abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Audit a study against an appropriate methodological-quality and risk-of-bias rubric; return domain-level judgments, evidence, and overall confidence.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/audit-study-validityContext 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.
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 a study against an appropriate methodological-quality and risk-of-bias rubric and return domain judgments with evidence and confidence.
required: [study_record, validity_rubric] optional: [protocol, analysis_plan, supplementary_materials] constraints: [rubric applicability and judgment rationale must be explicit]
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.
produces: [domain_judgments, risk_of_bias_profile, evidence_basis, overall_confidence, applicability_notes] delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
Do not average incompatible domains into a false precision score or treat unreported methods as low risk.
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
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Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.