smed-analyzer
Single Minute Exchange of Die analysis skill for changeover time reduction.
Best use case
smed-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Single Minute Exchange of Die analysis skill for changeover time reduction.
Teams using smed-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/smed-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How smed-analyzer Compares
| Feature / Agent | smed-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Single Minute Exchange of Die analysis skill for changeover time reduction.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# smed-analyzer
You are **smed-analyzer** - a specialized skill for analyzing and reducing changeover times using the Single Minute Exchange of Die (SMED) methodology.
## Overview
This skill enables AI-powered SMED analysis including:
- Changeover video analysis
- Internal vs external activity separation
- Activity timing and sequencing
- Conversion opportunity identification
- Parallel work assignment
- Quick-release mechanism suggestions
- Before/after comparison reports
- Standard changeover documentation
## Prerequisites
- Video recording capability
- Stopwatch or timing software
- Understanding of changeover process
## Capabilities
### 1. Changeover Activity Recording
```python
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional
import datetime
class ActivityType(Enum):
INTERNAL = "internal" # Machine must be stopped
EXTERNAL = "external" # Can be done while running
@dataclass
class ChangeoverActivity:
id: int
description: str
start_time: float # seconds from changeover start
end_time: float
activity_type: ActivityType
operator: str
tools_required: List[str]
notes: Optional[str] = None
@property
def duration(self):
return self.end_time - self.start_time
class ChangeoverAnalysis:
"""
Record and analyze changeover activities
"""
def __init__(self, machine_name: str, from_product: str, to_product: str):
self.machine_name = machine_name
self.from_product = from_product
self.to_product = to_product
self.activities: List[ChangeoverActivity] = []
self.timestamp = datetime.datetime.now()
def add_activity(self, description, start, end, activity_type,
operator, tools=None, notes=None):
activity = ChangeoverActivity(
id=len(self.activities) + 1,
description=description,
start_time=start,
end_time=end,
activity_type=activity_type,
operator=operator,
tools_required=tools or [],
notes=notes
)
self.activities.append(activity)
return activity
def summary(self):
internal = [a for a in self.activities if a.activity_type == ActivityType.INTERNAL]
external = [a for a in self.activities if a.activity_type == ActivityType.EXTERNAL]
return {
"total_changeover_time": max(a.end_time for a in self.activities),
"internal_time": sum(a.duration for a in internal),
"external_time": sum(a.duration for a in external),
"num_activities": len(self.activities),
"num_internal": len(internal),
"num_external": len(external)
}
```
### 2. Internal/External Separation Analysis
```python
def analyze_internal_external(activities):
"""
Identify activities that could be converted from internal to external
"""
conversion_opportunities = []
for activity in activities:
if activity.activity_type == ActivityType.INTERNAL:
# Check for conversion potential
potential = assess_conversion_potential(activity)
if potential['can_convert']:
conversion_opportunities.append({
"activity_id": activity.id,
"description": activity.description,
"current_duration": activity.duration,
"conversion_method": potential['method'],
"estimated_savings": potential['savings'],
"investment_required": potential['investment']
})
return conversion_opportunities
def assess_conversion_potential(activity):
"""
Assess if internal activity can become external
"""
# Keywords indicating conversion potential
prep_keywords = ['get', 'find', 'look for', 'search', 'locate', 'bring']
adjustment_keywords = ['adjust', 'set', 'calibrate', 'tune']
removal_keywords = ['remove', 'take off', 'disconnect']
desc_lower = activity.description.lower()
# Preparation activities can often be done externally
if any(kw in desc_lower for kw in prep_keywords):
return {
'can_convert': True,
'method': 'Pre-stage tools and materials before stopping machine',
'savings': activity.duration * 0.9, # 90% reduction
'investment': 'Low - organization and staging area'
}
# Adjustments might be eliminated with presetting
if any(kw in desc_lower for kw in adjustment_keywords):
return {
'can_convert': True,
'method': 'Use preset tooling or jigs for instant settings',
'savings': activity.duration * 0.7,
'investment': 'Medium - preset tooling investment'
}
return {'can_convert': False}
```
### 3. Parallel Work Analysis
```python
def analyze_parallel_opportunities(activities, available_operators):
"""
Identify activities that can be done in parallel
"""
# Group activities by time window
timeline = []
for activity in activities:
timeline.append({
'time': activity.start_time,
'type': 'start',
'activity': activity
})
timeline.append({
'time': activity.end_time,
'type': 'end',
'activity': activity
})
timeline.sort(key=lambda x: x['time'])
# Analyze operator utilization
parallel_opportunities = []
current_activities = []
for event in timeline:
if event['type'] == 'start':
current_activities.append(event['activity'])
else:
current_activities.remove(event['activity'])
# Check if operators are idle
active_operators = len(set(a.operator for a in current_activities))
idle_operators = available_operators - active_operators
if idle_operators > 0 and len(current_activities) > 0:
parallel_opportunities.append({
'time': event['time'],
'idle_operators': idle_operators,
'active_activities': [a.description for a in current_activities]
})
return parallel_opportunities
def optimize_parallel_work(activities, available_operators):
"""
Reassign activities for parallel execution
"""
# Simple greedy assignment
assignments = {i: [] for i in range(available_operators)}
operator_end_times = [0] * available_operators
# Sort by start time
sorted_activities = sorted(activities, key=lambda a: a.start_time)
for activity in sorted_activities:
# Find operator who finishes earliest
earliest_op = min(range(available_operators),
key=lambda i: operator_end_times[i])
# Assign to this operator
new_start = max(activity.start_time, operator_end_times[earliest_op])
new_end = new_start + activity.duration
assignments[earliest_op].append({
'activity': activity.description,
'original_start': activity.start_time,
'new_start': new_start,
'end': new_end
})
operator_end_times[earliest_op] = new_end
new_total_time = max(operator_end_times)
original_total_time = max(a.end_time for a in activities)
return {
'assignments': assignments,
'original_time': original_total_time,
'optimized_time': new_total_time,
'time_savings': original_total_time - new_total_time,
'reduction_percent': (1 - new_total_time/original_total_time) * 100
}
```
### 4. Quick-Release Mechanism Suggestions
```python
def suggest_quick_release_mechanisms(activities):
"""
Suggest engineering improvements for faster changeovers
"""
suggestions = []
for activity in activities:
desc_lower = activity.description.lower()
# Fastener improvements
if any(word in desc_lower for word in ['bolt', 'screw', 'nut', 'fasten']):
suggestions.append({
'activity': activity.description,
'current_method': 'Threaded fasteners',
'improvement': 'Quick-release clamps, cam locks, or quarter-turn fasteners',
'typical_reduction': '70-90%',
'investment_level': 'Medium'
})
# Tool changes
if 'tool' in desc_lower and 'change' in desc_lower:
suggestions.append({
'activity': activity.description,
'current_method': 'Manual tool change',
'improvement': 'Quick-change tool holders with preset tooling',
'typical_reduction': '80-95%',
'investment_level': 'Medium-High'
})
# Positioning/alignment
if any(word in desc_lower for word in ['align', 'position', 'center']):
suggestions.append({
'activity': activity.description,
'current_method': 'Manual alignment',
'improvement': 'Locating pins, guides, or self-centering fixtures',
'typical_reduction': '60-80%',
'investment_level': 'Low-Medium'
})
# Settings/adjustments
if any(word in desc_lower for word in ['adjust', 'set', 'calibrate']):
suggestions.append({
'activity': activity.description,
'current_method': 'Trial and error adjustment',
'improvement': 'Digital presets, scales, or stops',
'typical_reduction': '50-70%',
'investment_level': 'Medium'
})
return suggestions
```
### 5. Before/After Comparison
```python
def generate_comparison_report(before_analysis, after_analysis):
"""
Generate before/after SMED comparison report
"""
before_summary = before_analysis.summary()
after_summary = after_analysis.summary()
return {
'changeover': {
'machine': before_analysis.machine_name,
'product_change': f"{before_analysis.from_product} -> {before_analysis.to_product}"
},
'time_comparison': {
'before': {
'total_minutes': before_summary['total_changeover_time'] / 60,
'internal_minutes': before_summary['internal_time'] / 60,
'external_minutes': before_summary['external_time'] / 60
},
'after': {
'total_minutes': after_summary['total_changeover_time'] / 60,
'internal_minutes': after_summary['internal_time'] / 60,
'external_minutes': after_summary['external_time'] / 60
}
},
'improvement': {
'time_reduction_minutes': (before_summary['total_changeover_time'] -
after_summary['total_changeover_time']) / 60,
'percent_reduction': (1 - after_summary['total_changeover_time'] /
before_summary['total_changeover_time']) * 100,
'internal_reduction_percent': (1 - after_summary['internal_time'] /
before_summary['internal_time']) * 100
},
'activities_comparison': {
'before_count': before_summary['num_activities'],
'after_count': after_summary['num_activities'],
'eliminated': before_summary['num_activities'] - after_summary['num_activities']
}
}
```
### 6. Standard Changeover Documentation
```python
def generate_standard_changeover(optimized_analysis):
"""
Create standard work document for changeover
"""
document = {
'title': f"Standard Changeover: {optimized_analysis.machine_name}",
'revision': '1.0',
'date': datetime.datetime.now().isoformat(),
'target_time_minutes': optimized_analysis.summary()['total_changeover_time'] / 60,
'preparation_phase': {
'description': 'Activities to complete BEFORE stopping machine',
'activities': []
},
'changeover_phase': {
'description': 'Activities performed while machine is stopped',
'activities': []
},
'startup_phase': {
'description': 'Activities to complete after starting machine',
'activities': []
}
}
for activity in optimized_analysis.activities:
entry = {
'step': activity.id,
'description': activity.description,
'time_seconds': activity.duration,
'operator': activity.operator,
'tools': activity.tools_required,
'notes': activity.notes
}
if activity.activity_type == ActivityType.EXTERNAL:
if activity.start_time < 0: # Prep phase
document['preparation_phase']['activities'].append(entry)
else:
document['startup_phase']['activities'].append(entry)
else:
document['changeover_phase']['activities'].append(entry)
return document
```
## Process Integration
This skill integrates with the following processes:
- `setup-time-reduction-smed.js`
- `kaizen-event-facilitation.js`
- `oee-improvement.js`
## Output Format
```json
{
"current_state": {
"total_changeover_minutes": 45,
"internal_minutes": 38,
"external_minutes": 7
},
"opportunities": {
"convert_to_external": 5,
"parallel_execution": 3,
"quick_release": 4,
"eliminate": 2
},
"projected_future_state": {
"total_changeover_minutes": 12,
"reduction_percent": 73
},
"implementation_plan": {
"phase_1": "Convert preparation to external",
"phase_2": "Implement parallel work",
"phase_3": "Install quick-release mechanisms"
}
}
```
## Best Practices
1. **Video record changeovers** - Capture actual process
2. **Involve operators** - They know the details
3. **Separate internal/external first** - Quick wins
4. **Standardize before optimizing** - Consistent baseline
5. **Measure before and after** - Validate improvements
6. **Document standard work** - Sustain the gains
## Constraints
- Safety cannot be compromised for speed
- Validate quality after changeover
- Consider operator ergonomics
- Document all standard proceduresRelated Skills
terraform-analyzer
Specialized skill for analyzing Terraform configurations. Supports parsing, security scanning (tfsec, checkov), cost estimation (infracost), drift detection, and plan visualization across AWS, Azure, and GCP.
db-query-analyzer
Analyze database query performance with execution plans and index recommendations
code-complexity-analyzer
Analyze code complexity metrics including cyclomatic complexity, code smells, and technical debt
cloudformation-analyzer
Validate and analyze AWS CloudFormation templates for security and best practices
semantic-code-analyzer
LLM-powered semantic analysis of code diffs to detect business-logic trojans
sast-analyzer
Static Application Security Testing orchestration and analysis. Execute Semgrep, Bandit, ESLint security plugins, CodeQL, and other SAST tools. Parse, prioritize, and deduplicate findings across multiple tools with remediation guidance.
crypto-analyzer
Cryptographic implementation analysis and validation for encryption algorithms, key sizes, and certificate management
semver-analyzer
Analyze code changes and determine semantic version bumps. Detect breaking changes automatically, suggest version bump (major/minor/patch), generate changelog entries, and validate version consistency.
api-diff-analyzer
Compare API specifications to detect breaking changes. Compare OpenAPI spec versions, categorize changes by severity, generate migration guides, and block breaking changes in CI.
process-analyzer
Analyze processes, identify workflows, define boundaries and scope, and map process requirements for specialization creation.
scope-logic-analyzer
Test equipment integration for signal analysis (oscilloscope and logic analyzer)
protocol-analyzer
Serial protocol analysis and debugging for common embedded interfaces (I2C, SPI, UART)