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/normalize-comparison-scale

Normalize heterogeneous values onto a declared comparison scale while preserving direction, units, uncertainty, and the transformation rule. The normalized object may be criterion scores, resource budgets, or another comparable quantity.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill normalize-comparison-scale --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/normalize-comparison-scale

Context preview

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

Normalize heterogeneous values onto a declared comparison scale while preserving direction, units, uncertainty, and the transformation rule. The normalized object may be criterion scores, resource budgets, or another comparable quantity.

SKILL.md

normalize-comparison-scale.SKILL.md
name: normalize-comparison-scale
description: "Normalize heterogeneous values onto a declared comparison scale while preserving direction, units, uncertainty, and the transformation rule. The normalized object may be criterion scores, resource budgets, or another comparable quantity."

normalize-comparison-scale

Purpose

Normalize heterogeneous values onto a declared comparison scale while preserving direction, units, uncertainty, and transformation rule.

Input contract

required: [value_set, criterion_definitions, normalization_rule]
optional: [bounds, missing_value_policy, uncertainty_model]
constraints: [direction and units are preserved; rule is declared before transformation]

Procedure

1. Validate units, direction, bounds, and missingness. 2. Apply the caller-supplied transformation to each value. 3. Preserve uncertainty and retain original values for audit. 4. Return normalized values and out-of-bound diagnostics.

Output contract

produces: [normalized_values, transformation_record, uncertainty_preservation, diagnostics]
delta_fields: [evidence_updates, uncertainties, open_questions]

Quality gates

  • All normalized values lie in the caller-declared range, commonly [0, 1].
  • Maximize/minimize directions are explicit and correctly oriented.
  • Missing and extrapolated values are labeled, never silently imputed.

Parameterization

Caller supplies value schema, units, direction labels, bounds, transformation formula, and missingness policy.

Failure and counterexamples

Reject mixed units, hidden inversion, or normalization that discards uncertainty and source values.

Provenance map

  • concept: convergence/normalization
  • intermediate: Pass4/normalize-scores
  • concept: knowledge-acquisition/compute-normalization
  • intermediate: Pass4/normalize-compute-budget
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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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Python
Language
Apache-2.0
License
15h ago
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7mo ago
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Repo: yogsoth-ai/de-anthropocentric-research-engine