Agent Skills Hub

synapse

Autonomous pattern detection and skill recommendation engine that monitors project memory, logs, and task lists to evolve your AI agent's capabilities automatically.

flow-nexus-swarm

Enterprise-grade multi-agent swarm orchestration, event-driven workflow automation, and intelligent agent coordination for Claude Code.

patterns

A collection of design patterns for the Langroid multi-agent framework, covering agent configuration, tool handling, task orchestration, and external integrations.

ring:writing-skills

A framework for applying Test-Driven Development to process documentation, ensuring agent reliability by using pressure scenarios to identify and patch rationalization loopholes.

parallel-agents

Orchestrate complex workflows by coordinating multiple specialized AI agents for multi-perspective code analysis, feature implementation, and system-wide reviews.

orchestrator

Autonomous multi-agent orchestration framework for Claude Code with memory-driven workflows, parallel-first task execution, Aristotle-based deconstruction, and multi-stage quality gates.

claude-ralph

An autonomous AI agent loop that executes Claude Code repeatedly to build features from structured PRDs until completion.

brainwriting

Structured parallel brainstorming agent for ideation and conceptual expansion. Uses multi-agent perspectives to evolve vague ideas into practical, actionable visions. Ideation only, not for task planning.

skill-memory

A Git-backed memory store for agent skills. Download, version, edit, and share custom agent behaviors and procedural knowledge using a CLI.

evolving-skill-creator

A framework for creating, iterating, and managing reusable evolving workflows that combine structured documentation with custom Python automation tools.

synapse-a2a

Framework for multi-agent collaboration using the Google A2A protocol. Enables messaging, task delegation, and cross-agent coordination for CLI-based AI tools.

ship-learn-next

Transform passive learning content like transcripts and tutorials into actionable Ship-Learn-Next cycles with concrete implementation plans and progress-oriented quests.