causal-claim-extraction

$npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/causal-claim-extraction

Extracts cause-effect relationships from artifacts as structured claims

  • Identifies explicit and implicit causal relationships in text
  • Uses linguistic analysis and subagent execution for accuracy
  • Analyzes artifact type and content to detect X causes Y patterns
  • Returns structured claims list and causal graph for downstream use
SKILL.md
.github/skills/causal-claim-extractionView on GitHub ↗
---
name: causal-claim-extraction
description: Extract all causal claims (X causes Y, X leads to Y, X enables Y) from an artifact, producing a structured list of cause-effect pairs.
execution: subagent
prompt: ./prompt.md
input: artifact (string), artifact_type (string)
used-by: [counterfactual-probing]
---

# Causal Claim Extraction

Extracts all explicit and implicit causal claims from an artifact.

## Execution

Subagent — spawned via subagent-spawning/spawn-agent.

## Why Subagent

Causal claim extraction requires careful linguistic analysis of the entire artifact. Isolated context prevents premature evaluation of claims.

## Input

- **artifact**: The artifact to analyze
- **artifact_type**: Type of artifact (gap, hypothesis, claim, etc.)

## Output

- **causal_claims**: List of {cause, effect, strength, evidence, location}
- **causal_graph**: Directed graph of cause-effect relationships
- **claim_count**: Total number of causal claims found

## Budget

One unit = one extraction pass per artifact.
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