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Found 188 Skills
Production-grade engineering skills for AI coding agents - lifecycle commands, workflow automation, and best practices for software development.
Run a coding agent in an autonomous loop via a /ralph command, gated by a preflight check that every CLI is installed, linked, and authenticated. Use when driving long-running autonomous development from a wide, outcome-focused prompt.
Configure AI coding agents like Cursor, GitHub Copilot, or Claude Code with project-specific patterns, coding guidelines, and MCP servers for consistent AI-assisted development.
Effective communication strategies for AI-assisted development. Learn context-first prompting, phased interactions, iterative refinement, and validation techniques to get better results from Claude and other AI coding assistants.
Write effective AGENTS.md files that give coding agents the context they need to work in a repository. Use when creating a new AGENTS.md, improving an existing one, setting up a repo for AI coding agents, or onboarding agents to a codebase. Triggers on: "write AGENTS.md", "create AGENTS.md", "agent instructions", "set up repo for agents", "configure coding agent", "onboard agent to codebase", "agent context file".
Universal principles for agentic development when collaborating with AI agents. Defines divide-and-conquer, context management, abstraction level selection, and an automation philosophy. Applicable to all AI coding tools.
Core patterns for AI coding agents based on analysis of Claude Code, Codex, Cline, Aider, OpenCode. Triggers when: Building an AI coding agent or assistant, implementing tool-calling loops, managing context windows for LLMs, setting up agent memory or skill systems, or designing multi-provider LLM abstraction. Capabilities: Core agent loop with while(true) and tool execution, context management with pruning and compression and repo maps, tool safety with sandboxing and approval flows and doom loop detection, multi-provider abstraction with unified API for different LLMs, memory systems with project rules and auto-memory and skill loading, session persistence with SQLite vs JSONL patterns.
Exit copilot mode — return to autonomous mode with full worktree enforcement.
Use when the user wants to author, refine, or audit a Product Requirements Document for AI coding agents. Walks through an 8-phase pipeline (Socratic discovery → PRD draft → acceptance criteria → adversarial review → task decomposition → AI-readiness gate → test generation → handoff). Triggers on "write a PRD", "spec this feature", "draft requirements", "prepare X for Claude/Cursor/Copilot/Windsurf/Aider to build", "audit my PRD", "is this PRD AI-ready", "score this spec".
Curated collection of 1000+ agent skills compatible with Claude Code, Codex, Gemini CLI, Cursor, and more
Add persistent memory to AI coding agents using agentmemory - remembers context, preferences, and decisions across sessions
Research collection of reconstructed prompt patterns and architectures for agentic AI coding assistants