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Manage and compress conversation context in long sessions. Detect when context is growing large, summarize completed work phases, archive old findings while preserving key decisions. Prevents context degradation.
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SOC 職業分類に基づく
| name | context-compression |
| description | Manage and compress conversation context in long sessions. Detect when context is growing large, summarize completed work phases, archive old findings while preserving key decisions. Prevents context degradation. |
| when_to_use | When a session has 20+ turns, when context feels repetitive, when the agent is losing track of earlier work, or when the user says 'summarize what we've done'. NOT for short sessions. |
| effort | low |
Keep sessions productive by compressing completed work while preserving key decisions.
Long sessions (30+ turns) cause context degradation — the AI loses track of earlier work, repeats itself, or forgets decisions. Context compression proactively summarizes completed phases so the context window stays focused on active work.
Token Impact: Recovers 5,000-15,000 tokens in long sessions by replacing verbose tool outputs with semantic summaries.
| Signal | Action |
|---|---|
| Session has 20+ turns | Consider proactive compression |
| Agent repeats earlier suggestions | Context is saturated — compress now |
| User says "we already discussed this" | Compress immediately |
| Switching to a new phase of work |
| Compress the completed phase |
| Large tool output (500+ lines) | Micro-compact the output |
Compress individual tool outputs while retaining semantic content:
❌ Before (raw grep output — 200 lines, ~4,000 tokens):
src/auth/jwt.ts:15: import { verify } from 'jsonwebtoken'
src/auth/jwt.ts:23: export function validateToken(token: string) {
src/auth/jwt.ts:24: try {
src/auth/jwt.ts:25: const decoded = verify(token, SECRET)
... (195 more lines)
✅ After (micro-compact — 5 lines, ~100 tokens):
Grep results for "jwt": Found 8 files, 42 matches.
Key files: src/auth/jwt.ts (main JWT logic), src/middleware/auth.ts (middleware),
src/api/login.ts (token creation). Token validation at jwt.ts:23-40.
Error handling at jwt.ts:42-55. Secret loaded from env at jwt.ts:8.
Replace a completed work phase with a summary:
❌ Before (full research transcript — ~3,000 tokens):
[turn 1] Read package.json...
[turn 2] Read src/index.ts...
[turn 3] Grep for "auth"...
[turn 4] Found 8 files related to auth...
[turn 5] Read src/auth/jwt.ts...
... (10 more turns of exploration)
✅ After (phase summary — ~200 tokens):
## Research Phase Complete
- Project: Next.js 15 app with JWT auth
- Auth files: 8 files in src/auth/, src/middleware/, src/api/
- Token flow: login → create JWT → store in httpOnly cookie → validate in middleware
- Bug location: src/auth/jwt.ts:45 — expiry check uses `<` instead of `<=`
- Decision: Fix the comparison operator, add edge case test
Full session summary for long-running work:
## Session Checkpoint (Turn 35)
### Completed
- [x] Researched auth system (8 files, JWT flow mapped)
- [x] Fixed token expiry bug in jwt.ts:45
- [x] Added edge case test in jwt.test.ts
- [x] Verified: all 42 tests passing
### In Progress
- [ ] Update API documentation
- [ ] Review related middleware
### Key Decisions
1. Keep httpOnly cookies (not localStorage) for token storage
2. Use `<=` for expiry check to include exact-moment expiry
3. Add 5-minute grace period for clock skew
### Files Modified
- src/auth/jwt.ts (line 45: comparison fix)
- tests/auth/jwt.test.ts (added 3 edge case tests)
What work is DONE and won't be revisited?
→ Research findings already synthesized
→ Implementation already verified
→ Decisions already made and applied
From the completed phase, preserve:
✅ Decisions made and WHY
✅ File paths and line numbers of changes
✅ Key findings that inform ongoing work
✅ Error messages or test results (summarized)
Discard:
❌ Step-by-step tool invocation details
❌ Full file contents that were read
❌ Exploratory dead-ends that didn't lead anywhere
❌ Verbose error stack traces (keep the message only)
Use the Phase Summary format above.
Keep to 100-300 tokens per completed phase.
Include enough detail to resume work without re-reading.
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.