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.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model -a windsurf -y
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.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
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