analyze-heterogeneity
SkillDev toolsLets your agent analyze claude skill evidence records to find sources of variation across studies and pick follow-up investigations.
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 analyze-heterogeneity skill
About this skill
Identify clinical, methodological, and statistical heterogeneity sources and prioritize subgroup/meta-regression/outlier investigation.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/analyze-heterogeneity/SKILL.md and read by ahel’s review.
Purpose
Identify clinical, methodological, and statistical heterogeneity sources and prioritize subgroup, meta-regression, or outlier investigation.
Input contract
required: [evidence_records, outcome_schema, comparison_context]
optional: [covariate_schema, effect_estimates, subgroup_hypotheses]
constraints: [heterogeneity claims require record-level conditions and provenance]
Procedure
- Group records by declared design, population, intervention, outcome, and condition dimensions.
- Compare effect or performance patterns within and across groups.
- Identify plausible moderators, outliers, and confounding condition differences.
- 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.
Output contract
produces: [heterogeneity_sources, moderator_candidates, outlier_list, investigation_priorities]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]
Quality gates
- Each heterogeneity source must point to affected records and a comparison dimension.
- Do not infer a moderator from a single discrepant record without an uncertainty marker.
Failure and counterexamples
Do not label ordinary measurement noise as a causal subgroup effect, and do not pool records whose condition vectors are incomparable.
Provenance map
resolved: heterogeneity-source-analysisresolved: heterogeneity-investigation
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
analyze-heterogeneity- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine