Open-source SwiftUI testing skill for Claude Code uses Computer Use to visually test apps
An open-source skill for Claude Code called swiftui-autotest-skill leverages Claude's new Computer Use capability to visually test SwiftUI applications without writing test code.
How it works
You run /ios-test and the agent:
- Finds your
.xcodeprojfile - Picks a Simulator
- Builds the app
- Installs it
- Navigates through every single screen using Computer Use
The agent interacts with the app exactly like a real user would: tapping buttons, scrolling lists, following navigation links, and switching tabs.
What it catches
- Layout bugs (overflow, overlapping views, truncated text)
- Crashes (analyzes Simulator crash logs with stack traces mapped to your source code)
- Broken navigation (tests every tab, every link, back navigation)
- Non-responsive interactive elements
- Missing accessibility identifiers (and offers to auto-fix them)
Extra flags
--states→ tests empty, error, and loading states via launch arguments--performance→ measures RAM per screen, detects memory leaks--flow=onboarding→ tests a specific user flow end-to-end--screenshot-all→ captures every step
Additional feature
The skill also includes /add-accessibility which scans all SwiftUI views and auto-adds missing .accessibilityIdentifier() using a clean {screen}-{type}-{name} convention. This makes testing more reliable and prepares your app for VoiceOver.
The approach requires no XCUITest, no test targets, and no boilerplate. The agent visually inspects your app and reports issues.
📖 Read the full source: r/ClaudeAI
👀 See Also
Natural Language Autoencoders: Turning Claude's Internal Representations into Text
Transformer Circuits Thread publishes Natural Language Autoencoders that decode Claude's internal activations into readable text. GitHub repo and interactive demo available.
Krasis LLM Runtime Shows 8.9x Prefill and 4.7x Decode Speed Improvements Over Llama.cpp
Krasis LLM runtime now runs both prefill and decode entirely on GPU with different optimization strategies, achieving 8.9x faster prefill and 4.7x faster decode than llama.cpp on Qwen3.5-122B with a single 5090 GPU.
Engram: Open-source memory layer for Claude Code and MCP clients
Engram is an open-source memory layer that works as an MCP server with any client like Claude Code, Cursor, or Windsurf. It stores unlimited memories with semantic vector search, achieves 80% accuracy on LOCOMO benchmark, and uses about 800 tokens per query versus 5K+ for file-based approaches.
Upfront: A Claude Code Plugin That Forces Thinking Before Coding
Upfront is a Claude Code plugin with 20 skills that challenges developers before generating code. It uses three commands: /upfront:feature to push back on vague requirements, /upfront:plan to break work into ~400 LOC phases, and /upfront:build to execute with TDD and review per phase.