Files
yao-meta-skill/reports/output_execution_runs.md
T
YAO 31ce04c655 Split meta skill CLI and review gates
Merge the beta-ready Yao Meta Skill architecture, report, evidence gate, and release-boundary updates.\n\nRelease boundary: beta/public testing is allowed; formal world-class, fully reviewed, or superiority claims remain blocked until the pending evidence gates are accepted.
2026-06-17 18:43:02 +08:00

2.0 KiB

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: 10
  • Recorded fixtures: 0
  • Timing observed: 10
  • Token observed: 10
  • Token estimated: 0
  • Delta: 20.0
  • Gate pass: True

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 model deepseek-v4-flash 5980.01 484 0.0 pass
skill-package-contract with_skill model deepseek-v4-flash 11217.19 1848 75.0 pass
output-eval-expectation baseline model deepseek-v4-flash 3178.95 236 0.0 pass
output-eval-expectation with_skill model deepseek-v4-flash 4611.78 1121 25.0 pass
ir-before-packaging baseline model deepseek-v4-flash 6772.59 765 25.0 pass
ir-before-packaging with_skill model deepseek-v4-flash 17471.19 2986 25.0 pass
near-neighbor-boundary baseline model deepseek-v4-flash 3091.33 198 33.33 pass
near-neighbor-boundary with_skill model deepseek-v4-flash 4152.85 1015 33.33 pass
file-backed-governed-package baseline model deepseek-v4-flash 4750.01 502 60.0 pass
file-backed-governed-package with_skill model deepseek-v4-flash 9406.25 1610 60.0 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.