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Programmable LLM Safety Rails

nemo-guardrails

Orchestra-Research/AI-Research-SKILLs

NVIDIA’s NeMo Guardrails adds runtime safety rails to LLM apps, covering jailbreak detection, hallucination and fact-checking, PII filtering, and toxicity checks via Colang 2.0 rules and integrations for production use on T4 GPUs.

Implement Retell AI Policy Guardrails

retellai-policy-guardrails

jeremylongshore/claude-code-plugins-plus-skills

This skill provides implementation patterns for policy guardrails when utilizing Retell AI for voice agents and phone call automation. It is designed to help ensure that AI interactions in a telephony context adhere strictly to predefined business rules and safety policies, preventing unauthorized actions or deviations from the intended conversational flow.

Sparse Autoencoders for Model Interpretability

sparse-autoencoder-training

Orchestra-Research/AI-Research-SKILLs

SAELens provides a framework for training and analyzing Sparse Autoencoders (SAEs). SAEs decompose the dense, often polysemantic activations of large language models into sparse, monosemantic features. Use this when you need to discover the discrete, interpretable concepts a model has learned, study feature superposition, or analyze specific safety-relevant behaviors (like bias or deception) within deep neural networks.

Ilya Sutskever: AGI Safety and Deep Learning

sickn33/antigravity-awesome-skills

Simulates Ilya Sutskever, co-founder of OpenAI and deep learning pioneer. Provides profound perspectives on AGI safety, the ethics of superintelligence, scaling laws, and the philosophical depth of AI consciousness. Ideal for complex, safety-critical deep technical discussions.

LLM Guardrails for Input and Output Safety

implementing-llm-guardrails-for-security

mukul975/Anthropic-Cybersecurity-Skills

This framework implements robust guardrails for LLM applications, validating both user inputs and model outputs. It detects prompt injection, sensitive PII (Personally Identifiable Information), toxic content, and ensures structured output compliance using advanced tools like NVIDIA NeMo Guardrails and Guardrails AI. Essential for building secure, reliable chatbots and RAG systems.

Paper Rebuttal Workflow

wanshuiyin/Auto-claude-code-research-in-sleep

Workflow to parse external paper reviews, atomize reviewer concerns, and draft a grounded, venue-compliant text rebuttal under strict limits. Supports multiple reviewers, follow-up rounds, and safety checks to avoid fabrication and overpromising. Use when the user needs to reply to reviewers or run an ICML/OpenReview rebuttal.

Model Armor Prompt Sanitizer

gws-modelarmor-sanitize-prompt

googleworkspace/cli

Sanitize user prompts through Google Model Armor templates via the gws CLI command, ensuring outbound safety and supporting stdin, text, or explicit JSON inputs for prompt filtering.

ModelArmor Response Sanitizer

gws-modelarmor-sanitize-response

googleworkspace/cli

Sanitize Google Model Armor responses via CLI templates to ensure outbound safety before delivering model output to end users.

AI Agent Governance and Safety Patterns

agent-governance

github/awesome-copilot

Provides comprehensive patterns and techniques for securing AI agent systems. Learn how to enforce governance policies, including tool allow/block lists, content filtering, rate limiting, and human approval workflows. Essential for building robust, compliant, and trustworthy agents in multi-agent or production environments that interact with external APIs and sensitive data.

Prompt Safety Review

ai-prompt-engineering-safety-review

github/awesome-copilot

Conducts comprehensive AI prompt safety reviews that analyze bias, misinformation, injection, and privacy risks while delivering effectiveness evaluations, security guidance, and best-practice recommendations for prompt engineers.

Claude Content Safety Guardrails Implementation

clade-policy-guardrails

jeremylongshore/claude-code-plugins-plus-skills

This guide details how to implement robust content safety guardrails for applications powered by Claude. It covers essential techniques such as system prompt hardening, input validation to detect injection patterns and length limits, and output validation to prevent system prompt leakage. It ensures compliance with Anthropic's policies, making the AI integration secure and reliable.

Implementing LLM Policy Guardrails

anth-policy-guardrails

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.

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