How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidenceBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
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
- Define category semantics and assignment rules.
- Code each record using the declared evidence fields.
- Review boundary cases and preserve multi-label or unresolved assignments.
- 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-papersconcept: knowledge-structuring/source-categorization-patternsintermediate: Pass4/map-field-taxonomy
