name: autoresearch-ios description: Autonomously optimize iOS app cold launch time using Karpathy's autoresearch pattern. Runs a hypothesize → modify → build → measure → keep/discard loop on your SwiftUI app. disable-model-invocation: false context: fork

autoresearch-ios

Optimize the iOS app's cold launch time autonomously using the autoresearch pattern.

What this skill does

This skill runs an autonomous optimization loop on your iOS app's startup code. It reads your Swift source files, forms a hypothesis about what might reduce cold launch time, makes the change, builds and measures on the iOS Simulator, and keeps the change if it improved — otherwise reverts. This loop repeats until you stop it.

The approach is adapted from Karpathy's autoresearch which originally optimized LLM training. We replace val_bpb with cold_launch_ms and train.py with Swift source files.

Before you start

1. Read program.md in this repository for the full experiment protocol, constraints, optimization strategies, and output format.
2. Read prepare.py to understand the build-measure harness (do not modify it).
3. Update the constants in prepare.py to point to your iOS project:
   • TARGET_PATH — path to your Xcode project
   • WORKSPACE — your .xcworkspace path
   • SCHEME — your Xcode scheme name
   • BUNDLE_ID — your app's bundle identifier
   • DEVICE_UDID — your target simulator UDID (find with xcrun simctl list devices)
4. Update MUTABLE_FILES in prepare.py with the Swift files the agent is allowed to modify.
5. Update BASELINE in prepare.py with your app's current cold launch metrics.
6. Update program.md with your app's file paths, baseline metrics, and any app-specific constraints.

Usage

/autoresearch-ios [number-of-experiments]

If no argument is given, the loop runs indefinitely until manually stopped.

How it works

The skill follows the protocol defined in program.md:

1. Read the mutable Swift files listed in program.md
2. Hypothesize a change that might reduce cold_launch_ms
3. Edit the Swift file(s) in the target app
4. Commit the change in the target app repo
5. Measure by running python prepare.py (builds with xcodebuild, installs on simulator, runs 3 cold launches, takes median)
6. Keep or discard — if cold_launch_ms improved, keep; otherwise git checkout to revert
7. Log the result and repeat

Constraints (from program.md)

• Only modify the files listed in MUTABLE_FILES
• Never modify prepare.py
• Never add new files or dependencies
• App must still launch and function correctly
• Primary metric: cold_launch_ms (lower = better)
• Each experiment should complete in 1-2 minutes

Multi-model mode

For running multiple AI models in parallel comparison (like the original 10-model benchmark), use run_models.py instead:

export OPENROUTER_API_KEY="sk-or-..."
python run_models.py --experiments 15
python dashboard.py  # live results at http://localhost:8050

Reference

See program.md for the complete experiment protocol including:

• Detailed optimization strategies (high/medium/low impact)
• Output format and result logging
• Crash handling guidelines
• The autonomous experiment loop specification

Results from Middle Earth Explorer

202 experiments across 10 models optimized cold launch from 558ms to 189ms (-66%) for $17.05 total on OpenRouter. Full results at github.com/alpozcan/autoresearch.