One-click install
npx skills add https://github.com/fusengine/kimi-code --skill guardrails-fusengine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: guardrails
Source: https://github.com/fusengine/kimi-code/tree/main/plugins/prompt-engineer/skills/guardrails
Command: npx skills add https://github.com/fusengine/kimi-code --skill guardrails-fusengine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? LLM agents and prompts deployed without security controls are vulnerable to jailbreak attempts, PII leakage, hallucinated outputs, and unsafe tool calls. This Skill provides a 4-layer security architecture and ready-to-use templates to audit and harden agents before deployment. ## Core Features & Use Cases - 4-Layer Security Architecture: Covers input screening (jailbreak detection, PII redaction, topical checks), system-level ethical guardrails, output validation (format, hallucination, compliance), and monitoring (logging, alerts, rate limiting). - Ethical Guardrails Template: A drop-in system-prompt block defining required behaviors, forbidden behaviors, and refusal handling that resists jailbreak attempts. - Security Checklists: Agent-level and prompt-level checklists covering least-privilege tools, logging, capability limits, and hardcoded secret detection. - Use Case: Before shipping a customer-support agent, run the checklist to confirm input guardrails block jailbreak patterns, PII is redacted, and output validation rejects non-compliant responses. ## Quick Start Audit my agent's system prompt against the guardrails security checklist and add the ethical guardrails template to it.

Frequently Asked Questions about guardrails

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add guardrails to an LLM agent?▼

Add guardrails at four layers: screen inputs with a lightweight classifier for jailbreaks and PII, embed ethical rules and capability limits in the system prompt, validate outputs for format and hallucinations, and log interactions with rate limiting. The included checklist verifies each layer before deployment.

How to detect jailbreak attempts in user prompts?▼

Detect jailbreaks by matching patterns like "ignore your previous instructions", DAN roleplay, or developer-mode requests, using either regex or a classifier prompt that replies SAFE or JAILBREAK. Run this check before the input reaches the main LLM.

How do I redact PII from LLM inputs?▼

Redact PII by detecting emails, phone numbers, names, addresses, card numbers, and SSNs with regex patterns, then replacing them with placeholders like [EMAIL] or [SSN]. This anonymizes personal data before it reaches the model.

Can output guardrails catch LLM hallucinations?▼

Yes, hallucination detection extracts factual claims from the generated output and verifies each against provided sources, labeling them VERIFIED, UNVERIFIED, or CONTRADICTED. Unverified or contradicted claims can trigger a retry or error response.

What are the limitations of prompt-based guardrails?▼

Prompt-based guardrails rely on classifier LLMs and pattern matching, so novel jailbreak phrasings may bypass regex rules and classifiers can misclassify edge cases. They should be combined with monitoring, logging, and rate limiting rather than used as the sole defense.