# Output Execution Runs This report records how output-eval variants were produced and whether timing or token evidence is observed or estimated. - Cases: `5` - Variant runs: `10` - Command executed: `10` - Model executed: `0` - Recorded fixtures: `0` - Timing observed: `10` - Token observed: `0` - Token estimated: `10` - Delta: `100.0` - Gate pass: `True` No model-executed runs are recorded yet. Use `python3 scripts/yao.py output-exec --provider-runner openai` or `--runner-command` with a reviewed provider-backed runner to replace recorded fixtures with real model output evidence. Command runner evidence is present. This proves the eval harness executed an external command, but it is not provider-backed model evidence unless the runner reports model metadata. ## Runs | Case | Variant | Mode | Model | Duration ms | Tokens | Score | Status | | --- | --- | --- | --- | ---: | ---: | ---: | --- | | skill-package-contract | baseline | command | local-output-eval-runner | 28.76 | 33 | 0.0 | pass | | skill-package-contract | with_skill | command | local-output-eval-runner | 27.9 | 73 | 100.0 | pass | | output-eval-expectation | baseline | command | local-output-eval-runner | 27.6 | 36 | 0.0 | pass | | output-eval-expectation | with_skill | command | local-output-eval-runner | 28.21 | 80 | 100.0 | pass | | ir-before-packaging | baseline | command | local-output-eval-runner | 28.73 | 33 | 0.0 | pass | | ir-before-packaging | with_skill | command | local-output-eval-runner | 28.14 | 80 | 100.0 | pass | | near-neighbor-boundary | baseline | command | local-output-eval-runner | 28.71 | 36 | 0.0 | pass | | near-neighbor-boundary | with_skill | command | local-output-eval-runner | 28.58 | 65 | 100.0 | pass | | file-backed-governed-package | baseline | command | local-output-eval-runner | 28.49 | 37 | 0.0 | pass | | file-backed-governed-package | with_skill | command | local-output-eval-runner | 27.46 | 98 | 100.0 | pass | ## Next Fixes - Keep recorded fixtures as reproducible baselines, but do not describe them as model-executed evidence. - Use `scripts/provider_output_eval_runner.py` for provider-backed holdout cases when release confidence depends on real generation behavior. - Compare timing, token cost, and assertion deltas before promoting a skill to governed reuse.