executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
result-loggingUnderstand, validate, summarize, compare, and safely route lm-evaluation-harness result artifacts, sample logs, cache files, and optional external logging integrations.
Independent GitHub snapshot · Automated classification · Original documentation governs
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.