review-log
Read an entire runs/<id>/log.md and assess research narrative coherence, identify stuck hypotheses, detect overfitting signs, and write a review markdown. Use after a full run completes or when a loop has been stuck for many rounds.
来源信息
- 仓库
- qualit527/qec-ai-decoder
- 最近来源活动
- 2026年4月22日 05:58
- 检测到的 SKILL.md 语言
- 英语
- 星标
- 3
- 分支
- 0
安装方式
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
检查来源文件
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
正在显示 SKILL.md
SKILL.md
来源说明 · 只读预览- name
- review-log
- description
- Read an entire runs/<id>/log.md and assess research narrative coherence, identify stuck hypotheses, detect overfitting signs, and write a review markdown. Use after a full run completes or when a loop has been stuck for many rounds.
# /review-log
## When to use
- A run of 10+ rounds has completed.
- User asks for a "research review" or "is the agent stuck?"
## Inputs
- `run_dir`: path to `runs/<id>/`
## Behavior
1. Run `python -m cli.autoqec review-log <run_dir>` to get structured stats (round count, Pareto size, top hypotheses, killed_by_safety count).
2. Read `log.md` fully.
3. LLM-reason:
- Narrative coherence: does each round build on previous findings?
- Stuck patterns: ≥3 rounds with near-identical hypotheses?
- Overfitting signs: Δ_LER monotonically improving on train seeds but not holdout?
- Safety-kill clustering: are VRAM or wall-clock kills bunched around one hypothesis family?
4. Write `runs/<id>/review.md` with: summary (5 sentences), top 3 concerns, recommended next actions.
## Output
- Path to `review.md`.
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