abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Extract causal structure from source material at requested granularity: individual causal claims, ordered mechanism chains, mediators, assumptions, and stated boundary conditions.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/extract-causal-structureContext preview
The summary Claude sees to decide when to auto-load this skill.
Extract causal structure from source material at requested granularity: individual causal claims, ordered mechanism chains, mediators, assumptions, and stated boundary conditions.
name: extract-causal-structure description: "Extract causal structure from source material at requested granularity: individual causal claims, ordered mechanism chains, mediators, assumptions, and stated boundary conditions."
Extract causal claims, mechanism chains, mediators, assumptions, and boundary conditions at caller-specified granularity.
required: [source_material, extraction_granularity, causal_schema] optional: [theory_description, domain_ontology, evidence_links] constraints: [preserve direction and scope; distinguish observed association from asserted causation; attach evidence]
1. Identify candidate cause, mediator, effect, conditions, and temporal order. 2. Extract X->mediator->Y chains and direct X->Y claims at the requested granularity. 3. Record assumptions, boundary conditions, and evidence for each edge. 4. Assemble the causal graph and flag unsupported or ambiguous links.
If the extracted causal relations contain incompatible directions or effects under shared scope, consider `detect-contradiction` as the next tactic.
produces: [causal_claims, mechanism_chains, causal_graph, boundary_conditions, evidence_links] delta_fields: [findings, evidence_updates, hypothesis_updates, uncertainties]
The caller must provide source material, theory or artifact schema, extraction granularity, causal edge vocabulary, boundary-condition fields, and evidence-link format.
Reject chains with missing direction, no stated mechanism, or evidence that supports only correlation while the output claims causation.
| source | physical line | kind | source criterion | |---|---:|---|---| | hypothesis-formation/mechanism-extraction | 22 | numeric | Produce X->mediator->Y mechanism chains, at least 1 per theory and at least 2 total where applicable. |
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.