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Get Started Free →Use FIRST when user requests any disease market sizing report (e.g. "做 X 市场调研" / "Run market sizing for X"). Elicits 4-6 startup questions (evidence window, geography, international comparison, report depth, user role) and writes .cache/<slug>/contract.json. ALWAYS invoke before disease-market-sizing-orchestration. Validates against schemas/contract.schema.json.
.claude/skills/ethanyoq-contract-elicitor/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -30% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -18% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -49% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -30% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 70% | 0% |
Parses the user's one-liner to extract disease + locale, then asks 4-6 follow-up questions to fill the contract.
parse_disease_and_locale(s) -> (disease, locale) — strips action verbs, detects CJKelicit_contract(one_liner, project_root, ask_user) -> contract.json path.cache/<slug>/contract.json — schema: schemas/contract.schema.json
pythonfrom elicitor import assert_contract_complete # see Task 7 assert_contract_complete(contract_path)
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 7,594 | 5,343 | -30% | 1 | 1 | 0% | 1,734 | 1,216 | -30% | 0 | 0 | — |
case-02 | fail→pass | 7,745 | 5,091 | -34% | 1 | 1 | 0% | 1,666 | 1,368 | -18% | 0 | 0 | — |
case-03 | pass→pass | 1,288 | 1,964 | +52% | 1 | 1 | 0% | 220 | 462 | +110% | 0 | 0 | — |
case-04 | pass→pass | 1,935 | 2,706 | +40% | 1 | 1 | 0% | 280 | 632 | +126% | 0 | 0 | — |
case-05 | fail→pass | 9,224 | 3,951 | -57% | 1 | 1 | 0% | 1,999 | 1,015 | -49% | 0 | 0 | — |
case-06 | fail→fail | 10,183 | 3,311 | -67% | 1 | 1 | 0% | 2,085 | 866 | -58% | 0 | 0 | — |
case-07 | fail→pass | 8,168 | 4,894 | -40% | 1 | 1 | 0% | 1,961 | 1,376 | -30% | 0 | 0 | — |
case-08 | fail→pass | 23,128 | 8,342 | -64% | 1 | 1 | 0% | 1,322 | 2,248 | +70% | 0 | 0 | — |
case-09 | fail→pass | 5,871 | 1,035 | -82% | 1 | 1 | 0% | 1,160 | 356 | -69% | 0 | 0 | — |
case-10 | pass→pass | 8,573 | 5,162 | -40% | 1 | 1 | 0% | 1,705 | 1,138 | -33% | 0 | 0 | — |
case-11 | fail→pass | 9,390 | 3,058 | -67% | 1 | 1 | 0% | 1,962 | 814 | -59% | 0 | 0 | — |
case-12 | fail→pass | 9,573 | 6,155 | -36% | 1 | 1 | 0% | 1,060 | 826 | -22% | 0 | 0 | — |
case-13 | pass→pass | 1,813 | 1,500 | -17% | 1 | 1 | 0% | 297 | 420 | +41% | 0 | 0 | — |
case-14 | pass→pass | 1,888 | 2,260 | +20% | 1 | 1 | 0% | 422 | 665 | +58% | 0 | 0 | — |
case-15 | fail→pass | 5,534 | 3,457 | -38% | 1 | 1 | 0% | 1,202 | 875 | -27% | 0 | 0 | — |
case-16 | pass→pass | 4,411 | 2,811 | -36% | 1 | 1 | 0% | 783 | 706 | -10% | 0 | 0 | — |
case-17 | pass→pass | 9,629 | 6,154 | -36% | 1 | 1 | 0% | 2,475 | 1,876 | -24% | 0 | 0 | — |
case-18 | pass→pass | 1,431 | 1,960 | +37% | 1 | 1 | 0% | 229 | 506 | +121% | 0 | 0 | — |
case-19 | fail→pass | 2,651 | 2,869 | +8% | 1 | 1 | 0% | 614 | 912 | +49% | 0 | 0 | — |
case-20 | fail→fail | 7,172 | 4,584 | -36% | 1 | 1 | 0% | 1,503 | 1,310 | -13% | 0 | 0 | — |
case-21 | fail→pass | 4,016 | 3,463 | -14% | 1 | 1 | 0% | 1,045 | 1,050 | +0% | 0 | 0 | — |
case-22 | pass→fail | 9,358 | 2,927 | -69% | 1 | 1 | 0% | 1,806 | 415 | -77% | 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, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +45 percentage points is the difference between those two pass rates over the 21 comparable cases. 1 case got worse with the skill loaded, and it is 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.