Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
108 skills found
Systematically evaluate scholarly work using the ScholarEval framework, providing structured, quantitative, and qualitative assessment across research quality dimensions with actionable feedback.
Debugging guide for AReaL distributed training issues, including hangs, NCCL errors, OOM, and numerical consistency in FSDP2/TP/CP/EP.
Generate publication-quality figures, charts, and LaTeX tables from experiment data for academic papers.
An advanced development guide for Claude Code, covering REPL environments, MCP integration, development workflows, and best practices for AI-assisted coding.
Automate quality observability with DORA metrics, defect density tracking, and intelligent quality gate configuration for continuous delivery pipelines.
Evaluate scientific claims and research methodology for rigor, bias, and validity. Use evidence-based frameworks like GRADE and Cochrane to analyze experiments, protocols, and study conclusions.
Implement production-grade data quality validation using Great Expectations, dbt tests, and data contracts to ensure reliable pipelines.
Classify and group meteorological and environmental variables into specific driver categories for consistent attribution analysis and environmental modeling.
Create publication-quality plots and visualizations using matplotlib and seaborn. Works locally with any LLM.
A runtime skill discovery engine for AI agents. Search and retrieve specialized agent skills (SKILL.md) on-demand via REST API or MCP to inject procedural knowledge into your agent's context.
Maintain a structured DEBUG_LOG.md for recording bugs, debugging processes, and solutions to ensure project stability and knowledge retention.
Foundational Python library for static, animated, and interactive data visualization. Provides fine-grained control over plot elements for scientific, publication-ready figures.