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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.

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de-anthropocentric-research-engine
499200 skills
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure --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/extract-causal-structure

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

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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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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Repo: yogsoth-ai/de-anthropocentric-research-engine