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