AI Agents & Automation

Reverse-engineered system prompt of Claude Design from Anthropic. A system prompt and skill library that turns an LLM into an opinionated, accessibility-aware, AI-slop-resistant design collaborator.

A self-improving multi-agent orchestration framework for Claude Code, Gemini CLI, and Codex CLI. Coordinate 18 specialized AI agents and 13 orchestration skills through a complete software development lifecycle, from issue to merged PR, with recursive orchestration, parallel review gates, and a git-native knowledge base.

A collection of security and utility hooks for Claude Code. Hooks allow you to extend Claude Code's behavior by running custom scripts at key points during execution.

A collection of AI agent skills focused on resume optimization, job applications, and career development. Built for job seekers, career changers, and professionals who want Claude Code to help with resume writing, ATS optimization, interview prep, and strategic job search.

An Agent Skill that transforms AI assistants into expert economics paper writers, synthesizing best practices from 50+ authoritative guides by Nobel laureates, Clark Medal winners, and leading economists. --- Econ Writing Skill is an open-source Agent Skill that gives AI coding assistants deep knowledge of how to write economics papers.

ai-business-skills Fullstack marketing skills cho AI agent — 63 skills bilingual (VN + Global). Vietnamese-first + Global (US/EU/SEA/LATAM) | Over Powers Agency --- Windows: Test ngay trong Claude Code: → Skill 00-ke-hoach-mkt tự kích hoạt, output file .md chi tiết với benchmark VN 2025-2026, KPI 3 kịch bản, budget allocation, weekly timeline, risk matrix.

The open-source codebase context engine: give any AI instant automated knowledge of your entire codebase (and infrastructure) — at scale, zero configuration, fully private, completely free. Kindly sponsored by Altaire Limited One thing, done well: deep codebase intelligence — zero setup, no bloat, fully automatic.

CowAgent is lightweight, easy to deploy, and built to extend. Plug in any major LLM provider and run it 24/7 on a personal computer or server, across the web and all major IM platforms.

Transform vague prompts into expert-level, structured prompts using 31 frameworks across 7 intent categories. Works with Claude Code, ChatGPT, Gemini CLI, Cursor, GitHub Copilot, Windsurf, OpenAI Codex, and 30+ Agent Skills compatible tools.

PM-Skills A curated library of 68 best-practice, plug-and-play product management skills covering the complete product lifecycle - plus templates, workflows, and 200+ real-world sample outputs that set the quality bar. The catalog is frozen at the v2.9.2 build; subsequent v2.9.x patches do not change the catalog.

Build better enterprise architecture through structured strategy, design, delivery, and assurance workflows. The 13 community plugins (arckit-uae, arckit-fr, arckit-ca, arckit-eu, arckit-at, arckit-au, arckit-au-energy, arckit-us, arckit-uk-finance, arckit-uk-nhs, arckit-uk-gcloud, arckit-togaf-adm, arckit-agent-architecture) require the arckit core plugin.

GPT Image 2 Prompt Gallery + Agentic Skill + CLI OpenAI GPT Image 2 prompt gallery, image prompt library, agentic skill, and CLI — curated, copy-paste prompts and runnable examples for skill-capable agents. It includes reusable AI image prompts for research paper figures, posters, UI mockups, game HUDs, anime / manga, photography, typography, maps, tattoo design, and reference-image editing workflows.

Goal: Replace the brainstorm server's vendored nodemodules with a single zero-dependency server.js using Node built-ins. Architecture: Single file with WebSocket protocol (RFC 6455 text frames), HTTP server (http module), and file watching (fs.watch).

Goal: Add spec and plan document review loops to the brainstorming and writing-plans skills. Architecture: Create reviewer prompt templates in each skill directory.

--- name: writing-plans description: Use when you have a spec or requirements for a multi-step task, before touching code --- Write comprehensive implementation plans assuming the engineer has zero context for our codebase and questionable taste. Document everything they need to know: which files to touch for each task, code, testing, docs they might need to check, how to test it.

NEVER: INSTEAD: Example: your human partner's rule: "External feedback - be skeptical, but check carefully" your human partner's rule: "You and reviewer both report to me. If we don't need this feature, don't add it." Push back when: How to push back: If you're uncomfortable pushing back out loud: Name that tension, then tell your partner about the issue you've seen.

English | 한국어 | 中文 | 日本語 Game character voice lines + visual overlay notifications when your AI coding agent needs attention — or let the agent pick its own sound via MCP. AI coding agents don't notify you when they finish or need permission.

Load this reference when: writing or changing tests, adding mocks, or adding cleanup/helper methods for tests. Two principles govern everything here: Strict TDD produces both naturally: a test written first and watched failing against real code has already proven it can fail, and only earns a mock when the real dependency proves slow or external.

LLMs respond to the same persuasion principles as humans. Understanding this psychology helps you design more effective skills - not to manipulate, but to ensure critical practices are followed even under pressure.

--- name: finishing-a-development-branch description: Use when implementation is complete, all tests pass, and you need to decide how to integrate the work --- Core principle: Verify tests → Detect environment → Present options → Execute choice → Clean up. Announce at start: "I'm using the finishing-a-development-branch skill to complete this work." Run the project's full test suite (npm test / cargo test / pytest / go test ./...).