Agent Skills Hub

skill-authoring-workflow

A standardized workflow for converting raw PM notes, workshops, or rough drafts into polished, validated, and repository-compliant AI skills.

recursive-decomposition

Handles large-scale tasks by automatically breaking them down into manageable, recursive sub-tasks to overcome context window limits and improve reasoning accuracy on large codebases and document sets.

docs-generator

Generate hierarchical, AI-optimized documentation structures (AGENTS.md, agent.d) to streamline codebase context, setup, and navigation for AI coding assistants and developers.

milady-development

Self-modify your Milady agent by managing plugins. Edit code, rebuild, and restart the runtime to develop new capabilities or improve agent workflows locally.

data-storytelling

Transform raw data into compelling, decision-driving narratives using visualization strategies, story frameworks, and persuasive structures for analytics and executive reporting.

working-with-spreadsheets

Creates and edits Excel spreadsheets with professional formatting, formulas, and financial modeling standards using openpyxl and pandas.

markdown-token-optimizer

Analyzes markdown files to identify token-wasting patterns, providing actionable suggestions to optimize documentation for LLM consumption and token efficiency.

research-review

Get deep, critical, NeurIPS/ICML-style peer reviews of your research, paper drafts, and experimental setups using external LLMs via Codex MCP.

proactive-agent

Transform AI agents into proactive partners using WAL Protocol, persistent memory buffers, and autonomous cron scheduling to anticipate needs and improve performance.

kagent

Expert guide for kagent: the Kubernetes-native framework for building, deploying, and managing AI agents, MCP tools, and A2A protocols.

openai-agents-sdk

Build AI agents with the OpenAI Agents SDK for Python. Supports multi-agent handoffs, function tools, stateful sessions, streaming, and Azure OpenAI integration via LiteLLM.

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.