# Causal Claim Extraction

> Causal Claim Extraction is an Agent Skill for documents published in the yogsoth-ai/de-anthropocentric-research-engine repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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.

- Skill name: `causal-claim-extraction`
- Category: [Documents](https://www.openparable.com/skill/documents/1)
- Source: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/causal-claim-extraction/SKILL.md
- Repository: yogsoth-ai/de-anthropocentric-research-engine (386 GitHub stars when collected)

## SKILL.md

# 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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## Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use |
| --- | --- |
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |

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Source: https://www.openparable.com/skill/documents/causal-claim-extraction (OpenParable). Cite the canonical URL above.