massgen-develops-massgen

What this skill does

# MassGen Develops MassGen

This skill provides guidance for using MassGen to develop and improve itself. Choose the appropriate workflow based on what you're testing.

## Two Workflows

1. **Automation Mode** - Test backend functionality, coordination logic, agent responses
2. **Visual Evaluation** - Test terminal display, colors, layout, UX

---

## Workflow 1: Automation Mode

Use this to test functionality without visual inspection. Ideal for programmatic testing.

### Running MassGen with Automation

Run MassGen in the background (exact mechanism depends on your tooling):

```bash
uv run massgen --automation --config massgen/configs/basic/multi/two_agents_gemini.yaml "What is 2+2?"
```

**For MassGen agents**: Use `custom_tool__start_background_tool` targeting `mcp__command_line__execute_command`, then poll with `custom_tool__get_background_tool_status` / `custom_tool__get_background_tool_result`.
**For Claude Code**: Use Bash tool's `run_in_background` parameter.

### Why Automation Mode

| Feature | Benefit |
|---------|---------|
| Clean output | ~10 parseable lines vs 3,000+ ANSI codes |
| LOG_DIR printed | First line shows log directory path |
| status.json | Real-time monitoring file |
| Exit codes | 0=success, 1=config, 2=execution, 3=timeout, 4=interrupted |
| Workspace isolation | Safe parallel execution |

### Expected Output

```
LOG_DIR: .massgen/massgen_logs/log_20251120_143022_123456
STATUS: .massgen/massgen_logs/log_20251120_143022_123456/status.json

🤖 Multi-Agent Mode
Agents: gemini-2.5-pro1, gemini-2.5-pro2
Question: What is 2+2?

============================================================
QUESTION: What is 2+2?
[Coordination in progress - monitor status.json for real-time updates]

WINNER: gemini-2.5-pro1
DURATION: 33.4s
ANSWER_PREVIEW: The answer is 4.

COMPLETED: 2 agents, 35.2s total
```

Parse `LOG_DIR` from the first line to find the log directory.

### Monitoring Progress

Read the status.json file (updated every 2 seconds):

```bash
cat .massgen/massgen_logs/log_20251120_143022_123456/status.json
```

**Key fields:**

```json
{
  "coordination": {
    "completion_percentage": 65,
    "phase": "enforcement"
  },
  "results": {
    "winner": null  // null = running, "agent_id" = done
  },
  "agents": {
    "agent_a": {
      "status": "streaming",
      "error": null
    }
  }
}
```

**Agent status values:** `waiting`, `streaming`, `answered`, `voted`, `completed`, `error`

### Reading Results

After completion (exit code 0):

```bash
# Read final answer
cat [log_dir]/final/[winner]/answer.txt
```

### Timing Expectations

- **Standard tasks**: 2-10 minutes
- **Complex/meta tasks**: 10-30 minutes
- **Check if stuck**: Read status.json - if `completion_percentage` increases, it's working

### Advanced: Multiple Background Monitors

You can create multiple background monitoring tasks that run independently alongside the main MassGen process. Each monitor can track different aspects and write to separate log files for later inspection.

#### Approach

Create small Python scripts that run in background shells. Each script:
- Monitors a specific aspect (tokens, errors, progress, coordination, etc.)
- Writes timestamped data to its own log file
- Runs in a loop with `sleep()` intervals
- Can be checked anytime without blocking the main task

#### Example Monitor Scripts

**Token Usage Monitor** (`token_monitor.py`):
```python
import json, time, sys
from pathlib import Path

log_dir = Path(sys.argv[1])  # Pass LOG_DIR as argument
while True:
    if (log_dir / "status.json").exists():
        with open(log_dir / "status.json") as f:
            data = json.load(f)
        with open("token_monitor.log", "a") as log:
            log.write(f"=== {time.strftime('%H:%M:%S')} ===\n")
            log.write(f"Tokens: {data.get('total_tokens_used', 0)}\n")
            log.write(f"Cost: ${data.get('total_cost', 0):.4f}\n\n")
    time.sleep(5)
```

**Error Monitor** (`error_monitor.py`):
```python
import time, sys
from pathlib import Path

log_dir = Path(sys.argv[1])
while True:
    if log_dir.exists():
        with open("error_monitor.log", "a") as log:
            log.write(f"=== {time.strftime('%H:%M:%S')} ===\n")
            errors = []
            for logfile in log_dir.glob("*.log"):
                with open(logfile) as f:
                    for line in f:
                        if any(x in line.lower() for x in ['error', 'warning', 'failed']):
                            errors.append(line.strip())
            log.write('\n'.join(errors[-5:]) if errors else "No errors\n")
            log.write("\n")
    time.sleep(5)
```

**Progress Monitor** (`progress_monitor.py`):
```python
import json, time, sys
from pathlib import Path

log_dir = Path(sys.argv[1])
while True:
    if (log_dir / "status.json").exists():
        with open(log_dir / "status.json") as f:
            data = json.load(f)
        with open("progress_monitor.log", "a") as log:
            log.write(f"=== {time.strftime('%H:%M:%S')} ===\n")
            progress = data.get('completion_percentage', 0)
            active = sum(1 for a in data.get('agents', {}).values()
                        if a.get('status') == 'active')
            log.write(f"Progress: {progress}% Active agents: {active}\n\n")
    time.sleep(5)
```

**Coordination Monitor** (`coordination_monitor.py`):
```python
import json, time, sys
from pathlib import Path

log_dir = Path(sys.argv[1])
while True:
    if (log_dir / "status.json").exists():
        with open(log_dir / "status.json") as f:
            data = json.load(f)
        coord = data.get('coordination', {})
        with open("coordination_monitor.log", "a") as log:
            log.write(f"=== {time.strftime('%H:%M:%S')} ===\n")
            log.write(f"Phase: {coord.get('phase', 'unknown')}\n")
            log.write(f"Round: {coord.get('round', 0)}\n")
            log.write(f"Total answers: {coord.get('total_answers', 0)}\n\n")
    time.sleep(5)
```

#### Workflow

1. **Launch main task**, parse the LOG_DIR from output
2. **Create monitor scripts** as needed (write Python files)
3. **Launch monitors in background shells**: `python3 token_monitor.py [LOG_DIR] &`
4. **Check monitor logs anytime** by reading the .log files
5. **When complete**, kill monitor processes and analyze logs

#### Custom Monitors

Create monitors for any metric you want to track:
- Model-specific performance metrics
- Memory/context usage patterns
- Real-time cost accumulation
- Answer quality trends
- Agent coordination patterns
- Specific error categories

**Benefits:**
- Non-blocking inspection of specific metrics on demand
- Historical data captured for post-run analysis
- Independent monitoring streams for different aspects
- Easy to add new monitors without modifying configs

---

## Workflow 2: Visual Evaluation

Use this to analyze and improve MassGen's terminal display quality. Requires tools from `custom_tools/_multimodal_tools/`.

**Important:** This workflow records the rich terminal display, so the actual recording does NOT use `--automation` mode. However, you should ALWAYS pre-test with `--automation` first.

### Prerequisites

You should have these tools available in your workspace:
- `run_massgen_with_recording` - Records terminal sessions as video
- `understand_video` - Analyzes video frames with GPT-4.1 vision

### Step 0: Pre-Test with Automation (REQUIRED)

**Before recording the video**, verify the config works and API keys are valid:

```bash
# Start with --automation to verify everything works
uv run massgen --automation --config [config_path] "[question]"
```

**Wait 30-60 seconds** (enough to verify API keys, config parsing, tool initialization), then kill the process.

**Why this is critical:**
- Detects config errors before wasting recording time
- Validates API keys are present and working
- Ensures tools initialize correctly
- Prevents recording a broken session

**If the automation test fails**, fix the issues before proceeding to reco