eval-agentic-cleanup

eval-agentic-cleanup is an LLM evaluation & observability skill for AI agents, published by open-thoughts in the OpenThoughts-Agent repository on GitHub. Skillspace rates eval-agentic-cleanup D (55/100) and it passed the safety scan; the rating is based on static analysis only.

Audit + recover a finished agentic eval. ALWAYS start with the read-only, idempotent completeness/health audit (§0): job finished? score present + non-zero + not obviously broken? HF traces present + linked? trial count ≈ n_rep × benchmark_size? — it writes nothing and recommends an action per check. Then run only the flagged remediations: manual HF-trace upload + Supabase DB-registration via manual_db_eval_push.py, the vLLM-numeric-ID → real-HF-model-name fix (with cross-user FK safety), verify, free disk. Use to verify an eval is truly complete, when a sweep finds an eval that didn't upload/register or has a broken/zero score or short trial count, or to re-register/correct an eval's model/traces. Distinct from the model- publishing cleanups (rl-agentic-job-cleanup / sft-job-cleanup) and datagen-job-cleanup — this is the EVAL path.

Grade

D

Quality

55 / 100

Safety

Passed

GitHub stars

259

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