safety-interlocks
Implement safety interlocks and protective mechanisms to prevent equipment damage and ensure safe control system operation.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install benchflow-ai-skillsbench-safety-interlocks
Repository
Skill path: tasks-no-skills/hvac-control/environment/skills/safety-interlocks
Implement safety interlocks and protective mechanisms to prevent equipment damage and ensure safe control system operation.
Open repositoryBest for
Primary workflow: Ship Full Stack.
Technical facets: Full Stack.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: benchflow-ai.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install safety-interlocks into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/benchflow-ai/SkillsBench before adding safety-interlocks to shared team environments
- Use safety-interlocks for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: safety-interlocks
description: Implement safety interlocks and protective mechanisms to prevent equipment damage and ensure safe control system operation.
---
# Safety Interlocks for Control Systems
## Overview
Safety interlocks are protective mechanisms that prevent equipment damage and ensure safe operation. In control systems, the primary risks are output saturation and exceeding safe operating limits.
## Implementation Pattern
Always check safety conditions BEFORE applying control outputs:
```python
def apply_safety_limits(measurement, command, max_limit, min_limit, max_output, min_output):
"""
Apply safety checks and return safe command.
Args:
measurement: Current sensor reading
command: Requested control output
max_limit: Maximum safe measurement value
min_limit: Minimum safe measurement value
max_output: Maximum output command
min_output: Minimum output command
Returns:
tuple: (safe_command, safety_triggered)
"""
safety_triggered = False
# Check for over-limit - HIGHEST PRIORITY
if measurement >= max_limit:
command = min_output # Emergency cutoff
safety_triggered = True
# Clamp output to valid range
command = max(min_output, min(max_output, command))
return command, safety_triggered
```
## Integration with Control Loop
```python
class SafeController:
def __init__(self, controller, max_limit, min_output=0.0, max_output=100.0):
self.controller = controller
self.max_limit = max_limit
self.min_output = min_output
self.max_output = max_output
self.safety_events = []
def compute(self, measurement, dt):
"""Compute safe control output."""
# Check safety FIRST
if measurement >= self.max_limit:
self.safety_events.append({
"measurement": measurement,
"action": "emergency_cutoff"
})
return self.min_output
# Normal control
output = self.controller.compute(measurement, dt)
# Clamp to valid range
return max(self.min_output, min(self.max_output, output))
```
## Safety During Open-Loop Testing
During calibration/excitation, safety is especially important because there's no feedback control:
```python
def run_test_with_safety(system, input_value, duration, dt, max_limit):
"""Run open-loop test while monitoring safety limits."""
data = []
current_input = input_value
for step in range(int(duration / dt)):
result = system.step(current_input)
data.append(result)
# Safety check
if result["output"] >= max_limit:
current_input = 0.0 # Cut input
return data
```
## Logging Safety Events
Always log safety events for analysis:
```python
safety_log = {
"limit": max_limit,
"events": []
}
if measurement >= max_limit:
safety_log["events"].append({
"time": current_time,
"measurement": measurement,
"command_before": command,
"command_after": 0.0,
"event_type": "limit_exceeded"
})
```
## Pre-Control Checklist
Before starting any control operation:
1. **Verify sensor reading is reasonable**
- Not NaN or infinite
- Within physical bounds
2. **Check initial conditions**
- Measurement should be at expected starting point
- Output should start at safe value
3. **Confirm safety limits are configured**
- Maximum limit threshold set
- Output clamping enabled
```python
def pre_control_checks(measurement, config):
"""Run pre-control safety verification."""
assert not np.isnan(measurement), "Measurement is NaN"
assert config.get("max_limit") is not None, "Safety limit not configured"
return True
```
## Best Practices
1. **Defense in depth**: Multiple layers of protection
2. **Fail safe**: When in doubt, reduce output
3. **Log everything**: Record all safety events
4. **Never bypass**: Safety code should not be conditionally disabled
5. **Test safety**: Verify interlocks work before normal operation