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/smileynet/teach-menpx agentmods add skills/smileynet/teach-me/visual-qaWrote 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/smileynet/teach-me/visual-qa)<a href="https://agentmods.dev/skills/smileynet/teach-me/visual-qa"><img src="https://agentmods.dev/badge/skills/smileynet/teach-me/visual-qa/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/smileynet/teach-me/visual-qa"><img src="https://agentmods.dev/badge/skills/smileynet/teach-me/visual-qa.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 | $0.00054 | $0.01756 |
| Opus 5.5 | $0.00022 | $0.00702 |
| Sonnet 5.5 | $0.00011 | $0.00351 |
| Haiku 4.5 | $0.00005 | $0.00176 |
Grade A, and why
visual-qa scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual QA
Verify that UI components render and behave correctly by exercising them and analyzing the evidence.
General Check
Run the automated tool to exercise all components across all pages:
mise run visual-qa
This produces .scratch/visual-qa/manifest.json + screenshots per page. If it exits 0, all behavioral checks pass (tooltips appear, trays open, quizzes give feedback, SVGs render). If it exits 1, something is broken — read the manifest for which checks failed.
The tool is a behavioral check. It answers "does this work?" not "does this look right?"
Navigation Journey (per-domain)
mise run visual-qa exercises components on a page. To verify the cross-page USER JOURNEY
(does clicking through actually navigate?), run the navigation suite:
mise run test:nav
It discovers all library domains from the aggregate index #page-data island (no hardcoded
slugs), self-serves the library/ root headless, and for EACH domain walks
aggregate → domain map → a lesson → its quiz → breadcrumb back-nav, plus the index resume
cue. Navigation is asserted by act-then-verify (click → URL changes → landed <h1>), not by
link-presence. Per-domain pass/fail + screenshots land in test-results/ (navigation-report.md
screenshots/nav-*). Exit 0 = every domain's journey navigates correctly.
NOT in core mise run verify (slower browser journey) — run it after nav/breadcrumb/map/quiz
changes, or when adding a domain. The two-view Tree|Map toggle + tree keyboard model are
covered separately by mise run verify's interactive gate (index_two_view_toggle,
index_tree_keyboard).
Feature-Specific Visual Review
After building or modifying a specific feature, run the tool with --focus to scope screenshots, then analyze those screenshots against the feature's design intent.
python tools/visual-qa.py --serve --focus glossary
Then load the screenshots and analyze. The analysis prompt should be tailored to what the feature is supposed to look and feel like.
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.
- 1mo ago Changed · +21 lines 25fda432bb96
- 1mo ago First seen · 130 lines · 54 tokens per session scan A 086a1e12ac3e
visual-qa is a skill published in the GitHub repository smileynet/teach-me (3 stars, last pushed 25d ago), licensed MIT. It adds 54 tokens to every session and 1,756 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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