AI Coding
What separates AI coding environments that amplify output from ones that slow you down?
The answer is signal discipline — less context, not more. Most setups fail by adding too much.
Providers
- Config Architecture — Agent-agnostic setup: rules, hooks, skills
- Claude Code — Hooks, settings, Claude-specific patterns
- Gemini CLI — 1M+ context window, repo-wide analysis
- Codex CLI — AGENTS.md standard, second-opinion verification
Principles
- Agent Context Prompts
- Command Prompts
- Skills
Context Files
Agent MD files (CLAUDE.md, AGENTS.md) shape what the model sees. Used well, they correct consistent errors. Overused, they add noise: LLM-generated context files decrease performance by 3% on average and increase costs by 20%+.
| Principle | What to Do |
|---|---|
| Minimize context | Include only essential information — irrelevant context distracts |
| Correct consistent errors only | Add rules when the model reliably makes the same mistake |
| Prioritize codebase | If the model struggles, fix the codebase first — move confused elements, improve tests |
| Read agent struggles | Confusion signals an unclear codebase, not a prompting problem |
| Watch outputs | Note what files the agent reads and how long tasks take — builds intuition |
Agent Prompts
System prompt (Mindset)
Command Prompts
Instructions (JTBD)
Skills
- skills.sh — Open source AI coding skills library