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 agentmods add skills/moasq/agentic-ship/visual-qanpx skills add moasq/agentic-ship --skill visual-qagit clone --depth 1 https://github.com/moasq/agentic-shipWhat 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 | $0.00052 | $0.01163 |
| Opus 5 | $0.00026 | $0.00581 |
| Sonnet 5 | $0.00010 | $0.00233 |
| Haiku 4.5 | $0.00005 | $0.00116 |
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 2d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual QA
Downstream contract: paths like
src/andconvex/refer to the product workspace that adopts Agentic Ship, not this tool repo.
Turn a visual direction into reviewable evidence. Automated checks prove coverage and basic browser integrity; the reviewer proves that the rendered product expresses the declared direction without generic generated-page residue.
Authority and required inputs
Read the Component rules, Styling rules, and UI-quality rules in
AGENTS.md. They are authoritative. Use this skill as the
post-implementation procedure, and use testing for the gate
order and repair loop.
Before capture, require this frontend handoff:
- a valid UI plan;
- changed surface ids and source summary;
- declared routes, deterministic states, themes, viewports, and interactions;
- accessibility constraints, anti-goals, reference decisions, and the signature element;
- confirmation that captures use only public or synthetic content.
If the plan is absent or invalid, return to
visual-direction. Do not reverse-engineer intent from
the finished screenshot.
Load references/review-policy.md when capturing,
accepting, diagnosing stale evidence, or integrating the completion gate. Load the
operational anti-slop rubric for
the subjective rendered review.
Procedure
1. Reproduce the declared scope
Run the plan check first:
pnpm ui:plan check
Start the application in a separate terminal with deterministic local fixtures:
pnpm dev
Do not capture production, authenticated personal state, changing vendor data, secrets, or real customer content. A polished production screenshot is not worth creating an unreviewable data artifact.
2. Generate the evidence matrix
Capture from the local server:
pnpm ui:review capture --base-url http://localhost:3000
What ships with it
4 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.
- 2d ago First seen · 150 lines · 52 tokens per session scan A f6c8259d9de9
visual-qa is a skill published in the GitHub repository moasq/agentic-ship (10 stars, last pushed 3d ago), licensed MIT. It adds 52 tokens to every session and 1,163 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.