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Get Started Free →CI/CD accessibility agent. Conversational agent for setting up, managing, and troubleshooting accessibility CI pipelines. Supports baseline management (fail only on regressions), SARIF output for GitHub code scanning, PR annotations, and threshold configuration. Works with GitHub Actions, Azure DevOps, GitLab CI, CircleCI, and Jenkins.
.claude/skills/community-access-ci-accessibility/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 19% | 0% |
Derived from .claude/agents/ci-accessibility.md. Treat platform-specific tool names or delegation instructions as Codex equivalents.
You are a CI/CD accessibility specialist. You help teams set up, maintain, and troubleshoot automated accessibility scanning in their continuous integration pipelines.
axe-baseline.json files that track known violations so CI only fails on regressions.github/workflows/, azure-pipelines.yml, .gitlab-ci.yml, Jenkinsfile, .circleci/config.yml)package.json for @axe-core/cli, pa11y, lighthouse)axe-baseline.json, .a11y-cache.json).a11y-web-config.json)Ask the user about:
Generate the appropriate CI config with:
wcag2a,wcag2aa,wcag21a,wcag21aa,wcag22aa)The baseline pattern is critical for brownfield adoption:
axe-baseline.json| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 22,910 | 24,238 | +6% | 1 | 1 | 0% | 4,527 | 5,768 | +27% | 0 | 0 | — |
case-02 | fail→fail | 21,002 | 17,743 | -16% | 1 | 1 | 0% | 3,980 | 4,747 | +19% | 0 | 0 | — |
case-03 | fail→fail | 20,293 | 20,045 | -1% | 1 | 1 | 0% | 3,803 | 5,012 | +32% | 0 | 0 | — |
case-04 | fail→fail | 6,751 | 6,766 | +0% | 1 | 1 | 0% | 1,213 | 2,190 | +81% | 0 | 0 | — |
case-05 | fail→fail | 13,221 | 11,141 | -16% | 1 | 1 | 0% | 2,405 | 3,122 | +30% | 0 | 0 | — |
case-06 | fail→fail | 20,279 | 21,188 | +4% | 1 | 1 | 0% | 3,758 | 4,550 | +21% | 0 | 0 | — |
case-07 | fail→fail | 9,026 | 6,845 | -24% | 1 | 1 | 0% | 1,700 | 2,289 | +35% | 0 | 0 | — |
case-08 | fail→pass | 11,033 | 5,015 | -55% | 1 | 1 | 0% | 1,818 | 1,783 | -2% | 0 | 0 | — |
case-09 | fail→pass | 7,342 | 3,339 | -55% | 1 | 1 | 0% | 1,336 | 1,555 | +16% | 0 | 0 | — |
case-10 | pass→pass | 7,532 | 2,126 | -72% | 1 | 1 | 0% | 1,322 | 1,301 | -2% | 0 | 0 | — |
case-11 | pass→pass | 12,041 | 7,181 | -40% | 1 | 1 | 0% | 1,814 | 2,066 | +14% | 0 | 0 | — |
case-12 | pass→fail | 12,746 | 12,806 | +0% | 1 | 1 | 0% | 1,974 | 3,038 | +54% | 0 | 0 | — |
case-13 | pass→pass | 15,985 | 13,094 | -18% | 1 | 1 | 0% | 2,488 | 3,038 | +22% | 0 | 0 | — |
case-14 | pass→pass | 13,542 | 12,266 | -9% | 1 | 1 | 0% | 2,246 | 2,898 | +29% | 0 | 0 | — |
case-15 | fail→pass | 20,289 | 18,547 | -9% | 1 | 1 | 0% | 3,430 | 4,765 | +39% | 0 | 0 | — |
case-16 | pass→pass | 18,562 | 29,861 | +61% | 1 | 1 | 0% | 3,591 | 4,008 | +12% | 0 | 0 | — |
case-17 | pass→pass | 14,632 | 14,976 | +2% | 1 | 1 | 0% | 2,402 | 3,490 | +45% | 0 | 0 | — |
case-18 | pass→pass | 14,514 | 15,529 | +7% | 1 | 1 | 0% | 2,419 | 3,608 | +49% | 0 | 0 | — |
case-19 | fail→fail | 14,657 | 14,994 | +2% | 1 | 1 | 0% | 2,246 | 3,445 | +53% | 0 | 0 | — |
case-20 | fail→pass | 13,544 | 10,446 | -23% | 1 | 1 | 0% | 2,212 | 2,634 | +19% | 0 | 0 | — |
case-21 | pass→pass | 7,966 | 6,629 | -17% | 1 | 1 | 0% | 1,401 | 1,985 | +42% | 0 | 0 | — |
case-22 | pass→pass | 16,173 | 6,154 | -62% | 1 | 1 | 0% | 2,731 | 2,021 | -26% | 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. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 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.