design-experiment
SkillMediaLets your agent design claude skill experiments that turn a falsifiable hypothesis into a statistically sound test plan.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the design-experiment skill
About this skill
Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness designs are modes, not strategy nodes.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/design-experiment/SKILL.md and read by ahel’s review.
Purpose
Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness are modes.
When to use / not applicable
Use after a hypothesis has an operational construct and discriminating prediction. Not applicable when the hypothesis or measurable outcome is absent.
Input contract
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_input
Execution protocol
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
- You MUST load skill
identify-variablesto operationalize the outcome, factors, controls, and their functional roles. You MUST load skillenumerate-dimension-valuesto define admissible levels or perturbation values. If the resource envelope or feasibility constraints cannot support an executable design, consideranalyze-constraints-readinessbefore committing to a mode. - Choose
factorial,ablation,comparison,scaling, orrobustnessmode. - You MUST load skill
specify-metricsto preregister metrics, estimands, directionality, and decision thresholds. You MUST load skillestimate-sample-sizeto derive the sample or repetition requirement. You MUST load skillselect-statistical-methodto 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, consideraudit-validator-independencebefore freezing the analysis plan. - You MUST load skill
construct-design-matrixto construct the runnable matrix for the selected mode. You MUST load skilldesign-randomness-protocolto define seeds, repetitions, and propagation rules. You MUST load skillspecify-execution-environmentto capture interpretation-relevant hardware, software, data, configuration, and versions. You MUST load skillspecify-reproducibility-protocolto define and test the intended reproduction level. You MUST load skilloptimize-design-under-budgetto 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-resultsmay be the better next tactic.
Mode branches
For comparison or robustness, You MUST load skill select-experimental-baseline to choose a controlled baseline matched to the claim.
factorial: vary multiple factors in a structured design so main effects and interactions are estimable within the declared resource envelope.ablation: remove or replace components systematically to attribute the outcome to individual parts and suspected interactions. You MUST load skillmap-ablation-componentsto define ablatable units, dependencies, and legal removal or replacement operations.comparison: evaluate a target against controlled baselines with matched confounds, compute, tuning effort, and preregistered tests.scaling: instantiate geometric or otherwise justified scale points to test how the outcome changes across the declared regime.robustness: perturb relevant conditions or inputs and measure whether the claimed effect survives the defined stress space.
Output contract
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]
Thresholds and quality gates
experiment-designHARD-GATE and Budget Gate remain mandatory; no exit before declared minimum yield.- Resource and run-scale gates are relative to the declared resource envelope and eligible factor/condition space; record numerator, denominator, batch increment, stopping reason, and source references.
- Scale, ablation, comparison, and robustness coverage must reach a justified relative floor over the declared design space; do not substitute an unreasoned fixed run count.
- Factor levels, comparison baseline, statistical test, significance threshold, sample-size rationale, and stopping rule must all be explicit.
- Significance threshold must be pre-registered, not chosen post-hoc.
- Budget-constrained design must report at least one feasible design under the stated resource envelope.
- Scaling mode retains the source criterion of a geometric progression, typically 4-8 points, while evaluating coverage relative to the declared scale domain. Relative audit: declared universe = eligible scale domain; numerator = scale points instantiated; batch increment = points added per design pass; stopping reason = predeclared coverage or saturation rule; source references = scaling-design ledger and design records; direction/threshold rationale = geometric spacing preserves regime sensitivity, with 4-8 retained as the source band.
Failure and counterexamples
Reject post-hoc factor selection, outcome leakage, missing control, unpowered comparison, or a design whose claimed inference exceeds its measured outcome.
Provenance map
experiment-design, factor-level-design, ablation-design, comparison-design, scaling-design, robustness-design, statistical-method-selection, reproducibility-protocol, budget-constrained-design; mode-specific steps retained, agent dispatch removed.
Legacy context checkpoint / Delta notes
Record hypothesis ID, design mode, factors/levels, analysis plan, budget, preregistration status, and unresolved threats.
Preserved source criteria ledger
| source | source line | kind | source criterion |
|---|---|---|---|
| experiment-design | 45 | textual | ## HARD-GATE |
| experiment-design | 47 | textual | Before entering this campaign, the following must be satisfied: |
| experiment-design | 64 | textual | 4. How to ensure reproducibility |
| experiment-design | 79 | textual | ## Budget Gate |
| experiment-design | 83 | numeric | \ |
| experiment-design | 84 | numeric | \ |
| experiment-design | 85 | numeric | \ |
| experiment-design | 86 | numeric | \ |
| experiment-design | 95 | textual | ## Minimum Yield |
| experiment-design | 97 | textual | Every campaign invocation must produce at minimum: |
| ablation-design | 43 | textual | ## Budget Gate |
| ablation-design | 47 | numeric | \ |
| ablation-design | 48 | numeric | \ |
| ablation-design | 49 | numeric | \ |
| ablation-design | 50 | numeric | \ |
| ablation-design | 51 | numeric | \ |
| comparison-design | 34 | textual | - Fair Comparison Protocol (Bouthillier 2021): Control all confounds, same compute budget, same tuning effort. |
| comparison-design | 47 | textual | 6. reproducibility-protocol (tactic) -> Ensure all results are reproducible |
| comparison-design | 50 | textual | ## Budget Gate |
| comparison-design | 54 | numeric-table | \ |
| comparison-design | 55 | numeric | \ |
| comparison-design | 56 | numeric | \ |
| comparison-design | 57 | numeric-table | \ |
| scaling-design | 43 | numeric | 2. level-specification -> Define scale points (geometric progression, typically 4-8 points) |
| scaling-design | 49 | textual | ## Budget Gate |
| scaling-design | 53 | numeric | \ |
| scaling-design | 54 | numeric | \ |
| scaling-design | 55 | numeric | \ |
| scaling-design | 56 | numeric | \ |
| robustness-design | 50 | textual | ## Budget Gate |
| robustness-design | 54 | numeric | \ |
| robustness-design | 55 | numeric | \ |
| robustness-design | 56 | numeric | \ |
| robustness-design | 57 | numeric | \ |
| robustness-design | 58 | numeric-table | \ |
| statistical-method-selection | 30 | numeric-table | \ |
| statistical-method-selection | 31 | numeric-table | \ |
| statistical-method-selection | 32 | numeric-table | \ |
| statistical-method-selection | 33 | numeric-table | \ |
| statistical-method-selection | 34 | numeric-table | \ |
| statistical-method-selection | 36 | numeric-table | \ |
| statistical-method-selection | 48 | textual | - Is the significance threshold pre-registered (not chosen post-hoc)? |
| reproducibility-protocol | 17 | textual | # Tactic: Reproducibility Protocol |
| reproducibility-protocol | 21 | textual | 1. Assess Reproducibility Requirements -> Determine level needed (exact, statistical, conceptual) |
| reproducibility-protocol | 22 | textual | 2. seed-protocol-design -> Design random seed strategy for all stochastic components |
| reproducibility-protocol | 24 | textual | 4. Define Verification Plan -> How to confirm reproducibility (re-run subset, cross-machine test) |
| reproducibility-protocol | 25 | textual | 5. Document Non-Determinism -> Identify and document unavoidable sources of variance |
| reproducibility-protocol | 29 | textual | \ |
| reproducibility-protocol | 37 | textual | - Are all random seeds documented and controllable? |
| reproducibility-protocol | 38 | textual | - Is the full software environment captured (versions, dependencies)? |
| reproducibility-protocol | 39 | textual | - Are hardware-specific non-determinisms identified (GPU atomics, cuDNN)? |
| reproducibility-protocol | 40 | textual | - Is there a verification protocol (re-run N times, check variance)? |
| reproducibility-protocol | 44 | textual | - Is there a plan for cross-machine reproducibility testing? |
| budget-constrained-design | 34 | numeric | \ |
| budget-constrained-design | 35 | numeric | \ |
| budget-constrained-design | 36 | numeric | \ |
| budget-constrained-design | 37 | numeric | \ |
| budget-constrained-design | 38 | numeric-table | \ |
| budget-constrained-design | 53 | textual | - Are early stopping criteria pre-defined (not post-hoc)? |
Context checkpoint / Delta notes
Record hypothesis ID, design mode, factors/levels, analysis plan, budget, preregistration status, and unresolved threats.
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
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design-experiment-yogsoth-ai- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine