abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
@yogsoth-aiInstall SkillAutomation
Skill
/construct-evidence-network
Construct a network-meta-analysis evidence graph and evaluate connectivity/transitivity prerequisites.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-evidence-network --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-evidence-network -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-evidence-network -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-evidence-network -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-evidence-network -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-evidence-network -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-evidence-network -a windsurf -y
SKILL.md
construct-evidence-network.SKILL.mdname: 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
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
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`
Read more
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
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
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`
Ships withde-anthropocentric-research-engine
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
507200 skills
Stars on GitHub
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- 507
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Questions about construct-evidence-network.
- Which coding agents can use construct-evidence-network?
- construct-evidence-network installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.