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/extract-causal-structure

Extract causal structure from source material at requested granularity: individual causal claims, ordered mechanism chains, mediators, assumptions, and stated boundary conditions.

From plugin
de-anthropocentric-research-engine
507200 skills

Claude Code

$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure --agent claude-code

SKILL.md

extract-causal-structure.SKILL.md
name: extract-causal-structure
description: "Extract causal structure from source material at requested granularity: individual causal claims, ordered mechanism chains, mediators, assumptions, and stated boundary conditions."

extract-causal-structure

Purpose

Extract causal claims, mechanism chains, mediators, assumptions, and boundary conditions at caller-specified granularity.

Input contract

required: [source_material, extraction_granularity, causal_schema]
optional: [theory_description, domain_ontology, evidence_links]
constraints: [preserve direction and scope; distinguish observed association from asserted causation; attach evidence]

Procedure

1. Identify candidate cause, mediator, effect, conditions, and temporal order. 2. Extract X->mediator->Y chains and direct X->Y claims at the requested granularity. 3. Record assumptions, boundary conditions, and evidence for each edge. 4. Assemble the causal graph and flag unsupported or ambiguous links.

If the extracted causal relations contain incompatible directions or effects under shared scope, consider `detect-contradiction` as the next tactic.

Output contract

produces: [causal_claims, mechanism_chains, causal_graph, boundary_conditions, evidence_links]
delta_fields: [findings, evidence_updates, hypothesis_updates, uncertainties]

Quality gates

  • Mechanism-extraction mode produces at least 1 chain per supplied theory and at least 2 chains in total where that source protocol applies.
  • Every edge has direction, scope, and evidence status; association is not upgraded to causation.
  • Biological strategy extraction preserves mechanism-level details of how function is achieved.

Parameterization

The caller must provide source material, theory or artifact schema, extraction granularity, causal edge vocabulary, boundary-condition fields, and evidence-link format.

Failure and counterexamples

Reject chains with missing direction, no stated mechanism, or evidence that supports only correlation while the output claims causation.

Provenance map

  • resolved: hypothesis-formation/mechanism-extraction
  • resolved: creative-ideation/biological-strategy-extraction
  • resolved: stress-test/causal-claim-extraction
  • intermediate: Pass3/extract-mechanism
  • intermediate: Pass3/extract-causal-claims

Preserved source criteria ledger

| source | physical line | kind | source criterion | |---|---:|---|---| | hypothesis-formation/mechanism-extraction | 22 | numeric | Produce X->mediator->Y mechanism chains, at least 1 per theory and at least 2 total where applicable. |

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@yogsoth-ai
Ships withde-anthropocentric-research-engine

A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.

Stars on GitHub

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Star history of yogsoth-ai/de-anthropocentric-research-engine on GitHub, a chart by star-history.com

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Questions about extract-causal-structure.

Which coding agents can use extract-causal-structure?
extract-causal-structure 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.