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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.

Stars
781
Hot score
99
Updated
March 20, 2026
Overall rating
C4.8
Composite score
4.8
Best-practice grade
B77.6

Install command

npx @skill-hub/cli install benchflow-ai-skillsbench-safety-interlocks

Repository

benchflow-ai/SkillsBench

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 repository

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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

Claude CodeCodex CLIGemini CLIOpenCode

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
safety-interlocks | SkillHub