abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Cluster an evidence corpus by theme, method, chronology, mechanism, or another declared schema.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/categorize-evidenceContext 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.
name: categorize-evidence description: "Cluster an evidence corpus by theme, method, chronology, mechanism, or another declared schema."
Cluster an evidence corpus by a declared thematic, methodological, chronological, mechanistic, or other schema.
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]
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.
produces: [categorized_corpus, category_definitions, boundary_cases, coverage_summary] delta_fields: [findings, evidence_updates, uncertainties, open_questions]
Do not force records into categories whose definitions do not fit, and do not confuse frequency with evidential importance.
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Evaluate competing arguments against stated criteria and produce a reasoned verdict with uncertainty.
Move a scientific object up/down in abstraction or narrow/broaden selected scope dimensions (population, mechanism, context, outcome, timeframe, system…
Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner. Perspective, target, escalation depth,…
Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.