# IOS Debugger Agent

> Debug the current iOS project on a booted simulator with XcodeBuildMCP.

- Skill: `techwavedev/ios-debugger-agent` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add techwavedev/ios-debugger-agent`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/ios-debugger-agent/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/ios-debugger-agent

---


# iOS Debugger Agent

## Overview
Use XcodeBuildMCP to build and run the current project scheme on a booted iOS simulator, interact with the UI, and capture logs. Prefer the MCP tools for simulator control, logs, and view inspection.

## When to Use

- When the user asks to run, debug, or inspect an iOS app on a simulator.
- When you need simulator UI interaction, screenshots, or runtime logs via XcodeBuildMCP.

## Core Workflow
Follow this sequence unless the user asks for a narrower action.

### 1) Discover the booted simulator
- Call `mcp__XcodeBuildMCP__list_sims` and select the simulator with state `Booted`.
- If none are booted, ask the user to boot one (do not boot automatically unless asked).

### 2) Set session defaults
- Call `mcp__XcodeBuildMCP__session-set-defaults` with:
  - `projectPath` or `workspacePath` (whichever the repo uses)
  - `scheme` for the current app
  - `simulatorId` from the booted device
  - Optional: `configuration: "Debug"`, `useLatestOS: true`

### 3) Build + run (when requested)
- Call `mcp__XcodeBuildMCP__build_run_sim`.
- **If the build fails**, check the error output and retry (optionally with `preferXcodebuild: true`) or escalate to the user before attempting any UI interaction.
- **After a successful build**, verify the app launched by calling `mcp__XcodeBuildMCP__describe_ui` or `mcp__XcodeBuildMCP__screenshot` before proceeding to UI interaction.
- If the app is already built and only launch is requested, use `mcp__XcodeBuildMCP__launch_app_sim`.
- If bundle id is unknown:
  1) `mcp__XcodeBuildMCP__get_sim_app_path`
  2) `mcp__XcodeBuildMCP__get_app_bundle_id`

## UI Interaction & Debugging
Use these when asked to inspect or interact with the running app.

- **Describe UI**: `mcp__XcodeBuildMCP__describe_ui` before tapping or swiping.
- **Tap**: `mcp__XcodeBuildMCP__tap` (prefer `id` or `label`; use coordinates only if needed).
- **Type**: `mcp__XcodeBuildMCP__type_text` after focusing a field.
- **Gestures**: `mcp__XcodeBuildMCP__gesture` for common scrolls and edge swipes.
- **Screenshot**: `mcp__XcodeBuildMCP__screenshot` for visual confirmation.

## Logs & Console Output
- Start logs: `mcp__XcodeBuildMCP__start_sim_log_cap` with the app bundle id.
- Stop logs: `mcp__XcodeBuildMCP__stop_sim_log_cap` and summarize important lines.
- For console output, set `captureConsole: true` and relaunch if required.

## Troubleshooting
- If build fails, ask whether to retry with `preferXcodebuild: true`.
- If the wrong app launches, confirm the scheme and bundle id.
- If UI elements are not hittable, re-run `describe_ui` after layout changes.

---

<!-- AGI-INTEGRATION-START -->

## 🧠 AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Qdrant Memory Integration

Before executing complex tasks with this skill:
```bash
python3 execution/memory_manager.py auto --query "<task summary>"
```
- **Cache hit?** Use cached response directly — no need to re-process.
- **Memory match?** Inject `context_chunks` into your reasoning.
- **No match?** Proceed normally, then store results:
```bash
python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags ios-debugger-agent <relevant-tags>
```

### Agent Team Collaboration

- This skill can be invoked by the `orchestrator` agent via intelligent routing.
- In **Agent Teams mode**, results are shared via Qdrant shared memory for cross-agent context.
- In **Subagent mode**, this skill runs in isolation with its own memory namespace.

### Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:
- Embeddings: `nomic-embed-text` via Qdrant memory system
- Lightweight analysis: Local models reduce API costs for repetitive patterns

<!-- AGI-INTEGRATION-END -->