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/decompose-evaluation-metric

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

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill decompose-evaluation-metric --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/decompose-evaluation-metric

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

decompose-evaluation-metric.SKILL.md
name: decompose-evaluation-metric
description: "Decompose an evaluation metric into rewarded signals, aggregation choices, polarity, ceiling effects, and Goodhart vulnerabilities."

decompose-evaluation-metric

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`
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Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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