zenml-quick-wins

# ZenML Quick Wins Implementation

Guides users through discovering and implementing high-impact ZenML features that take ~5 minutes each. Investigates current setup, recommends priorities, and implements chosen improvements.

## Workflow Overview

```
┌─────────────────────┐
│  1. INVESTIGATE     │  Understand current stack + codebase
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│  2. CONFIRM         │  ⏸️ Check understanding with user
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│  3. GATHER CONTEXT  │  ⏸️ Get additional context from user
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│  4. RECOMMEND       │  Prioritize quick wins based on findings
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│  5. PREPARE         │  ⏸️ Verify branch setup before changes
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│  6. IMPLEMENT       │  Apply selected quick wins
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│  7. VERIFY          │  Confirm implementation works
└─────────────────────┘

⏸️ = User checkpoint (uses AskUserQuestion tool)
```

---

## Phase 1: Investigation

**Use subagents to gather information efficiently.** This keeps verbose output out of the main conversation while enabling parallel investigation.

### Recommended: Parallel Subagent Investigation

Spawn **both subagents in parallel** using a single Task tool call with multiple invocations:

```
Use the Task tool to launch BOTH agents simultaneously:

1. zenml-quick-wins:zenml-stack-investigator agent:
   - Prompt: "Investigate the ZenML stack configuration for this project.
     Run all the ZenML CLI commands to understand stacks, components,
     and recent pipeline activity. Return a structured summary."

2. zenml-quick-wins:zenml-codebase-analyzer agent:
   - Prompt: "Analyze the Python codebase for ZenML patterns and quick win
     opportunities. Search for pipeline definitions, current feature usage,
     and areas