yogsoth-ai Gap-Driven Generation
Detect coverage gaps in the solution space, then generate targeted solutions using failure analysis and factorial design.
Stages
Stage 1: Coverage Gap Detection
Run coverage-gap-detection SOP to identify uncovered regions in the solution space. Output: gap list with priority ranking based on impact and feasibility.
Stage 2: Failure-Driven Generation
Run failure-driven-generation SOP to generate solutions that specifically target identified failure modes or gaps. Output: one or more targeted solutions per gap.
Stage 3: Factor-Level Design
Run factor-level-design SOP to identify key factors in the gap space, define levels, and design an experiment matrix for systematic exploration. Output: factorial design ready for implementation.
Minimum Yield
| Metric | Floor |
|---|---|
| Coverage gaps identified | ≥3 |
| Gaps with priority ranking | 100% |
| Solutions generated per gap | ≥1 |
| Total targeted solutions | ≥3 |
| Factor-level matrix produced | yes |
Available SOPs
| SOP | Role |
|---|---|
| coverage-gap-detection | Stage 1 — detect uncovered regions |
| failure-driven-generation | Stage 2 — generate targeted solutions |
| factor-level-design | Stage 3 — factorial experiment design |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| coverage-gap-detection | Detect uncovered regions in the solution space, producing a prioritized gap list. |
| creative-ideation-factor-level-design | Identify factors and their levels for a problem, then design an experiment matrix for systematic exploration. |
| creative-ideation-failure-mode-cataloging | Systematically catalog all failure modes in a domain or method, producing a classified failure taxonomy. |
| failure-driven-generation | Generate targeted solutions for each identified failure mode, ensuring every failure has at least one proposed mitigation. |