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.
Source facts
- Repository
- qualit527/qec-ai-decoder
- Last source activity
- April 22, 2026 at 05:58
- Detected SKILL.md language
- English
- Stars
- 3
- Forks
- 0
Install options
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Review the source files
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Showing SKILL.md
SKILL.md
Source instructions · Read-only preview- 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`.
View on GitHub