abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Interpret completed experimental outputs after host/runtime execution using pre-declared statistical tests, effect/uncertainty estimates, reproducibility checks, and calibrated synthesis.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-experiment-results --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/analyze-experiment-resultsContext preview
The summary Claude sees to decide when to auto-load this skill.
Interpret completed experimental outputs after host/runtime execution using pre-declared statistical tests, effect/uncertainty estimates, reproducibility checks, and calibrated synthesis.
name: analyze-experiment-results description: "Interpret completed experimental outputs after host/runtime execution using pre-declared statistical tests, effect/uncertainty estimates, reproducibility checks, and calibrated synthesis."
Interpret completed experimental outputs after host/runtime execution using pre-declared statistical tests, effect/uncertainty estimates, reproducibility checks, and calibrated synthesis.
required: [experiment_results, predeclared_analysis_plan, reproducibility_target] optional: [assumptions, prior_findings, evidence_updates] constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
1. You MUST load skill `statistical-testing` to run the preregistered statistical tests and retain effect uncertainty. 2. You MUST load skill `verify-reproducibility` to verify the declared reproduction level. If the results must be assembled into claims, evidence, and counterclaims, consider `construct-argument-map`. If several interventions or methods require comparative selection, consider `rank-candidates`.
Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.
produces: [effect_estimates, uncertainty_summary, reproducibility_assessment, interpretation] delta_fields: [uncertainties]
Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.
| source | criterion | treatment | |---|---|---| | resolved v3 entries above | node-specific criteria | retained and specialized to the v4 object contract | | experiment-execution/statistical-testing | $\alpha$ = 0.05 | fixed value retained where applicable | | experiment-execution/sample-size-estimation | power = 0.8 | fixed value retained where applicable |
Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Evaluate competing arguments against stated criteria and produce a reasoned verdict with uncertainty.
Move a scientific object up/down in abstraction or narrow/broaden selected scope dimensions (population, mechanism, context, outcome, timeframe, system…
Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner. Perspective, target, escalation depth,…
Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.