- 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.
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 currentThis 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 summariesKey facts → appropriate domain/project file:
## Project X Facts
- Tech stack: React + Node + Postgres
- Main user pain point: slow onboarding
- Current velocity: 5 story points/sprintPatterns → ~/self-improving/memory.md:
## User Preferences
- Always explain the "why" before the "what"
- Prefers bullet points over paragraphsStep 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 reviewStep 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 suggestionsscripts/summarize_session.py— Create session summaryscripts/extract_decisions.py— Pull out decisions and key factsreferences/patterns.md— Common summarization patterns