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/strategic-compact

@c752aac

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction. Use when a session is approaching a context limit and a task phase is a natural place to compact.

Use this Skill: https://skilld.dev/gh/affaan-m/everything-claude-code/strategic-compact

This session only. Nothing lands on disk.

SKILL.md

≈63 tokens always: the name and description. ≈2.1k when used: this file.

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

Tool count alone is a weak proxy for window pressure: a few large file reads or MCP responses can fill the window in very few calls, while many tiny calls can cross 50 with a near-empty window. The context-size signal fires when it actually matters.

Hook Setup

Installed as a plugin? No setup is needed. The plugin's hooks/hooks.json already registers suggest-compact.js (hook id pre:edit-write:suggest-compact, active in the standard and strict hook profiles). Do not copy the block below into ~/.claude/settings.json — ~/.claude/scripts/ does not exist on plugin installs, and duplicating a plugin hook causes double execution.

If installed manually (./install.sh), add to your ~/.claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Edit",
        "hooks": [{ "type": "command", "command": "node ~/.claude/scripts/hooks/suggest-compact.js" }]
      },
      {
        "matcher": "Write",
        "hooks": [{ "type": "command", "command": "node ~/.claude/scripts/hooks/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)
  • COMPACT_STATE_TTL_DAYS — Days before stale per-session state files in the temp dir are swept (default: 14)
  • 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:

Phase Transition Compact? Why
Research → Planning Yes Research context is bulky; plan is the distilled output
Planning → Implementation Yes Plan is written down (a file, or the task list if you have one); free up context for code
Implementation → Testing Maybe Keep if tests reference recent code; compact if switching focus
Debugging → Next feature Yes Debug traces pollute context for unrelated work
Mid-implementation No Losing variable names, file paths, and partial state is costly
After a failed approach Yes Clear the dead-end reasoning before trying a new approach

What Survives Compaction

Understanding what persists helps you compact with confidence:

Persists Lost
CLAUDE.md instructions Intermediate reasoning and analysis
Files on disk File contents you previously read
Memory files (~/.claude/memory/) Multi-step conversation context
Git state (commits, branches) Tool call history and counts
The task list — only if you have the todo tools (see below) Nuanced user preferences stated verbally

Don't rely on the task list surviving — it may not exist

Claude Code 2.1.233 removed the todo/task tools by default on Opus 4.8, Sonnet 5, Fable 5, Mythos 5 and newer models (TodoWrite, TaskCreate/Get/Update/List). CLAUDE_CODE_ENABLE_TODO_TOOLS=1 brings them back, but that is a per-machine environment setting — it does not travel with this skill, so you cannot assume the reader has it.

This matters because "my todo list survives compaction" is a reason people compact instead of writing state down. If the tools are absent there is no list to survive, and the plan is simply gone. Write the plan to a file before compacting — a file persists on every version and every model. Treat the task list as a convenience that may be missing, never as your durable record.

Best Practices

  1. Compact after planning — Once the plan is finalized and written to a file, 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

Token Optimization Patterns

Trigger-Table Lazy Loading

Instead of loading full skill content at session start, use a trigger table that maps keywords to skill paths. Skills load only when triggered, reducing baseline context by 50%+:

Trigger Skill Load When
"test", "tdd", "coverage" tdd-workflow User mentions testing
"security", "auth", "xss" security-review Security-related work
"deploy", "ci/cd" deployment-patterns Deployment context

Context Composition Awareness

Monitor what's consuming your context window:

  • CLAUDE.md files — Always loaded, keep lean
  • Loaded skills — Each skill adds 1-5K tokens
  • Conversation history — Grows with each exchange
  • Tool results — File reads, search results add bulk

Duplicate Instruction Detection

Common sources of duplicate context:

  • Same rules in both ~/.claude/rules/ and project .claude/rules/
  • Skills that repeat CLAUDE.md instructions
  • Multiple skills covering overlapping domains

Context Optimization Tools

  • token-optimizer MCP — Automated 95%+ token reduction via content deduplication
  • context-mode — Context virtualization (315KB to 5.4KB demonstrated)

Related

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

Source: SKILL.md on GitHub

1 warning1mo5 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    The skill manages context usage by suggesting compaction at strategic intervals. It presents a low risk due to the ingestion of session transcripts, which acts as a potential surface for indirect prompt injection.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

  • Runlayer6mo

    2/2 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at c752aac. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 10 hours ago.

Activeupdated 2 months ago
metadata
{
  "origin": "ECC"
}
  • context-management
  • claude
  • token-optimization
  • workflow
  • session-management
  • compaction
  • hooks

README badge

README badge for affaan-m/everything-claude-code/strategic-compact

Triggers manual `/compact` at logical task boundaries—after research, planning phases, or debugging—rather than relying on auto-compaction that fires mid-task. Uses a PreToolUse hook to monitor context size and tool-call count, suggesting compaction at 160K+ tokens or after 50+ tool invocations, with configurable thresholds.

Generated from the current SKILL.md.

When should I manually compact instead of waiting for auto-compaction?
Compact at logical task boundaries — after completing research before implementation, after finishing a milestone, or when switching between unrelated tasks. The skill suggests compaction when context approaches your window limit (160k tokens on 200k window, 250k on 1M) or after 50 tool calls, but you decide whether to accept based on phase completion.
What information survives a `/compact` and what gets lost?
CLAUDE.md instructions, TodoWrite task lists, memory files, and git state persist after compaction. Lost are intermediate reasoning, previously-read file contents, conversation history, and tool call logs.
How do I configure when the skill suggests compaction?
Set environment variables: `COMPACT_THRESHOLD` (tool calls before first suggestion, default 50), `COMPACT_CONTEXT_THRESHOLD` (context tokens, default 160k on 200k window), and `COMPACT_CONTEXT_INTERVAL` (tokens between repeated suggestions, default 60k). Set `COMPACT_CONTEXT_THRESHOLD` to 0 to disable context-based suggestions.
Does this skill work with different context window sizes?
Yes. The skill auto-detects your window size from a `[1m]` model marker or infers it when observed tokens exceed 200k, then scales thresholds accordingly (160k for 200k windows, 250k for 1M windows).

Generated from the current SKILL.md. These answers refresh after source changes.