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Get Started Free →Read every docs/benchmarks/runs/*.json and surface drift in win rate, latency, escalation rate, and LLM-baseline cost over time
.claude/skills/ruvnet-cost-trend/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -55% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -84% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -35% | 0% |
The smoke gate is binary (winRate ≥ 0.80 → pass/fail). The corpus benchmarks captured over time form a curve — and curves catch regressions the gate misses (win rate slowly creeping from 100% to 85% is "still passing" by smoke but a real degradation).
This skill reads every persisted run in docs/benchmarks/runs/*.json and reports first→last deltas plus a per-run series, flagging regressions in win rate or latency.
agent-booster — surface latency / strategy changes.bash node plugins/ruflo-cost-tracker/scripts/trend.mjs
Optional env:
TREND_FORMAT=json — emit JSON instead of markdownTREND_LIMIT=10 — consider only the most recent N runsBENCH_ANTHROPIC=1 at run time).> ⚠ Regression callouts when:cost-benchmark — the producer of the run JSONs this skill consumesbench/booster-corpus.json — the corpus version is recorded in each run, so trends across corpus versions remain interpretabledocs/benchmarks/runs/latest.json — the most-recent run; smoke step 23 gates on winRate ≥ 0.80 from this file| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 4,127 | 2,130 | -48% | 1 | 1 | 0% | 720 | 731 | +2% | 0 | 0 | — |
case-02 | fail→fail | 4,786 | 4,468 | -7% | 1 | 1 | 0% | 322 | 742 | +130% | 0 | 0 | — |
case-03 | fail→fail | 5,248 | 3,958 | -25% | 1 | 1 | 0% | 907 | 666 | -27% | 0 | 0 | — |
case-04 | fail→pass | 8,895 | 2,523 | -72% | 1 | 1 | 0% | 1,548 | 992 | -36% | 0 | 0 | — |
case-05 | pass→fail | 10,148 | 9,292 | -8% | 1 | 1 | 0% | 2,087 | 2,145 | +3% | 0 | 0 | — |
case-06 | fail→pass | 12,781 | 6,404 | -50% | 1 | 1 | 0% | 2,557 | 1,807 | -29% | 0 | 0 | — |
case-07 | fail→pass | 13,773 | 3,485 | -75% | 1 | 1 | 0% | 2,490 | 1,120 | -55% | 0 | 0 | — |
case-08 | fail→pass | 25,994 | 1,639 | -94% | 1 | 1 | 0% | 5,054 | 785 | -84% | 0 | 0 | — |
case-09 | fail→pass | 8,304 | 2,610 | -69% | 1 | 1 | 0% | 1,533 | 990 | -35% | 0 | 0 | — |
case-10 | pass→pass | 9,286 | 3,338 | -64% | 1 | 1 | 0% | 1,510 | 1,160 | -23% | 0 | 0 | — |
case-11 | fail→pass | 9,801 | 2,120 | -78% | 1 | 1 | 0% | 1,790 | 866 | -52% | 0 | 0 | — |
case-12 | fail→pass | 13,298 | 2,873 | -78% | 1 | 1 | 0% | 2,619 | 1,050 | -60% | 0 | 0 | — |
case-13 | pass→pass | 13,333 | 3,092 | -77% | 1 | 1 | 0% | 2,115 | 964 | -54% | 0 | 0 | — |
case-14 | fail→pass | 41,819 | 1,878 | -96% | 1 | 1 | 0% | 382 | 795 | +108% | 0 | 0 | — |
case-15 | pass→pass | 7,686 | 4,128 | -46% | 1 | 1 | 0% | 1,241 | 1,195 | -4% | 0 | 0 | — |
case-20 | fail→pass | 8,475 | 2,669 | -69% | 1 | 1 | 0% | 1,448 | 939 | -35% | 0 | 0 | — |
case-16 | fail→pass | 9,419 | 2,729 | -71% | 1 | 1 | 0% | 1,602 | 1,003 | -37% | 0 | 0 | — |
case-17 | fail→pass | 5,965 | 2,227 | -63% | 1 | 1 | 0% | 1,174 | 873 | -26% | 0 | 0 | — |
case-18 | fail→pass | 11,468 | 2,302 | -80% | 1 | 1 | 0% | 2,039 | 886 | -57% | 0 | 0 | — |
case-19 | fail→pass | 11,352 | 3,957 | -65% | 1 | 1 | 0% | 1,959 | 1,215 | -38% | 0 | 0 | — |
case-21 | pass→pass | 5,857 | 3,191 | -46% | 1 | 1 | 0% | 1,021 | 1,018 | -0% | 0 | 0 | — |
case-22 | pass→pass | 4,386 | 3,241 | -26% | 1 | 1 | 0% | 690 | 1,043 | +51% | 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 +55 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.