---
name: yogsoth-ai/budget-constrained-design
source: https://app.decimal.ai/s/yogsoth-ai-budget-constrained-design@1/SKILL.md
source_sha256: 9f672b622b49
---

# Tactic: Budget-Constrained Design

## Orchestration Pattern

1. **Assess Budget** → Determine available GPU-hours, wall-clock time, and cost ceiling
2. **factor-identification** → Identify all candidate factors
3. **Estimate Cost Per Run** → Calculate time/compute for a single experiment run
4. **Compute Maximum Runs** → budget / cost_per_run = max feasible runs
5. **level-specification** → Constrain levels to fit within run budget
6. **Select Design Type** → Choose most information-efficient design for the budget
7. **design-matrix-construction** → Build the constrained design matrix

## Decision Criteria

| Available Runs | Recommended Approach |
|---------------|---------------------|
| < 10 | One-factor-at-a-time or Plackett-Burman screening |
| 10-30 | Fractional factorial (Resolution III-IV) |
| 30-60 | Fractional factorial (Resolution V) or Taguchi |
| 60-120 | Full factorial on top factors + screening on rest |
| 120+ | Full factorial or RSM with replication |

## Optimization Strategies

- **Sequential Design**: Run screening first, then detailed study on important factors
- **Adaptive Allocation**: Allocate more runs to high-variance conditions
- **Early Stopping**: Define stopping criteria for clearly dominated configurations
- **Transfer from Pilot**: Use pilot study results to inform main study design
- **Shared Controls**: Reuse control/baseline runs across multiple comparisons

## Quality Checks

- Does the design have sufficient power for the primary hypothesis?
- Are the most important factors given priority in the allocation?
- Is there a contingency plan if budget is cut mid-experiment?
- Are early stopping criteria pre-defined (not post-hoc)?
- Is the design balanced despite budget constraints?

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## Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use |
| --- | --- |
| design-matrix-construction | Build the experiment design matrix with proper orthogonality and balance |
| factor-identification | Identify independent, dependent, and control variables for an experiment |
| level-specification | Determine appropriate levels for each experimental factor |

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