decompose-evaluation-metric
SkillMonitoring & opsLets 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.
No other account needed.
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
- Split the metric into primitive signals and aggregation operations.
- Record polarity, scale, weighting, normalization, and ceiling/floor behavior.
- Map rewarded shortcuts and construct-irrelevant incentives.
- 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