abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
@yogsoth-aiInstall SkillAutomation
Skill
/categorize-evidence
Cluster an evidence corpus by theme, method, chronology, mechanism, or another declared schema.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence -a windsurf -y
SKILL.md
categorize-evidence.SKILL.mdname: 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`
Read more
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`
Ships withde-anthropocentric-research-engine
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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Questions about categorize-evidence.
- Which coding agents can use categorize-evidence?
- categorize-evidence installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.