Construct a network-meta-analysis evidence graph and evaluate connectivity/transitivity prerequisites.
Scanned 9/24/2026
npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-evidence-network --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: construct-evidence-network
description: "Construct a network-meta-analysis evidence graph and evaluate connectivity/transitivity prerequisites."
---
# construct-evidence-network
## Purpose
Construct an evidence graph for network synthesis and evaluate connectivity, transitivity, and comparison support.
## Input contract
```yaml
required: [evidence_records, intervention_nodes, outcome_schema]
optional: [study_designs, effect_sizes, covariate_schema]
constraints: [edges require a shared outcome definition and traceable comparison evidence]
```
## Procedure
1. Normalize interventions, comparators, outcomes, and study-level edges.
2. Build the direct and indirect comparison graph with effect-support provenance.
3. Check connectivity and transitivity assumptions across connected components.
4. Report usable components, unsupported links, and synthesis limitations.
## Output contract
```yaml
produces: [evidence_graph, connected_components, transitivity_assessment, unsupported_links, synthesis_readiness]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
```
## Quality gates
- Every edge points to a study record and outcome definition.
- Disconnected components are not silently combined.
- Transitivity judgments list the effect modifiers examined.
## Failure and counterexamples
Do not create indirect evidence from incomparable populations or outcomes, and do not treat graph connectivity as proof of exchangeability.
## Provenance map
- `resolved: knowledge-acquisition-evidence-network-construction`
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