abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Extract causal structure from source material at requested granularity: individual causal claims, ordered mechanism chains, mediators, assumptions, and stated boundary conditions.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-causal-structure -a windsurf -y
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 claims, mechanism chains, mediators, assumptions, and boundary conditions at caller-specified granularity.
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]
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.
produces: [causal_claims, mechanism_chains, causal_graph, boundary_conditions, evidence_links] delta_fields: [findings, evidence_updates, hypothesis_updates, uncertainties]
The caller must provide source material, theory or artifact schema, extraction granularity, causal edge vocabulary, boundary-condition fields, and evidence-link format.
Reject chains with missing direction, no stated mechanism, or evidence that supports only correlation while the output claims causation.
| 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. |
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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