agentmods.dev

sim-use: Skill for Claude Code

.agents/skills/run-evals/SKILL.md

run-evals is a skill for Claude Code, Codex from lycorp-jp/sim-use. It costs 99 tokens per session (1,410 once invoked), scanned A, original, Apache-2.0.

A runner for end-to-end tests of coding-agent skills. It has an AI agent use a skill against sample apps, then checks whether the expected results happened.

In plain words
What is it for?
Use it before release to evaluate a skill, choose the program version under test, run the documented evaluation cases, and detect mismatches between instructions and behavior.
Why use it?
It tests whether an agent can follow the written instructions in realistic tasks, including problems that ordinary scripted tests may miss.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is lycorp-jp/sim-use's own configuration. It tells Claude Code and Codex how to work on sim-use itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything sim-use configures →

not rated 1.5krepo +16 13d ago A scan SkillSpector: warn 99 tokens original Apache-2.0
View source ↗ lycorp-jp/sim-use
About the project

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.

lycorp-jp/sim-use · 1,532 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to lycorp-jp/sim-use. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/lycorp-jp/sim-use/main/.agents/skills/run-evals/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/lycorp-jp/sim-use

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for run-evals

README.md
[![agentmods](https://agentmods.dev/badge/skills/lycorp-jp/sim-use/run-evals/github.svg)](https://agentmods.dev/skills/lycorp-jp/sim-use/run-evals)
Your own site
<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/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.

agentmods 80×15 button for run-evals

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,410 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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 Agent Snooping · line 88
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00099 $0.01410
Opus 5.5 $0.00040 $0.00564
Sonnet 5.5 $0.00020 $0.00282
Haiku 4.5 $0.00010 $0.00141

Measured 2mo ago against content hash 9c6cd37f756f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.agents/skills/run-evals/SKILL.md · 106 lines

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:

  1. Ask (or infer from the user's request) which binary to evaluate:
    • Installed release (default): whatever sim-use resolves 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 with make build.
  2. Confirm the resolution and report it to the user before running: 
    python3 -c 'import pathlib,shutil; print(pathlib.Path(shutil.which("sim-use")).resolve())'
sim-use --version
    The wrapper prints sim-use under test: <real path> (<version>) and the run report records it under sim-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

  1. Device Hub (Xcode 27) must be CLOSED — pgrep dtuhidd must 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.
  2. Boot a simulator and wait: xcrun simctl boot <UDID> && xcrun simctl bootstatus <UDID>.
  3. The Playground fixture must be installed. Check: xcrun simctl listapps <UDID> | grep -c com.cameroncooke.SimUsePlayground — if missing, install with scripts/test-runner.sh -b (builds sim-use + Playground, ~2-3 min).

Read the full file on GitHub · 106 lines

Changes

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.

  1. 2mo ago First seen · 106 lines · 99 tokens per session scan A 9c6cd37f756f

Subscribe to this mod's changes

run-evals is a skill published in the GitHub repository lycorp-jp/sim-use (1,532 stars, last pushed 13d ago), licensed Apache-2.0. It adds 99 tokens to every session and 1,410 once invoked, about $0.0004 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 2026-08-30.

E2E testingTestingaccessibilityai-agentsai-developmentandroid-emulatorios-simulatormobile-automation
Related

run-evals compared with skills from other repositories

run-evals next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
run-evalslycorp-jp/sim-useSkill1.5k13d agoApache-2.0A99Claude Code, Codex
critiqueplugin87/ux-ui-agent-skillsSkill3.1k10d agoMITA37Claude Code
a11y-allyproffesor-for-testing/agentic-qeSkill525yesterdayMITA44Claude Code
accessibility-testingproffesor-for-testing/agentic-qeSkill525yesterdayMITA50Claude Code
emulate-seedyonatangross/orchestkitSkill299yesterdayMITA47Claude Code
kb-github-a11y-scannerCommunity-Access/accessibility-agentsSkill4357d agoMITA45Claude Code, Codex
kb-playwright-testingCommunity-Access/accessibility-agentsSkill4357d agoMITA36Claude Code, Codex