yogsoth-ai Strategy: Assumption Constraint
Methodology
Systematic assumption vulnerability analysis:
- Extraction: Surface all implicit and explicit assumptions
- Scoring: Quantify vulnerability (confidence × evidence / testability)
- Impact assessment: Blast radius × recovery cost
- Prioritization: Vulnerability × Impact = Priority
- Validation planning: Cheapest test that resolves uncertainty
Assumption categories:
| Category | Examples |
|---|---|
| Technical | Method convergence, architecture suitability |
| Data | Availability, quality, representativeness |
| Resource | Sufficiency of compute, time, expertise |
| Environmental | Tool stability, API access, policy |
| Theoretical | Effect existence, measurability, magnitude |
Execution Flow
Challenge Assumptions → call
assumption-challengingSOP- Input: experiment plan, hypothesis
- Output: assumption inventory with validity assessment
Quantify Validation Cost → call
resource-quantificationSOP- Input: validation experiments for top assumptions
- Output: cost to validate each assumption
Rank Sensitivity → invoke
sensitivity-rankingtactic- Determine which assumptions are most binding
Report → synthesize vulnerability assessment
- Top-5 fragile assumptions with validation paths
- Binding assumption constraint identification
Budget Gate
| Resource | Budget | Notes |
|---|---|---|
| Subagent calls | ≤5 | 2 SOPs + synthesis |
| Iterations | ≤2 | Re-rank if new assumptions surface |
| Output size | ≤3000 tokens | Ranked table + validation plan |
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| sensitivity-ranking | Rank constraints by sensitivity — which ones most impact the outcome if they shift |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| assumption-challenging | Challenge each assumption's validity — shared cross-repo SOP |
| resource-quantification | Quantify resource demand vs supply vs gap for each resource category |