By sancovp
Program agent teams for any coding agent — the programmable, provider-agnostic version of Claude Code Teams. Compose agents with a tiny DSL (>> and |), loop/contest/evolve topologies, and game-worlds. CAVE = Coding Agent Virtualization Environment.
N agents read and write a shared state over rounds, with a mutator that folds each contribution in and an optional adjudicator (stigmergy arena). Use for agents collaborating on a shared board, a market, a workspace they all see. `blackboard()`.
Run different agents depending on a condition (guarded choice / router). Use for 'if X do this else that', routing, triage, picking a path by the state. `choice()` / `router()`.
Program WHEN each agent fires — the message state machine. Use to gate agents on runtime state: after these two finish, only if a flag/budget holds, only once a check passed — any rule you can write. THIS is what makes cave-teams programmable where Claude Code Teams' flow is fixed.
Run agents on a dependency graph — each fires when its inputs are ready (the general scheduler; sequential and parallel are its limits). Use for 'merge after frontend AND backend', diamond/fan-in dependencies, partial orders. `dag()`.
Hand off specific named data from one agent to the next, typed (load files, validate expected outputs, map outputs→inputs). Use when step B needs particular named inputs produced by step A, not just the raw text. `dovetail()`.
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Connect AI agents like code. Wire your whole team of AI agents with one line instead of hundreds of lines of glue — add one, swap one, or reuse a whole team just by changing a word.
cave-teams is the programmable, provider-agnostic version of Claude Code Teams. The agents underneath can be anything (Claude Code, Codex, MiniMax, a model call, a shell command, a Python function); you control the flow with code. CAVE = Coding Agent Virtualization Environment.
pip install cave-teams # one small dependency (pydantic)
Everything is the same shape — a building block. An agent is one, a team is one, a whole world is
one. A composition of building blocks is a building block, so teams nest inside teams forever
(agent = team = world). That is why two operators wire anything:
import cave_teams
from cave_teams import AgentLink
research = AgentLink("research", "Find 3 key facts.")
writer = AgentLink("writer", "Turn the facts into a paragraph.")
team = research >> writer # >> run in order
flow = research >> (security | perf | tests) >> ship # | run at the same time
result = await flow.execute({"goal": "ship the feature"})
when_flag, after, any
predicate). The message state machine, not a markdown to-do list.AgentLink (Claude Code / MiniMax), HeavenMiniMaxLink (a real coding agent
with bash + file-edit), or lift() any function / callable.GameWorld, an economic crafter sim, agents that compete and evolve.>> / | DSL and the pattern functions.cave() is the metacontrol function on top: it drives the whole API from a data spec, in any
sequence — serialize a team, run it from JSON, save/reuse a proven team, federate caves.This repo is also a Claude Code plugin (plugin/). It ships a skill per pattern — the language
(cave-teams), the metacontrol (cave), and one each for sequential / parallel / branch / gate /
conditions / dovetail / dag / blackboard / tournament / evolve / season / world / sim / metacog.
.claude/skills, .codex/skills, and .agent/skills symlink to the same plugin/skills, so any
coding agent that clones the repo picks them up.
MIT.
npx claudepluginhub sancovp/cave-teams --plugin cave-teamsAutopoiesis Plugin - Self-maintaining promise loops for honest AI work
A compiler-compiler for cognition: invent a notation, gate it so the model can't malform it, compile it into a loadable skill, compose and organize them. Ships the meta-APE capability skills (make-prompt, make-persona, big-generator, persona-generator, forge-attention-chain, forge-cor, forge-skillchain, construct-language) — one per CC/*CC build-function — plus bandit-chain-system, glyphsteer, glyphsteer-dense and the Self-Interpreter MCP. Built on agent-prompt-engineering (APE, the prompt engine) over pydantic-stack-core, with the *CC (accc/corcc/sccc) made FROM chaincompiler. Requires the Python packages: ./install.sh, then `pip install fastmcp` for the MCP.
CodeNose - Configurable code smell detection for LLMs. Sniffs out duplicate logic, architecture violations, missing logging, and other blind spots.
Instantiate a TWI Jobworld — AI-powered company with agents that run themselves. Enable this plugin and a CEO agent spawns, ready to bootstrap your company.
SDNA (Sanctuary DNA) - Gnostic agent workflow DSL with skills for chain development
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Lazy senior dev mode. Forces the simplest, shortest solution that actually works: YAGNI, stdlib first, no unrequested abstractions.
Develop, test, build, and deploy Godot 4.x games with Claude Code. Includes GdUnit4 testing, web/desktop exports, CI/CD pipelines, and deployment to Vercel/GitHub Pages/itch.io.
Evidence-gated AI coding workflow: scan → analyze → plan → TDD → execute → fix → verify → review, powered by Codebase Memory MCP >= 0.9.0 with optional Serena LSP intelligence. Includes blast-radius planning, test/cycle gates, independent review, and Windows Git Bash hook auto-resolution.
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex, antigravity, and grok CLIs when installed) to get diverse perspectives on coding problems
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review