abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness designs are modes, not strategy nodes.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-experiment --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/design-experimentContext preview
The summary Claude sees to decide when to auto-load this skill.
Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness designs are modes, not strategy nodes.
name: design-experiment description: "Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness designs are modes, not strategy nodes."
Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness are modes.
Use after a hypothesis has an operational construct and discriminating prediction. Not applicable when the hypothesis or measurable outcome is absent.
mode_contracts:
factorial: &experiment_input
required: [falsifiable_hypothesis, outcome, factors, constraints]
optional: [baseline, candidate_models, budget, robustness_axes]
constraints: [analysis_plan_must_be_preregistered]
ablation: *experiment_input
comparison: *experiment_input
scaling: *experiment_input
robustness: *experiment_inputDo not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
1. You MUST load skill `identify-variables` to operationalize the outcome, factors, controls, and their functional roles. You MUST load skill `enumerate-dimension-values` to define admissible levels or perturbation values. If the resource envelope or feasibility constraints cannot support an executable design, consider `analyze-constraints-readiness` before committing to a mode. 2. Choose `factorial`, `ablation`, `comparison`, `scaling`, or `robustness` mode. 3. You MUST load skill `specify-metrics` to preregister metrics, estimands, directionality, and decision thresholds. You MUST load skill `estimate-sample-size` to derive the sample or repetition requirement. You MUST load skill `select-statistical-method` to select the inference or estimation method before observing outcomes; specify power and the stopping rule. If the proposed metric or validator may share artifacts, labels, or assumptions with the system under test, consider `audit-validator-independence` before freezing the analysis plan. 4. You MUST load skill `construct-design-matrix` to construct the runnable matrix for the selected mode. You MUST load skill `design-randomness-protocol` to define seeds, repetitions, and propagation rules. You MUST load skill `specify-execution-environment` to capture interpretation-relevant hardware, software, data, configuration, and versions. You MUST load skill `specify-reproducibility-protocol` to define and test the intended reproduction level. You MUST load skill `optimize-design-under-budget` to select a feasible information-efficient design under the declared resource envelope; include resource and failure checks. Once the design has produced observations and the task changes from planning to inference, `analyze-experiment-results` may be the better next tactic.
For `comparison` or `robustness`, You MUST load skill `select-experimental-baseline` to choose a controlled baseline matched to the claim.
mode_contracts:
factorial:
produces: [factor_level_matrix, factor_level_catalog, estimands_main_effects_interactions, metric_significance_plan, sample_power_plan]
delta_fields: [findings, decisions, uncertainties, open_questions]
ablation:
produces: [ablation_matrix, baseline_anchors_full_minimal, attribution_contrasts, component_interaction_plan]
delta_fields: [findings, decisions, uncertainties, open_questions]
comparison:
produces: [controlled_baseline_comparison, matched_confound_controls, seed_environment_protocol, statistical_comparison_plan, reproducibility_protocol]
delta_fields: [findings, decisions, uncertainties, open_questions]
scaling:
produces: [scaling_axes, geometric_scale_points, scaling_experiment_grid, curve_fit_plan, scale_budget_plan]
delta_fields: [findings, decisions, uncertainties, open_questions]
robustness:
produces: [perturbation_stress_matrix, severity_axes, baseline_comparison, degradation_metrics, survival_criteria]
delta_fields: [findings, decisions, uncertainties, open_questions]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.