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Found 147 Skills
AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
Write Project Guardrails, i.e. project engineering specifications. Applicable scenarios: when you need to define frontend, backend, API, data, security, operation and maintenance, and release standards during new project launch, tech stack change, multi-team collaboration, incident review, or code specification drift.
Analyze existing repositories, maintain structure, setup guardrails and best practices
Expert skill for using Future AGI — the open-source end-to-end platform for evaluating, observing, and improving LLM and AI agent applications with tracing, evals, simulations, datasets, gateway, and 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.
Build and operate multi-agent workflows with OpenAI Agents SDK (Python): define agents/tools/handoffs, add guardrails, run conversations, and debug orchestration behavior. Use when users ask for agent orchestration with OpenAI-native patterns, handoff routing, or production-ready agent loops.
Amazon Bedrock Runtime API for model inference including Claude, Nova, Titan, and third-party models. Covers invoke-model, converse API, streaming responses, token counting, async invocation, and guardrails. Use when invoking foundation models, building conversational AI, streaming model responses, optimizing token usage, or implementing runtime guardrails.
Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore. Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching setup and debugging, quota health checks and throttling diagnosis, cost attribution and tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. NOT for custom model training, Rekognition, or Comprehend.
LLM guardrails with NeMo, Guardrails AI, and OpenAI. Input/output rails, hallucination prevention, fact-checking, toxicity detection, red-teaming patterns. Use when building LLM guardrails, safety checks, or red-team workflows.
Implement Exa lint rules, policy enforcement, and automated guardrails. Use when setting up code quality rules for Exa integrations, implementing pre-commit hooks, or configuring CI policy checks for Exa best practices. Trigger with phrases like "exa policy", "exa lint", "exa guardrails", "exa best practices check", "exa eslint".
Creates, updates, validates, and displays the architectural DNA of a project through two shared documents: docs/specs/architecture.md (technology stack, architectural rules, security constraints, AI guardrails) and docs/specs/ontology.md (domain glossary / Ubiquitous Language). Use BEFORE brainstorm as a project setup step, or at any point in the SDD lifecycle to validate specs/tasks against architecture principles. Triggers on 'create constitution', 'update constitution', 'constitution check', 'validate against constitution', 'project principles', 'architectural guardrails', 'setup project architecture', 'define ontology'.
Find, compare, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Use when a user asks for a loop, recurring agent workflow, automation cadence, iterative improvement process, an existing Loop Library recommendation, or help turning an outcome into a bounded copy-ready loop through a short question-led design session.