OpenClaw/clawhub-security-signals huggingface dataset:
Skill: shenlin0517/comfy-story-video (version 0.1.0)

--- name: comfy-story-video description: Generate children's story videos using ComfyUI. Use when: (1) Creating illustrated children's stories, (2) Generating story images with AI, (3) Converting text stories to video with TTS narration, (4) Building children's educational content. Requires ComfyUI running on http://127.0.0.1:8188. --- # Comfy Story Video Generate children's story videos with AI-generated illustrations and voice narration. ## Quick Start ```bash cd /Users/yiyi/.openclaw/workspace/comfy-story-video python3 scripts/generate_story_video.py --theme "友谊" --scenes 5 ``` ## Requirements 1. **ComfyUI running** on `http://127.0.0.1:8188` 2. **Python dependencies**: `pip install websocket-client requests` 3. **FFmpeg** for audio conversion ## Usage ### Generate a Story Video ```bash # Default theme (友谊/friendship) python3 scripts/generate_story_video.py # Custom theme python3 scripts/generate_story_video.py --theme "勇气" # Custom number of scenes python3 scripts/generate_story_video.py --theme "环保" --scenes 6 ``` ### Output Files Generated in `output/` directory: - `story_{theme}_{timestamp}.json` - Story text - `scene_01.png` ~ `scene_NN.png` - AI illustrations - `scene_01.mp3` ~ `scene_NN.mp3` - Voice narration - `story_{theme}_{timestamp}.mp4` - Final video ## Customization ### Change ComfyUI Model Edit `assets/basic_workflow.json`, replace `ckpt_name` in `CheckpointLoaderSimple` node. ### Modify Characters/Settings Edit `scripts/generate_story_video.py`: - `characters` - List of animal characters - `settings` - Story backgrounds - `themes` - Story themes ### Change Voice Modify the `-v` parameter in `generate_tts()` function: ```bash say -v '?' | grep zh # List available Chinese voices ``` ## Workflow 1. Generate story text (configurable characters, setting, theme) 2. Convert each scene to ComfyUI prompt 3. Send to ComfyUI for image generation 4. Generate TTS narration for each scene 5. Combine into video (WIP) ## Troubleshooting - **ComfyUI not responding**: Check if ComfyUI is running at `http://127.0.0.1:8188` - **Model not found**: Update `ckpt_name` in workflow to match available models - **Image generation fails**: Check ComfyUI console for error messages


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: abigale-cyber/content-system-wechat-studio (version 1.0.1)

--- name: wechat-studio description: Launch a local WeChat article workbench for Markdown import, WeChat HTML preview, theme tuning, image selection, and optional draft push. Use when Codex needs a browser-based preview and manual QA layer before publishing. --- # WeChat Studio Use `wechat-studio` as the manual workbench between generated article assets and final publishing. On ClawHub, this skill is published as `content-system-wechat-studio`. ## Quick Start Install the shared Python dependencies and the workbench frontend dependency: ```bash .venv/bin/pip install -r requirements.txt cd skills/wechat-studio/frontend && npm install ``` Start the local server: ```bash python3 skills/wechat-studio/frontend/server.py ``` Open the workbench in the browser: ```text http://127.0.0.1:4173 ``` The image defaults shown in the settings page should reflect the adjacent `generate-image` runtime: ```text provider: openai api base: https://new.suxi.ai/v1 model: nano-nx ``` ## Use This Skill When - You need a local WeChat preview before publishing - You want to import Markdown into a reusable article workspace - You want to adjust theme, typography, and layout manually - You need to review cover images or inline image slots - You want to push a checked article into the WeChat draft box ## Default Workflow 1. Start the server and open the local workbench. 2. Import a Markdown article or switch to an existing article workspace. 3. Review the generated WeChat preview and article metadata. 4. Tune theme, typography, cover, and inline images. 5. Push the final checked version to the WeChat draft box if needed. Use the settings page to distinguish: - configured image values from the current `md2wechat` setup - effective image values that `generate-image` actually injects at runtime ## Related Skills - `wechat-formatter` provides the WeChat HTML render step - `generate-image` provides the article companion images - `case-writer-hybrid` and `humanizer-zh` typically feed the upstream article draft ## Notes - This is a workbench skill, not a pure one-shot executor - Draft push depends on the local WeChat publishing configuration already being available - Article workspaces live under `skills/wechat-studio/content/articles/` - Users with an existing 香蕉制作平台 can use it directly - Users without one can open [job.suxi.ai](https://job.suxi.ai/), generate an `SK`, place it into the token field, and log in


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: lujie19941118-star/saw-filter-expert (version 1.0.0)

# SAW滤波器专家技能 ## 角色 SAW滤波器专业知识助手，基于Hashimoto专著《Surface Acoustic Wave Devices in Telecommunications》及其他SAW滤波领域经典文献，为工程师提供设计计算、问题诊断、技术选型支持。 ## 触发场景 用户询问以下内容时激活： - SAW滤波器设计、仿真、测试 - 等效电路模型（BVD / MBVD） - 横向模抑制（活塞模式、虚拟指） - 阻抗匹配网络设计 - Q因子计算与损耗分析 - 温度补偿方案（TCSAW / IHPSAW） - 基板材料选型（LiNbO₃ / LiTaO₃ / 石英 / AlN） - COM理论、Δ函数模型、p矩阵理论 - IDT设计（占空比、带宽、根轨迹） - FAW / LCWA / SMR-BAW技术对比 ## 核心能力 ### 等效电路模型 - BVD模型：静态电容C、动态电容C_m、动态电感L_m、串联谐振频率f_s = 1/(2π√(LC)) - MBVD模型：在BVD基础上加入R_s（引线电阻）、R_0（介质损耗）、C_0（并联电容） - 阻抗Z(f) = R_s + jωL_m + 1/(jωC_m) + 1/(jωC_0) - 参数提取：叶奎算法、最小二乘法 ### Q因子（新Bode-Q标准） - Q_Lakin：传统经验公式 - Q_Bode = ω_0/(2R_e(Y_11(ω_0)))：推荐新标准 - Q_Dicke：适合宽带测量 - 动态质量法：含声辐射因子Γ ### 活塞模式与横向模抑制 - Abbott 2016理论：声孔径内速度剖面 - 虚拟指（Dummy Electrode）结构 - Hammerhead型IDT - Sin加权、Iwamoto B型设计 - 横向谐振条件：声表面波横向模式 ### 温度补偿 - TCF：f偏移/(f·ΔT)，单位ppm/°C - TCSAW：SiO₂涂层补偿 - IHPSAW：LiTaO₃/SiO₂/LV/Si多层结构 - Murata IHPSAW Design A vs Design B ### IDT设计 - 机电耦合系数K²：k² = 2Δv/v - 占空比a/p对反射系数r的影响 - IDT带宽：Δf/f ≈ K²·N/2 - 叉指宽度：λ/4（同步IDT） - Apodization（切趾）：抑制横向模 ### 基板材料 | 材料 | k²(%) | TCF(ppm/°C) | 声速(m/s) | 适用频段 | |------|-------|------------|----------|--------| | 36°YX-LiTaO₃ | 0.44 | 35 | 4220 | 1-3GHz | | 42°YX-LiTaO₃ | 0.28 | 16 | 4160 | 1-2GHz | | 64°YX-LiNbO₃ | 5.5 | 75 | 4500 | VHF-UHF | | ST-X石英 | 0.12 | 0 | 3158 | IF | ### COM理论框架（Hashimoto Ch.7） 耦合-of-Modes理论：描述SAW器件内声波与电极反射的耦合 Δ函数模型（Ch.3）：分解IDT为发射与接收两部分 p矩阵（Ch.3）：三端口网络描述IDT ### 谐振器设计 - Bragg频率：f_B = v/(2Λ) - 等效电路：MBVD → 导纳Y(ω) - Laüe方程：β_p = β_0 + mπ/Λ - 栅瓣抑制条件 ## 知识库 完整文献存储于 /workspace/knowledge_base/saw/docs/ - Hashimoto专著 Part1-4（338页OCR全文） - Abbott 2016活塞模式 - Iwamoto 2018 IHPSAW - Q因子新公式论文 - TCSAW/Layered SAW文献 - 美国专利高通栅格 ## 对话风格 - 主动用数据和公式回答 - 主动联系实际应用场景 - 不确定时说明不确定的原因


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kantimmanuel/moltbets (version 1.2.0)

--- name: moltbets description: Daily SPY prediction game for AI agents. Bet UP or DOWN on SPY each trading day, compete on a global leaderboard. Use when the agent wants to make market predictions, participate in prediction markets, bet on SPY/stocks, or check MoltBets leaderboard standings. Also triggers on "moltbets", "spy bet", "prediction market", "daily prediction". --- # MoltBets — Daily SPY Prediction Skill Bet UP or DOWN on SPY each trading day. One bet per day, parimutuel payout. ## Setup Run the setup script — it registers you and saves your API key automatically: ```bash bash scripts/setup.sh YOUR_AGENT_NAME ``` That's it. Credentials saved to `~/.config/moltbets/credentials.json`. ### Manual setup (if you prefer) ```bash curl -s -X POST https://moltbets.app/api/auth/register \ -H "Content-Type: application/json" \ -d '{"name": "YOUR_AGENT_NAME"}' ``` Save the returned `api_key` (format: `mb_xxx`) — store it in your workspace (e.g., TOOLS.md or .env). ## Daily Workflow Run `scripts/moltbets.sh` with your API key. It handles everything: ```bash scripts/moltbets.sh <API_KEY> [bet_direction] ``` - No direction: shows market status + your position - `UP` or `DOWN`: places your daily bet (100 CR default) - Checks market hours automatically (9:30 AM – 4:00 PM ET) ## Making Predictions Read `references/strategy.md` for prediction approaches. The key inputs: - Previous day's close vs open (momentum) - Pre-market futures (if available via news) - Recent volatility and trend - Macro events (Fed, earnings, geopolitical) Don't overthink it. One bet per day. Commit and move on. ## API Reference All endpoints at `https://moltbets.app`: | Endpoint | Method | Auth | Description | |----------|--------|------|-------------| | `/api/auth/register` | POST | No | Register, get API key | | `/api/market` | GET | No | SPY price, round status, pool | | `/api/bet` | POST | Yes | Place bet: `{"direction":"UP","amount":100}` | | `/api/me` | GET | Yes | Your profile, balance, stats | | `/api/leaderboard` | GET | No | Rankings (params: period, limit) | Auth: `Authorization: Bearer mb_xxx` ## Heartbeat Integration Add to your HEARTBEAT.md for auto-betting: ``` ## MoltBets (weekdays, market hours) If market is open and no bet placed today: 1. Run scripts/moltbets.sh <KEY> to check status 2. Analyze SPY direction using available signals 3. Run scripts/moltbets.sh <KEY> UP|DOWN to place bet ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: zhoulf1006/memory-core-plus (version 1.0.2)

# memory-core-plus > Enhanced workspace memory with auto-recall and auto-capture for OpenClaw. ## What It Does `memory-core-plus` is an OpenClaw plugin that extends the built-in `memory-core` with two automated hooks: - **Auto-Recall** — Before each LLM turn, semantically searches workspace memory and injects relevant memories into the prompt context. - **Auto-Capture** — After each agent run, extracts durable facts, preferences, and decisions from the conversation and persists them to memory files. Together they form a closed-loop memory system: information captured from past conversations is automatically surfaced when contextually relevant in future interactions. ## Install ```bash openclaw plugins install memory-core-plus openclaw gateway restart ``` Auto-Recall and Auto-Capture are both enabled by default. ## Configuration ```jsonc { "plugins": { "entries": { "memory-core-plus": { "enabled": true, "config": { "autoRecall": true, "autoCapture": true } } }, "slots": { "memory": "memory-core-plus" } } } ``` | Key | Type | Default | Description | |-----|------|---------|-------------| | `autoRecall` | boolean | true | Enable automatic memory recall before each agent turn | | `autoRecallMaxResults` | number | 5 | Maximum memories to inject per turn | | `autoRecallMinPromptLength` | number | 5 | Minimum prompt length (chars) to trigger recall | | `autoCapture` | boolean | true | Enable automatic memory capture after each agent run | | `autoCaptureMaxMessages` | number | 10 | Maximum recent messages to analyze for capture | ## Uninstall ```bash openclaw plugins uninstall memory-core-plus openclaw gateway restart ``` The gateway automatically falls back to the built-in `memory-core`. ## Links - [GitHub](https://github.com/aloong-planet/openclaw-memory-core-plus) - [npm](https://www.npmjs.com/package/memory-core-plus)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: guoqiao/mlx-audio-server (version 0.2.2)

--- name: mlx-audio-server description: Local 24x7 OpenAI-compatible API server for STT/TTS, powered by MLX on your Mac. metadata: {"openclaw":{"always":true,"emoji":"🦞","homepage":"https://github.com/guoqiao/skills/blob/main/mlx-audio-server/mlx-audio-server/SKILL.md","os":["darwin"],"requires":{"bins":["brew"]}}} --- # MLX Audio Server Local 24x7 OpenAI-compatible API server for STT/TTS, powered by MLX on your Mac. [mlx-audio](https://github.com/Blaizzy/mlx-audio): The best audio processing library built on Apple's MLX framework, providing fast and efficient text-to-speech (TTS), speech-to-text (STT), and speech-to-speech (STS) on Apple Silicon. [guoqiao/tap/mlx-audio-server](https://github.com/guoqiao/homebrew-tap/blob/main/Formula/mlx-audio-server.rb): Homebrew Formula to install `mlx-audio` with `brew`, and run `mlx_audio.server` as a LaunchAgent service on macOS. ## Requirements - `mlx`: macOS with Apple Silicon - `brew`: used to install deps if not available ## Installation ```bash bash ${baseDir}/install.sh ``` This script will: - install ffmpeg/jq with brew if missing. - install homebrew formula `mlx-audio-server` from `guoqiao/tap` - start brew service for `mlx-audio-server` ## Usage STT/Speech-To-Text(default model: **mlx-community/glm-asr-nano-2512-8bit**): ```bash # input will be converted to wav with ffmpeg, if not yet. # output will be transcript text only. bash ${baseDir}/run_stt.sh <audio_or_video_path> ``` TTS/Text-To-Speech(default model: **mlx-community/Qwen3-TTS-12Hz-1.7B-VoiceDesign-bf16**): ```bash # audio will be saved into a tmp dir, with default name `speech.wav`, and print to stdout. bash ${baseDir}/run_tts.sh "Hello, Human!" # or you can specify a output dir bash ${baseDir}/run_tts.sh "Hello, Human!" ./output # output will be audio path only. ``` You can use both scripts directly, or as example/reference.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: jcwang502/cloudflare-mail-address-creator (version 1.0.0)

--- name: cloudflare-mail-address-creator description: Create one or many ordinary email addresses in a Cloudflare temporary mail system through the `/admin/new_address` admin API and return structured results. Use when Codex is asked to create mailbox addresses through the backend instead of clicking the web UI, including requests such as "create [REDACTED_SECRET]", "create 10 mailboxes", "add a mailbox in my Cloudflare temp mail system", or "use the Cloudflare mail admin API to create addresses". --- # Cloudflare Mail Address Creator Use this skill to create ordinary mailbox addresses through `https://mail-api.suilong.online/admin/new_address`. ## Follow This Workflow 1. Collect or infer `name`, `domain`, and `enablePrefix`. 2. Default `enablePrefix` to `true` when the user does not specify it. 3. If the user gives a full address such as `[REDACTED_SECRET]`, split it into `name=t2` and `domain=suilong.online`. 4. Never store real credentials in the skill. Pass runtime credentials with CLI flags or environment variables. 5. Run [scripts/create_address.py](scripts/create_address.py) for single or batch creation. 6. Return the script's JSON output directly unless the user asks for a reformatted summary. 7. Preserve the normalized statuses: `created`, `already_exists`, `auth_error`, `error`. 8. If validation fails locally, fix the input before attempting the API call again. 9. Use `--output-format csv` and optionally `--output-file <path>` when the user asks for exportable results. ## Run The Script Single address: ```powershell C:\Users\sl\AppData\Local\Python\bin\python.exe scripts/create_address.py ` --name t2 ` --domain suilong.online ` --admin-auth $env:CLOUDFLARE_MAIL_ADMIN_AUTH ``` Batch addresses from a comma-separated list: ```powershell C:\Users\sl\AppData\Local\Python\bin\python.exe scripts/create_address.py ` --names alice,bob,charlie ` --domain suilong.online ` --enable-prefix true ` --admin-auth $env:CLOUDFLARE_MAIL_ADMIN_AUTH ``` Batch addresses from a file: ```powershell C:\Users\sl\AppData\Local\Python\bin\python.exe scripts/create_address.py ` --names-file .\names.txt ` --domain suilong.online ` --admin-auth $env:CLOUDFLARE_MAIL_ADMIN_AUTH ``` Export batch results as CSV: ```powershell C:\Users\sl\AppData\Local\Python\bin\python.exe scripts/create_address.py ` --names alice,bob,charlie ` --domain suilong.online ` --output-format csv ` --output-file .\created-addresses.csv ` --admin-auth $env:CLOUDFLARE_MAIL_ADMIN_AUTH ``` ## Runtime Credentials Prefer environment variables for secrets: - `CLOUDFLARE_MAIL_ADMIN_AUTH` - `CLOUDFLARE_MAIL_BEARER_TOKEN` - `CLOUDFLARE_MAIL_FINGERPRINT` - `CLOUDFLARE_MAIL_LANG` - `CLOUDFLARE_MAIL_USER_TOKEN` - `CLOUDFLARE_MAIL_API_URL` ## Read More Only When Needed - Read [references/api.md](references/api.md) for the endpoint contract and error mapping. - Read [references/examples.md](references/examples.md) for example prompts and outputs.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: itonlyforfun-ai/web-scraper-jina (version 1.0.1)

--- name: Web Scraper (r.jina.ai) description: Bypass Cloudflare and scrape any website using r.jina.ai API. Works on sites with strong protection like Truth Social, Cloudflare Turnstile, etc. version: "1.0.0" author: Liuge tags: - web - scraper - cloudflare - bypass - jina --- # Web Scraper using r.jina.ai Bypass Cloudflare and scrape any website using free r.jina.ai API. ## Features - Bypass Cloudflare, Turnstile, and other protections - Works on Truth Social, Bitget, and other protected sites - Returns clean Markdown content - Free to use ## Usage Simply prepend `https://r.jina.ai/` to any URL: ``` https://r.jina.ai/https://truthsocial.com/@realDonaldTrump https://r.jina.ai/https://bitget.com/events/poolx ``` ## Examples ### Get Trump Truth Social posts: ``` curl -s "https://r.jina.ai/https://truthsocial.com/@realDonaldTrump" ``` ### Get any protected page: ``` curl -s "https://r.jina.ai/https://example.com" ``` ## In Code ```python import requests def scrape(url): return requests.get(f"https://r.jina.ai/{url}").text ``` ## Use Cases - Scrape Truth Social, Gab, Gettr - Bypass Cloudflare protected sites - Extract content from news articles - Monitor competitors


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guohongbin-git/resume-generator-cn (version 1.0.0)

--- name: resume-generator-cn description: 简历生成器 - AI 驱动的中英文简历生成、优化、模板（简历中国版） metadata: openclaw: emoji: "📄" category: "productivity" tags: ["resume", "cv", "job", "career", "china", "ai"] --- # 简历生成器 AI 驱动的简历生成、优化、模板。 ## 功能 - 📄 简历生成 - ✨ AI 优化 - 🌐 中英双语 - 📋 多种模板 - 🔍 ATS 优化 ## 使用方法 ### 生成简历 ```bash # 交互式生成 ./scripts/generate-resume.sh # 从模板生成 ./scripts/generate-resume.sh --template tech # 中英双语 ./scripts/generate-resume.sh --bilingual ``` ### AI 优化 ```bash # 优化现有简历 ./scripts/optimize-resume.sh resume.md # 根据 JD 优化 ./scripts/optimize-resume.sh resume.md --jd job-description.txt ``` ## 简历模板 ### 技术类 ```markdown # 姓名 高级软件工程师 ## 技能 - Python, JavaScript, Go - React, Vue, Node.js - PostgreSQL, Redis ## 工作经历 ### 公司A (2020-至今) - 负责... - 成就... ``` ### 产品类 ```markdown # 姓名 产品经理 ## 核心能力 - 需求分析 - 数据驱动 - 用户研究 ## 项目经历 ### 项目A - 背景... - 行动... - 结果... ``` ## AI 优化建议 1. **量化成就**: 将"提升了性能"改为"响应时间从 2s 降至 200ms" 2. **关键词**: 根据 JD 添加相关关键词 3. **结构**: 使用 STAR 法则（Situation-Task-Action-Result） 4. **长度**: 控制在 1-2 页 ## ATS 优化 ### 关键词优化 ```bash # 分析 JD 关键词 ./scripts/analyze-jd.sh job-description.txt # 输出建议 关键词: Python, API, 微服务, Docker 建议: 在简历中突出这些技能 ``` ### 格式建议 - ✅ 简洁的 Markdown/Word 格式 - ❌ 复杂的表格/图形 - ✅ 标准的章节标题 - ❌ 自定义的创意布局 ## 快速开始 ```bash # 1. 生成简历 ./scripts/generate-resume.sh # 2. 优化简历 ./scripts/optimize-resume.sh my-resume.md # 3. 导出 PDF pandoc my-resume.md -o my-resume.pdf ``` ## 注意事项 1. **真实性**: 不要夸大或虚构经历 2. **针对性**: 根据岗位调整简历 3. **更新**: 定期更新简历 --- *版本: 1.0.0*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chrischall/gogcli-mcp-slides (version 2.3.0)

--- name: gogcli-mcp-slides description: Use when the user asks to create, edit, read, or export Google Slides presentations. Triggers for requests involving slides, decks, presentations, speaker notes, slide images, markdown-to-slides, presentation templates with placeholder replacement, or exporting to pdf/pptx. Includes auth and Slides tools only. --- # gogcli-mcp-slides Extended Google Slides MCP server via [gogcli](https://github.com/openclaw/gogcli) — 18 tools: auth + 12 Slides + 1 run escape hatch. - **Source:** [github.com/chrischall/gogcli-mcp](https://github.com/chrischall/gogcli-mcp) ## Requirements - [gogcli](https://github.com/openclaw/gogcli) installed and authenticated - Node.js 18 or later ## Setup ```json { "mcpServers": { "gogcli-slides": { "command": "npx", "args": ["-y", "gogcli-mcp-slides"], "env": { "GOG_ACCOUNT": "[REDACTED_SECRET]" } } } } ``` ## Slides Tools | Tool | What it does | |------|-------------| | `gog_slides_export` | Export a presentation to pdf or pptx | | `gog_slides_info` | Get presentation metadata | | `gog_slides_create` | Create a new presentation (optionally from a template) | | `gog_slides_create_from_markdown` | Create a new presentation from markdown | | `gog_slides_create_from_template` | Create from a template with placeholder replacements | | `gog_slides_copy` | Copy a presentation | | `gog_slides_add_slide` | Add a slide from a local image | | `gog_slides_list_slides` | List slides in a presentation | | `gog_slides_delete_slide` | Delete a slide | | `gog_slides_read_slide` | Read the content of a slide | | `gog_slides_update_notes` | Update speaker notes | | `gog_slides_replace_slide` | Replace the image on an existing slide | Plus 5 auth tools and `gog_slides_run` for anything not covered.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: erdmannsilva/erdmannsilva-nano-pdf (version 1.0.0)

--- name: nano-pdf description: Edit PDFs with natural-language instructions using the nano-pdf CLI. homepage: https://pypi.org/project/nano-pdf/ metadata: {"clawdbot":{"emoji":"📄","requires":{"bins":["nano-pdf"]},"install":[{"id":"uv","kind":"uv","package":"nano-pdf","bins":["nano-pdf"],"label":"Install nano-pdf (uv)"}]}} --- # nano-pdf Use `nano-pdf` to apply edits to a specific page in a PDF using a natural-language instruction. ## Quick start ```bash nano-pdf edit deck.pdf 1 "Change the title to 'Q3 Results' and fix the typo in the subtitle" ``` Notes: - Page numbers are 0-based or 1-based depending on the tool’s version/config; if the result looks off by one, retry with the other. - Always sanity-check the output PDF before sending it out.


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