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.
Quellinformationen
- Repository
- qualit527/qec-ai-decoder
- Letzte Quellaktivität
- 22. April 2026 um 05:58
- Erkannte Sprache von SKILL.md
- Englisch
- Sterne
- 3
- Forks
- 0
Installationsoptionen
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
SKILL.md wird angezeigt
SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau- 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`.
Auf GitHub ansehen