Explorar skills

Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition

"setup" = install testing infra only. "check" = audit gaps and report, don't write tests. "full" or omitted = setup + audit + write + run + verify.

Fix PageSpeed Insights/Lighthouse accessibility "!" errors caused by contrast audit failures (CSS filters, OKLCH/OKLAB, low opacity, gradient text, image backgrounds). Use for accessibility-driven SEO/performance debugging and remediation.

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

Build automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, scheduled generation. Triggers: ai automation, workflow automation, batch processing, ai pipeline, automated content,

Exploração colaborativa de design que refina ideias em especificações validadas através de questionamento iterativo. Use antes de qualquer trabalho criativo, incluindo a criação de funcionalidades, construção de componentes, adição de funcionalidade ou modificação de comportamento.

Scrapes content based on a preset URL list, filters high-quality technical information, and generates daily Markdown reports.

SDK de Consulta do Azure Monitor para Java. Execute consultas Kusto em workspaces do Log Analytics e consulte métricas de recursos do Azure.

Async testing patterns with pytest-asyncio. Use when writing tests, mocking async code, testing database operations, or debugging test failures.

When the user needs to identify at-risk accounts, understand why customers are leaving, reduce churn rate, build health scores, design save plays, or create win-back campaigns.

Phase 7 — Test Strategy with pyramid, coverage gates, load tests, and Critical User Journeys

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.