extract-causal-structure
SkillDev toolsLets your agent pull causal claims, mechanism chains, and assumptions out of source material as a structured graph.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the extract-causal-structure skill
About this skill
Extract causal structure from source material at requested granularity: individual causal claims, ordered mechanism chains, mediators, assumptions, and stated boundary conditions.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/extract-causal-structure/SKILL.md and read by ahel’s review.
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
- Identify candidate cause, mediator, effect, conditions, and temporal order.
- Extract X->mediator->Y chains and direct X->Y claims at the requested granularity.
- Record assumptions, boundary conditions, and evidence for each edge.
- 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. |
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
extract-causal-structure- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine