Explorar skills

Workflow automation is the infrastructure that makes AI agents

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

AI-powered product design tool for transforming sketches into full-fidelity 3D renders.

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

Build resilient applications with robust error handling strategies that gracefully handle failures and provide excellent debugging experiences.

Creates comprehensive technical documentation from existing codebases. Analyzes architecture, design patterns, and implementation details to produce long-form technical manuals and ebooks.

Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity.

Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories.

Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops

AI-powered browser automation — navigate sites, fill forms, extract structured data, log in with stored credentials, and build reusable workflows.

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.

Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.