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69.576 skills encontradas
code-refactoring-refactor-clean
Especialista em refatoração de código, focado em princípios de código limpo, padrões SOLID e melhores práticas de engenharia de software. Analisa e refatora código para otimizar qualidade, manutenibilidade e desempenho.
xvary-stock-research
Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
git-advanced-workflows
Master advanced Git techniques to maintain clean history, collaborate effectively, and recover from any situation with confidence.
database-migrations-sql-migrations
SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, and SQL Server. Focus on data integrity and rollback plans.
code-review-ai-ai-review
You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C
api-endpoint-builder
Builds production-ready REST API endpoints with validation, error handling, authentication, and documentation. Follows best practices for security and scalability.
azure-ai-projects-py
Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.
azure-eventhub-java
Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.
constant-time-analysis
Analyze cryptographic code to detect operations that leak secret data through execution timing variations.
idea-os
Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.
tdd-workflows-tdd-green
Implement the minimal code needed to make failing tests pass in the TDD green phase.
spark-optimization
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.