Results for “guardrails”

Interacts with Google Cloud services using the gcloud CLI safely and efficiently, with command validation, data reduction, safety guardrails, and workflows for discovery and investigation.

Analyzes AWS Batch compute environments, job queues, scheduling policies, and array jobs with parallel execution and anti-hallucination guardrails.

Analyze AWS GuardDuty findings, detectors, suppression rules, and member accounts with parallel execution and anti-hallucination guardrails.

Analyzes AWS Glue ETL jobs, crawlers, Data Catalog, and schema registry using parallel AWS CLI queries with anti-hallucination guardrails.

Manages Ray clusters, jobs, Serve deployments, and distributed workloads via the Dashboard API and CLI, with discovery-first checks and safety guardrails.

Analyzes AWS billing data with anti-hallucination guardrails, covering cost breakdowns, trends, anomaly detection, RI/SP utilization, forecasting, and multi-account comparisons using Cost Explorer.

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.

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.

Analyzes AWS ECS clusters, services, and tasks, including health, deployment status, task failures, capacity providers, and resource utilization, with parallel execution and anti-hallucination guardrails.

Map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads using Akamai Guardicore Segmentation.

Deep-dive analysis of AWS RDS instances using Performance Insights, event subscriptions, proxy health, and global database status, with parallel execution and anti-hallucination guardrails.

Analyzes AWS CloudTrail events and trails, including trail health, API activity, security investigations, resource changes, and event selector audits, with parallel execution and anti-hallucination guardrails.

Orchestrates a multi-agent build session, acting as manager to cut goals into units, spawn implementer and tester subagents, and run test-and-review loops with anti-thrash guardrails.

Design and implement Arduino integration with Azure IoT Hub and IoT Edge, including secure provisioning, resilient telemetry, command handling, and production guardrails.

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.

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.

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.

Security controls for RAG. Indirect prompt-injection via retrieved documents, PII detection/redaction (Microsoft Presidio, AWS Comprehend), multi-tenant isolation, ACL-aware retrieval with row-level/metadata filtering, data-leakage prevention, jailbreak hardening on retrieved context, GDPR right-to-be-forgotten in vector DBs. USE WHEN: user mentions "prompt injection RAG", "indirect prompt injection", "PII redaction", "Presidio", "ACL RAG", "row-level security", "multi-tenant RAG isolation", "GDPR vector DB", "right to be forgotten", "jailbreak", "data leakage RAG" DO NOT USE FOR: hallucination detection - use `rag-guardrails`; tenancy scaling patterns - use `rag-production`; audit tracing schema - use `rag-observability`

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.