analyze-heterogeneity

SkillDev tools

Lets 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.

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

  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.

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-analysis
  • resolved: 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