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Get Started Free →Run, monitor, resume, merge, and debug Ralph loops. Use this skill whenever the user asks to operate `ralph run` or `ralph loops`, inspect loop state, recover suspended loops, analyze diagnostics, or unblock merge queue issues.
.claude/skills/mikeyobrien-ralph-loop/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -66% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -65% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -69% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -64% | 0% |
Use this skill to operate Ralph loops from the outside.
-c and -H inputsralph loops list or ralph loops list --json to establish thecurrent state.
ralph run ... with the right core configand hats source.
logs, history, and diffbefore changing state.
.ralph/suspend-state.json and useralph loops resume <id>.
needs-review, inspect the diff first, then usemerge, process, retry, or discard as appropriate.
calls, parse errors, or performance.
.ralph state files.scratchpad as the primary state model.
dead.
.ralph/ are last-resort recovery steps and should becalled out explicitly when used.
references/commands.mdreferences/diagnostics.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 4,827 | 3,964 | -18% | 1 | 1 | 0% | 246 | 609 | +148% | 0 | 0 | — |
case-01 | fail→fail | 7,441 | 4,277 | -43% | 1 | 1 | 0% | 1,164 | 674 | -42% | 0 | 0 | — |
case-03 | fail→fail | 5,727 | 3,978 | -31% | 1 | 1 | 0% | 1,130 | 655 | -42% | 0 | 0 | — |
case-04 | fail→pass | 6,714 | 2,419 | -64% | 1 | 1 | 0% | 1,291 | 850 | -34% | 0 | 0 | — |
case-05 | pass→pass | 10,901 | 6,602 | -39% | 1 | 1 | 0% | 1,990 | 1,693 | -15% | 0 | 0 | — |
case-06 | pass→pass | 8,055 | 3,309 | -59% | 1 | 1 | 0% | 1,386 | 988 | -29% | 0 | 0 | — |
case-19 | pass→pass | 7,982 | 1,514 | -81% | 1 | 1 | 0% | 1,475 | 596 | -60% | 0 | 0 | — |
case-07 | pass→pass | 6,463 | 3,435 | -47% | 1 | 1 | 0% | 1,260 | 1,028 | -18% | 0 | 0 | — |
case-08 | fail→pass | 12,280 | 1,912 | -84% | 1 | 1 | 0% | 2,121 | 719 | -66% | 0 | 0 | — |
case-09 | fail→pass | 8,880 | 1,331 | -85% | 1 | 1 | 0% | 1,658 | 574 | -65% | 0 | 0 | — |
case-10 | fail→pass | 11,030 | 1,963 | -82% | 1 | 1 | 0% | 2,019 | 625 | -69% | 0 | 0 | — |
case-20 | pass→fail | 28,230 | 7,195 | -75% | 1 | 1 | 0% | 3,031 | 1,828 | -40% | 0 | 0 | — |
case-11 | fail→pass | 9,319 | 1,154 | -88% | 1 | 1 | 0% | 1,627 | 579 | -64% | 0 | 0 | — |
case-12 | pass→pass | 10,635 | 2,597 | -76% | 1 | 1 | 0% | 1,915 | 857 | -55% | 0 | 0 | — |
case-13 | fail→pass | 8,149 | 2,507 | -69% | 1 | 1 | 0% | 1,334 | 842 | -37% | 0 | 0 | — |
case-14 | pass→pass | 10,013 | 3,102 | -69% | 1 | 1 | 0% | 1,643 | 922 | -44% | 0 | 0 | — |
case-15 | fail→pass | 7,285 | 2,188 | -70% | 1 | 1 | 0% | 1,259 | 780 | -38% | 0 | 0 | — |
case-16 | fail→pass | 10,059 | 5,415 | -46% | 1 | 1 | 0% | 1,909 | 1,366 | -28% | 0 | 0 | — |
case-17 | fail→pass | 17,033 | 5,034 | -70% | 1 | 1 | 0% | 2,848 | 1,366 | -52% | 0 | 0 | — |
case-18 | pass→pass | 9,875 | 2,590 | -74% | 1 | 1 | 0% | 1,619 | 839 | -48% | 0 | 0 | — |
case-21 | pass→pass | 14,388 | 14,009 | -3% | 1 | 1 | 0% | 3,023 | 3,157 | +4% | 0 | 0 | — |
case-22 | pass→pass | 11,290 | 9,611 | -15% | 1 | 1 | 0% | 2,081 | 2,209 | +6% | 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 19 counted toward the lift figure. The other 3 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 +36 percentage points is the difference between those two pass rates over the 19 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.