Context Engineering for AI Agents

Problem to Solve

When a coding agent starts using wrong patterns, calling nonexistent APIs, or ignoring project conventions, the issue is often not model intelligence but context delivery. Too little context invites hallucination; too much context dilutes focus. This skill addresses context engineering for AI agents, focusing on providing the right information at the right time. It is especially useful when starting a new coding session, switching codebase areas, setting up an AI-assisted project, or when agent output quality has declined.

How It Works

The core approach is to organize context by persistence rather than dumping all guidance into a conversation. The documented key steps include:
- Create persistent rules files: for example CLAUDE.md for Claude Code, preserving project boundaries across sessions.
- Add high-leverage information: project name, tech stack, common commands, and conventions such as npm run build, npx tsc --noEmit, and test-file naming rules.
- Constrain structure instead of piling examples: front-load component style, export rules, and error handling to reduce ungrounded improvisation.
Boundary: it is a context-organization method, not a substitute for debugging, testing, or architecture design. If project documentation is missing, conventions are inconsistent, or large-scale code search is needed, combine it with codebase exploration and human review.