Agent skill · writing content · affaan-m
strategic-compact
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.
Why this skill is useful
Adds an executable script that automates context management, enhancing the AI's ability to handle large sessions effectively.
What it needs
Requires node installed locally. About 6k tokens when loaded. Last updated 2026-08-06. 238,342 stars on the source repository.
What this skill does
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 (transcriptpath in the hook payload) and sums inputtokens + cachereadinputtokens + cachecreationinputtokens (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. A valid native compaction budget can lower the default reminder threshold to 80% of that budget; model-capacity percentages still use the resolved model window 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills affaan-m/strategic-compact--c2d3ea