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Get Started Free →Run Nx generators with prioritization for workspace-plugin generators. Use this when generating code, scaffolding new features, or automating repetitive tasks in the monorepo.
.claude/skills/microck-run-nx-generator/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 122% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -47% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -29% | 0% |
This skill helps you execute Nx generators efficiently, with special focus on workspace-plugin generators from your internal tooling.
Use the mcp__nx-mcp__nx_generator_schema tool to get more information about how to use the generator
Choose which generators to run in this priority order:
These are your custom internal tools in tools/workspace-plugin/
Only use these if workspace-plugin generators don't fit:
nx generate @nx/devkit:... - DevKit utilitiesnx generate @nx/node:... - Node.js librariesnx generate @nx/react:... - React components and appsUse the mcp__nx-mcp__nx_generator_schema tool to get more information about how to use the generator
bash nx generate [generator-path] [options]
nx affected -t testnpx prettier --write [files]--dry-run flag to preview changes before applyingWhen updating the Maven plugin version, use the workspace-plugin generator:
bashnx generate @nx/workspace-plugin:bump-maven-version \ --newVersion 0.0.10 \ --nxVersion 22.1.0-beta.7
This automates all the version bumping instead of manual file edits.
For creating a new create-nodes plugin:
bashnx generate @nx/workspace-plugin:create-nodes-plugin \ --name my-custom-plugin
Use this skill when you need to:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 9,433 | 6,627 | -30% | 1 | 1 | 0% | 1,592 | 1,000 | -37% | 0 | 0 | — |
case-01 | fail→fail | 3,717 | 5,783 | +56% | 1 | 1 | 0% | 285 | 829 | +191% | 0 | 0 | — |
case-03 | fail→fail | 3,586 | 5,985 | +67% | 1 | 1 | 0% | 217 | 806 | +271% | 0 | 0 | — |
case-04 | pass→pass | 4,496 | 2,817 | -37% | 1 | 1 | 0% | 829 | 1,023 | +23% | 0 | 0 | — |
case-05 | pass→pass | 7,153 | 2,915 | -59% | 1 | 1 | 0% | 1,106 | 933 | -16% | 0 | 0 | — |
case-06 | fail→pass | 3,445 | 1,970 | -43% | 1 | 1 | 0% | 395 | 877 | +122% | 0 | 0 | — |
case-07 | pass→pass | 5,724 | 2,969 | -48% | 1 | 1 | 0% | 935 | 1,087 | +16% | 0 | 0 | — |
case-08 | fail→pass | 10,135 | 1,881 | -81% | 1 | 1 | 0% | 1,612 | 848 | -47% | 0 | 0 | — |
case-09 | pass→pass | 11,707 | 7,065 | -40% | 1 | 1 | 0% | 1,818 | 1,618 | -11% | 0 | 0 | — |
case-10 | pass→pass | 3,223 | 2,672 | -17% | 1 | 1 | 0% | 565 | 975 | +73% | 0 | 0 | — |
case-11 | pass→pass | 5,008 | 2,669 | -47% | 1 | 1 | 0% | 867 | 975 | +12% | 0 | 0 | — |
case-12 | pass→pass | 3,441 | 1,512 | -56% | 1 | 1 | 0% | 440 | 723 | +64% | 0 | 0 | — |
case-13 | fail→pass | 7,935 | 1,953 | -75% | 1 | 1 | 0% | 1,261 | 834 | -34% | 0 | 0 | — |
case-14 | pass→fail | 5,603 | 4,648 | -17% | 1 | 1 | 0% | 937 | 733 | -22% | 0 | 0 | — |
case-15 | pass→pass | 12,794 | 3,696 | -71% | 1 | 1 | 0% | 2,054 | 1,145 | -44% | 0 | 0 | — |
case-16 | fail→pass | 12,215 | 3,921 | -68% | 1 | 1 | 0% | 1,940 | 1,253 | -35% | 0 | 0 | — |
case-17 | fail→pass | 9,767 | 3,556 | -64% | 1 | 1 | 0% | 1,596 | 1,140 | -29% | 0 | 0 | — |
case-18 | pass→pass | 4,756 | 2,137 | -55% | 1 | 1 | 0% | 877 | 885 | +1% | 0 | 0 | — |
case-19 | pass→pass | 10,593 | 2,751 | -74% | 1 | 1 | 0% | 1,746 | 977 | -44% | 0 | 0 | — |
case-20 | pass→pass | 10,791 | 4,994 | -54% | 1 | 1 | 0% | 1,890 | 1,402 | -26% | 0 | 0 | — |
case-21 | pass→pass | 16,423 | 19,171 | +17% | 1 | 1 | 0% | 1,473 | 2,577 | +75% | 0 | 0 | — |
case-22 | pass→pass | 8,048 | 10,137 | +26% | 1 | 1 | 0% | 1,521 | 1,867 | +23% | 0 | 0 | — |
case-23 | pass→pass | 17,542 | 12,204 | -30% | 1 | 1 | 0% | 2,557 | 2,582 | +1% | 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. 23 cases were attempted, and 19 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 +17 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.