Context Engineering

Context Engineering

Four operations control everything about how context flows through an AI coding session. Master them and you control the quality of every response.

The Four Operations

1. Write — Persist Info Outside Context

Move information out of the context window into durable storage so it survives compaction and session boundaries.

Where to write:

Pattern — Scratchpad workflow:

1. Start complex task → create NOTES.md with goals and constraints
2. After research → write findings to NOTES.md
3. After compaction → NOTES.md survives, context does not
4. Resume → read NOTES.md to recover full state

2. Select — Retrieve Relevant Info

Pull the right information into context at the right time. Precision matters more than volume.

Methods ranked by precision:

1. @file references — exact file injection
2. grep / Glob — targeted pattern search
3. Subagent exploration — delegated deep search
4. RAG / embeddings — semantic retrieval for large codebases

Key principle: Focused 300 tokens > unfocused 113K tokens.

A surgical grep result that returns the exact function signature beats dumping an entire module into context. Every irrelevant token dilutes attention.

Pattern — Progressive retrieval:

1. Start with file names (Glob)
2. Narrow to specific functions (Grep)
3. Read only the relevant lines (Read with offset+limit)
4. Never read entire large files when you need one function

3. Compress — Reduce Tokens, Preserve Signal

Shrink context without losing the information that matters.

Compaction strategies:

Compaction triggers:

• After planning, before implementation
• After completing a feature or milestone
• When context exceeds 50% (set CLAUDE_AUTOCOMPACT_PCT_OVERRIDE=50)
• Before switching task domains
• After heavy search/read operations

PostCompact hook — Re-inject critical context:

{
  "type": "PostCompact",
  "command": "cat .claude/critical-context.md"
}

Use this to ensure project rules, current task state, or architecture constraints survive every compaction.

4. Isolate — Partition Across Execution Spaces

Don't load everything into one context. Split work across independent execution spaces.

Pattern — Subagent delegation:

Main session: planning, coordination, commits
Subagent 1: explore auth module, report findings
Subagent 2: run test suite, report failures
Subagent 3: generate migration script

Main context stays clean. Subagents handle the volume.

Context Budget Planning

Example baseline (calibrate with /context): 200K total window, ~20K overhead (CLAUDE.md, tool definitions, MCP schemas). Plan around **180K usable** — actual budgets vary by model and configuration.

Put static context first. CLAUDE.md and tool definitions load before conversation. Keeping them stable maximizes prompt cache hits — saves cost and latency.

When to /clear vs /compact vs Subagent

Anti-Patterns

• Loading entire files when you need one function
• Keeping MCP tool results in context after extracting what you need
• Running 15+ MCPs (each adds tool schema overhead to every request)
• Vague prompts that force Claude to search broadly ("fix the code")
• Never compacting until auto-compact triggers at 95%

Add to CLAUDE.md

## Context Engineering

Write to NOTES.md for working state that must survive compaction.
Select with precision — grep first, read specific lines, never dump whole files.
Compact at 50% or task boundaries. Set CLAUDE_AUTOCOMPACT_PCT_OVERRIDE=50.
Isolate heavy work to subagents. Main session stays for coordination and commits.