audit-modeling-evidence

name	audit-modeling-evidence
description	Audit existing mathematical-modeling data, backend ownership, experiments, innovation claims, results, constraints, reproducibility, and claim-to-evidence links. Use when the primary request is to check or validate an existing modeling project rather than solve a complete new problem.

Audit Modeling Evidence

Audit without silently repairing, rerunning, or promoting unsupported claims. Return the smallest defensible repair and a clear status: pass, warn, failed, or blocked.

Workflow

1. Identify source-of-truth files, active subquestions, evidence/data mode, ambition, workflow stage, result status, and audit scope.
2. Select only applicable lanes: intake/data; backend; method/experiment; candidate validation; simulator/decision trace; innovation/readiness; frozen evidence/manuscript.
3. Resolve SKILL_ROOT as this Skill directory and invoke scripts by absolute quoted path.
4. Run the smallest deterministic check first. Preserve input hashes, commands, environment, and artifact paths.
5. Distinguish observed, inferred, diagnostic-only, validated, frozen, and out-of-scope evidence. Rewriting a claim cannot turn failed evidence into a pass.
6. Report each issue with file, field/row, observed condition, required condition, affected downstream artifacts, and smallest repair.

Script routing

• Data/leakage: scripts/audit_dataset.py.
• MATLAB capability and numerical ownership: scripts/audit_backend_contract.py.
• Experiment budgets/runs: scripts/register_experiment.py, scripts/run_experiment.py.
• Candidate evidence/promotion: scripts/audit_candidate_evidence.py, scripts/promote_validated_candidate.py.