Agent Skills Hub

anthropic-api

Expert guidance for building production-ready applications with Anthropic's Claude API. Covers SDKs, prompt caching, batch processing, streaming, tool use, and cost optimization strategies.

hf-mcp

Connect your AI agent to the Hugging Face Hub via MCP. Search models, datasets, and papers, manage repos, run cloud compute jobs, and invoke Gradio Spaces as functional AI tools.

skill-repo

Standardize, validate, and manage Netresearch AI agent skill repositories with automated structure enforcement, distribution workflows, and licensing compliance tools.

opper-api

Direct access to the Opper REST API for LLM orchestration, model management, task execution, and seamless migration from OpenAI, Anthropic, or OpenRouter.

ai-llm-patterns

Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.

research-pipeline

End-to-end autonomous research agent: from idea generation and literature review to experiment execution, adversarial review loops, and paper writing.

ops-devops-platform

DevOps and platform engineering patterns: Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, and cloud-native ops.

data-analysis

Data Analysis Specialist for EDA, statistical modeling, SQL queries, and Python-based visualization. Turn raw datasets into actionable insights through rigorous quantitative methods.

gitlab-ci-patterns

Implement professional GitLab CI/CD pipelines with multi-stage workflows, caching strategies, and Kubernetes deployment patterns for scalable automation.

trading-strategies

Framework for building, testing, and deploying automated trading strategies for prediction markets using Python.

devops

Manage CI/CD workflows, Docker containerization, and infrastructure configurations for the multi-chain crypto wallet system.

scikit-learn

Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.