Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xuansenpa1/skillrevise --skill safety-interlocksgit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xuansenpa1/skillrevise/safety-interlocks)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/safety-interlocks"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/safety-interlocks/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/safety-interlocks"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/safety-interlocks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.00870 |
| Opus 5 | $0.00012 | $0.00435 |
| Sonnet 5 | $0.00005 | $0.00174 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
safety-interlocks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to safety-interlocks — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
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
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))
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 145 lines · 23 tokens per session scan A 2f655c05c1a8
safety-interlocks is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 6d ago), licensed MIT. It adds 23 tokens to every session and 870 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to safety-interlocks, differing in 0 lines, and is treated as a copy.
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