Cluster an evidence corpus by theme, method, chronology, mechanism, or another declared schema.
Scanned 9/24/2026
npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: categorize-evidence
description: "Cluster an evidence corpus by theme, method, chronology, mechanism, or another declared schema."
---
# categorize-evidence
## Purpose
Cluster an evidence corpus by a declared thematic, methodological, chronological, mechanistic, or other schema.
## Input contract
```yaml
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
```yaml
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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