🧪 Agent Skill Model-Release Regression Harness

Agent Skill Model-Release Regression Harness turns recorded runner observations into a deterministic before-and-after release decision. For each fixture, provide four comparable observations: prior-model baseline, prior-model skill-assisted, candidate-model baseline, and candidate-model skill-assisted. The local Python helper validates exact model versions and dated HTTPS sources, calculates prior and candidate skill uplift, checks required output paths, compares tools and permissions, applies explicit cost and latency ratios, preserves CANNOT_ASSESS for missing runs, and returns CLEAR, REVIEW, or HOLD with stable finding codes and a SHA-256 evidence receipt. It never calls a provider, spends credits, accesses credentials, or creates a provider-specific adapter. The result is bounded evidence for the supplied fixtures and thresholds, not a general compatibility or safety certification.