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Get Started Free →MIMIC (Medical Information Mart for Intensive Care) database toolkit. Curated ICU data: vitals, labs, medications, notes, diagnoses. Tools for querying MIMIC-III/IV, building ML features, and reproducing benchmarks.
.claude/skills/mkurman-mimic/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✓→✗ | ▼ Worse | 8% | 0% |
| case-11 | ✓→✓ | = Same ✓ | -31% | 0% |
| case-02 | ✓→✓ | = Same ✓ | -23% | 0% |
| case-03 | ✓→✓ | = Same ✓ | -31% | 0% |
| case-04 | ✓→✓ | = Same ✓ | -62% | 0% |
MIMIC (Medical Information Mart for Intensive Care) provides ICU data: vitals, labs, medications, notes, diagnoses. Tools for querying MIMIC-III/IV, building ML features, and reproducing clinical benchmarks.
Apply for access at https://physionet.org/content/mimiciv/ -- requires CITI data use training.
bashuv pip install psycopg2 pandas
pythonimport pandas as pd from sqlalchemy import create_engine engine = create_engine("postgresql://user:pass@localhost:5432/mimiciv") # First 24h vitals query = """ SELECT subject_id, charttime, valuenum FROM mimiciv_icu.chartevents WHERE itemid = 220045 AND valuenum IS NOT NULL LIMIT 100 """ hr = pd.read_sql(query, engine)
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | pass→pass | 5,448 | 2,846 | -48% | 1 | 1 | 0% | 883 | 610 | -31% | 0 | 0 | — |
case-01 | fail→fail | 12,997 | 11,143 | -14% | 1 | 1 | 0% | 2,461 | 2,322 | -6% | 0 | 0 | — |
case-02 | pass→pass | 9,376 | 6,285 | -33% | 1 | 1 | 0% | 1,595 | 1,225 | -23% | 0 | 0 | — |
case-03 | pass→pass | 5,221 | 2,306 | -56% | 1 | 1 | 0% | 926 | 638 | -31% | 0 | 0 | — |
case-04 | pass→pass | 9,059 | 2,763 | -69% | 1 | 1 | 0% | 1,612 | 618 | -62% | 0 | 0 | — |
case-05 | pass→pass | 11,276 | 2,099 | -81% | 1 | 1 | 0% | 1,840 | 616 | -67% | 0 | 0 | — |
case-06 | pass→pass | 9,029 | 5,735 | -36% | 1 | 1 | 0% | 1,770 | 1,347 | -24% | 0 | 0 | — |
case-07 | pass→pass | 4,026 | 2,647 | -34% | 1 | 1 | 0% | 691 | 690 | -0% | 0 | 0 | — |
case-08 | pass→pass | 5,117 | 2,752 | -46% | 1 | 1 | 0% | 903 | 745 | -17% | 0 | 0 | — |
case-09 | pass→pass | 9,068 | 4,804 | -47% | 1 | 1 | 0% | 1,560 | 1,121 | -28% | 0 | 0 | — |
case-10 | pass→pass | 10,548 | 6,908 | -35% | 1 | 1 | 0% | 1,942 | 1,598 | -18% | 0 | 0 | — |
case-12 | pass→pass | 10,605 | 1,782 | -83% | 1 | 1 | 0% | 1,673 | 508 | -70% | 0 | 0 | — |
case-13 | pass→fail | 16,143 | 15,371 | -5% | 1 | 1 | 0% | 2,840 | 3,072 | +8% | 0 | 0 | — |
case-14 | pass→pass | 15,420 | 15,725 | +2% | 1 | 1 | 0% | 2,650 | 2,958 | +12% | 0 | 0 | — |
case-15 | pass→pass | 15,640 | 14,243 | -9% | 1 | 1 | 0% | 2,567 | 2,807 | +9% | 0 | 0 | — |
case-16 | fail→fail | 14,443 | 12,335 | -15% | 1 | 1 | 0% | 2,352 | 2,317 | -1% | 0 | 0 | — |
case-17 | pass→pass | 6,282 | 3,952 | -37% | 1 | 1 | 0% | 988 | 947 | -4% | 0 | 0 | — |
case-18 | pass→pass | 6,396 | 3,520 | -45% | 1 | 1 | 0% | 959 | 913 | -5% | 0 | 0 | — |
case-19 | pass→pass | 8,999 | 2,341 | -74% | 1 | 1 | 0% | 1,561 | 616 | -61% | 0 | 0 | — |
case-20 | pass→pass | 9,842 | 7,387 | -25% | 1 | 1 | 0% | 1,644 | 1,537 | -7% | 0 | 0 | — |
case-21 | pass→pass | 11,040 | 10,729 | -3% | 1 | 1 | 0% | 1,970 | 2,203 | +12% | 0 | 0 | — |
case-22 | pass→pass | 13,371 | 14,812 | +11% | 1 | 1 | 0% | 2,410 | 2,976 | +23% | 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 -100 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Other measured skills in the registry, with their headline benchmark lift.