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Get Started Free →Evaluate Clojure code via nREPL using the standalone scripts/nrepl-eval.mjs CLI tool.
.claude/skills/penpot-nrepl-eval/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -65% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -53% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -86% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -55% | 0% |
Evaluate Clojure (or ClojureScript) code via a running nREPL server using scripts/nrepl-eval.mjs.
Full documentation: mem:scripts/nrepl-eval (file: .serena/memories/scripts/nrepl-eval.md)
sessions (backend 6064, frontend 3447) — live inspection, patching, or debugging.
bash./scripts/nrepl-eval.mjs [options] [<code>]
| Flag | Description | Default | |------|-------------|---------| | --backend | Connect to backend nREPL (port 6064) | — | | --frontend | Connect to frontend nREPL (port 3447) | — | | -p, --port PORT | nREPL server port | 6064 | | -H, --host HOST | nREPL server host | 127.0.0.1 | | -t, --timeout MS | Timeout in milliseconds | 120000 | | --reset-session | Discard stored session and start fresh | — | | -e, --last-error | Evaluate *e to retrieve the last exception | — | | -h, --help | Show help message | — |
bash./scripts/nrepl-eval.mjs '(+ 1 2 3)' ./scripts/nrepl-eval.mjs --backend '(+ 1 2 3)' ./scripts/nrepl-eval.mjs --frontend '(js/alert "hi")' ./scripts/nrepl-eval.mjs -e
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 6,268 | 12,692 | +102% | 1 | 1 | 0% | 829 | 879 | +6% | 0 | 0 | — |
case-02 | fail→fail | 6,928 | 6,736 | -3% | 1 | 1 | 0% | 242 | 659 | +172% | 0 | 0 | — |
case-03 | fail→fail | 6,910 | 6,823 | -1% | 1 | 1 | 0% | 1,009 | 646 | -36% | 0 | 0 | — |
case-04 | fail→pass | 16,000 | 3,126 | -80% | 1 | 1 | 0% | 2,317 | 818 | -65% | 0 | 0 | — |
case-05 | fail→pass | 10,983 | 4,998 | -54% | 1 | 1 | 0% | 1,969 | 1,300 | -34% | 0 | 0 | — |
case-06 | fail→pass | 9,636 | 3,008 | -69% | 1 | 1 | 0% | 1,622 | 764 | -53% | 0 | 0 | — |
case-07 | fail→pass | 24,342 | 2,723 | -89% | 1 | 1 | 0% | 5,312 | 736 | -86% | 0 | 0 | — |
case-08 | pass→pass | 13,158 | 5,251 | -60% | 1 | 1 | 0% | 2,054 | 1,204 | -41% | 0 | 0 | — |
case-09 | pass→pass | 9,229 | 6,902 | -25% | 1 | 1 | 0% | 1,511 | 971 | -36% | 0 | 0 | — |
case-10 | fail→pass | 11,791 | 3,157 | -73% | 1 | 1 | 0% | 1,882 | 842 | -55% | 0 | 0 | — |
case-11 | fail→pass | 25,739 | 3,281 | -87% | 1 | 1 | 0% | 4,774 | 863 | -82% | 0 | 0 | — |
case-12 | pass→pass | 6,700 | 2,255 | -66% | 1 | 1 | 0% | 844 | 628 | -26% | 0 | 0 | — |
case-13 | fail→fail | 4,027 | 2,194 | -46% | 1 | 1 | 0% | 541 | 664 | +23% | 0 | 0 | — |
case-14 | fail→pass | 16,627 | 3,311 | -80% | 1 | 1 | 0% | 2,767 | 671 | -76% | 0 | 0 | — |
case-15 | pass→pass | 6,924 | 2,523 | -64% | 1 | 1 | 0% | 1,206 | 698 | -42% | 0 | 0 | — |
case-16 | fail→pass | 16,730 | 4,538 | -73% | 1 | 1 | 0% | 2,830 | 943 | -67% | 0 | 0 | — |
case-17 | pass→pass | 25,076 | 2,908 | -88% | 1 | 1 | 0% | 1,356 | 579 | -57% | 0 | 0 | — |
case-18 | pass→pass | 8,424 | 5,425 | -36% | 1 | 1 | 0% | 1,269 | 1,055 | -17% | 0 | 0 | — |
case-19 | fail→fail | 20,789 | 2,592 | -88% | 1 | 1 | 0% | 1,612 | 744 | -54% | 0 | 0 | — |
case-20 | pass→fail | 11,012 | 15,109 | +37% | 1 | 1 | 0% | 1,792 | 2,446 | +36% | 0 | 0 | — |
case-21 | pass→fail | 13,046 | 9,091 | -30% | 1 | 1 | 0% | 2,121 | 823 | -61% | 0 | 0 | — |
case-22 | pass→pass | 18,984 | 19,114 | +1% | 1 | 1 | 0% | 1,757 | 2,474 | +41% | 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 18 counted toward the lift figure. The other 4 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 +27 percentage points is the difference between those two pass rates over the 18 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.