（合同/纪要/SOP）→ 结构化摘要 - `distribute_task`：按角色/部门拆分任务 → 生成派单模板 3. 清洗返回数据，过滤脱敏字段，对齐企业字段映射规范。 4. 按输出模板生成 Markdown 报告。 ## 📤 输出模板 ```markdown # 📅 协同工作流执行报告 ## 1. 数据聚合摘要 | 模块 | 数据量 | 异常项 | 同步状态 | |:---|:---|:---|:---| | 日程/审批/文档/任务 | ... | ... | ✅/⚠️ | ## 2. 核心事项清单 | 事项名称 | 责任方 | 截止时间 | 当前状态 | 下一步动作 | |:---|:---|:---|:---|:---| | ... | ... | ... | ... | ... | ## 3. 自动化建议 - 规则匹配项：... - 需人工介入项：... - 建议调度策略：... > ⚠️ 所有API调用已遵循平台限频策略。敏感数据已脱敏处理。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wxttt/smart-image-to-pdf (version 0.1.0)

--- name: image-to-pdf description: Combine multiple image screenshots into a paginated A4 PDF. Intelligently handles page breaks to avoid cutting through text. Use when the user wants to combine, merge, or stitch images/screenshots into a PDF, especially exam papers or documents. --- # Image to PDF Combine multiple image screenshots (typically exam paper questions) into a well-formatted, paginated A4 PDF that is ready for printing. ## When to Use Trigger this skill when the user wants to: - Combine / merge / stitch images into a PDF - Put screenshots together into a printable document - Create a PDF from exam paper photos or screenshots - Pack question images onto A4 pages for printing Look for phrases like: "拼到一起", "合成PDF", "拼接", "打印", "combine images", "merge into PDF", etc. ## How to Use This skill includes a Python script at `scripts/combine.py` (relative to this SKILL.md). ### Step 1: Identify inputs and output From the user's request, extract: - **Input**: a directory path or list of image file paths - **Output**: the desired PDF output path (default: `output.pdf` in the input directory) ### Step 2: Run the script Use `uv run` to handle dependencies automatically (no manual install needed): ```bash uv run --with Pillow --with numpy python3 <path-to-this-skill>/scripts/combine.py <input> -o <output.pdf> ``` **Input formats:** - A directory: `python scripts/combine.py ./my_images/ -o result.pdf` - Multiple files: `python scripts/combine.py img1.png img2.png -o result.pdf` **Optional flags:** - `--margin <px>`: page margin in pixels at 300dpi (default: 80, ~7mm) - `--no-trim`: disable automatic whitespace border trimming - `--dpi <n>`: output DPI (default: 300) ### Step 4: Report result Tell the user: - How many images were processed - How many PDF pages were generated - The output file path ## What the Script Does 1. Loads images, auto-trims whitespace borders 2. Scales all images to the same width (A4 printable width) 3. Greedily packs images onto A4 pages (tight layout, no gaps) 4. When a page break is needed, uses pixel analysis to find whitespace rows so text is never cut 5. Outputs a 300 DPI PDF ready for printing


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: parker-xferops/xferops-sentry (version 0.1.0)

--- name: xferops-sentry description: Query Sentry issues, events, and projects via MCP for XferOps incident investigation and triage. Requires @sentry/mcp-server and a Sentry API token. --- # Sentry MCP Error tracking and triage via MCP (Model Context Protocol). ## Setup Install the MCP server: ```bash npx -y @sentry/mcp-server --skills=inspect,triage ``` Configure in your MCP client (e.g., `~/.mcporter/mcporter.json`): ```json { "mcpServers": { "sentry": { "command": "npx", "args": ["-y", "@sentry/mcp-server", "--skills=inspect,triage"], "env": { "SENTRY_ACCESS_TOKEN": "your-token-here" } } } } ``` Set `SENTRY_ACCESS_TOKEN` to a Sentry User Auth Token with API access. ## Tools (8 total) ### Organizations & Projects - `list_organizations` — List organizations available to the token - `list_projects` — List projects in an organization ### Issues - `list_issues` — List issues in an organization (requires `organizationSlug`) - `get_issue` — Get details for a specific issue (requires `organizationSlug`, `issueId`) ### Events - `list_events` — List events for an issue (requires `organizationSlug`, `issueId`) - `get_event` — Get details for an event (requires `organizationSlug`, `eventId`) ### AI Workflows - `inspect` — AI-powered issue inspection - `triage` — AI-powered triage assistance ## Common Patterns ### List organizations ```bash mcporter call sentry.list_organizations ``` ### List projects ```bash mcporter call sentry.list_projects organizationSlug=xferops ``` ### List issues ```bash mcporter call sentry.list_issues organizationSlug=xferops ``` ### Get issue details ```bash mcporter call sentry.get_issue organizationSlug=xferops issueId=XFEROPS-AUTH-1 ``` ### List events for an issue ```bash mcporter call sentry.list_events organizationSlug=xferops issueId=XFEROPS-AUTH-1 ``` ## XferOps Notes - Default organization: `xferops` - Token may be stored in Secrets Manager as `xferops/sentry/api-token` - This setup is already configured on Adam's machine


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ethan-china/miniest-validate (version 1.0.0)

--- name: validate-idea description: > 使用极简主义创业者框架验证商业创意。 当用户有一个商业创意，想在构建任何东西之前测试它是否值得追求时使用。 核心：验证通过销售而非构建来实现。 trigger_keywords: - 验证想法 - 测试创意 - 这个想法好吗 - validate idea - 值不值得做 - 有没有市场 read_when: - 用户有了一个商业创意想验证 - 用户在考虑要不要开始一个项目 - 用户想知道是否有人会为此付费 --- # 验证创意 — 极简主义创业者 你是一位极简主义创业顾问，遵循Sahil Lavingia《极简主义创业者》的理念。帮助用户在写一行代码或花一分钱之前验证他们的商业创意。 ## 核心原则 **验证通过销售，而非构建来实现。** 大多数创始人花几个月构建没人需要的产品。取而代之，先手动销售你解决方案的手工版本来验证。 ## 极简主义验证流程 ### 第1步：定义问题（而非解决方案） 询问用户： - 谁具体有这个问题？（要精确——不是"企业"，而是"在invoicing方面挣扎的自由职业平面设计师"） - 他们今天是如何解决的？（当前的变通方法才是你真正的竞争对手） - 这个问题有多痛苦？（轻微烦恼 vs. 火烧眉毛） - 他们会付费让这个问题消失吗？ ### 第2步：你能先手动解决它吗？ 在构建任何东西之前，你能手动为人们解决这个问题吗？ - Sahil称之为**"流程化"（processizing）**——创建一个手动的有价值流程 - 先自己动手做，雇用自己，把每个步骤写在纸上 - 如果你能手动为几个人解决，最终就可以自动化 - 例子：Gumroad最初是Sahil手动收集PayPal信息，一个个地向创作者付款 ### 第3步：人们会付费吗？ 最终的验证是一笔交易： - 你现在可以为这项手动服务收费吗？ - 你和至少10个潜在客户谈过吗？ - 至少3个人说他们会付款（或实际已付款）吗？ - 什么价位感觉自然？ ### 第4步：构建前的四个问题 来自书中的问题——问自己： 1. **我能在一个周末内发布吗？** 第一个迭代应该在2-3天内完成原型。 2. **它让客户的生活稍微好一点了吗？** 这就是最小可行产品。 3. **客户愿意为此付费吗？** 从第一天起就要盈利。 4. **我能快速获得反馈吗？** 反馈循环越快，你就能越快构建出值得付费的东西。 ## 🔴 红旗（如果符合则不要构建） - 目前没有人试图解决这个问题（没有现有的变通方法） - 你无法说出10个有这个问题的具体的人 - 唯一的验证是"我的朋友认为这是个酷创意" - 你需要教育人们让他们意识到他们有这个问题 - 你在为自己不属于的社群构建 ## 🟢 绿旗（如果符合则值得追求） - 人们已经在为劣质解决方案付费 - 你手动为几个人解决了这个问题，他们喜欢它 - 社群在积极抱怨这个问题 - 你能用一句话描述客户和他们的痛点 - 你在解决自己的痛点 ## 输出 给用户一个明确的判断： - ✅ **已验证**：强烈信号，继续进行MVP - ⚠️ **需要更多验证**：收集证据的具体后续步骤 - 🔄 **转型**：创意需要根本性改变——建议方向


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: minguncle/a-share-market-sweep (version 1.0.0)

--- name: a-share-market-sweep description: 从零抓取 A 股当日盘面，按指数、广度、成交、板块、情绪、领涨股顺序重建临时全市场工作包。适用于今天盘面、盘中复核、收盘总结、找主线和为后续选股做底稿。 --- # A Share Market Sweep ## Overview 这个 skill 负责把“今天市场到底在交易什么”重建出来，并形成当次会话专用的全市场临时库。它不依赖旧状态，不把上次扫描结果直接沿用到这次。 ## Hard Rules 1. 先看指数，再看广度，再看板块，最后看个股。 2. 用户说“今天”“现在”“刚刚”时，必须抓当日最新数据。 3. 任何主线判断都必须至少有三个证据支撑，例如指数结构、板块强度、成交或领涨股联动。 4. 允许建“临时全市场库”，但仅限当前响应内使用，不写成长期状态依赖。 5. 若指数与情绪不一致，必须单列说明“指数强弱”和“情绪强弱”是分裂还是共振。 ## When To Use - 用户问“今天 A 股怎么样” - 要做盘中复核、收盘复盘、主线扫描 - 要给选股或调仓建议，但还没完成市场底稿 - 要判断某个板块是不是主线或只是脉冲 - 要把宏观或事件观点落到今天盘面上 ## Workflow ### Step 1: 时间与市场状态 - 锚定日期、时间、是否午盘前、午盘后或收盘后 - 明确本次扫描是“盘中结构跟踪”还是“收盘定稿” ### Step 2: 指数层 至少覆盖： - 上证、深成指、创业板、科创或中证核心宽基 - 涨跌幅、成交额、分时强弱 - 指数之间的风格差异 ### Step 3: 广度与量能层 至少覆盖： - 上涨家数、下跌家数、涨停、跌停 - 成交额与相对前值变化 - 是放量普涨、缩量轮动、抱团防守还是退潮杀跌 ### Step 4: 板块层 输出强弱分层： - 第一梯队主线 - 第二梯队轮动方向 - 明显承压方向 对每个主线至少说明： - 领涨逻辑 - 资金承接是否稳定 - 是趋势主线、事件脉冲还是超跌反抽 ### Step 5: 个股层 从板块里抽取： - 龙头 - 跟风 - 分歧转强候选 - 高位风险样本 ### Step 6: 建立临时全市场库 把当前会话内的可交易信息归档成临时表： - 主线板块 - 领涨标的 - 次强板块 - 观察名单 - 明显回避名单 顺序模板见 [sweep-order.md](references/sweep-order.md)。 ### Step 7: 输出交易框架 给出而不是泛泛评论： - 今日最该做什么 - 今日最不该做什么 - 适合追涨、低吸、等分歧还是等确认 - 下一步应该衔接哪个 skill ## Tool Rules - 大盘页、板块资金、东财数据中心：优先网页抓取 - 个股与板块定向数据：优先 `cn_a_stock` 和 `stock_mcp` - 网页信息必须落到具体数字、日期和栏目，不要只复述标题 - 若工具之间数据冲突，优先标记时间差异，再说明采用哪一个口径 ## Output Contract 默认输出： - `今日结论` - `关键证据` - `临时全市场库` - `交易框架` - `风险清单` - `观察清单` ## Practical Notes - 若市场高度混沌，不强行制造单一主线，直接承认“多脉冲、低持续” - 若资源、科技、红利同时有表现，要拆成不同风格篮子，不要混写 - 扫描结束后，若用户要处理持仓，优先交给组合校准或动作路由


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nickchristensen/paperless (version 1.1.0)

--- name: paperless description: Interact with Paperless-NGX document management system via ppls CLI. Search, retrieve, upload, and organize documents. emoji: 📄 metadata: {"clawdbot":{"requires":{"bins":["ppls"],"env":["PPLS_HOSTNAME","PPLS_TOKEN"]},"install":[{"id":"node","kind":"node","package":"@nickchristensen/ppls","bins":["ppls"],"label":"Install ppls CLI (npm/bun)"}]}} --- # Paperless-NGX CLI Search and manage documents in Paperless-NGX using `ppls`. ## Setup ```bash npm install -g @nickchristensen/ppls ppls config set hostname http://your-paperless-host ppls config set token your-api-token ``` ## Searching Documents ```bash # By name ppls documents list --name-contains "invoice" --json # By date range ppls documents list --created-after 2024-01-01 --created-before 2024-12-31 --json # By tag (OR — any of these tags) ppls documents list --tag 5 --tag 12 --json # By tag (AND — must have all) ppls documents list --tag-all 5,12 --json # Exclude tags ppls documents list --tag-not 3 --json # By correspondent ppls documents list --correspondent 7 --json # By document type ppls documents list --document-type 2 --json # Documents missing metadata ppls documents list --no-correspondent --json ppls documents list --no-tag --json # Recently added/modified ppls documents list --added-after 2024-06-01 --json ppls documents list --modified-after 2024-06-01 --json # Combine filters ppls documents list --correspondent 7 --created-after 2024-01-01 --tag 5 --json ``` ## Viewing & Downloading ```bash # Get full document details (includes OCR content) ppls documents show 1234 --json # Download single document ppls documents download 1234 --output ~/Downloads/doc.pdf # Download multiple documents ppls documents download 1234 5678 --output-dir ~/Downloads # Download original (pre-processed) version ppls documents download 1234 --original ``` ## Uploading Documents ```bash # Simple upload (Paperless auto-processes) ppls documents add scan.pdf # With metadata ppls documents add receipt.pdf \ --title "Store Receipt" \ --correspondent 5 \ --document-type 2 \ --tag 10 ``` ## Managing Metadata ```bash # List tags/correspondents/document-types ppls tags list --json ppls correspondents list --json ppls document-types list --json # Create new ppls tags add "Tax 2024" --color "#ff0000" ppls correspondents add "New Vendor" ppls document-types add "Contract" # Update document metadata ppls documents update 1234 --title "New Title" --correspondent 5 --tag 10 ``` ## Tips - **Always use `--json`** for AI/automation — it's the most parseable format - **Date formats:** `YYYY-MM-DD` or full ISO 8601 - **IDs are numeric** — use `list --json` commands to find them - **Filters are repeatable:** `--tag 1 --tag 2` or `--tag 1,2` both work - **Pagination:** Use `--page` and `--page-size` for large result sets ## Links - [ppls on GitHub](https://github.com/NickChristensen/ppls) - [Paperless-NGX Docs](https://docs.paperless-ngx.com/)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/mood (version 1.0.0)

--- name: "mood" version: "1.0.0" description: "Mood and emotional wellness reference — mood science, regulation strategies, journaling techniques, emotional literacy. Use when understanding emotional patterns, designing mood tracking systems, or improving emotional regulation." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [mood, emotions, wellness, mental-health, emotional-regulation, journaling, psychology] category: "general" --- # Mood — Mood & Emotional Wellness Reference Quick-reference skill for understanding mood science, emotional regulation, and wellness practices. ## When to Use - Understanding the science of moods and emotions - Learning emotional regulation techniques - Designing mood tracking or journaling systems - Identifying mood patterns and triggers - Building emotional literacy and resilience ## Commands ### `intro` ```bash scripts/script.sh intro ``` Mood science fundamentals — emotions vs moods, affect theory, neuroscience basics. ### `wheel` ```bash scripts/script.sh wheel ``` Emotion taxonomy — Plutchik's wheel, granular emotion vocabulary, intensity levels. ### `regulation` ```bash scripts/script.sh regulation ``` Emotional regulation strategies — CBT techniques, reappraisal, grounding, distress tolerance. ### `triggers` ```bash scripts/script.sh triggers ``` Mood triggers — HALT framework, environmental factors, cognitive distortions. ### `journaling` ```bash scripts/script.sh journaling ``` Mood journaling techniques — structured prompts, tracking patterns, gratitude practice. ### `lifestyle` ```bash scripts/script.sh lifestyle ``` Lifestyle factors — sleep, exercise, nutrition, social connection, nature exposure. ### `tracking` ```bash scripts/script.sh tracking ``` Mood tracking systems — scales, frequency, pattern recognition, data visualization. ### `resilience` ```bash scripts/script.sh resilience ``` Building emotional resilience — stress inoculation, growth mindset, social support. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: instinct323/latex-scaffold (version 1.0.0)

--- name: latex-scaffold description: Scaffold LaTeX papers into modular structure. Use for organizing .tex files, generating figure code, and table highlighting. Trigger on LaTeX paper organization, figure management, or academic paper formatting. --- # LaTeX Scaffold Standardize LaTeX academic paper projects with modular structure and automated asset management. ## Project Structure ``` project-root/ ├── assets/ # Images and auto-generated .tex files │ ├── figure1.png │ ├── figure1.tex # Auto-generated │ └── table1.tex ├── src/ # Modular sections │ ├── introduction.tex │ ├── methods.tex │ └── references.bib └── main.tex # Entry point with \input{src/...} ``` ## Workflow ### Step 0: Get Template (if needed) If user has no LaTeX project, download the default IEEE conference template: `https://ras.papercept.net/conferences/support/files/ieeeconf.zip` ### Step 1: Structure the Project Split large `.tex` files. In `main.tex`, use: ```latex \input{src/introduction} \input{src/methods} \input{src/results} ``` ### Step 2: Generate LaTeX for Assets ```bash python <skill-dir>/scripts/generate_tex_for_assets.py <project-root> ``` Requires in preamble: ```latex \usepackage{graphicx} \usepackage{caption} ``` ### Step 3: Add Table Highlighting ```latex \usepackage[table]{xcolor} % \cellcolor{top1}3.14 \definecolor{top1}{rgb}{0.996, 0.851, 0.380} % Gold \definecolor{top2}{rgb}{0.675, 0.843, 0.557} % Light green \definecolor{top3}{rgb}{0.663, 0.914, 0.894} % Cyan ``` ## Trigger Guidelines **USE this skill when:** - User has a `.tex` file to organize - "Organize my LaTeX paper" - "Split this tex file into sections" - "Generate LaTeX for my figures" - "Set up table colors in LaTeX" - "Make my LaTeX project modular" **DO NOT use when:** - "How do I install LaTeX?" (installation question) - "What does \\textbf do?" (syntax question) - "Create a LaTeX presentation" (wrong document type) - "Convert PDF to LaTeX" (unrelated task) - User just mentions "LaTeX" without context of organizing/managing papers ## Notes - Main filename is flexible - Generated `.tex` files in `assets/` can be customized after generation - Color definitions are optional but recommended


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