Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
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Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
---name: context-engineering
description: Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
user-invocable: true
---# 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:**
| Target | When | Example |
|--------|------|---------|
| CLAUDE.md | Permanent project rules | "Always use pnpm, never npm" |
| NOTES.md / scratchpad | Working state for current task | Architecture decisions, open questions |
| `.claude/memory/` | Learnings and patterns | `[LEARN]` rules from corrections |
| External files | Data too large for context | Test plans, migration checklists |
**Pattern — Scratchpad workflow:**
```text
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
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:**
```text
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.
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.
