decompose-evaluation-metric

SkillMonitoring & ops

Lets your agent break down an evaluation metric into its signals, aggregation rules, and gaming risks.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the decompose-evaluation-metric skill

About this skill

Decompose an evaluation metric into rewarded signals, aggregation choices, polarity, ceiling effects, and Goodhart vulnerabilities.

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/decompose-evaluation-metric/SKILL.md and read by ahel’s review.

Purpose

Decompose an evaluation metric into rewarded signals, aggregation choices, polarity, ceiling effects, and Goodhart vulnerabilities.

Input contract

required: [metric_definition, scored_outputs]
optional: [reference_standard, aggregation_rule, known_failure_cases]
constraints: [each component must have a declared direction and interpretation]

Procedure

  1. Split the metric into primitive signals and aggregation operations.
  2. Record polarity, scale, weighting, normalization, and ceiling/floor behavior.
  3. Map rewarded shortcuts and construct-irrelevant incentives.
  4. State interpretation limits and diagnostic needs.

If metric components are explicit but their link to the intended construct remains uncertain, consider assess-construct-validity as the next tactic.

Output contract

produces: [metric_components, aggregation_map, polarity_and_scale, ceiling_analysis, goodhart_risks]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]

Quality gates

  • Component contributions and aggregation are reconstructible.
  • A high score is not treated as capability evidence without construct support.

Failure and counterexamples

Do not infer metric meaning from its name or ignore nonlinear aggregation and clipping.

Provenance map

  • resolved: metric-decomposition

Signals

GitHub stars
501
Forks
41
Last commit
Sep 2026
Advanced
Catalog kind
skill
Key
decompose-evaluation-metric
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine