计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
result-logging【效率与协作】Understand, validate, summarize, compare, and safely route lm-evaluation-harness result artifacts, sample logs, cache files, and optional external logging integrations.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when the user needs help interpreting saved lm-eval outputs, validating result JSON, comparing finished runs, preparing logging arguments, or converting local result files into tables.
results_*.json files and optional per-task samples_*.jsonl logs.results, groups, configs, versions, n-shot, higher_is_better, n-samples, samples, config, task_hashes, tokenizer metadata, and environment metadata.--output_path, --log_samples, --predict_only, --wandb_args, --trackio_args, and --hf_hub_log_args without exposing credentials.../evaluation-runs/.../model-backends/.../task-authoring/.references/result-schema.md.references/result-workflows.md.references/troubleshooting.md.python scripts/summarize_results.py path/to/results.json
python scripts/compare_results.py run_a.json run_b.json --metric acc
HF_TOKEN, W&B, Trackio, Zeno, or config files.predict_only is used, expect predictions/sample logs but no aggregate metrics.log_samples is requested, require an output_path; sample logs are not useful if they are never written.