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.
Code standards locked in wikis are code standards ignored. Coding.md puts your conventions, patterns, and best practices in markdown files alongside your code. Version them. Review them. Let AI assistants enforce them automatically. Standards become living documentation that evolves with your codebase.
New developers shouldn't spend weeks learning unwritten rules. Coding.md documents your git workflows, debugging approaches, and common gotchas in structured markdown. AI assistants can instantly surface relevant guidance. What took weeks now takes hours. Knowledge transfer becomes deterministic.
AI-assisted code review needs your standards to be effective. Coding.md provides the context layer that transforms generic suggestions into specific, project-aligned feedback. Your quality bar is codified, versioned, and consistently applied. Every PR benefits from institutional knowledge.
"The difference between good code and great code is context. Coding.md helps teams capture and share the patterns, practices, and principles that define their engineering culture - making implicit knowledge explicit and available to both humans and AI."
Built by development teams who believe great code starts with great documentation.
We are committed to changing how teams think about coding standards. They shouldn't be locked in wikis or lost in Slack threads. Coding standards belong in .md files, versioned with your code, enforced by AI assistants, and evolved through pull requests. When standards live as markdown, they become living documentation that actually gets used.
Our vision is simple: every codebase should have its conventions, patterns, and best practices captured in markdown files that both humans and AI can understand. This isn't just documentation - it's the foundation of consistent, high-quality code at scale. Standards as infrastructure, not afterthought.
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.
Have coding standards you want to share? Questions about documentation best practices? Let's collaborate.