/context-engineering

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:**

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:**

| Strategy | How | When | |----------|-----|------| | `/compact` with focus | `/compact focus: auth module changes` | Task boundaries | | Microcompact | Ask Claude to summarize tool output inline | After large reads/searches | | Head+tail | Read first 20 + last 20 lines of large output | Log analysis, test results | | Tool result clearing | Subagent results auto-clear after reporting | Heavy exploration | | Semantic selection | Summarize findings, discard raw data | Research phases |

**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.

| Method | Isolation Level | Use When | |--------|----------------|----------| | Subagents | Forked context | Heavy exploration, test runs, doc generation | | Worktrees (`claude -w`) | Full repo copy | Parallel features, competing approaches | | `/btw` (built-in Claude Code) | Temporary overlay | Quick questions without entering conversation history | | Agent teams | Independent sessions | Cross-layer changes, parallel reviews | | Fresh session (`/resume`) | Clean slate | Unrelated work, degraded context |

**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.

| Allocation | Budget | What Goes Here | |------------|--------|----------------| | Static context | 20-30K | CLAUDE.md, tool schemas, MCP definitions | | Dynamic context | 150-180K | Code, conversation, tool results |

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

| Phase | Target Usage | Action If Over | |-------|-------------|----------------| | Planning | < 20% | Keep plans concise, write to scratchpad | | Implementation | < 50% | Compact between files, delegate reads | | Testing | < 70% | Delegate test runs to subagents | | Review | < 85% | Start fresh session if degraded |

When to /clear vs /compact vs Subagent

| Situation | Action | |-----------|--------| | Task boundary, want to keep learnings | `/compact` with focus | | Context degraded, Claude repeating itself | `/compact`, then `/resume` if still bad | | Starting unrelated work | `/clear` or new session | | Heavy read/search operation | Delegate to subagent | | Quick side question | `/btw` (doesn't pollute main context) | | Exploring multiple approaches | Worktrees or agent teams |

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 N