llm-guardrails-spec
Specify the safety and reliability guardrails for an LLM feature before it ships. Use when asked to define LLM guardrails, add safety controls to an AI feature, prevent prompt injection or jailbreaks, or harden a chatbot/agent against misuse. Produces a guardrails spec — threats, input/output controls, refusal and escalation policy, logging, and a red-team test set — mapped to where each control runs.
npx skills add mohitagw15856/pm-claude-skills --skill llm-guardrails-spec --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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): - **The feature** — what the LLM does, who uses it, and what it can access (data, tools, actions). - **Trust boundary** — is input from untrusted users? Does the model call tools or take actions? - **Sensitivity** — what data is in scope (PII, financial, health), and the regulated/brand constraints. - **Acceptable behaviour** — what's in scope to answer, what must be refused, and the
- Working from a brief
- Required Inputs
- Output Format
- Guardrails Spec: [feature]
- Quality Checks
- Anti-Patterns
- Based On
What does the llm-guardrails-spec skill do?
Specify the safety and reliability guardrails for an LLM feature before it ships. Use when asked to define LLM guardrails, add safety controls to an AI feature, prevent prompt injection or jailbreaks, or harden a chatbot/agent against misuse. Produces a guardrails spec — threats, input/output controls, refusal and escalation policy, logging, and a red-team test set — mapped to where each control runs.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill llm-guardrails-spec --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.