abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Extract a repeatable empirical pattern from observations, state its support and exceptions, and generalize cautiously without importing an unsupported mechanism.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity -a windsurf -y
name: extract-empirical-regularity description: "Extract a repeatable empirical pattern from observations, state its support and exceptions, and generalize cautiously without importing an unsupported mechanism."
Extract a repeatable empirical pattern from records while preserving conditions, exceptions, and uncertainty.
required: [observations, variable_schema, condition_schema] optional: [replication_records, measurement_uncertainty] constraints: [regularity claims require multiple comparable observations or an explicit single-case limitation]
1. Normalize observations, units, and conditions. 2. Identify repeated associations, trends, or invariants. 3. Test exceptions, alternative explanations, and measurement artifacts. 4. State the regularity with scope and uncertainty boundaries.
If the regularity is stable enough to express through measurable factors and outcomes, consider `identify-variables` as the next tactic.
produces: [regularity_statement, supporting_records, exception_set, scope_conditions] delta_fields: [findings, evidence_updates, uncertainties, open_questions]
Do not generalize a pattern across changed populations or protocols without a comparability check.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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