audit-study-validity

SkillDev tools

Lets your agent check a research study for methodological quality and risk of bias using a suitable rubric.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the audit-study-validity skill

About this skill

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

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/audit-study-validity/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
501
Forks
41
Last commit
Sep 2026
Advanced
Catalog kind
skill
Key
audit-study-validity
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine