abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Identify approximately independent dimensions/axes that span the relevant problem, design, or validity space and define their semantics.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill define-analysis-dimensions --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill define-analysis-dimensions -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill define-analysis-dimensions -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill define-analysis-dimensions -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill define-analysis-dimensions -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill define-analysis-dimensions -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill define-analysis-dimensions -a windsurf -y
name: define-analysis-dimensions description: "Identify approximately independent dimensions/axes that span the relevant problem, design, or validity space and define their semantics."
Define approximately independent, measurable axes that span a problem, design, or validity space.
required: [problem_or_artifact, target_outcome, dimension_ontology] optional: [candidate_axes, domain_constraints, measurement_plan] constraints: [each axis has semantics, type, direction, and measurement rule; correlated axes are flagged]
1. Extract factors and conditions relevant to the target outcome. 2. Group candidates into dimensions and test approximate independence. 3. Define each dimension's domain, units, direction, and observability. 4. Return the dimension set with exclusions and unresolved dependencies.
If each analysis dimension has a clear meaning and admissible type, consider `enumerate-dimension-values` as the next tactic.
produces: [dimension_set, axis_definitions, independence_notes, coverage_scope] delta_fields: [findings, evidence_updates, uncertainties, open_questions]
The caller must provide the target problem, outcome, axis ontology, candidate factors, domain bounds, measurement units, and independence criterion.
Reject duplicate axes, dimensions with no observable values, or an independence claim unsupported by a comparison.
| source | physical line | kind | source criterion | |---|---:|---|---| | deep-insight/variation-axis-definition | 14 | structural | Identify orthogonal axes, ensuring they are independent, measurable, and span the parameter space. |
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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