abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Decompose an evaluation metric into rewarded signals, aggregation choices, polarity, ceiling effects, and Goodhart vulnerabilities.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill decompose-evaluation-metric --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/decompose-evaluation-metricContext 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.
name: decompose-evaluation-metric description: "Decompose an evaluation metric into rewarded signals, aggregation choices, polarity, ceiling effects, and Goodhart vulnerabilities."
Decompose an evaluation metric into rewarded signals, aggregation choices, polarity, ceiling effects, and Goodhart vulnerabilities.
required: [metric_definition, scored_outputs] optional: [reference_standard, aggregation_rule, known_failure_cases] constraints: [each component must have a declared direction and interpretation]
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.
produces: [metric_components, aggregation_map, polarity_and_scale, ceiling_analysis, goodhart_risks] delta_fields: [findings, evidence_updates, uncertainties, open_questions]
Do not infer metric meaning from its name or ignore nonlinear aggregation and clipping.
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
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Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.