Borrowing it
Nothing to install: this file belongs to mtarcure/claude-vibe-squad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mtarcure/claude-vibe-squad/main/.agents/skills/visual-regression-baseline/SKILL.mdgit clone --depth 1 https://github.com/mtarcure/claude-vibe-squadWrote 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/mtarcure/claude-vibe-squad/visual-regression-baseline)<a href="https://agentmods.dev/skills/mtarcure/claude-vibe-squad/visual-regression-baseline"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/visual-regression-baseline/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/mtarcure/claude-vibe-squad/visual-regression-baseline"><img src="https://agentmods.dev/badge/skills/mtarcure/claude-vibe-squad/visual-regression-baseline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.00786 |
| Opus 5 | $0.00030 | $0.00393 |
| Sonnet 5 | $0.00012 | $0.00157 |
| Haiku 4.5 | $0.00006 | $0.00079 |
Grade A, and why
visual-regression-baseline 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 9d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Regression Baseline
Capture stable, comparable visual references so a UI/render change can be judged as intended design movement vs. accidental drift — a deterministic diff, not a screenshot glance.
Steps
- Define the capture set. Enumerate the states that must be verified: routes/screens, key components, breakpoints (mobile/tablet/desktop), theme variants (light/dark), and salient interaction states (hover/focus/error/empty/loading). Each entry is one named, reproducible capture.
- Establish the baseline. On the known-good build, capture each entry to an immutable, named reference
(via
chrome-devtools/playwrighttake_screenshot/browser_take_screenshot, fresh Chrome). Record the capture conditions with it: viewport size, device-scale factor, color scheme, locale, and the app build/commit. A baseline without its conditions is not a baseline. - Neutralize non-determinism BEFORE diffing. Freeze or mask sources of pixel noise that are not the change under test: fixed clock/seeded RNG, disabled animations/transitions, stable fonts (wait for webfont load), and masked dynamic regions (timestamps, avatars, ads, carousels, generated IDs). Masks are declared per-entry and versioned with the baseline — an unmasked dynamic region is a false FAIL.
- Re-capture deterministically. On the candidate build, re-capture the SAME set under the SAME recorded conditions. Any condition mismatch (viewport, scale, theme, locale) invalidates the comparison — re-capture, do not diff across conditions.
- Diff and compare. Compare candidate vs. baseline per entry. Use a pixel/perceptual diff with a declared tolerance (anti-aliasing/sub-pixel threshold) and the entry's masks applied. Produce a diff artifact (highlighted delta image) for every non-identical entry.
- Human/
view_imagereview of diffs. A non-zero diff is a SIGNAL, not a verdict. Review each diff image (view_imageon the codex lane, or a lane image-read) and classify: intended (accept → promote to the new baseline, with a note on what changed and why), or regression (reject). Never auto-accept a diff to silence it.
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.
- 9d ago First seen · 48 lines · 59 tokens per session scan A 54a327eccdc5
visual-regression-baseline is a skill published in the GitHub repository mtarcure/claude-vibe-squad (122 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 786 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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