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Get Started Free →Generate and maintain documentation with drift detection. Use when the user asks to write/update/refresh docs, detect doc drift against code, or schedule recurring documentation maintenance.
.claude/skills/ruvnet-doc-gen/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -62% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -54% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -51% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -66% | 0% |
Generate docs via MCP worker dispatch: mcp__plugin_ruflo-core_ruflo__hooks_worker-dispatch({ trigger: "document" })
For continuous doc maintenance via CronCreate: CronCreate({ schedule: "0 */2 * * *", prompt: "Run document worker" })
Detect drift by comparing current code against existing docs and flagging inconsistencies.
Scoped generation:
npx @claude-flow/cli@latest hooks worker dispatch --trigger document --scope apinpx @claude-flow/cli@latest hooks worker dispatch --trigger document --scope fullStore the approach: mcp__plugin_ruflo-core_ruflo__memory_store({ key: "doc-pattern", value: "APPROACH", namespace: "patterns" })
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 3,021 | 4,568 | +51% | 1 | 1 | 0% | 564 | 983 | +74% | 0 | 0 | — |
case-01 | fail→fail | 4,964 | 3,951 | -20% | 1 | 1 | 0% | 829 | 833 | +0% | 0 | 0 | — |
case-02 | fail→fail | 6,207 | 8,488 | +37% | 1 | 1 | 0% | 1,154 | 863 | -25% | 0 | 0 | — |
case-03 | fail→fail | 8,422 | 7,976 | -5% | 1 | 1 | 0% | 1,402 | 793 | -43% | 0 | 0 | — |
case-05 | pass→fail | 4,627 | 5,274 | +14% | 1 | 1 | 0% | 1,026 | 1,210 | +18% | 0 | 0 | — |
case-06 | pass→fail | 12,174 | 5,775 | -53% | 1 | 1 | 0% | 2,443 | 632 | -74% | 0 | 0 | — |
case-07 | fail→fail | 8,520 | 5,593 | -34% | 1 | 1 | 0% | 1,620 | 492 | -70% | 0 | 0 | — |
case-08 | fail→fail | 4,877 | 8,082 | +66% | 1 | 1 | 0% | 851 | 940 | +10% | 0 | 0 | — |
case-09 | fail→pass | 4,234 | 5,228 | +23% | 1 | 1 | 0% | 843 | 1,180 | +40% | 0 | 0 | — |
case-10 | fail→fail | 7,208 | 8,106 | +12% | 1 | 1 | 0% | 670 | 769 | +15% | 0 | 0 | — |
case-11 | fail→fail | 9,494 | 4,104 | -57% | 1 | 1 | 0% | 1,730 | 897 | -48% | 0 | 0 | — |
case-12 | fail→fail | 13,324 | 11,228 | -16% | 1 | 1 | 0% | 2,266 | 1,256 | -45% | 0 | 0 | — |
case-21 | fail→pass | 8,102 | 4,580 | -43% | 1 | 1 | 0% | 1,401 | 529 | -62% | 0 | 0 | — |
case-13 | pass→pass | 8,119 | 1,770 | -78% | 1 | 1 | 0% | 1,422 | 523 | -63% | 0 | 0 | — |
case-14 | fail→pass | 4,488 | 1,279 | -72% | 1 | 1 | 0% | 809 | 371 | -54% | 0 | 0 | — |
case-15 | pass→pass | 7,509 | 1,447 | -81% | 1 | 1 | 0% | 1,405 | 466 | -67% | 0 | 0 | — |
case-16 | fail→pass | 5,618 | 1,700 | -70% | 1 | 1 | 0% | 968 | 475 | -51% | 0 | 0 | — |
case-17 | fail→pass | 8,695 | 1,905 | -78% | 1 | 1 | 0% | 1,476 | 508 | -66% | 0 | 0 | — |
case-18 | pass→pass | 3,899 | 2,662 | -32% | 1 | 1 | 0% | 765 | 658 | -14% | 0 | 0 | — |
case-19 | fail→pass | 6,145 | 1,132 | -82% | 1 | 1 | 0% | 1,068 | 339 | -68% | 0 | 0 | — |
case-20 | fail→pass | 7,967 | 1,948 | -76% | 1 | 1 | 0% | 1,480 | 564 | -62% | 0 | 0 | — |
case-22 | pass→pass | 9,884 | 1,831 | -81% | 1 | 1 | 0% | 1,685 | 467 | -72% | 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 17 counted toward the lift figure. The other 5 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 +23 percentage points is the difference between those two pass rates over the 17 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.