lifelong-learning

context: fork user-invocable: false name: lifelong-learning description: | Continuous learning pipeline that captures session experiences, performs batch learning via GRPO, and transfers validated knowledge between System 1 and System 2 caches. Auto-activates when: session end, pattern discovery during routing, knowledge transfer triggers, manual /learn command. Triggers: learn, experience, knowledge, transfer, promote, demote, grpo, batch, pattern, skill development, growth, continuous improvement lang: [en] platforms: [claude-code, gemini-cli, codex-cli, cursor] level: 2 triggers: - "learn" - "skill development" - "knowledge" - "growth" - "continuous improvement" agents: - "planner" tokens: "~2K" category: "learning" source_hash: d5a5a1f0 whenNotToUse: "One-off tasks or throwaway experiments where no routing pattern or user preference is worth persisting; do not trigger during active task execution."

Lifelong Learning

Contents

• When This Skill Applies
• Core Guidance
• Configuration
• Workflow Checklist
• Human Checkpoints
• Freedom Levels
• Quick Reference

When This Skill Applies

• Session end (automatic via nightly-learner hook)
• Pattern discovery during routing decisions
• Knowledge transfer between System 1 and System 2
• Periodic consolidation of routing experience
• Manual learning triggers via /learn command

Core Guidance

1. Learning Pipeline

Session Experiences
        |
        v
+------------------+
| Experience       |  Collect routing decisions + outcomes
| Collector        |  during the session
+--------+---------+
         |
         v
+------------------+
| Batch Learner    |  Process experiences in batches (size: 50)
| (GRPO)           |  Group Relative Policy Optimization
+--------+---------+
         |
         v
+------------------+
| Knowledge        |  Promote/demote patterns between
| Transfer         |  System 1 and System 2 caches
+--------+---------+
         |
         v
+------------------+
| Persistence      |  Save updated caches to disk
| Layer            |  ~/.claude/artibot/
+------------------+

2. Experience Collection

Each routing decision is recorded as an experience entry:

3. GRPO (Group Relative Policy Optimization)

Batch learning algorithm that groups similar experiences and optimizes routing thresholds:

1. Group experiences by domain + complexity range (group size: 5)
2. For each group:
   a. Calculate success rate per routing decision
   b. Compare System 1 vs System 2 outcomes
   c. Compute relative advantage: advantage = s2_success - s1_success
3. Update routing threshold:
   - If System 2 consistently better -> lower threshold (more System 2)
   - If System 1 reliably handles -> raise threshold (more System 1)
4. Adjustment step: adaptRate * advantage (clamped to [-0.1, 0.1])

4. Knowledge Transfer

Validated patterns move between System 1 and System 2 caches:

Promotion (System 2 -> System 1)

• Pattern succeeds promotionThreshold (3) consecutive times in System 2
• Confidence consistently > 0.8
• Action: Cache pattern in System 1 for fast retrieval

Demotion (System 1 -> System 2)

• Pattern fails demotionThreshold (2) consecutive times in System 1
• Confidence drops below minConfidence
• Action: Remove from System 1 cache, flag for System 2 analysis

System 2 Cache                              System 1 Cache
+-------------------+    promote (3x)     +-------------------+
| Complex patterns  | =================> | Fast patterns     |
| Deep analysis     |                    | Cached heuristics |
| New discoveries   | <================= | Quick matches     |
+-------------------+    demote (2x)     +-------------------+

5. Knowledge Transfer Parameters

6. Persistence

Learning state is saved to ~/.claude/artibot/:

7. Integration with Cognitive Routing

The lifelong learning system feeds back into the cognitive router:

• Updated thresholds are loaded at session start
• Promoted patterns are available to System 1 immediately
• Demoted patterns are flagged for System 2 re-evaluation
• Transfer history informs meta-cognitive monitoring

Configuration

Settings in artibot.config.json under learning.lifelong and learning.knowledgeTransfer:

{
  "learning": {
    "lifelong": { "batchSize": 50, "grpoGroupSize": 5 },
    "knowledgeTransfer": { "promotionThreshold": 3, "demotionThreshold": 2 },
    "schedule": { "enabled": false, "nightlyLearner": "3 2 * * *", "driftCheck": "7 6 * * 1" }
  }
}

Automatic Scheduling (CronCreate)

When learning.schedule.enabled is true, the learning pipeline can be automatically scheduled within the current Claude Code session via the CronCreate tool. Jobs are session-only (in-memory) and auto-expire after 7 days. See the scheduled-learning skill for full setup details.

Workflow Checklist

Copy this checklist and track progress:

Progress:
- [ ] Step 1: Collect routing experiences during session
- [ ] Step 2: Batch experiences (size: 50) for GRPO processing
- [ ] Step 3: Group by domain + complexity range (group size: 5)
- [ ] Step 4: Compare System 1 vs System 2 outcomes per group
- [ ] Step 5: Update routing threshold (adaptRate * advantage)
- [ ] Step 6: Transfer knowledge — promote/demote between caches
- [ ] Step 7: Persist updated caches to disk

Human Checkpoints

Checkpoint 1: GRPO 비교 결과 검토 (After Step 4)

Context: System 1과 System 2의 성공률 비교가 완료된 시점. 그룹별 결과가 합리적인지 확인해야 라우팅 임계값 조정의 신뢰성이 보장된다. Ask: "Step 4 GRPO 그룹 비교 결과를 확인했습니다. 각 그룹의 성공률 차이가 합리적으로 보이나요?" Options:

1. Accept — 결과가 합리적, Step 5 임계값 조정으로 진행
2. Reset group data — 그룹 데이터를 초기화하고 재집계 Default: 1 (데이터가 충분할 경우 GRPO 결과는 신뢰 가능) Skippable: No — 잘못된 비교 결과로 임계값이 왜곡될 수 있음 Freedom: LOW

Checkpoint 2: 임계값 조정 방향 확인 (After Step 5)

Context: adaptRate * advantage 공식으로 라우팅 임계값이 조정된 시점. 조정 방향(올리기/내리기)이 실제 관찰된 패턴과 일치하는지 검증이 필요하다. Ask: "라우팅 임계값이 조정되었습니다. 조정 방향(System 2 비중 증가/감소)이 세션에서 관찰된 패턴과 맞나요?" Options:

1. Apply — 조정값 적용, Step 6 지식 이전으로 진행
2. Revert adjustment — 이번 조정 취소, 기존 임계값 유지 Default: 1 (공식 범위 [-0.1, 0.1]로 클램핑되어 있어 안전) Skippable: No — 잘못된 방향 조정은 라우팅 품질을 누적 저하시킬 수 있음 Freedom: LOW

Checkpoint 3: 승격/강등 결정 검토 (After Step 6)

Context: 패턴의 System 1 ↔ System 2 이동 결정이 완료된 시점. 자동 기준(3회 연속 성공/2회 연속 실패)이 맥락에 맞는지 사람의 판단이 필요할 수 있다. Ask: "지식 이전 결정이 생성되었습니다. 각 패턴의 승격/강등/보류 결정이 타당해 보이나요?" Options:

1. Promote — 해당 패턴을 System 1로 승격
2. Demote — 해당 패턴을 System 2로 강등
3. Hold — 이번 사이클에서는 현재 위치 유지 Default: 자동 기준 결과 적용 (임계값 기반 결정이 기본값) Skippable: Yes (기본값 사용) — 자동 기준으로 결정하고 Step 7 퍼시스턴스로 진행 Freedom: MEDIUM

Freedom Levels

Quick Reference

Learning Cycle: Collect -> Batch (GRPO) -> Transfer -> Persist Promotion: 3 consecutive System 2 successes -> System 1 cache Demotion: 2 consecutive System 1 failures -> System 2 re-analysis Storage: ~/.claude/artibot/

Rationalizations

The following table captures common excuses agents make to skip the discipline of this skill, paired with factual rebuttals.