Agent Skills Hub

mcp-development

Guidance for Model Context Protocol (MCP) server development, including tool design, resource handling, and AI/ML integration patterns.

evidence-first-debugging

Enforces a strict evidence-based debugging workflow using structured observation, hypothesis testing, and causality validation to eliminate speculation in technical investigations.

trench

High-performance Solana meme coin trading for AI agents: sniping, MEV-protected execution, rug detection, and automated position management.

overview

Foundational mental model and operational rules for using TraceMem to ensure secure, auditable, and compliant AI agent execution.

screenpipe-cli

Manage screenpipe pipes (AI-driven automations) and integrations via CLI. Create, run, schedule, and debug local agents to automate tasks based on your computer activity.

Quality Assurance

Automated quality assurance system that validates markdown deliverables against defined checklists for PB-000 market research workflows.

data-storytelling

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

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.

MultiQC

Automates the triage, prioritization, and feedback process for new MultiQC module requests by analyzing repository activity, community engagement, and technical feasibility.

competitive-analysis

Framework for building professional competitive landscape decks, including market positioning, peer benchmarking, and strategic synthesis for finance professionals.

workiq

Query Microsoft 365 Copilot for workplace intelligence—emails, meetings, documents, and team communication—to ground your AI agent in organizational context.

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.