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Strategic Compact

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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.

Overview

Publisheraffaan-m
RepositoryECC
Skill namestrategic-compact
Stars
270.2K
Forks
40.4K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by affaan-m on GitHub. Read the source before you install it.

Installation

Install the Strategic Compact AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/affaan-m/ECC/tree/main/.agents/skills/strategic-compact
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/.agents/skills/strategic-compact .claude/skills/strategic-compact
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Strategic Compact in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Strategic Compact on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Strategic Compact is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

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:

Phase TransitionCompact?Why
Research → PlanningYesResearch context is bulky; plan is the distilled output
Planning → ImplementationYesPlan is written down (a file, or the task list if you have one); free up context for code
Implementation → TestingMaybeKeep if tests reference recent code; compact if switching focus
Debugging → Next featureYesDebug traces pollute context for unrelated work
Mid-implementationNoLosing variable names, file paths, and partial state is costly
After a failed approachYesClear the dead-end reasoning before trying a new approach

What Survives Compaction

Understanding what persists helps you compact with confidence:

PersistsLost
CLAUDE.md instructionsIntermediate reasoning and analysis
Files on diskFile 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

Related

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Strategic Compact AI skill do?

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 use Strategic Compact on TypingMind?

Because you install it once and use it with any model. Strategic Compact is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Strategic Compact in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/.agents/skills/strategic-compact. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Strategic Compact?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Strategic Compact?

As many as you like. As long as a model supports skills, you can use Strategic Compact with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Strategic Compact AI skill free?

Yes. It is published on GitHub by affaan-m under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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