abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-data-extraction --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/design-data-extractionContext preview
The summary Claude sees to decide when to auto-load this skill.
Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.
name: design-data-extraction description: "Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis."
Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.
required: [evidence_question, record_schema] optional: [coding_guidance, unit_rules, quality_fields] constraints: [field definitions and missing-data states must be explicit]
1. Derive fields from the evidence question and synthesis outputs. 2. Define types, units, allowed values, coding rules, and provenance fields. 3. Specify unknown, not reported, not applicable, and ambiguous states. 4. Pilot the form on boundary records and revise only documented ambiguities.
If the extraction schema is fixed and studies are ready for methodological appraisal, consider `audit-study-validity` as the next tactic.
produces: [extraction_schema, coding_rules, missing_data_policy, pilot_issues] delta_fields: [decisions, assumption_updates, uncertainties, open_questions]
Do not add fields that cannot affect the synthesis, and do not collapse incomparable units.
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.