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 nexus-labs-automation/agent-observability --skill guardrails-safetygit clone --depth 1 https://github.com/nexus-labs-automation/agent-observabilityWrote 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/nexus-labs-automation/agent-observability/guardrails-safety)<a href="https://agentmods.dev/skills/nexus-labs-automation/agent-observability/guardrails-safety"><img src="https://agentmods.dev/badge/skills/nexus-labs-automation/agent-observability/guardrails-safety/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/nexus-labs-automation/agent-observability/guardrails-safety"><img src="https://agentmods.dev/badge/skills/nexus-labs-automation/agent-observability/guardrails-safety.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 | $0.00018 | $0.02831 |
| Opus 5 | $0.00009 | $0.01416 |
| Sonnet 5 | $0.00004 | $0.00566 |
| Haiku 4.5 | $0.00002 | $0.00283 |
Grade A, and why
guardrails-safety 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 439 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guardrails & Safety Instrumentation
Instrument safety checks to catch issues before users see them.
Core Principle
Guardrails run at two points:
- Input guardrails: Before the LLM sees user input
- Output guardrails: Before the user sees LLM output
Both must be instrumented to:
- Know what was blocked and why
- Measure false positive rate (blocking good content)
- Track latency overhead of safety checks
Guardrail Span Attributes
# P0 - Always capture
span.set_attribute("guardrail.name", "pii_filter")
span.set_attribute("guardrail.type", "output") # or "input"
span.set_attribute("guardrail.triggered", True)
span.set_attribute("guardrail.action", "block") # block, warn, redact, pass
# P1 - For analysis
span.set_attribute("guardrail.category", "pii")
span.set_attribute("guardrail.confidence", 0.95)
span.set_attribute("guardrail.latency_ms", 45)
# P2 - For debugging (be careful with PII)
span.set_attribute("guardrail.matched_pattern", "SSN")
span.set_attribute("guardrail.redacted_count", 2)
Input Guardrails
Prompt Injection Detection
from langfuse.decorators import observe, langfuse_context
@observe(name="guardrail.input.injection")
def check_prompt_injection(user_input: str) -> dict:
"""Detect prompt injection attempts."""
# Simple heuristic checks
injection_patterns = [
r"ignore.*previous.*instructions",
r"you are now",
r"new instructions:",
r"system prompt:",
r"<\|.*\|>", # Special tokens
]
triggered = False
matched = []
for pattern in injection_patterns:
if re.search(pattern, user_input, re.IGNORECASE):
triggered = True
matched.append(pattern)
langfuse_context.update_current_observation(
metadata={
"guardrail_name": "prompt_injection",
"guardrail_type": "input",
"triggered": triggered,
"patterns_matched": len(matched),
}
)
return {
"passed": not triggered,
"action": "block" if triggered else "pass",
"reason": "prompt_injection" if triggered else None,
}
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.
- 12d ago First seen · 439 lines · 18 tokens per session scan A 422d59929bc1
guardrails-safety is a skill published in the GitHub repository nexus-labs-automation/agent-observability (7 stars, last pushed 8mo ago), licensed MIT. It adds 18 tokens to every session and 2,831 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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