abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Identify clinical, methodological, and statistical heterogeneity sources and prioritize subgroup/meta-regression/outlier investigation.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-heterogeneity --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-heterogeneity -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-heterogeneity -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-heterogeneity -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-heterogeneity -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-heterogeneity -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-heterogeneity -a windsurf -y
name: analyze-heterogeneity description: "Identify clinical, methodological, and statistical heterogeneity sources and prioritize subgroup/meta-regression/outlier investigation."
Identify clinical, methodological, and statistical heterogeneity sources and prioritize subgroup, meta-regression, or outlier investigation.
required: [evidence_records, outcome_schema, comparison_context] optional: [covariate_schema, effect_estimates, subgroup_hypotheses] constraints: [heterogeneity claims require record-level conditions and provenance]
1. Group records by declared design, population, intervention, outcome, and condition dimensions. 2. Compare effect or performance patterns within and across groups. 3. Identify plausible moderators, outliers, and confounding condition differences. 4. Prioritize subgroup or meta-regression investigations and record uncertainty.
If unexplained variation may reflect selective reporting rather than substantive moderators, consider `assess-publication-bias` as the next tactic.
produces: [heterogeneity_sources, moderator_candidates, outlier_list, investigation_priorities] delta_fields: [findings, evidence_updates, uncertainties, open_questions]
Do not label ordinary measurement noise as a causal subgroup effect, and do not pool records whose condition vectors are incomparable.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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