lycorp-jp/sim-use is a command-line tool that lets AI agents observe and control iOS simulators, Android emulators, and devices through accessibility information and input actions. It helps agents test mobile applications by reading compact screen descriptions and interacting with named elements, while the catalogue skills and instructions teach agents how to use those capabilities.
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/lycorp-jp/sim-use/run-evalsnpx skills add lycorp-jp/sim-use --skill run-evalsgit clone --depth 1 https://github.com/lycorp-jp/sim-useWrote 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/lycorp-jp/sim-use/run-evals)<a href="https://agentmods.dev/skills/lycorp-jp/sim-use/run-evals"><img src="https://agentmods.dev/badge/skills/lycorp-jp/sim-use/run-evals.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 | $0.00099 | $0.01410 |
| Opus 5 | $0.00049 | $0.00705 |
| Sonnet 5 | $0.00020 | $0.00282 |
| Haiku 4.5 | $0.00010 | $0.00141 |
Grade A, and why
run-evals 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 5d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill orchestrates the agent-eval suite: natural-language cases executed
by a headless claude -p agent using the bundled skill (skills/sim-use/)
against the Playground fixture apps, judged by deterministic post-condition
checks. It verifies the layer the scripted E2E suites cannot: that an agent
reading SKILL.md reaches for the right verbs and survives the documented
pitfalls. A failure here with a green scripted layer usually means
skill-prose drift, not a CLI bug.
Execution is delegated to scripts/eval.sh / e2e/agent-evals/run.py — do
not reimplement their logic. Case anatomy, tags, and authoring rules live in
e2e/agent-evals/README.md. Run from the repo root.
Step 1: Decide WHICH sim-use is under test
The whole run — device probing, the agent's commands, the verification layer
— resolves sim-use from PATH unless overridden. Never let this be implicit:
- Ask (or infer from the user's request) which binary to evaluate:
- Installed release (default): whatever
sim-useresolves to on PATH. - A development build: pass
-b <path>, e.g.-b .build/out/Products/Debug/sim-use(SwiftBuild layout) or-b .build/debug/sim-use(classic). Build it first withmake build.
- Installed release (default): whatever
- Confirm the resolution and report it to the user before running:
The wrapper printspython3 -c 'import pathlib,shutil; print(pathlib.Path(shutil.which("sim-use")).resolve())' sim-use --versionsim-use under test: <real path> (<version>)and the run report records it undersim-use under test:— quote that line back in your summary so the human knows exactly what was evaluated.
Step 2: Prepare devices and fixtures
For each platform you intend to cover (the wrapper auto-detects reachable
ones; use -p ios|android to restrict):
iOS
- Device Hub (Xcode 27) must be CLOSED —
pgrep dtuhiddmust be empty. A simulator booted while Device Hub is open has legacy HID disconnected; sim-use's guard will (correctly) fail every case on it. If dtuhidd is running: quit Device Hub, then shutdown && boot the simulator. - Boot a simulator and wait:
xcrun simctl boot <UDID> && xcrun simctl bootstatus <UDID>. - The Playground fixture must be installed. Check:
xcrun simctl listapps <UDID> | grep -c com.cameroncooke.SimUsePlayground— if missing, install withscripts/test-runner.sh -b(builds sim-use + Playground, ~2-3 min).
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.
- 5d ago First seen · 106 lines · 99 tokens per session scan A 9c6cd37f756f
run-evals is a skill published in the GitHub repository lycorp-jp/sim-use (1,310 stars, last pushed yesterday), licensed Apache-2.0. It adds 99 tokens to every session and 1,410 once invoked, about $0.0005 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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