metrics-dashboard

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• SKILL.md

SKILL.md (first lines)

# Metrics Dashboard

Aggregates pipeline run data to track velocity, success rates, and identify improvement areas.

## Configuration

Uses the **production API** by default. No configuration needed for read operations.

**Defaults (no setup required):**

- API URL: `https://api-gateway-856475788601.us-central1.run.app`
- Read-only endpoints at `/public/*` require no authentication

**For write operations**, set:

```bash
export PIPELINE_API_KEY="..."  # Get from GCP Secret Manager or ask admin
```

## Workflow

### 1. Fetch Ticket Data from API

```typescript
import { PipelineClient, PipelineAPIError } from '@pipeline/client'

const client = new PipelineClient({
  apiUrl: process.env.PIPELINE_API_URL || 'https://api-gateway-856475788601.us-central1.run.app',
  agentId: process.env.AGENT_ID!,
})

// Fetch all tickets for metrics calculation
const { tickets, total } = await client.listTickets({
  limit: 1000,
})

// Filter by state if needed
const { tickets: completedTickets } = await client.listTickets({
  state: 'done',
  limit: 1000,
})

// Filter by agent
const { tickets: myTickets } = await client.listTickets({
  agent_id: process.env.AGENT_ID,
})
```

### 2. Calculate Metrics

```typescript
interface TicketMetrics {
  total: number
  completed: number
  failed: number
  inProgress: number
  successRate: number
  avgCycleTimeHours: number
  minCycleTimeHours: number
  maxCycleTimeHours: number
}

function calculateMetrics(tickets: Ticket[]): TicketMetrics {
  const completed = tickets.filter((t) => t.state === 'done')
  const failed = tickets.filter((t) => t.state === 'failed')
  const inProgress = tickets.filter((t) =>
    ['intake', 'research', 'planning', 'implementation', 'pr_created'].includes(t.state)
  )

  // Calculate cycle times from timestamps
  const cycleTimes = completed
    .filter((t) => t.created_at && t.state_changed_at)
    .map((t) => {
      const start = new Date(t.created_at).getTime()
      const end = new Date(t.state_changed_at).getTime()
      return (end - start) / (1000 * 60 * 60) // hours
    })

  const avgCycleTime = cycleTimes.length > 0 ? cycleTimes.reduce((a, b) => a + b, 0) / cycleTimes.length : 0

  return {
    total: tickets.length,
    completed: completed.length,
    failed: failed.length,
    inProgress: inProgress.length,
    successRate:
      completed.length + failed.length > 0 ? (completed.length / (completed.length + failed.length)) * 100 : 0,
    avgCycleTimeHours: avgCycleTime,
    minCycleTimeHours: cycleTimes.length > 0 ? Math.min(...cycleTimes) : 0,
    maxCycleTimeHours: cycleTimes.length > 0 ? Math.max(...cycleTimes) : 0,
  }
}

const metrics = calculateMetrics(tickets)
```

### 3. Generate Dashboard

```markdown
# Pipeline Metrics Dashboard

**Period:** Last 30 days | **Generated:** {timestamp}

## Summary

| Metric            | Value   | Trend      |
| ----------------- | ------- | ---------- |
| Tickets Completed | 12      | ↑ +3       |
| Success Rate      | 83%     | ↑ +5%      |
| Avg Cycle Time    | 2.5 hrs | ↓ -0.5 hrs |

## Cycle Time Breakdown

Intake ████ 5 min | Research ████████ 15 min | Planning ██████ 10 min
Implement █████████████ 25 min | Quality ████ 8 min | Review ██████ 12 min

## Success by Type

| Type     | Count | Success | Avg Time |

From DrJKL/ticket-to-pr-pipeline, MIT.

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