47 boosters for "bun" — open source, verified from GitHub, ready to install
Create a Codex-compatible animated pet from a concept, brand cue, company/prospect name, one or more reference images, or any combination of those inputs. This workflow keeps the deterministic hatch-pet pipeline for atlas geometry, validation, visual QA, and packaging, while using concise state-spec
Generates spoken audio from text using OpenAI's TTS API, supporting single clips, batch operations, and accessibility reads. Developers building voiceovers, IVR systems, or accessible content will find this directly useful.
Automates real browser interactions from the terminal using Playwright CLI for tasks like navigation, form filling, and data extraction. Useful for developers and AI assistants building UI automation workflows without writing test frameworks.
Helps developers quickly create structured, reproducible Jupyter notebooks for experiments and tutorials using built-in templates and helper scripts. Ideal for data scientists, researchers, and educators who need consistent notebook scaffolding.
This booster equips AI coding assistants with specialized guidance for reading, creating, and editing Word documents programmatically while maintaining formatting and layout fidelity. Developers working with `.docx` files—especially those requiring professional formatting, tables, or visual validation—will find this booster invaluable.
Sora enables Claude Code users to generate, remix, and manage AI videos directly through OpenAI's video API. Developers building products with video content (demos, marketing, UI mocks) benefit from integrated video generation workflows.
Enables AI assistants to programmatically create and edit PowerPoint presentations using PptxGenJS with layout helpers and validation utilities. Developers and content creators benefit from automating slide deck generation, modification, and troubleshooting.
A performance optimization skill that identifies and fixes loading speed, rendering, animations, images, and bundle size issues to create faster, smoother user experiences. Developers building web applications benefit from automated performance diagnostics and improvements.
Transformers.js enables running state-of-the-art machine learning models directly in JavaScript, both in browsers and Node.js environments, with no server required. Use this skill when you need to: The pipeline API is the easiest way to use models. It groups together preprocessing, model inference,
"name": "designlang", "description": "Extract any website's design language and ship it. Eleven slash commands \u2014 /extract, /site, /grade, /battle, /remix, /pack, /theme-swap, /brand, /pair, /studio, /verify \u2014 wrap the designlang CLI to pull DTCG tokens, Tailwind/shadcn/Figma vars, motion +
  
Arbeitsfokus: Eu Mittel Deckung Finden. Prüfe diese Anker am Sachverhalt; ergänze nur Normen, die denselben Output, dieselbe Frist oder dieselbe Beweisfrage tragen: Rechtsprechung nur ergänzen, wenn Gericht, Datum, Aktenzeichen und eine frei prüfbare Quelle vorliegen; keine BeckRS-/juris-Blindzitate
LLM-first SEO analysis skill with 16 specialized sub-skills, 10 specialist agents, and 33 scripts for website, blog, and GitHub repository optimization. For prompt reliability in Codex/agent IDEs, map common user wording to a fixed workflow: When the user requests SEO analysis, follow this routing:
"description": "AI-native job queue for Bun — skills, MCP server (73 tools), custom agent, SQLite persistence, cron, priorities, retries, DLQ, webhooks", "name": "egeominotti" "homepage": "https://github.com/egeominotti/bunqueue",
Custom codegen'd CDP SDK (every method from browserprotocol.json + jsprotocol.json gets a typed wrapper) plus a tiny HTTP server that holds one persistent CDP . The CLI auto-starts the server on first use and forwards JS snippets to it. The SDK lives in the skill's directory. In the rest of this d
Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.
Procedural memory for AI coding agents. Transforms scattered sessions into persistent, cross-agent memory. Uses a three-layer cognitive architecture that mirrors human expertise development. AI coding agents accumulate valuable knowledge but it's: You've solved auth bugs three times this month acros
"name": "orchestrator-supaconductor", "description": "Conductor v3 — Multi-agent orchestration with Evaluate-Loop, parallel execution, Board of Directors, and bundled SupaConductor skills for Claude Code", "orchestrator-supaconductor",
Use this skill whenever the user wants to: This skill is organized to match the Rspack official documentation structure (https://rspack.rs/zh/guide/start/introduction, https://rspack.rs/zh/config/, https://rspack.rs/zh/plugins/, https://rspack.rs/zh/api/). When working with Rspack: 1. Identify the t
"name": "bundles-forge", "description": "Bundle-plugin engineering toolkit: scaffolding, platform adaptation, version management, and skill lifecycle", "email": "odradekai@outlook.com"
"name": "qt-development-skills", "description": "Agentic engineering skills for Qt software development, including Qt C++/QML code review, QML coding, and Qt C++/QML code documentation. Bundles the hosted Qt Documentation MCP server for live Qt API lookups. These skills use AI and can make mistakes.
A Cursor rules booster that guides AI to prefer Bun as the default runtime and build tool over Node.js, npm, and other alternatives, with specific API recommendations. Developers using Bun in their projects benefit from consistent AI suggestions aligned with their tooling preferences.
When reviewing or writing code, ensure:
"name": "workflow-audit", "description": "SwiftUI workflow audit — find dead ends, broken promises, and UX friction. Bundled with a phased plan generator.", "name": "Terry Nyberg"
