The Problem
AI agents are great until you want them to do more than spit out one-off answers. Persistent memory, context switching, security checks, and scaling up workflows? Good luck wiring all that together without creating a spaghetti mess of YAML files and custom scripts. If you’ve tried building or optimizing an agent harness, you’ve probably hit a wall with inconsistent performance, manual skill updates, or flaky evaluations.
What This Does
everything-claude-code is the Swiss Army knife for AI agent harnesses. It’s a complete system—not just some configs—with modular tools for skills, memory optimization, and parallelized workflows. The real meat is in .opencode/: Skills: Skills are defined with SKILL.md files (e.g., .agents/skills/security-review/SKILL.md) and paired with YAML configs like agents/openai.yaml. These skills are reusable, so you don’t reinvent the wheel every time you need a new agent behavior. Hooks: Persistent memory? Context switching? The .cursor/hooks/ folder has you covered with pre-built lifecycle hooks like session-start.js and before-submit-prompt.js. Performance Tools: Want to know where your agent’s choking? Check out .opencode/tools/security-audit.ts or run-tests.ts for automated checks and benchmarks.
The system also includes commands/ for day-to-day operations like checkpoint.md for evaluations or quality-gate.md for ensuring new code doesn’t break the harness.
Real-World Use
Let’s say you’re building an agent system for automatic code reviews. You’d start by defining the skill in .agents/skills/code-reviewer/SKILL.md and setting up the behavior in .agents/skills/code-reviewer/agents/openai.yaml. Then, hook up .cursor/hooks/after-file-edit.js to ensure the agent reacts in real time to file changes, saving context between sessions. Run npm run security (defined in .opencode/package.json) to make sure your agent isn’t exposing sensitive data.
Bonus: If you're scaling this setup, use the cascade method from the Longform Guide to spin up parallel agents across multiple instances.
The Bottom Line
This repo is a beast. If you’re building production-grade AI agents, it’s a goldmine of tools and patterns. That said, it’s not for the faint of heart—expect to spend time deciphering the structure and documentation. If you’re working on a small project or just need a chatbot, skip this. But if you’re serious about building scalable, smart agent systems, this is worth the effort.