Maximize the effectiveness of your .md context files with these battle-tested strategies for working with AI coding assistants like Claude Code, Cursor, and GitHub Copilot.
Your .md should be a high-level guide with strategic pointers, not a comprehensive manual. Document what your AI consistently gets wrong. If explaining something requires more than 3 paragraphs, the problem is your tooling, not your docs.
Avoid @-mentioning files unnecessarily - it bloats context windows. Instead, sell the AI on when to read: "For database errors, see /docs/db.md" vs embedding the entire file. Save tokens for code, not documentation.
Never say "Never" without offering alternatives. "Don't use --force" leaves AI stuck. Instead: "Prefer --safe-mode. Use --force only in dev with approval." Prescriptive > restrictive.
If you need paragraphs to explain a command, the command is the problem. Build a wrapper script with a better API. Short .md files force codebase simplification. Complexity documented is complexity that should be eliminated.
Avoid /compact - it's opaque and lossy. Simple restart: /clear + /catchup. Complex work: dump state to .md, /clear, resume from file. Document > compact. Always.
For large changes, always use planning mode. Align on approach and define checkpoint reviews before implementation. Planning builds AI intuition about your context needs. Code without planning wastes both your time.
One good example beats three paragraphs of explanation. Instead of describing patterns abstractly, show concrete code. AI learns faster from // Example: than from "The pattern is...". Prefer copy-pasteable snippets.
Context files belong in git with your code. When code evolves, context must evolve. Treat CONTEXT.md changes like code changes - review in PRs, test effectiveness, document breaking changes. Stale context is worse than no context.
Use global (~/.claude/context.md), project (CONTEXT.md), and file-level context. Global for your personal patterns, project for codebase conventions, inline for file-specific nuances. Don't repeat yourself across layers.
Explicitly state what's in-scope and out-of-scope. "Don't modify files in /vendor" or "Test coverage required for /src only". Clear boundaries prevent AI from over-helping or making incorrect assumptions.
Verify AI uses your context. Try /clear + task that should use context. Does AI follow patterns? If not, your context isn't working. Iterate until behavior matches intent. Context untested is context unused.
Context rots faster than code. When you change patterns, update context immediately. Outdated context trains AI on deprecated patterns. Set calendar reminders to review quarterly. Fresh context compounds value.
Context files are infrastructure, not documentation. Your .md should be executable specification - concise, versioned, and tested. Think "API contract for AI" not "reference manual for humans."
Slash commands are shortcuts. Context files are strategy. Commands trigger actions. Context shapes behavior. Master both, but invest in context - it compounds over time while commands stay transactional.
Development environment setup shouldn't be tribal knowledge passed down through Slack threads. Codev.md documents every dependency, configuration, and secret in structured markdown. New developers get from zero to productive in minutes, not days. AI assistants can troubleshoot setup issues instantly.
Environment variables, service configurations, and tooling setup are critical context. Codev.md captures the why behind every config decision. AI assistants help developers understand not just what to set, but why it matters. Infrastructure knowledge becomes searchable and versionable.
Works on my machine stops being an excuse when environments are documented as code. Codev.md ensures every developer has access to the same setup procedures, troubleshooting guides, and configuration context. AI can validate environments match specs. Consistency becomes default.
"The best development teams eliminate environment setup as a friction point entirely. Codev.md turns scattered setup instructions into versioned infrastructure documentation - making onboarding instant and environment issues trivial to debug."
Built by DevOps engineers who are tired of "works on my machine" excuses.
We believe environment setup should be deterministic, not tribal knowledge. Every dependency, configuration choice, and secret should be documented in markdown - explaining not just what to set, but why it matters. When environment context lives in .md files, new developers get productive in minutes and AI assistants can troubleshoot setup instantly.
Our vision is that every development team has their environment knowledge captured in versioned markdown. No more hunting through Slack for that one configuration command. No more "works on my machine" mysteries. Infrastructure knowledge becomes searchable, versionable, and accessible to both humans and AI.
LLMs parse markdown better than any other format. Fewer tokens, cleaner structure, better results.
Context evolves with code. Git tracks changes, PRs enable review, history preserves decisions.
No special tools needed. Plain text that works everywhere. Documentation humans actually read.
Struggling with environment setup? Have a unique configuration challenge? Share your setup - we might have a solution.