abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Construct and validate an explicit causal model with measurable variables, mechanism edges, evidence links, feedback loops, interventions, counterevidence, and confidence.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/construct-causal-modelContext preview
The summary Claude sees to decide when to auto-load this skill.
Construct and validate an explicit causal model with measurable variables, mechanism edges, evidence links, feedback loops, interventions, counterevidence, and confidence.
name: construct-causal-model description: "Construct and validate an explicit causal model with measurable variables, mechanism edges, evidence links, feedback loops, interventions, counterevidence, and confidence."
Construct and validate an explicit causal model with measurable variables, mechanism edges, evidence links, feedback loops, interventions, counterevidence, and confidence.
required: [variable_records, mechanism_candidates, evidence_records] optional: [assumptions, prior_findings, evidence_updates] constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
1. You MUST load skill `identify-variables` to inventory outcomes, factors, mediators, moderators, confounders, and assumptions before drawing any edge. 2. You MUST load skill `extract-causal-structure` to extract directed cause-mediator-effect chains with temporal order, boundary conditions, and evidence anchors. 3. You MUST load skill `represent-mechanism-edge` to encode each mechanism edge with pathway, sign, enabling conditions, falsifier, and strength. 4. You MUST load skill `attach-evidence-to-relation` to attach independent supporting and contradicting evidence while retaining alternative interpretations. 5. You MUST load skill `detect-contradiction` to compare opposing causal claims under common scope before validation. 6. You MUST load skill `detect-feedback-loop` to find reinforcing and balancing cycles, delays, uncertain edges, and testable loop implications. 7. You MUST load skill `trace-causal-chain` to trace target outcomes through intermediate nodes, branches, and because-links. 8. You MUST load skill `analyze-intervention` to map intervention components, dose, timing, implementation fidelity, comparators, and heterogeneous effects. 9. You MUST load skill `construct-counterfactual` to propagate a declared intervention and classify the counterfactual outcome. 10. You MUST load skill `validate-causal-link` to apply CLR-style checks for existence, connection, sufficiency, omitted conditions, and alternatives. 11. You MUST load skill `update-confidence-from-evidence` to reweight confidence while preserving unresolved conflicts and permitted bounds. If the model should generate testable explanations, consider `formulate-hypotheses`. If a specific intervention needs minimal-flip and necessity analysis, consider `counterfactual-causal-analysis`.
Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.
produces: [causal_graph, mechanism_edges, intervention_implications, confidence_updates] delta_fields: [findings, decisions]
Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.
| source | criterion | treatment | |---|---|---| | resolved v3 entries above | node-specific criteria | retained and specialized to the v4 object contract | | experiment-execution/statistical-testing | $\alpha$ = 0.05 | fixed value retained where applicable | | experiment-execution/sample-size-estimation | power = 0.8 | fixed value retained where applicable |
Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as 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.