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/construct-causal-model

Construct and validate an explicit causal model with measurable variables, mechanism edges, evidence links, feedback loops, interventions, counterevidence, and confidence.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-causal-model --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/construct-causal-model

Context 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.

SKILL.md

construct-causal-model.SKILL.md
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-causal-model

Purpose

Construct and validate an explicit causal model with measurable variables, mechanism edges, evidence links, feedback loops, interventions, counterevidence, and confidence.

Input contract

required: [variable_records, mechanism_candidates, evidence_records]
optional: [assumptions, prior_findings, evidence_updates]
constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]

Execution protocol

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.

Output contract

produces: [causal_graph, mechanism_edges, intervention_implications, confidence_updates]
delta_fields: [findings, decisions]

Thresholds and quality gates

  • Each output is traceable to an input object, operation, and evidence reference.
  • Scope, assumptions, and unresolved alternatives remain explicit.
  • Retain $\alpha$ 0.05 and power 0.8 wherever the predeclared statistical design requires them.

Failure and counterexamples

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.

Provenance map

  • intermediate: knowledge-structuring/causal-modeling [campaign]
  • intermediate: variable-identification [strategy]
  • resolved: mechanism-mapping
  • resolved: evidence-collection
  • resolved: intervention-analysis
  • resolved: model-validation
  • resolved: counterfactual-reasoning
  • resolved: evidence-weighing

Preserved source criteria ledger

| 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 |

Context checkpoint / Delta notes

Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.

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