Results for “llm-guardrails”
13 skills
mukul975 implementing-llm-guardrails-for-security
Builds input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs using NeMo Guardrails, Presidio, and Guardrails AI.
24.6k · bundle
mukul975 defending-llms-with-guardrails
Deploy Llama Guard, NeMo Guardrails, and LLM Guard as runtime input/output scanners to block jailbreaks, prompt injection, and toxic content in production LLM applications.
24.6k · bundle
akillness opik
Run Comet's Opik — open-source LLM observability, evaluation, and optimization — from one routing-first skill: install the Python/TypeScript SDK, stand up a server (Comet.com cloud, Docker Compose via `./opik.sh`, or Kubernetes/Helm), wire tracing through `@opik.track` or one of 50+ framework integrations (OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, Ollama, Bedrock, Vercel AI SDK, …), score outputs with LLM-as-a-judge metrics (Hallucination, Moderation, Answer Relevance, Context Precision), and run Datasets/Experiments evaluations including PyTest CI gates. Use when the user wants LLM tracing, prompt evaluation, production LLM monitoring, agent optimization, or guardrails with Opik. Triggers on: opik, comet opik, opik configure, opik.sh, llm observability, llm tracing, llm as a judge, hallucination metric, prompt evaluation, opik dashboard, opik guardrails, agent optimizer.
42 · bundle
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redpanda-data adp
Operates Redpanda's Agentic Data Plane: manages AI agents, MCP servers, LLM providers, budgets, guardrails, and Cedar access policies via the `rpk ai` CLI and API, including OAuth wiring and spend/transcript insights.
6 · bundle
tianhao909 llamaguard
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
1
orchestra-research prompt-guard
Detect prompt injections and jailbreak attempts in LLM applications using Meta's 86M parameter classifier. Filter user inputs, third-party data, and RAG documents with low latency and multilingual support.
10.4k
tianhao909 prompt-guard
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
1
yanacuti1121 deepeval
DeepEval — LLM evaluation framework, RAG metrics, hallucination detection, red-teaming, CI/CD integration
2
qcmuu prompt-guard
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
0
qcmuu llamaguard
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
0
bobmatnyc terraform
Terraform infrastructure-as-code workflow patterns: state and environments, module design, safe plan/apply, drift control, and CI guardrails
71 · bundle
kensaurus audit-infra-cost
Read-only audit of hosting, database, storage, egress, and serverless spend (Supabase, Vercel, S3/R2, edge). Use when "hosting bill is high", "cut infra costs", or a bill jumps. CI minutes → audit-cicd. Model tokens → plan-llm-cost-guardrails. Consumes test-load numbers.
8
deep-chavda python-ai-precommit-setup
Set up pre-commit hooks on a Python project — standard file-hygiene checks plus a security gate (gitleaks secret scanning, Trivy filesystem scan for CVEs/secrets/misconfigs, and Bandit Python SAST). Use this whenever the user wants to add, configure, or fix pre-commit hooks on a Python repo, mentions .pre-commit-config.yaml, wants secret/vulnerability/SAST scanning on commits, or is setting up code-quality guardrails — even if they just say 'add pre-commit hooks' without naming the tools. Especially for uv-based GenAI/LLM backends. Handles the setup gotchas that break first-time installs: the Trivy binary, the required data/html.tpl report template, bandit[toml] + [tool.bandit] config, and the right .gitignore entries.