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Found 197 Skills
Generates BYO custom safety policies for NVIDIA Nemotron content-safety guardrails — Nemotron-Content-Safety-Reasoning-4B (text) and multimodal Nemotron-3-Content-Safety. Produces a Markdown policy, JSON taxonomy, and drop-in inference prompts. Maps rough words or an existing policy to V2 categories, adding custom categories or topic-following rules.
High-fidelity HTML design and prototype guidance for AI agents with structured design thinking and quality guardrails
Use this when you need to execute the AI SDLC (Spec Pack) process in the sdlc-dev repository, select/chain together skills from the demand side (raw/solution/prd/prototype/demo) and implementation side (plan/execute/finishing), and use guardrails to avoid context drift, incorrect directory writes, or skipping critical steps under pressure.
Configure Celigo AI agent and guardrail imports -- LLM-powered steps that classify, extract, validate, or generate data within flows. Use when creating agent imports (OpenAI, Gemini), guardrails (PII, moderation), or configuring prompts, structured output, or BYOK connections.
Implement Supabase lint rules, policy enforcement, and automated guardrails. Use when setting up code quality rules for Supabase integrations, implementing pre-commit hooks, or configuring CI policy checks for Supabase best practices. Trigger with phrases like "supabase policy", "supabase lint", "supabase guardrails", "supabase best practices check", "supabase eslint".
Reasoning skill for designing AWS GenAI and ML architectures for healthcare and life sciences workloads. Use when the user asks to choose between SageMaker and Bedrock, design a RAG system over medical literature, architect clinical NLP or medical imaging inference, plan genomics or drug discovery pipelines on AWS, address HIPAA/PHI compliance in ML systems, design MLOps for regulated clinical models, or optimize cost for HCLS ML workloads. Triggers include "AWS architecture", "SageMaker vs Bedrock", "HIPAA ML", "clinical RAG", "medical imaging inference", "genomics on AWS", "PHI training", "MLOps healthcare", "Bedrock guardrails", "HealthLake", "HCLS cloud architecture", "BAA compliance", "SageMaker endpoint", "Bedrock knowledge base", "clinical NLP on AWS", "FDA SaMD on AWS".
Safety guardrails for destructive commands. Warns before rm -rf, DROP TABLE, force-push, git reset --hard, kubectl delete, and similar destructive operations. User can override each warning. Use when touching prod, debugging live systems, or working in a shared environment. Use when asked to "be careful", "safety mode", "prod mode", or "careful mode".
Knowledge base for designing, reviewing, and linting agentic AI infrastructure. Use when: (1) designing a new agentic system and need to choose patterns, (2) reviewing an existing agentic architecture ADR or design doc for gaps/risks, (3) applying the lint script to an ADR markdown file to get structured findings, (4) looking up a specific agentic pattern (prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent collaboration, memory management, learning/adaptation, MCP, goal setting, exception handling, HITL, RAG, A2A, resource optimization, reasoning techniques, guardrails, evaluation, prioritization, exploration/discovery). All rules and guidance are grounded in the PDF "Agentic Design Patterns" (482 pages).
Designs, deepens, and hardens TypeScript codebase architecture in three modes: folder structures, module contracts, and middleware pipelines for a new app; domain-informed deepening of existing code; and the guardrail tooling, CI gates, and wayfinding that stop a structure decaying. Use when setting up project structure, organizing a monorepo, designing backend modules, writing an architecture brief, recovering domain terminology, recording an architecture decision, or asking "how should I structure this app", "find architecture improvements", "this module is a mess", "make this codebase agent-friendly", "set up guardrails for coding agents", "add a dead-code check", or "my agent can't find anything in this repo". For scaffolding a new repo use scaffold-nextjs or scaffold-cli, for multi-tenant isolation use multi-tenant-architecture, for the AGENTS.md file's own content use agents-md, and for review of a local diff use pr-reviewer.
Define or refresh a product North Star metric + driver tree and produce a shareable North Star Metric Pack (narrative, metric spec, inputs, guardrails, rollout).
Verify and validate AI output before it reaches users. Use when you need guardrails, output validation, safety checks, content filtering, fact-checking AI responses, catching hallucinations, preventing bad outputs, quality gates, or ensuring AI responses meet your standards before shipping them. Covers DSPy assertions, verification patterns, and generate-then-filter pipelines.
Core context and guardrails for OpenWork native app