audit-study-validity
SkillDev toolsLets 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.
No other account needed.
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
- Select and scope the rubric for the study design.
- Judge each domain from reported methods and supporting material.
- Record signaling evidence, uncertainty, and direction of likely bias.
- 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-assessmentresolved: knowledge-acquisition-risk-of-bias-assessmentresolved: knowledge-acquisition-quality-assessment-protocolintermediate: Pass3/assess-study-qualityintermediate: 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