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yogsoth-aiyogsoth-ai/de-anthropocentric-research-engine/skills/categorize-evidence/SKILL.md

categorize-evidence

Skill505 starsChanged 49 days agoApache-2.0View on GitHub

Cluster an evidence corpus by theme, method, chronology, mechanism, or another declared schema.

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 categorized sources now need claim-level structured records, consider extract-evidence-record as the next tactic.

    line 27
  2. Distinctive:

    Every assignment is traceable to record fields.

    line 39
  3. Distinctive:

    Do not force records into categories whose definitions do not fit, and do not confuse frequency with evidential importance.

    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

categorize-evidence

Purpose

Cluster an evidence corpus by a declared thematic, methodological, chronological, mechanistic, or other schema.

Input contract

required: [evidence_records, category_schema]
optional: [coding_rules, multi_label_policy, seed_categories]
constraints: [category assignments require record evidence and allow explicit multi-label or unknown states]

Procedure

  1. Define category semantics and assignment rules.
  2. Code each record using the declared evidence fields.
  3. Review boundary cases and preserve multi-label or unresolved assignments.
  4. Summarize category coverage and representative records.

If categorized sources now need claim-level structured records, consider extract-evidence-record as the next tactic.

Output contract

produces: [categorized_corpus, category_definitions, boundary_cases, coverage_summary]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]

Quality gates

  • Categories are mutually interpretable even when not mutually exclusive.
  • Every assignment is traceable to record fields.

Failure and counterexamples

Do not force records into categories whose definitions do not fit, and do not confuse frequency with evidential importance.

Provenance map

  • resolved: knowledge-acquisition-categorize-papers
  • concept: knowledge-structuring/source-categorization-patterns
  • intermediate: Pass4/map-field-taxonomy

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