Compile, verify, and run multi-agent DAGs across Pi, Codex, Claude Code, OpenCode, and Grok—with resume, replay, and incremental recomputation.

Anti-laziness skill for AI agents. Core: the Depth Tree method, which splits a task N layers deep and gives every leaf the full time budget of the whole task, so effort multiplies with depth. Grounded in 2025-2026 research on model laziness, underthinking and premature completion.

Claude Skill to do your tasks with subagents in Git worktrees

A Claude Code skill that 10x's your effective context window by dispatching tasks to background AI workers.

Durable MCP control plane for long-running Codex Desktop tasks

Use sidecar next to CLI agents for diffs, file trees, conversation history, and task management with td

Delegate tasks to DeepSeek right inside your Claude Code / Codex sessions.

One CLI to orchestrate them all. Manage a team of AI agents executing tasks in parallel from your terminal using a command line interface to manage them all. Manage a team of artificial intelligence agents who perform tasks in parallel from your terminal or offline on a local or server

GitHub-style CLI for Jenkins — manage contexts, runs, logs, and admin tasks from your terminal.

a recursive self-improving harness designed to help your agents (and future iterations of those agents) succeed on any task

Hand off tasks to Devin — a plugin/skill for Claude Code, Codex, Cursor, and any coding agent

Self-hosted control plane for AI agents: dispatch tasks, review runs, track spend, and operate OpenClaw, Claude Code, Codex, and other runtimes.

Multi-agent team operating system for Claude Code. 108 MCP tools, 40+ agent templates, 10 lifecycle hooks, 7 pipeline workflows. Persistent teams, structured meetings, task wall, real-time React dashboard. No LangChain/AutoGen — pure CC native integration.

Spec-driven development workflow for AI coding agents: architecture-first planning, task decomposition, GitHub Issue/PR tracking, Deep Discuss, and adaptive control for Claude Code, Codex, Cursor, and other Markdown-capable agents.

Delegate a coding task to a separate coding agent CLI, review the diff, land the commit yourself — one per implementer.

Tool-agnostic 13-phase AI development pipeline — turns a task description into reviewed, committed code through automated design, adversarial review, security, test, and code-review gates. One bash engine, balanced Opus/Sonnet routing, self-healing commit review.

Samurai-inspired multi-agent system for Claude Code. Orchestrate parallel AI tasks via tmux with shogun → karo → ashigaru hierarchy.

Open-World Self-Evolution for LLM Agents — agents that build both their skills and their own verification signals from scratch, with no target-task supervision. (Code coming soon.)

Define task-specific AI sub-agents in Markdown for any MCP-compatible tool.

Run multiple AI models against the same research, design, or coding task. Surface disagreements before you ship.

Forge Orchestrator: Multi-AI task orchestration. File locking, knowledge capture, drift detection. Rust.

The Agent Harness for AI-Human Collaboration, inspired by the AI-DLC (AI-Driven Development Lifecycle)

Unity MCP acts as a bridge between AI assistants and your Unity Editor. Give your LLM tools to manage assets, control scenes, edit scripts, and automate tasks within Unity.

Make AI coding agents architecture-aware: baseline-first, evidence-verified, drift-checked, and safe across long tasks.