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/vercel-labs/native/automationnpx skills add vercel-labs/native --skill automationgit clone --depth 1 https://github.com/vercel-labs/nativeWrote 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/vercel-labs/native/automation)<a href="https://agentmods.dev/skills/vercel-labs/native/automation"><img src="https://agentmods.dev/badge/skills/vercel-labs/native/automation.svg" alt="Measured on agentmods" 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 | $0.00069 | $0.04141 |
| Opus 5 | $0.00034 | $0.02070 |
| Sonnet 5 | $0.00014 | $0.00828 |
| Haiku 4.5 | $0.00007 | $0.00414 |
Grade A, and why
automation 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 4d 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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automate Native SDK apps
Every Native SDK app embeds an automation server — native-rendered apps and WebView-shell apps alike. It works through file-based IPC in .zig-cache/native-sdk-automation/ and is intended for smoke tests, CI checks with a GUI session, and quick runtime inspection: accessibility snapshots, widget driving through the real input paths, deterministic reference-renderer screenshots, readiness/state assertions, and bridge round-trips.
Automation is not browser DOM automation. It reports runtime/window/widget state, drives retained-canvas widgets, and can ask the runtime to reload or dispatch bridge requests. For DOM testing of the optional WebView path, use the frontend framework's tests or a browser automation tool against the dev server.
What automation can verify
- An automation-enabled app started and published
ready=true. - The runtime loaded the expected app name, source kind, and window metadata.
- The main window exists and is focused/open.
- The JavaScript-to-Zig bridge can round-trip a request through
native automate bridge. - Builtin window/WebView commands work when exercised by a smoke test.
- Reload requests are accepted by the runtime.
- Real pixels of retained-canvas (
gpu_surface) views:native automate screenshot <view-label>renders the view's current canvas frame through the deterministic CPU reference renderer and writes a PNG artifact. Two captures of an unchanged scene are byte-identical, so screenshots can back golden-image or "did the UI change" checks.
What automation cannot verify
- Screenshots of WebView content.
screenshotcoversgpu_surfacecanvas views only; there is no DOM/WebView pixel capture. - Arbitrary DOM queries and clicks.
- Browser network assertions.
Prerequisites
Build/run an app with automation enabled. Generated examples usually expose -Dautomation=true:
zig build run -Dplatform=macos -Dautomation=true
Repository examples may have specialized steps:
zig build run-webview -Dplatform=macos -Dautomation=true
zig build test-webview-smoke -Dplatform=macos
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.
- 4d ago First seen · 253 lines · 69 tokens per session scan A 8274dbc992fc
automation is a skill published in the GitHub repository vercel-labs/native (7,625 stars, last pushed yesterday), licensed Apache-2.0. It adds 69 tokens to every session and 4,141 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.
Other skills, from other repositories
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brainstorming
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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.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…