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PthahnixPthahnix/De-Anthropocentric-Research-Engine/skills/analyze-heterogeneity/SKILL.md

analyze-heterogeneity

Skill504 starsChanged 49 days agoApache-2.0View on GitHub

Identify clinical, methodological, and statistical heterogeneity sources and prioritize subgroup/meta-regression/outlier investigation.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If unexplained variation may reflect selective reporting rather than substantive moderators, consider assess-publication-bias as the next …

    line 27
  2. Distinctive:

    Do not infer a moderator from a single discrepant record without an uncertainty marker.

    line 39
  3. Distinctive:

    Do not label ordinary measurement noise as a causal subgroup effect, and do not pool records whose condition vectors are incomparable.

    line 43

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

analyze-heterogeneity

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

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