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Get Started Free →Strategy: Adjust research question scope — zoom in/out until the scope is appropriate
.claude/skills/yogsoth-ai-scope-calibration/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 91% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 80% | 0% |
Adjust research question scope — when a question is too broad or too narrow, find the right granularity through systematic zoom in/out.
Core logic: a good research question has "Goldilocks" characteristics — not too broad, not too narrow, just right.
Add constraints to narrow the scope:
Relax constraints to widen the scope:
| Tier | Iteration rounds | Output | |------|---------|------| | S | ≥1 round of scope adjustment | Appropriately scoped RQ | | M | ≥2 rounds of scope adjustment + comparison | Before/after comparison + final RQ | | L | ≥3 rounds + multi-direction exploration | Multiple granularity versions + rationale for the optimal choice |
After the strategy completes, context-checkpoint must be called, recording:
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | question-refinement-loop | Tactic: iteratively refine a research question until it passes all 5 FINER criteria |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 13,070 | 23,144 | +77% | 1 | 1 | 0% | 2,041 | 3,371 | +65% | 0 | 0 | — |
case-02 | fail→pass | 19,973 | 19,528 | -2% | 1 | 1 | 0% | 2,377 | 2,802 | +18% | 0 | 0 | — |
case-03 | fail→pass | 17,346 | 25,715 | +48% | 1 | 1 | 0% | 2,696 | 3,935 | +46% | 0 | 0 | — |
case-04 | fail→pass | 10,060 | 12,881 | +28% | 1 | 1 | 0% | 934 | 1,786 | +91% | 0 | 0 | — |
case-05 | fail→fail | 14,267 | 17,775 | +25% | 1 | 1 | 0% | 1,413 | 2,679 | +90% | 0 | 0 | — |
case-06 | pass→pass | 22,560 | 12,868 | -43% | 1 | 1 | 0% | 1,911 | 2,778 | +45% | 0 | 0 | — |
case-07 | pass→pass | 11,535 | 23,297 | +102% | 1 | 1 | 0% | 991 | 3,300 | +233% | 0 | 0 | — |
case-08 | fail→pass | 14,266 | 17,592 | +23% | 1 | 1 | 0% | 1,378 | 2,483 | +80% | 0 | 0 | — |
case-09 | pass→pass | 18,312 | 19,211 | +5% | 1 | 1 | 0% | 2,112 | 2,997 | +42% | 0 | 0 | — |
case-10 | pass→pass | 13,070 | 17,614 | +35% | 1 | 1 | 0% | 1,346 | 2,495 | +85% | 0 | 0 | — |
case-11 | fail→pass | 15,480 | 18,986 | +23% | 1 | 1 | 0% | 1,759 | 2,868 | +63% | 0 | 0 | — |
case-12 | pass→pass | 16,401 | 9,813 | -40% | 1 | 1 | 0% | 1,764 | 1,303 | -26% | 0 | 0 | — |
case-13 | pass→fail | 29,101 | 10,566 | -64% | 1 | 1 | 0% | 3,758 | 2,275 | -39% | 0 | 0 | — |
case-14 | fail→pass | 23,060 | 15,835 | -31% | 1 | 1 | 0% | 3,147 | 3,454 | +10% | 0 | 0 | — |
case-15 | fail→pass | 37,657 | 28,827 | -23% | 1 | 1 | 0% | 5,092 | 4,064 | -20% | 0 | 0 | — |
case-16 | pass→pass | 15,696 | 6,924 | -56% | 1 | 1 | 0% | 1,557 | 814 | -48% | 0 | 0 | — |
case-17 | fail→pass | 9,796 | 11,927 | +22% | 1 | 1 | 0% | 1,644 | 1,851 | +13% | 0 | 0 | — |
case-18 | fail→pass | 11,785 | 7,108 | -40% | 1 | 1 | 0% | 1,033 | 1,763 | +71% | 0 | 0 | — |
case-19 | pass→pass | 8,025 | 19,594 | +144% | 1 | 1 | 0% | 1,373 | 2,979 | +117% | 0 | 0 | — |
case-20 | pass→pass | 15,328 | 17,390 | +13% | 1 | 1 | 0% | 2,039 | 2,862 | +40% | 0 | 0 | — |
case-21 | pass→fail | 19,205 | 26,122 | +36% | 1 | 1 | 0% | 2,665 | 4,368 | +64% | 0 | 0 | — |
case-22 | pass→pass | 12,040 | 20,696 | +72% | 1 | 1 | 0% | 1,241 | 4,049 | +226% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.