Context Engineering — Optimize What Your AI Agent Sees and When

The problem

AI agents often hallucinate APIs, ignore project conventions, or lose focus as conversations grow. Without structured context, you waste time correcting refactors that break existing patterns or manually fixing imports.

What it does

• Establishes a multi-level context hierarchy to balance focus and knowledge.
• Standardizes project-wide rules via CLAUDE.md, .cursorrules, and .windsurfrules configurations.
• Implements confusion management patterns to prompt for clarification instead of guessing.
• Optimizes token usage through selective file inclusion and hierarchical project mapping.

Frameworks & tools

Compatible with Claude Code, Cursor, Windsurf, GitHub Copilot, and any LLM-based IDE extension. Supports MCP servers for live documentation, database schema, and browser state integration.

Why this beats prompting it yourself

One-off prompts lack persistence and lead to context drift as the session progresses. This skill provides a systematic framework for long-term project alignment, ensuring the agent adheres to your specific stack and architectural boundaries without repeated manual instructions.

Use cases

• Setting up a new repository with strict linting, testing, and component patterns.
• Recovering agent performance when output quality begins to degrade in long sessions.
• Onboarding an agent to a complex codebase with conflicting legacy and modern patterns.
• Managing multi-step features by enforcing inline planning before code execution.

Known limitations

Performance may degrade if context exceeding 5,000 lines is loaded without filtering. Requires manual creation of initial rules files to capture project-specific tribal knowledge.