Strategic Compact

Strategic Compact Skill

Suggests manual /compact at strategic points in your workflow rather than relying on arbitrary auto-compaction.

When to Activate

• Running long sessions that approach context limits (200K+ tokens)
• Working on multi-phase tasks (research → plan → implement → test)
• Switching between unrelated tasks within the same session
• After completing a major milestone and starting new work
• When responses slow down or become less coherent (context pressure)

Why Strategic Compaction?

Auto-compaction triggers at arbitrary points:

• Often mid-task, losing important context
• No awareness of logical task boundaries
• Can interrupt complex multi-step operations

Strategic compaction at logical boundaries:

• After exploration, before execution — Compact research context, keep implementation plan
• After completing a milestone — Fresh start for next phase
• Before major context shifts — Clear exploration context before different task

How It Works

The suggest-compact.js script runs on PreToolUse (Edit/Write) and combines two signals:

1. Context size (primary) — Reads the latest usage record from the session transcript (transcript_path in the hook payload) and sums input_tokens + cache_read_input_tokens + cache_creation_input_tokens (the true context size of the turn). Suggests /compact at a window-scaled threshold — 160k tokens on a 200k window, 250k on a 1M window (detected from a [1m] model marker, or inferred when observed tokens already exceed 200k) — and re-reminds after every additional 60k tokens of context growth
2. Tool-call count (secondary) — Counts tool invocations in session; suggests at a configurable threshold (default: 50 calls), then every 25 calls after

Hook Setup

Add to your ~/.claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Edit",
        "hooks": [{ "type": "command", "command": "node ~/.claude/skills/strategic-compact/suggest-compact.js" }]
      },
      {
        "matcher": "Write",
        "hooks": [{ "type": "command", "command": "node ~/.claude/skills/strategic-compact/suggest-compact.js" }]
      }
    ]
  }
}

Configuration

Environment variables:

• COMPACT_THRESHOLD — Tool calls before first suggestion (default: 50)
• COMPACT_CONTEXT_THRESHOLD — Context tokens before the context-size suggestion (default: 160000 on a 200k window, 250000 on a 1M window; 0 disables the context signal)
• COMPACT_CONTEXT_INTERVAL — Additional context tokens before the suggestion repeats (default: 60000)
• ECC_CONTEXT_WINDOW_TOKENS — Explicit context-window size, in tokens, overriding auto-detection. Set this for large-window models whose reported id lacks a [1m] marker (e.g. 400k Opus 4.x, or a new 1M-window model family) so the threshold scales to the real window instead of defaulting to 200k and overstating context usage.
• CLAUDE_CODE_AUTO_COMPACT_WINDOW — Claude Code's native window-size override, in tokens; honored as a fallback when ECC_CONTEXT_WINDOW_TOKENS is unset.

The context window is otherwise auto-detected from a [1m] model marker or inferred when observed tokens already exceed 200k. On a large-window model that carries neither signal, set one of the overrides above so the /compact suggestion fires at the right point.

Compaction Decision Guide

Use this table to decide when to compact:

What Survives Compaction

Understanding what persists helps you compact with confidence:

Best Practices

1. Compact after planning — Once plan is finalized in TodoWrite, compact to start fresh
2. Compact after debugging — Clear error-resolution context before continuing
3. Don't compact mid-implementation — Preserve context for related changes
4. Read the suggestion — The hook tells you when, you decide if
5. Write before compacting — Save important context to files or memory before compacting
6. Use /compact with a summary — Add a custom message: /compact Focus on implementing auth middleware next

Related

• The Longform Guide — Token optimization section
• Memory persistence hooks — For state that survives compaction
• continuous-learning skill — Extracts patterns before session ends