Context Window Optimizer

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name: context-window-optimizer description: "Optimize context window usage by summarizing old conversation segments, extracting key facts and decisions to permanent memory, and keeping current context lean. Triggers when: (1) conversation history grows beyond ~50 messages or context feels heavy; (2) before long or complex tasks; (3) after significant decisions or work completions; (4) when explicitly asked to optimize context, compact context, or clean up context."

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Context Window Optimizer

Manage context strategically to prevent token waste and keep conversations effective.

Core Principle

Context is a shared resource. Keep it lean so there's room for actual work.

When to Optimize

Conversation exceeds ~50 messages

Context feels heavy before a new task

Starting a complex multi-step task

After significant decisions or completions

Explicit request to optimize/compact

Optimization Workflow

Step 1: Assess Context State

Run the analyzer to get context metrics:

python3 scripts/analyze_context.py --session current

This reports:

Message count and approximate token count

Age of oldest message

Density score (signal vs noise)

Step 2: Identify Optimization Targets

Look for:

Old已完成 tasks with verbose logs

Repeated explanations of same concept

Off-topic tangents

Raw tool outputs that could be summarized

Decisions that should move to permanent memory

Step 3: Extract to Memory

Decisions → MEMORY.md or relevant project file:

## Decisions (from 2026-03-25 session)
Chose PostgreSQL over MongoDB for project X
Agreed on 3-day sprint cadence
User prefers detailed explanations, not summaries

Key facts → appropriate domain/project file:

## Project X Facts
Tech stack: React + Node + Postgres
Main user pain point: slow onboarding
Current velocity: 5 story points/sprint

Patterns → ~/self-improving/memory.md:

## User Preferences
Always explain the "why" before the "what"
Prefers bullet points over paragraphs

Step 4: Summarize Dense Segments

For long work sessions, create a summary instead of keeping all details:

## Session Summary: 2026-03-25
Work Completed
Set up authentication flow
Fixed memory leak in worker process
Designed new API schema
Decisions Made
Use JWT over sessions (simpler, scales better)
Defer caching to v2 (not blocking)
Open Questions
Final tech stack for notifications (push vs polling)
Need user feedback on onboarding flow
Next Steps
Implement auth endpoints
Write tests for worker
Schedule design review

Step 5: Archive, Don't Delete

Never delete context — archive it:

Move summaries to memory/YYYY-MM-DD.md

Keep pointers in session for recovery

Use [[archived:filename.md]] notation

Context Density Rules

| Content Type | Action | |--------------|--------| | Completed tasks | Summarize outcome, archive details | | Decisions | Extract to MEMORY.md or project file | | Key facts | Extract to relevant domain/project | | Tool logs | Summarize if successful, keep if debugging | | Repeated concepts | Remove duplicates, keep one canonical | | Off-topic | Skip or summarize in notes | | System prompts | Never touch | | Skills metadata | Only load relevant ones |

Quick Commands

| Task | Command | |------|---------| | Analyze current context | python3 scripts/analyze_context.py --session current | | Summarize session | python3 scripts/summarize_session.py --session current --output summary.md | | Extract decisions | python3 scripts/extract_decisions.py --session current |

Files

scripts/analyze_context.py — Context metrics and optimization suggestions

scripts/summarize_session.py — Create session summary

scripts/extract_decisions.py — Pull out decisions and key facts

references/patterns.md — Common summarization patterns