LLM Guardrails Spec Skill

An LLM feature without guardrails fails in public: it leaks data, follows an injected instruction, answers out of scope, or says something the brand can't stand behind. This skill specifies the controls that prevent that — what to block, where to block it (input, model, output, or human), and how you'll prove it works — so safety is a reviewable spec, not a hope.

Working from a brief

Given "we're adding an AI chat to our support site", produce the full guardrails spec anyway — infer the threat surface from the feature type, label assumptions, and flag what to confirm. Never hand back only a list of risks with no controls; the controls and their placement are the deliverable.

Required Inputs

Ask for these only if they aren't already provided (else infer and label):