Communitygithub.com

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.

What is strategic-compact?

strategic-compact is a Claude Code agent skill that 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.

Works withClaude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/strategic-compact

Ask in your favorite AI

Open a new chat with this agent skill pre-loaded.

Documentation

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 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

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%+:

TriggerSkillLoad When
"test", "tdd", "coverage"tdd-workflowUser mentions testing
"security", "auth", "xss"security-reviewSecurity-related work
"deploy", "ci/cd"deployment-patternsDeployment 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

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

Related Skills