# downtime

> Run equipment downtime analysis on Stresscon maintenance data. Calculates Cost of Idleness metrics, generates category breakdowns, monthly trends, and top-offender rankings. Use when analyzing equipment availability, maintenance KPIs, or estimating financial impact of machine downtime.

- Author: BAM
- Repository: bamussel23/claude-skills
- Version: 20260207211010
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-08
- Source: https://github.com/bamussel23/claude-skills
- Web: https://mule.run/skillshub/@@bamussel23/claude-skills~downtime:20260207211010

---

---
name: downtime
description: >
  Run equipment downtime analysis on Stresscon maintenance data. Calculates
  Cost of Idleness metrics, generates category breakdowns, monthly trends,
  and top-offender rankings. Use when analyzing equipment availability,
  maintenance KPIs, or estimating financial impact of machine downtime.
---

# Downtime Analysis Dashboard

You run downtime analysis for Stresscon's precast concrete manufacturing
equipment using the Python analytics modules.

## Before running analysis

Read `references/api.md` for the full function signatures and data formats.

## Quick analysis workflow

### Step 1: Load data

```python
from stresscon.downtime_analyzer import (
    load_logs,
    calculate_downtime_minutes,
    aggregate_by_category,
    monthly_trends,
    top_offenders,
)
from stresscon.cost_calculator import cost_of_idleness, department_cost_report

# From CSV
df = load_logs("path/to/maintenance_logs.csv")

# Or from test data
df = load_logs("python/data/test_maintenance_data.csv")
```

### Step 2: Calculate downtime

```python
df = calculate_downtime_minutes(df)
# Adds "DowntimeMinutes" column from DowntimeEnd - DowntimeStart
```

### Step 3: Run analyses

```python
# Category breakdown (Mechanical, Electrical, Hydraulic, etc.)
categories = aggregate_by_category(df)
# Returns: IssueCategory, TotalDowntimeMinutes, Count

# Monthly trends (pivot table)
trends = monthly_trends(df)
# Returns: months as rows, categories as columns, minutes as values

# Top offending machines
worst_machines = top_offenders(df, n=5)
# Returns: AssetID, Title, TotalDowntimeMinutes
```

### Step 4: Calculate costs

```python
# Single downtime event
cost = cost_of_idleness(
    downtime_minutes=240.0,
    hourly_labor_rate=45.0,    # default from config
    production_rate_per_hour=1.0,  # default from config
)
# Returns: {downtime_minutes, downtime_hours, labor_cost, lost_production_units, total_cost}

# Department-wide cost report
dept_costs = department_cost_report(df, labor_rate=45.0, production_rate=1.0)
# Returns: IssueCategory, TotalDowntimeMinutes, DowntimeHours, LaborCost, LostUnits
```

### Step 5: Summarize findings

When presenting results, always include:
1. **Total downtime** — sum of all DowntimeMinutes, formatted as hours
2. **Top category** — which IssueCategory has highest downtime
3. **Top offender** — which machine (AssetID + name) has highest downtime
4. **Cost impact** — total LaborCost across all categories
5. **Trend direction** — is downtime increasing or decreasing month-over-month?

## CLI analysis runner

```bash
cd /Users/blakemusselman/claude/agent
python python/scripts/run_analysis.py --csv python/data/test_maintenance_data.csv
```

## Default cost parameters

From `python/stresscon/config.py`:
- `DEFAULT_LABOR_RATE = $45.00/hr`
- `DEFAULT_PRODUCTION_RATE = 1.0 units/hr`

These can be overridden by setting environment variables:
- `DEFAULT_LABOR_RATE`
- `DEFAULT_PRODUCTION_RATE`

## Data format requirements

The input DataFrame must have these columns:
- `AssetID` — equipment identifier (e.g., "CRN-01")
- `Title` — equipment name (e.g., "Overhead Crane #1")
- `IssueCategory` — one of: Mechanical, Electrical, Hydraulic, Pneumatic, Software, Safety
- `SeverityLevel` — one of: Critical (Line Down), High (Needs Repair), Routine (Scheduled)
- `DowntimeStart` — datetime (ISO format or parseable)
- `DowntimeEnd` — datetime (ISO format or parseable)