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Get Started Free →Comprehensive rule-based check. Triggered by "/check [target]" to evaluate all source files against project rules and report every violation.
.claude/skills/x-cmd-check/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 98% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -45% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -17% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -47% | 0% |
Use when:
/checkDo NOT use when:
/scan)/assess)/check [--ruleset <dir>] [target_root]Default: current directory.
bashx rule which # locate ruleset ls $ruleset_dir/*.yml cat $ruleset_dir/*.yml
For each file under target_root:
apply fieldRule apply values: | Apply Value | Files Matched | |-------------|---------------| | all posix shell files | *.sh | | all shell files | *.sh, *.bash, *.zsh | | all yaml files | *.yml, *.yaml | | all json files | *.json | | all source files | source code files | | all files | any file |
TSV with header:
root target ruleid score hintScoring:
If all pass: output header row only.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 10,806 | 12,909 | +19% | 1 | 1 | 0% | 1,708 | 2,314 | +35% | 0 | 0 | — |
case-02 | fail→fail | 5,832 | 6,522 | +12% | 1 | 1 | 0% | 855 | 698 | -18% | 0 | 0 | — |
case-03 | fail→fail | 53,766 | 7,589 | -86% | 1 | 1 | 0% | 7,501 | 843 | -89% | 0 | 0 | — |
case-04 | fail→fail | 11,918 | 32,388 | +172% | 1 | 1 | 0% | 1,057 | 2,054 | +94% | 0 | 0 | — |
case-05 | pass→fail | 33,185 | 5,770 | -83% | 1 | 1 | 0% | 5,152 | 1,343 | -74% | 0 | 0 | — |
case-06 | fail→pass | 19,962 | 8,309 | -58% | 1 | 1 | 0% | 694 | 1,371 | +98% | 0 | 0 | — |
case-07 | fail→pass | 12,124 | 3,923 | -68% | 1 | 1 | 0% | 1,838 | 1,012 | -45% | 0 | 0 | — |
case-08 | pass→pass | 4,168 | 3,661 | -12% | 1 | 1 | 0% | 626 | 943 | +51% | 0 | 0 | — |
case-09 | pass→pass | 10,189 | 3,060 | -70% | 1 | 1 | 0% | 1,491 | 814 | -45% | 0 | 0 | — |
case-10 | pass→pass | 8,950 | 3,316 | -63% | 1 | 1 | 0% | 1,399 | 924 | -34% | 0 | 0 | — |
case-11 | fail→pass | 14,544 | 4,155 | -71% | 1 | 1 | 0% | 1,282 | 1,067 | -17% | 0 | 0 | — |
case-12 | fail→pass | 28,274 | 4,267 | -85% | 1 | 1 | 0% | 1,341 | 966 | -28% | 0 | 0 | — |
case-13 | fail→fail | 11,240 | 3,226 | -71% | 1 | 1 | 0% | 1,715 | 905 | -47% | 0 | 0 | — |
case-14 | fail→pass | 10,018 | 5,917 | -41% | 1 | 1 | 0% | 1,572 | 832 | -47% | 0 | 0 | — |
case-15 | pass→pass | 16,324 | 1,914 | -88% | 1 | 1 | 0% | 1,468 | 598 | -59% | 0 | 0 | — |
case-16 | pass→pass | 30,378 | 2,524 | -92% | 1 | 1 | 0% | 5,285 | 664 | -87% | 0 | 0 | — |
case-17 | fail→pass | 9,837 | 3,374 | -66% | 1 | 1 | 0% | 1,286 | 918 | -29% | 0 | 0 | — |
case-18 | fail→pass | 11,428 | 3,390 | -70% | 1 | 1 | 0% | 1,921 | 818 | -57% | 0 | 0 | — |
case-19 | fail→pass | 9,485 | 3,143 | -67% | 1 | 1 | 0% | 1,496 | 788 | -47% | 0 | 0 | — |
case-20 | pass→pass | 3,743 | 3,108 | -17% | 1 | 1 | 0% | 552 | 898 | +63% | 0 | 0 | — |
case-21 | pass→pass | 4,550 | 2,911 | -36% | 1 | 1 | 0% | 565 | 788 | +39% | 0 | 0 | — |
case-22 | fail→pass | 26,766 | 2,791 | -90% | 1 | 1 | 0% | 2,037 | 728 | -64% | 0 | 0 | — |
case-23 | fail→pass | 9,303 | 3,307 | -64% | 1 | 1 | 0% | 1,593 | 843 | -47% | 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 21 counted toward the lift figure. The other 2 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 +39 percentage points is the difference between those two pass rates over the 21 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.