LLM guardrails for security — input/output validation with NeMo Guardrails, Presidio, and Guardrails AI
Harden LLM apps with input/output guardrails — NeMo Guardrails (Colang) flows, Python PII validators, and Guardrails AI schema checks against prompt injection, PII leaks, and hallucinations
- What
- Harden LLM apps with input/output guardrails — NeMo Guardrails (Colang) flows, Python PII validators, and Guardrails AI schema checks against prompt injection, PII leaks, and hallucinations
- Cost
- Free
- Needs
- a Python 3.10+ environment with pip; an OpenAI API key (billed) OR a local LLM endpoint for the NeMo Guardrails self-check rails
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @mukul975's guardrails skill: a complete defensive playbook for adding input/output safety controls to LLM applications. The agent installs the guardrail frameworks (NeMo Guardrails for Colang-based rail flows, Guardrails AI for structured output validation, Microsoft Presidio and spaCy for PII detection), runs a guardrails pipeline agent in multiple modes (full, input-only, output-only, PII redaction, JSON for dashboards), authors JSON content policies (allowed/blocked topics, blocked patterns, PII categories), writes Colang 2.0 flow definitions (input rails like self-check, jailbreak check, sensitive-data masking; output rails like hallucination check), and deploys the whole thing as validation middleware around an LLM app or RAG pipeline. It ships key-concept explanations, a tools inventory, verification checks (injection patterns blocked, PII redacted, <200 ms input-only latency), and an honest scope boundary: guardrails are defense-in-depth, not a replacement for authentication, authorization, or network security. Honest caveats: needs Python 3.10+ and real installs (nemoguardrails, presidio, a spaCy model); the self-check rails call a model — an OpenAI API key (billed) or a local LLM endpoint, so costs are avoidable with a local model. Apache-2.0 licensed (frontmatter and manifest agree). Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: a Python 3.10+ environment with pip; an OpenAI API key (billed) OR a local LLM endpoint for the NeMo Guardrails self-check rails Install "LLM guardrails for security — input/output validation with NeMo Guardrails, Presidio, and Guardrails AI" for me. It gives my agent @mukul975's defensive guardrails playbook: install the guardrail frameworks (nemoguardrails, guardrails-ai, presidio-analyzer, presidio-anonymizer, spacy + a language model), run the guardrails security agent in full/input-only/output-only/PII/JSON modes, author a JSON content policy (allowed and blocked topics, blocked patterns, PII categories, output limits), define Colang 2.0 rail flows (self check input, check jailbreak, mask sensitive data, self check output, hallucination check), integrate the pipeline as validation middleware around an LLM app or RAG pipeline, and review guardrail logs for block rates and bypass attempts. Apache-2.0 licensed. Repository: https://github.com/mukul975/anthropic-cybersecurity-skills/blob/main/skills/implementing-llm-guardrails-for-security/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via environment variables, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "implementing-llm-guardrails-for-security". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. pip install the guardrail packages and a spaCy model, set OPENAI_API_KEY or point the self-check rails at a local LLM endpoint, author my content policy JSON). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.