Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Branch skill for building and improving agents. Use when creating new agents, adapting marketplace agents, validating agent structure, writing system prompts, or improving existing agents. Triggers: 'create agent', 'improve agent', 'validate agent', 'fix agent', 'agent frontmatter', 'system prompt', 'adapt agent', 'customize agent', 'agent examples', 'agent tools'.

Write effective AGENTS.md files for AI coding agents.

Generate hierarchical AGENTS.md structures optimized for AI coding agents with minimal token usage.

Create or update root and nested AGENTS.md files that document scoped conventions, monorepo module maps, cross-domain workflows, and (optionally) per-module feature maps (feature -> paths, entrypoints, tests, docs). Use when the user asks for AGENTS.md, nested agent instructions, or a module/feature map.

Deploy project to the Agentuity Cloud. Requires authentication. Use for Agentuity cloud platform operations

Set an environment variable. Requires authentication. Use for Agentuity cloud platform operations

List storage resources or files in a bucket. Requires authentication. Use for Agentuity cloud platform operations

Create and maintain AgentV YAML evaluation files for testing AI agent performance. Use this skill when creating new eval files, adding eval cases, or configuring custom evaluators (code validators or LLM judges) for agent testing workflows.

Orchestrate multiple worker agents to implement groomed tasks. Use when multiple ready tasks need implementation, when you want autonomous multi-task execution, or when coordinating batch development work. Keywords: coordinator, orchestrator, multi-task, parallel, workers, batch, autonomous.

Master agile metrics with velocity, burn-down charts, cycle time, and team health indicators for data-driven improvement.

Generate agile release plans with sprints and roadmaps using unique sprint codes. Use when creating sprint schedules, product roadmaps, release planning, or when user mentions agile planning, sprints, roadmap, or release plans.

Plan and execute effective sprints using Agile methodologies. Define sprint goals, estimate user stories, manage sprint backlog, and facilitate daily standups to maximize team productivity and deliver value incrementally.

Orchestrate agile development workflows by invoking commands in sequence with checkpoint-based flow control. This skill should be used when the user asks to 'run the workflow', 'continue working', 'what's next', 'complete the task cycle', 'start my day', 'end the sprint', 'implement the next task', or wants guided step-by-step development assistance. Keywords: workflow, orchestrate, agile, task cycle, sprint, daily, implement, review, PR, standup, retrospective.

Agile product management, Scrum practices, and team collaboration for iterative product development.

Agno AI agent framework. Use for building multi-agent systems, AgentOS runtime, MCP server integration, and agentic AI development.

Expert in negotiating utility easements with farmers including farm operation impact assessment (crop production, livestock, equipment), compensation structure design (one-time vs. recurring, mitigation works), and multi-generational farm psychology. Use when negotiating transmission line, pipeline, or drainage easements with agricultural landowners. Key terms include agricultural easement, farm operation impacts, tower placement, crop loss, irrigation impacts, easement compensation, farm succession

Master AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design

Better proposals, faster closes. Use when creating AI agent pricing, automation proposals, ROI calculations, or sales materials. Generates professional pricing pages, case studies, and closing scripts for AI automation services.

Comprehensive L&D framework for upskilling DevOps/IaC/Automation teams to become AI Agent Engineers. Covers LLM literacy, RAG, agent frameworks, multi-agent systems, and LLMOps. Designed to help traditional automation teams compete with OpenAI and Anthropic.

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.

Production-grade AI agent patterns with MCP integration, agentic RAG, handoff orchestration, multi-layer guardrails, observability, token economics, ROI frameworks, and build-vs-not decision guidance (modern best practices)

Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Claude, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.

AI-powered development tools configuration and usage

Leveraging AI coding assistants and tools to boost development productivity, while maintaining oversight to ensure quality results.

AI-powered issue operations via gh-models. TRIGGERS - issue summarization, auto-labeling, issue insights.

Remove AI-generated code slop from branches. Use after AI-assisted coding

AI-powered code generation for boilerplate, tests, data, and scaffolding

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Design co-learning experiences using the Three Roles Framework (AI as Teacher/Student/Co-Worker). Use when teaching AI-driven development workflows, spec-first collaboration, or balancing AI assistance with foundational learning. NOT for curriculum without AI integration.

Use when collaborating with other AI assistants (Codex, Gemini, Aider, Cursor, OpenCode), delegating tasks, or requesting code review.

Methodology and templates for effective AI consultation workflows with external AI tools like Codex and Gemini.

AI content generation with OpenAI and Claude, callAIWithPrompt usage, prompt storage in app_settings, structured outputs, response format validation, multi-criteria scoring, rate limiting, JSON schema, and AI API best practices. Use when generating content, creating prompts, scoring articles, or working with OpenAI/Claude APIs.

You are a specialized cross-validation assistant that uses Google's Gemini 2.5 Pro API to provide independent, multi-perspective code validation alongside Claude's analysis.

Cross-verify Claude-generated plans and code using OpenAI Codex and Google Gemini CLI. Provides code review, plan validation, and comparative analysis. Use when needing second opinions on Claude's code or plans, validating technical decisions, or seeking consensus from multiple AI models.

Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create visualizations, or build predictive models with reproducible, code-based workflows.

Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

Synchronize and update Claude Code and GitHub Copilot development tool configurations to work similarly. Use when asked to update Claude Code setup, update Copilot setup, sync AI dev tools, add new skills/prompts/agents across both platforms, or ensure Claude and Copilot configurations are aligned. Covers skills, prompts, agents, instructions, workflows, and chat modes.

Use when deciding between HITL, OHOTL, and AHOTL modes in AI-DLC workflows. Covers decision frameworks for human involvement levels and mode transitions.

This skill should be used when writing, reviewing, or refactoring documentation that will be consumed as AI context. Optimizes documentation for LLM comprehension using principles of completeness, efficiency, and zero fluff—replacing prose with structured data, enforcing heading hierarchy, detecting meta-commentary, and validating that examples serve a purpose.

Build production-ready LLM applications, advanced RAG systems, and

In traditional software, inputs and outputs are defined. In AI, inputs and outputs are fuzzy. Evals (evaluations) are the "unit tests" for AI products. They allow you to move from "vibes-based" develo

Create an AI Evals Pack (eval PRD, test set, rubric, judge plan, results + iteration loop). Use for LLM evaluation, benchmarks, rubrics, error analysis/open coding, and ship/no-ship quality gates for AI features.

External AI API integration with retry logic, rate limiting, content safety detection, and multi-turn conversation support for image generation.

Operational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving at scale. Emphasizes production-grade performance, cost control, and observability.

Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.

Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference serving, and lifecycle operations with cost/safety controls.

Manage AI agents through the AI Maestro CLI. This skill provides commands for creating, updating, deleting, hibernating, and waking agents. It also handles plugin management and agent import/export.