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By DheerG
Launch and orchestrate multi-agent teams for coding, writing, diagnosis, and general tasks using configurable modes and governance. Includes automated code review refinement, triage diagnosis, and custom workflow scaffolding with access control enforcement.
npx claudepluginhub dheerg/swarms --plugin swarmLaunch a code-mode agent team
Scaffold a custom workflow — generates a mode skill and shortcut command
Launch a general-mode agent team
Interactively launch an agent team with guided setup
Walk through swarm's core concepts and launch your first team
Evaluates ambient context artifacts (CLAUDE.md, memory, local skills, settings hooks) for compatibility with swarm governance. Returns a classified report so users can address interference before launching a team.
Code mode operational spec for the team lead. Returns lead identity, facilitator identity, mode-specific rules, suggest-members guidance, and phase arc for code-mode teams.
Builds measurable validation criteria when no existing standards apply. Invoked by team agents during review.
General mode operational spec for the team lead. Returns lead identity, facilitator identity, suggest-members guidance, and phase arc for general-purpose teams.
Reframes implementation descriptions into outcome statements. Invoked by launch commands during outcome refinement.
Uses power tools
Uses Bash, Write, or Edit tools
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Dynamically assemble expert agent teams for complex tasks using Claude Code's agent teams feature
General-purpose agent superteam for Claude code - contract-gated verification loops with 7 outer-loop roles, 5-phase orchestration, task form-driven inner-loop cooperation, and structural prevention of premature exits.
Orchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams
Multi-agent coordination with agent-swarm MCP
Multi-agent task decomposition and coordination for Claude Code
AI team role and worker manager for multi-agent development workflows.
No model invocation
No model invocation
Executes directly as bash, bypassing the AI model
Executes directly as bash, bypassing the AI model
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Get consistent predictable, well tested results from claude code sessions.
This plugin gives users a few commands that greatly improve results using agent teams/swarms with outcome based directives. It works well with both coding and non-coding tasks.
Unlike out-of-the-box agent teams, swarm gives the team members much needed instruction on being better at communicating with each other, better at following instructions, staying active during long lasting sessions, applying great quality improvement processes, all while requiring fewer course corrections.
Swarm launches a small team of agents for each task: a lead, a Socratic facilitator, and specialists you pick for the work. They research independently and argue through disagreements before the team scores its own output. Work only reaches you after the team agrees it's at 9 out of 10. Most of the quality work happens in the cycles you never see.
/swarm:launch
New here? Start with /swarm:onboard, a short walkthrough of the four concepts before your first launch.
Install the plugin:
claude plugin marketplace add DheerG/swarms
claude plugin install swarm@swarms --scope project
Agent teams must also be enabled in Claude Code. Add to ~/.claude/settings.json:
{
"env": {
"CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
}
}
/swarm:launch checks for this and will enable it for you if it's missing. For local development:
claude --plugin-dir /path/to/swarms
Changes take effect in the next session.
If you ship in auto mode to an org that isn't one of this repo's configured remotes (a fork's upstream, a mirror, or a different org), the permission classifier may deny the first push. Press r in /permissions to approve and ship right then; to stop it recurring, add the destination to autoMode.environment in user scope (~/.claude/settings.json) or local scope (.claude/settings.local.json, gitignored) — Claude Code ignores autoMode in shared project scope. Keep $defaults (it already trusts this repo's own remotes), and replace the placeholders with your host/org:
{
"autoMode": {
"environment": [
"$defaults",
"Source control: <your-host>/<your-org>. Pushing branches and opening pull requests is part of the standard development workflow."
]
}
}
I built this across hundreds of sessions, pruning rules, memories, and skills until the quality stopped varying. When model quality shifted, small targeted changes kept it working, even on smaller models. Once the results were consistent enough to rely on, I started sharing with teammates and friends.
That's when the real problem showed up. I'd send a prompt to someone I work with and watch them get wildly different results. Their Claude had a different CLAUDE.md, different memories, different local skills, different settings hooks. All of that ambient context quietly rewrote what they were trying to do, not just their prompts. The prompt alone wasn't the problem. The environment around it was.
Swarm is the fix. It bundles the rules and the phases into one plugin you install and invoke, so what you share is what actually runs, on your machine or anyone else's. Portable quality, not just personal repeatability.
— Dheer
More on how I think about agents: dheer.co
Swarm is for you if:
Swarm is not:
Running /swarm:launch is a guided interaction. At every step you either see something or make a choice, with no silent spawns and no mystery setup.
/swarm:launch
The first question is always about outcomes. An outcome describes what success looks like when the work is done, rather than what to build.
Do you have outcomes defined, or would you like help?
- I'll provide my outcomes (Recommended) I know what success looks like and will describe it
- Help me define outcomes Use /swarm:refine-outcomes to reframe my ideas into outcome statements