llm-guardrails-designerlisted

A guardrail is the validation layer around an LLM that a system prompt can't be: programmatic checks on what goes *into* the model and what comes *out*, enforced in code rather than requested in text. This skill designs that layer — deciding which checks matter for your app, placing them as input or output rails, implementing them with a guardrails library, and making them fail closed — as defense in depth, not a wall. ## When to use this skill - Adding a safety/validation layer to an LLM app instead of trusting the prompt to police itself. - Enforcing output structure, policy, or PII/secret-leakage checks before responses reach users or downstream systems. - Hardening a RAG or agent app against injection and unsafe actions as part of [defending against prompt injection](/guides/ai-safety/defending-prompt-injection). ## Instructions 1. **Threat-model the app first.** Identify the untrusted inputs (user, retrieved content, tool output), the sensitive data/actions to protect, and the unacceptable outputs (leaked secrets, policy violations, malformed structure). Guardrails follow the threats — don't add checks with no threat behind them. 2. **Choose input rails.** On the way in, decide what to scan and reject/sanitize: prompt-injection patterns, PII/secret stripping (often via the [prompt-pii-redactor](/skills/security/prompt-pii-redactor)), banned topics, and input size/token limits. Input rails reduce what reaches the model. 3. **Choose output rails.** On the way out, vali