deterministic-metric-design

deterministic-metric-design is a Structured reasoning & decision frameworks skill for AI agents, published by pproenca in the dot-skills repository on GitHub. Skillspace rates deterministic-metric-design C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Inventing deterministic metrics — turning a fuzzy property like 'maintainability', 'risk', or 'how reducible this code is' into a deterministic, computable number an agent can trust and optimize. Covers the path from construct to adoption — operationalizing the construct, confronting computability limits (Kolmogorov, Rice) with sound proxies, picking the right measurement scale, proving properties (monotonicity, invariance, the Weyuker/Briand axioms), guaranteeing determinism, establishing construct validity (not just LOC in disguise), and hardening against Goodhart-style gaming when an agent optimizes the metric. Trigger when designing, reviewing, or validating a quantitative metric, score, measure, or index — and even when the user doesn't say 'metric' but wants to quantify, score, rank, or measure code/behavior, build a deterministic optimization target, or invent a measure for something previously unquantified (e.g., behavior-preserving codebase-size reduction).

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

180

Unproven: rated by static analysis only, not yet compared head-to-head.

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