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/extract-empirical-regularity

Extract a repeatable empirical pattern from observations, state its support and exceptions, and generalize cautiously without importing an unsupported mechanism.

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de-anthropocentric-research-engine
499200 skills
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity --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/extract-empirical-regularity

Context preview

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

Extract a repeatable empirical pattern from observations, state its support and exceptions, and generalize cautiously without importing an unsupported mechanism.

SKILL.md

extract-empirical-regularity.SKILL.md
name: extract-empirical-regularity
description: "Extract a repeatable empirical pattern from observations, state its support and exceptions, and generalize cautiously without importing an unsupported mechanism."

extract-empirical-regularity

Purpose

Extract a repeatable empirical pattern from records while preserving conditions, exceptions, and uncertainty.

Input contract

required: [observations, variable_schema, condition_schema]
optional: [replication_records, measurement_uncertainty]
constraints: [regularity claims require multiple comparable observations or an explicit single-case limitation]

Procedure

1. Normalize observations, units, and conditions. 2. Identify repeated associations, trends, or invariants. 3. Test exceptions, alternative explanations, and measurement artifacts. 4. State the regularity with scope and uncertainty boundaries.

If the regularity is stable enough to express through measurable factors and outcomes, consider `identify-variables` as the next tactic.

Output contract

produces: [regularity_statement, supporting_records, exception_set, scope_conditions]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]

Quality gates

  • Conditions and exceptions accompany every regularity.
  • Correlation is not labeled mechanism without supporting evidence.

Failure and counterexamples

Do not generalize a pattern across changed populations or protocols without a comparability check.

Provenance map

  • `resolved: extract-empirical-regularity`
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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
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Apache-2.0
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5h ago
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Repo: yogsoth-ai/de-anthropocentric-research-engine