Explorar skills

Integração do Spec-Kit com GitHub para desenvolvimento guiado por especificações baseado em constituição, com um fluxo de trabalho de 7 fases. Gatilhos incluem: "spec-kit", "speckit", "constitution", "specify", ".specify/", "规格驱动开发", "需求规格".

Crie comandos de barra personalizados para o Claude Code com estrutura adequada, frontmatter e melhores práticas. Use esta skill para criar novos comandos, adicionar comandos de barra, construir comandos personalizados ou transformar um fluxo de trabalho em um comando reutilizável.

Crie, refine e avalie habilidades de agente. Utilize ao desenvolver uma nova habilidade, atualizar uma existente, executar avaliações, verificar a qualidade do gatilho ou aprimorar a descrição de uma habilidade.

Use esta skill quando o usuário perguntar sobre o material do curso de MTC de Ni Haixia, incluindo Shang Han Lun, Jingui, Zhongjing Xinfa, casos clínicos, Bagang Bianzheng, Fuyang Forum, Yijinjing, diálogo Liang Dong, palestra de Stanford, Tianji, Huangdi Neijing, Shennong Bencao e acupuntura.

Generate a Python code skeleton from an experiment blueprint

Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces r

🦞+🔬 NanoResearch: The Autonomous AI Research Assistant

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.