Results for “agent-architecture”

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

Patterns and architectures for running Claude Code autonomously in loops, from simple sequential pipelines to RFC-driven multi-agent DAG systems.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.

Design agents that watch data sources, build candidate prediction-market baskets, draft parameter changes, and hand results to a human for review.

Agent profile for coordinate mobile app architecture across iOS, Android, Expo/React Native, offline states, permissions, and releases. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Technical Writer IA — Expert en documentation technique (API docs, README, architecture docs, ADRs, changelogs). Rend le savoir explicite et accessible.

MUX Architect IA — Expert en architecture UX technique, CSS systems, motion design systems, et implementation developer-friendly. Pont entre design et code.

ScepticAgent internal architecture reference. Use whenever the user asks how the extension works, wants to add a new AI provider, add a new highlight category, debug communication between components, understand the agent loop or streaming, or work with provider routing and the Gemini CORS proxy.

Skilled agent architecture replacing multi-agent system for RL training. Trigger when: (1) planning agent-guided training, (2) implementing tool-augmented LLM consultations, (3) comparing skilled vs multi-agent approaches, (4) designing simulate-verify loops for training, (5) implementing prompt evolution / learnable parameters, (6) understanding Claude Agent SDK integration in training, (7) debugging SkilledTrainer consultations or tool calls, (8) configuring agent safety bounds for training actions.

Maintains a PARA-structured knowledge base on disk for persistent project context, decisions, and architecture records across agent sessions.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. Covers supervisor, swarm, and hierarchical multi-agent patterns; subgraph composition; state management (checkpointers/stores); persistence; evals; and production debugging. Reach for this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns.

Expert knowledge for Azure Reliability development including best practices, decision making, architecture & design patterns, and limits & quotas. Use when choosing Azure regions/zones, AKS/DB/queue designs, Web PubSub scaling, or DR/failover architectures, and other Azure Reliability related development tasks. Not for Azure Resiliency (use azure-resiliency), Azure Monitor (use azure-monitor), Azure Service Health (use azure-service-health), Azure Sre Agent (use azure-sre-agent).

This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches.

Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.

Guides coding agents through architecture-first UI/UX design, from product truth and user journeys to rendered browser critique, covering art direction, interaction, responsive behavior, and motion.

Threat-model and harden AI agents, RAG systems, assistants, and tool-using workflows against direct, indirect, stored, cross-agent, and multimodal prompt injection. Use when reviewing an agent architecture, isolating untrusted content, constraining tools and egress, protecting secrets, adding injection-focused tests, investigating a suspected injection incident, or documenting residual prompt-injection risk.

This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration.