Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qenpx agentmods add skills/proffesor-for-testing/agentic-qe/a11y-allyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/a11y-ally)<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/a11y-ally"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/a11y-ally/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/a11y-ally"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/a11y-ally.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.14222 |
| Opus 5.5 | $0.00018 | $0.05689 |
| Sonnet 5.5 | $0.00009 | $0.02844 |
| Haiku 4.5 | $0.00004 | $0.01422 |
Grade A, and why
a11y-ally scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -L -A "Mozilla/5.0" --retry 3 -o /tmp/a11y-work/video.mp4 "FULL_VIDEO_URL" Copies of this mod
1 near-identical copy found in the catalogue:
- a11y-ally — 94% identical, 60 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,695 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/a11y-ally - Comprehensive Accessibility Audit
<default_to_action> When this skill is invoked with a URL, Claude executes ALL steps automatically without waiting for user prompts between steps.
THIS IS AN LLM-POWERED SKILL
The value of this skill is Claude's intelligence, not just running automated tools:
| Automated Tools Do | Claude (This Skill) Does |
|---|---|
| Flag "button has no name" | Analyze context: icon class, parent element, nearby text → generate "Add to wishlist" |
| Flag "image missing alt" | Use Vision to see the image → describe actual content |
| Flag "video has no captions" | Download video, extract frames, analyze each frame with Vision → generate real captions |
| Output generic templates | Generate context-specific, copy-paste ready fixes |
IF YOU SKIP THE LLM ANALYSIS, THIS SKILL HAS NO VALUE.
EXECUTION MODEL
CLAUDE EXECUTES ALL STEPS WITHOUT STOPPING.
Do NOT wait for user prompts between steps. Execute the full pipeline:
- Data Collection: Run multi-tool scan (axe-core, pa11y, Lighthouse) via Bash
- LLM Analysis: Read results and analyze context for each violation
- Vision Pipeline: If videos detected → download → extract frames → Read each frame → describe
- Intelligent Remediation: Generate context-specific fixes using your reasoning
- Generate Reports: Write all output files to
docs/accessibility-scans/{page-slug}/
WRONG:
Claude: "I found 5 violations. Should I analyze them?"
User: "Yes"
Claude: "I see a video. Should I run the video pipeline?"
User: "Yes"
RIGHT:
Claude: [Runs scan] → [Analyzes violations] → [Downloads video] → [Extracts frames] →
[Reads each frame with Vision] → [Generates captions] → [Writes all files]
"Audit complete. Generated 4 files in docs/accessibility-scans/example/"
STEP 1: BROWSER AUTOMATION - Content Fetching
Uses the qe-browser fleet skill as the browser engine. qe-browser wraps Vibium (WebDriver BiDi, 10MB Go binary) and provides the QE primitives we rely on. See .claude/skills/qe-browser/SKILL.md.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 1,695 lines · 44 tokens per session scan A 4f8a87563bf2
a11y-ally is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (504 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 14,222 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-11.
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