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Automation
Skill

/categorize-evidence

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

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/categorize-evidence

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

categorize-evidence.SKILL.md
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

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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Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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