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Maintain and apply RASS v1 scorecards with fixed weights, hard gates, and anti-gaming protections.
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Based on SOC occupation classification
| name | scorecard |
| description | Maintain and apply RASS v1 scorecards with fixed weights, hard gates, and anti-gaming protections. |
RASS v1 uses five weighted categories. Effectiveness is 35%. Efficiency is 20%. Reliability and safety are 20%. Memory and research quality are 15%. Maintainability and simplicity are 10%.
Every scorecard must include these hard gates. Tests pass. No secret leak. Eval files unchanged. No scope violation. No eval gaming.
Version scorecards explicitly. Keep metric names concrete and observable. Prefer a small set of metrics that a script can normalize to 0..1. Do not change a scorecard during the run that uses it.
Fixtures are protected eval inputs. Do not rewrite them during candidate evaluation. If a fixture or scorecard needs improvement, open a separate phase or commit and re-baseline before using it for candidate decisions.
Use scripts/evals/run_scorecard.py for single fixtures and suite-specific scripts for richer flows. A candidate can be kept only when the hard gates pass and the documented threshold is met.
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