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/audit-study-validity

Audit a study against an appropriate methodological-quality and risk-of-bias rubric; return domain-level judgments, evidence, and overall confidence.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity --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/audit-study-validity

Context preview

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

Audit a study against an appropriate methodological-quality and risk-of-bias rubric; return domain-level judgments, evidence, and overall confidence.

SKILL.md

audit-study-validity.SKILL.md
name: audit-study-validity
description: "Audit a study against an appropriate methodological-quality and risk-of-bias rubric; return domain-level judgments, evidence, and overall confidence."

audit-study-validity

Purpose

Audit a study against an appropriate methodological-quality and risk-of-bias rubric and return domain judgments with evidence and confidence.

Input contract

required: [study_record, validity_rubric]
optional: [protocol, analysis_plan, supplementary_materials]
constraints: [rubric applicability and judgment rationale must be explicit]

Procedure

1. Select and scope the rubric for the study design. 2. Judge each domain from reported methods and supporting material. 3. Record signaling evidence, uncertainty, and direction of likely bias. 4. Aggregate domain judgments without hiding critical domain failures.

If the validity-screened corpus may already support a stopping decision, consider `assess-evidence-saturation` as the next tactic. If validity differs materially across designs, populations, or conditions, consider `analyze-heterogeneity` as the next tactic. If the conclusion depends on study exclusions or uncertain validity judgments, consider `assess-sensitivity` as the next tactic.

Output contract

produces: [domain_judgments, risk_of_bias_profile, evidence_basis, overall_confidence, applicability_notes]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]

Quality gates

  • No overall label is emitted without domain-level evidence.
  • Rubric choice and missing-data handling are recorded.

Failure and counterexamples

Do not average incompatible domains into a false precision score or treat unreported methods as low risk.

Provenance map

  • `resolved: knowledge-acquisition-quality-assessment`
  • `resolved: knowledge-acquisition-risk-of-bias-assessment`
  • `resolved: knowledge-acquisition-quality-assessment-protocol`
  • `intermediate: Pass3/assess-study-quality`
  • `intermediate: Pass3/assess-risk-of-bias`
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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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Repo: yogsoth-ai/de-anthropocentric-research-engine