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.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity -a windsurf -y
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.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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