Borrowing it
Nothing to install: this file belongs to doncheli/don-cheli-sdd. 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/doncheli/don-cheli-sdd/main/.agent/skills/doncheli-visual-test/SKILL.mdgit clone --depth 1 https://github.com/doncheli/don-cheli-sddWrote 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/doncheli/don-cheli-sdd/doncheli-visual-test)<a href="https://agentmods.dev/skills/doncheli/don-cheli-sdd/doncheli-visual-test"><img src="https://agentmods.dev/badge/skills/doncheli/don-cheli-sdd/doncheli-visual-test/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/doncheli/don-cheli-sdd/doncheli-visual-test"><img src="https://agentmods.dev/badge/skills/doncheli/don-cheli-sdd/doncheli-visual-test.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 22 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00047 | $0.00465 |
| Opus 5 | $0.00023 | $0.00233 |
| Sonnet 5 | $0.00009 | $0.00093 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
doncheli-visual-test 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 10d 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.
What it actually says
Don Cheli: Visual Test
Instructions
- Detect the visual testing tool in use: Playwright, Cypress, Percy, Chromatic, Storybook, or custom
- Run the visual test suite and capture screenshots of the current state
- Compare against the stored baseline snapshots
- Report each visual difference:
- Component / page name
- Diff percentage (pixels changed vs. total)
- Category: layout shift, color change, missing element, new element
- Classify severity:
-
5% diff → blocker (likely regression)
- 1-5% diff → warning (review required)
- < 1% diff → info (minor or anti-aliasing)
-
- For new snapshots (no baseline exists), prompt the user to approve them as the new baseline
- Never auto-approve baselines — always require explicit human confirmation
- Generate a visual diff report with image paths or URLs if available
- Save report to
.dc/visual-test-report-<timestamp>.md
Output Format
## Visual Test Report — 2026-03-28T15:00Z
### Summary
- Compared: 24 snapshots
- Passed: 21
- Diffs: 3 (1 blocker, 2 warnings)
### Blocker 🔴
- CheckoutPage/desktop — 12.3% diff
Category: layout shift — payment form moved 40px down
Baseline: .visual/baselines/checkout-desktop.png
Current: .visual/current/checkout-desktop.png
### Warnings 🟡
- Button/primary — 2.1% diff — color shift (likely theme variable change)
- NavBar/mobile — 1.8% diff — font rendering difference (check font loading)
### New Snapshots (approval required)
- VoiceModal/desktop — no baseline found — run with --approve to set baseline
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.
- 10d ago First seen · 49 lines · 47 tokens per session scan A 400fb5eafc44
doncheli-visual-test is a skill published in the GitHub repository doncheli/don-cheli-sdd (57 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 465 once invoked, about $0.0002 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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browser-screenshot-diff
Visual + DOM diff between two recorded sessions at matching trajectory step ids; used for visual regression and replay verification.