Improvement Learner

Real Karpathy self-improvement loop: evaluate → modify → re-evaluate → keep/revert → repeat.

When to Use

• 查看一个 skill 在 6 个维度上的质量评分
• 运行自动改进循环（Pareto front 保护，不允许任何维度回退）
• 追踪 skill 评估分数的历史变化

When NOT to Use

• 给改进候选打语义分 → use improvement-discriminator
• 跑全流程（生成→打分→门禁→执行） → use improvement-orchestrator
• 只想改一个文件 → use improvement-executor

6 Evaluation Dimensions

Three-Layer Memory

\x3Cexample> 正确用法: 评估一个 skill 的质量 $ python3 scripts/self_improve.py --skill-path /path/to/skill --max-iterations 1 → 输出 JSON: {"final_scores": {"accuracy": 0.83, "coverage": 1.0, "reliability": 1.0, ...}} → accuracy 0.83 说明 SKILL.md 缺少部分检查项（如 Output Artifacts 或 Related Skills） \x3C/example>

\x3Canti-example> 错误判读: 纯文本 skill 的 reliability=1.0 不代表质量好 → 纯文本 skill 没有 scripts/，reliability 默认 1.0（没有代码就不需要测试） → 真正有意义的维度是 accuracy 和 trigger_quality \x3C/anti-example>

CLI

# 评估（不改动，只看分数）
python3 scripts/self_improve.py --skill-path /path/to/skill --max-iterations 1

# 自改进循环（5 轮）
python3 scripts/self_improve.py \
  --skill-path /path/to/skill \
  --max-iterations 5 \
  --memory-dir /path/to/memory \
  --state-root /path/to/state

# 追踪历史
python3 scripts/track_progress.py --skill-path /path/to/skill --output progress.json

Output Artifacts

Related Skills

• improvement-discriminator: Semantic scoring (LLM judge); learner focuses on structural quality
• improvement-orchestrator: Full pipeline; learner provides standalone quality scoring used by autoloop-controller and self-improvement loop (not a stage in the orchestrator pipeline)
• benchmark-store: Pareto front data shared between learner and benchmark-store