zc277584121/mermaid-to-image
Convert Mermaid code blocks in Markdown files to PNG images using the mermaid.ink API.
Convert Mermaid code blocks in Markdown files to PNG images using the mermaid.ink API.
npx skills add https://github.com/zc277584121/marketing-skills/tree/main/skills/mermaid-to-imageConvert Mermaid code blocks in Markdown files to PNG images using the mermaid.ink API.
This repo contains 12 individual skills — each has its own dedicated page.
Take focused, region-specific screenshots from web pages. Navigates to the right page based on user context (URL, search query, social media post), locates the target region via DOM selectors, and crops to a clean, focused screenshot.
Automate Chrome browser tasks using agent-browser CLI. Navigate pages, fill forms, click buttons, take screenshots, extract data, and replay recorded workflows — all inside the user's real Chrome session.
Adapt and rewrite content for different platforms (LinkedIn, X, Reddit, English blog, WeChat). Each platform has its own tone, format, and length requirements.
Fetch, store, and visualize GitHub repository traffic data (views, clones, referrers, stars) with trend charts. Requires repo push access.
Generate illustration images for articles and documentation with a Codex-first workflow, OpenAI API fallback, and Gemini fallback.
Write Milvus application-level Jupyter notebook examples using a Markdown-first workflow with jupyter-switch for format conversion.
Convert local Markdown files to Feishu (Lark) documents with automatic image uploading. Uses the feishu-docx CLI tool.
Convert Mermaid code blocks in .mmd or .md files to animated GIFs with customizable animation styles (progressive, highlight walk, pulse flow, wave).
Post-process raw screen recordings by removing silent segments and applying speed adjustments. Uses FFmpeg-based Python scripts to optimize video pacing automatically.
Review and adjust writing style to reduce AI-generated patterns, making text read more naturally and human-like. Supports Chinese and English.
Compress PNG and JPEG screenshots in place using pngquant and jpegoptim, keeping the original format for maximum compatibility.
Convert a video to multiple GIF variants with different quality/size tradeoffs. Generates a comparison set so the user can visually pick the best result.
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
Generates article cover images with 5 dimensions (type, palette, rendering, text, mood) combining 11 color palettes and 7 rendering styles. Supports cinematic (2.35:1), widescreen (16:9), and square (1:1) aspects. Use when user asks to "generate cover image", "create article cover", or "make cover".
Create Vega and Vega-Lite visualizations with ES|QL data sources in Kibana. Use when building custom charts, dashboards, or programmatic panel layouts beyond standard Lens charts.
Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.
Steganography detection and extraction playbook. Use when analyzing images (LSB, PNG chunks, JPEG DCT, EXIF), audio (spectrogram, DTMF), files (polyglots, appended data, ADS), and text (whitespace, zero-width, homoglyphs) for hidden data.
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.