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
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mmlu_answer_scoring
mmlu_answer_scoringCompute MMLU option-label accuracy and confidence calibration gap from model predictions.
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentation de la Skill
GitHubname: mmlu_answer_scoring description: Compute MMLU option-label accuracy and confidence calibration gap from model predictions.
mmlu_answer_scoring
Use this skill when a recovery or evaluation task needs the MMLU mechanism represented by this module. Do not use it as a substitute for a full model evaluation unless the run explicitly declares a reduced proxy target.
Inputs
Inputs are JSON-compatible dictionaries following the module document contract. Required fields are validated by the script in scripts/.
Outputs
Outputs are JSON-compatible dictionaries with normalized labels, prompts, scores, or recovery artifacts. Downstream modules should consume these outputs instead of parsing free-form text.
Workflow
- Read the paper profile and module plan to confirm the selected target.
- Call the script or import the helper functions from
scripts/. - Preserve the boundary between item construction, prompt construction, scoring, and recovery logging.
- Run the validation command after changes:
python <distiller>/module-to-skill/scripts/validate_skill_tree.py <skill_dir> --run-tests.
Limitations
This skill captures the evaluation mechanism, not the full hosted GPT-3 runtime or the full private benchmark distribution. Soft-mode recovery must label reduced results as proxy evidence.
Refinement Note
Cycle 02 confirms sentence-form answer extraction is part of the scoring contract.