Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gosha70/code-copilot-team --skill visual-reviewgit clone --depth 1 https://github.com/gosha70/code-copilot-teamWrote 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/gosha70/code-copilot-team/visual-review)<a href="https://agentmods.dev/skills/gosha70/code-copilot-team/visual-review"><img src="https://agentmods.dev/badge/skills/gosha70/code-copilot-team/visual-review/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/gosha70/code-copilot-team/visual-review"><img src="https://agentmods.dev/badge/skills/gosha70/code-copilot-team/visual-review.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 | $0.00060 | $0.01105 |
| Opus 5.5 | $0.00024 | $0.00442 |
| Sonnet 5.5 | $0.00012 | $0.00221 |
| Haiku 4.5 | $0.00006 | $0.00111 |
Grade A, and why
visual-review 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Review Loop Protocol
Invoke after UI is built against the design-system bundle. Generation alone can't
be trusted — this loop verifies the rendered result against the committed
DESIGN.md and fails the build until the bar is met.
The loop
build → boot dev server → rubric pre-filter (cheap, deterministic)
→ axe-core a11y gate (fail-fast)
→ Playwright screenshot @ 375 / 768 / 1440
→ CRITIC scores screenshots vs DESIGN.md rubric
→ triage [Blocker/High/Medium/Nitpick] → write critique-feedback.json
→ coding agent applies fixes → re-run → check exit criteria
Deterministic harness + pluggable critic. The harness/ runner does the
identical-for-every-copilot parts (boot, screenshot, axe, rubric pre-filter, emit
artifacts). The critic (aesthetic judgment) is either:
- Agent critic (Claude Code): the
visual-revieweragent reads the PNGs directly (a picture is worth 1000 tokens) and scores them. - Runner critic (any other copilot): the runner calls a vision-LLM (provider,
model, key from env) and writes
critique-feedback.json.
Same rubric, same gates, same exit criteria — only the critic swaps.
Running it
From the project root: npm run copilot:review (wraps harness/). It:
- runs the a11y + rubric gates; on failure writes
tmp/ui-review/critique-feedback.jsonand exits non-zero; - captures screenshots at the three breakpoints;
- runs the critic;
- exits
0only when all exit criteria pass (vision critic), or after emitting artifacts for the agent critic to judge.
The coding agent reads tmp/ui-review/critique-feedback.json ({passed, critiqueSummary, actionableFixes[]}), applies the fixes, and re-runs. Never
hand-wave a failing gate — fix the UI.
The critic rubric (score each; source of truth is DESIGN.md)
Phased, adapted from published design-review agents (OneRedOak, Anthropic frontend-design, shiplightai):
- Typography — modular scale, real hierarchy, committed pairing (no default fonts).
- Spacing & layout — 4/8pt rhythm, internal ≤ external spacing, deliberate layout grammar (no lone centered column, no cardocalypse).
- Color & theme — semantic-token discipline, dominant + sharp accent (not evenly distributed), light/dark parity, no banned gradients.
- Hierarchy — one primary CTA per screen; attention guided by size/weight/space.
- States — empty/loading/error/success/focus present for every data component.
- Anti-slop flags — any Step-3 tell from
design-systempresent ⇒ automatic fail. - Domain fit — does it match the archetype/density locked in DESIGN.md.
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 First seen · 89 lines · 60 tokens per session scan A 170a4943decf
visual-review is a skill published in the GitHub repository gosha70/code-copilot-team (7 stars, last pushed 17d ago), licensed MIT. It adds 60 tokens to every session and 1,105 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 2026-09-03.
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