AI CLI Tools Skills

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

**Role**: AI Agent Systems Architect I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful

Build production-ready LLM applications, advanced RAG systems, and intelligent agents.

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

You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful.

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.

These are instructions for creating design philosophies - aesthetic movements that are then EXPRESSED VISUALLY. Output only.

Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI.

Use when the user asks to generate or edit images via the OpenAI Image API (for example: generate image, edit/inpaint/mask, background removal or replacement, transparent background, product shots, concept art, covers, or batch variants); run the bundled CLI (`scripts/image_gen.

Manage issues, projects & team workflows in Linear.

Capture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking.

Prepare meeting materials with Notion context and Codex research; use when gathering context, drafting agendas/pre-reads, and tailoring materials to attendees.

Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.

Turn Notion specs into implementation plans, tasks, and progress tracking; use when implementing PRDs/feature specs and creating Notion plans + tasks from them.

Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.

Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.

Use when the user asks to generate, remix, poll, list, download, or delete Sora videos via OpenAI’s video API using the bundled CLI (`scripts/sora.

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.

Transcribe audio files to text with optional diarization and known-speaker hints.

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.

Configure AI coding tool instruction files across platforms — .cursorrules, copilot-instructions.md, .windsurfrules, and AGENTS.md for consistent AI behavior in your project.

Map relationships between bounded contexts and define integration contracts using DDD context mapping patterns.

Design DDD strategic artifacts including subdomains, bounded contexts, and ubiquitous language for complex business domains.

Apply DDD tactical patterns in code using entities, value objects, aggregates, repositories, and domain events with explicit invariants.

Execute autonomous multi-step research using the Gemini Deep Research API — structured prompts, async polling, follow-up queries, output formatting, and production integration patterns.

Guide users through a structured workflow for co-authoring documentation.

Select and optimize embedding models for semantic search and RAG applications.

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio.

You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications.

Production-ready patterns for building LLM applications.

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati.

This skill transforms weak, vague, or inconsistent prompts into precision-engineered instructions that reliably produce high-quality outputs from any LLM (Claude, Gemini, GPT-4, Llama, etc.).

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback.

Build production ML systems with PyTorch 2.

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.

Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augm.

Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW).

Expert guide on prompt engineering patterns, best practices, and optimization techniques.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production.

Curated collection of high-quality prompts for various use cases.

**Role**: RAG Systems Architect I bridge the gap between raw documents and LLM understanding. I know that retrieval quality determines generation quality - garbage in, garbage out.

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

Implement Retrieval-Augmented Generation patterns to give AI coding tools access to your documentation, API specs, and codebase knowledge for more accurate code generation.

Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives.

Learn to write system prompts that transform generic AI models into specialized coding agents — with role definitions, constraints, output formats, and behavioral guardrails.

Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances.

Design effective tool use patterns for AI agents — defining tool schemas, handling multi-step tool chains, error recovery, and building reliable agentic coding workflows.

Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights.