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Get Started Free →Drills the user on a framework with application-level scenario questions. Inspired by mattpocock/skills/grill-me. Tracks coverage in-session, evaluates answers against framework guidance, never reproduces normative standard text.
.claude/skills/grcengclub-socratic-drill/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-18 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 109% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 71% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 285% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 491% | 0% |
You are the skill invoked by /teach-me:quiz <framework>. Your job is to drill the learner on applying a framework's controls, not memorizing them. You ask scenario questions, evaluate answers, and adapt difficulty to keep them in the productive-struggle zone.
--focus=<area> was passed, restrict the control pool to that area.apprentice — definitions, scope, who-does-what. practitioner (default) — application and edge cases. audit-defense — the hard ones, where the user has to defend a decision to a hypothetical assessor.stop, enough, done, quit, exit, or anything similar. Print the final coverage summary on exit.After every 5 questions and at the end of the session:
Coverage so far:
Covered (n): <list of control families touched>
Uncovered (n): <list of control families not yet touched>
Weak spots (n): <list of families where answers were partial or wrong>
Suggested next focus: <family the skill recommends>grc-data/learning/.)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 11,791 | 8,164 | -31% | 1 | 1 | 0% | 1,694 | 2,188 | +29% | 0 | 0 | — |
case-18 | fail→pass | 7,783 | 2,293 | -71% | 1 | 1 | 0% | 1,448 | 1,330 | -8% | 0 | 0 | — |
case-02 | pass→fail | 16,050 | 6,707 | -58% | 1 | 1 | 0% | 2,613 | 2,103 | -20% | 0 | 0 | — |
case-03 | pass→fail | 12,987 | 8,688 | -33% | 1 | 1 | 0% | 2,327 | 2,352 | +1% | 0 | 0 | — |
case-04 | pass→fail | 14,178 | 9,904 | -30% | 1 | 1 | 0% | 2,017 | 2,420 | +20% | 0 | 0 | — |
case-05 | fail→pass | 5,657 | 5,238 | -7% | 1 | 1 | 0% | 840 | 1,759 | +109% | 0 | 0 | — |
case-06 | pass→pass | 8,174 | 7,281 | -11% | 1 | 1 | 0% | 1,145 | 2,006 | +75% | 0 | 0 | — |
case-07 | fail→fail | 8,132 | 15,237 | +87% | 1 | 1 | 0% | 1,165 | 3,209 | +175% | 0 | 0 | — |
case-08 | pass→pass | 13,999 | 9,482 | -32% | 1 | 1 | 0% | 1,965 | 2,323 | +18% | 0 | 0 | — |
case-09 | fail→fail | 6,618 | 4,351 | -34% | 1 | 1 | 0% | 1,029 | 1,662 | +62% | 0 | 0 | — |
case-10 | fail→pass | 10,777 | 11,249 | +4% | 1 | 1 | 0% | 1,635 | 2,799 | +71% | 0 | 0 | — |
case-11 | fail→pass | 2,578 | 2,123 | -18% | 1 | 1 | 0% | 331 | 1,276 | +285% | 0 | 0 | — |
case-12 | pass→pass | 15,790 | 5,773 | -63% | 1 | 1 | 0% | 2,327 | 1,770 | -24% | 0 | 0 | — |
case-13 | fail→pass | 2,214 | 5,562 | +151% | 1 | 1 | 0% | 297 | 1,754 | +491% | 0 | 0 | — |
case-14 | pass→pass | 7,063 | 5,614 | -21% | 1 | 1 | 0% | 1,098 | 1,790 | +63% | 0 | 0 | — |
case-15 | pass→pass | 10,094 | 7,445 | -26% | 1 | 1 | 0% | 1,609 | 2,066 | +28% | 0 | 0 | — |
case-16 | fail→pass | 14,163 | 6,283 | -56% | 1 | 1 | 0% | 2,166 | 1,945 | -10% | 0 | 0 | — |
case-17 | pass→fail | 9,050 | 6,176 | -32% | 1 | 1 | 0% | 1,393 | 1,872 | +34% | 0 | 0 | — |
case-19 | fail→pass | 8,976 | 3,694 | -59% | 1 | 1 | 0% | 1,277 | 1,527 | +20% | 0 | 0 | — |
case-20 | pass→pass | 5,160 | 6,901 | +34% | 1 | 1 | 0% | 741 | 1,987 | +168% | 0 | 0 | — |
case-21 | pass→pass | 7,239 | 4,842 | -33% | 1 | 1 | 0% | 1,056 | 1,759 | +67% | 0 | 0 | — |
case-22 | pass→fail | 12,588 | 7,167 | -43% | 1 | 1 | 0% | 1,873 | 2,000 | +7% | 0 | 0 | — |
case-23 | fail→pass | 14,333 | 5,121 | -64% | 1 | 1 | 0% | 1,970 | 1,749 | -11% | 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. 23 cases were attempted. The headline lift of +13 percentage points is the difference between those two pass rates over the 23 comparable cases. 5 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.