Agent Skills Hub

memcontext-autopilot

An automated memory middleware for AI agents, implementing a Retrieve-Respond-Save loop to maintain long-term persistent context across conversations.

ctx-planning

Generate daily and weekly planning reports from backlog and carryover state, applying WIP limits and priority rules from BaseContext.yaml with automatic git commit/push.

xurl

Unified CLI tool to read, query, discover, and write AI agent conversations using the agents:// URI scheme across multiple coding agents and providers.

Project-Management-Interactive-copilot-test

A universal skill for automating GitHub Project V2 Kanban boards, supporting status transitions, sprint management, and interactive workflows via CLI.

ctx-upgrade

Update context-mode to the latest version, rebuild assets, reinstall global NPM dependencies, and refresh hook configurations.

tool-design

Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.

tldr

Token-efficient codebase analysis skill for call graphs, semantic search, impact analysis, and data flow. Saves ~95% tokens vs. raw reads.

wormhole

Shared memory and collaboration layer for AI coding agents to track actions, manage sessions, detect conflicts, and preserve project context across tools.

manage-worktree

Manage git worktrees: create, move branches into, or remove worktrees. Simplifies parallel development, context switching, and cleanup for Apartment-based Rails projects.

strategic-compact

Enhance workflow efficiency by performing manual context compaction at logical task boundaries instead of relying on unpredictable auto-compaction.

supermemory

Supermemory is a long-term memory infrastructure for AI agents, enabling persistent context, user profiles, and semantic RAG across multi-modal knowledge bases.

planning-with-files

Implements Manus-style persistent markdown planning for complex workflows, project tracking, and research management to optimize agent attention and memory.