What does this skill do, and when should you use it?
ClawTeam is a CLI tool (clawteam) that orchestrates multiple AI coding agents as self-organizing swarms. It isolates each agent in its own git worktree, hosts sessions in tmux windows, and routes communication through filesystem-based inboxes. This skill (skills/openclaw/SKILL.md) teaches an OpenClaw agent to create teams, spawn workers, manage task dependencies, and monitor progress via natural language. The repository is a fork of HKUDS/ClawTeam deeply adapted to make OpenClaw the default agent backend. Three pre-built team templates ship out of the box: hedge-fund, code-review, and research-paper.
When the user expresses intent like 'build a team', 'spawn agents', or 'swarm', the skill guides the agent to: create a team with a leader via clawteam team spawn-team; build dependency chains with clawteam task create --blocked-by (dependents auto-unblock when blockers complete); give each worker its own tmux window plus a git worktree on branch clawteam/{team}/{agent} with an auto-injected coordination prompt via clawteam spawn; exchange point-to-point and broadcast messages via inbox send/broadcast/receive; monitor via board show/attach/serve (terminal kanban, tiled tmux view, or Web dashboard); track spend with cost report/show/budget; merge branches with workspace merge and tear down with team cleanup. All state lives as JSON files under ~/.clawteam/ with fcntl file locking for concurrent safety, and every command supports -- output.
- Full-stack development: say 'create a team to build a web app with auth' — the leader splits work into a dependency chain (API design → backend/frontend → tests), 5 agents develop in parallel in separate worktrees, then everything merges to main
- Investment analysis: clawteam launch hedge-fund boots a 7-agent team (5 parallel analysts, a risk manager, and a portfolio manager) from a single TOML template
- Code review: launch the code-review template so multiple agents divide up review work and consolidate findings
- Research writing: launch the research-paper template to organize literature survey and writing assignments
- Large-scale experimentation: a leader spawns multiple research agents in isolated worktrees and cross-pollinates best configs (README cites an autoresearch run: 2,430 experiments in ~30 GPU-hours)
How do you install this skill?
- The skill auto-confirms workspace trust prompts and suggests rewriting exec-approvals config; manually review allowlist changes before installing.
- clawteam team cleanup --force irreversibly deletes all team data; save results before use.
- No tests found covering the openclaw skill path itself; rehearse on a small team first.
- Depends on OpenClaw and other external services plus a GitHub install path; mainland-China reachability is undeclared — verify independently.
- Publisher is unverified and an npm name-squatting package exists; install from the local repository per the README only.
- Shell / CLI
- Local filesystem
Python 3.10+clawteam CLI (pip install -e . from this repo)tmux (Linux/macOS/WSL)OpenClaw CLI (default backend)git
Prerequisites: Python 3.10+, git, tmux on Linux/macOS, and at least one CLI coding agent (OpenClaw by default). Do NOT pip install clawteam (that's the upstream version defaulting to claude, without OpenClaw adaptations) and do NOT npm install -g clawteam (an unrelated name-squatting package). Correct install:
- git clone https://github.com/win4r/ClawTeam-OpenClaw.git && cd ClawTeam-OpenClaw
- pip install -e . (required — installs from the local repo, not PyPI)
- Ensure clawteam is on PATH: mkdir -p ~/bin && ln -sf "$(which clawteam)" ~/bin/clawteam
- Install this skill: mkdir -p ~/.openclaw/workspace/skills/clawteam && cp skills/openclaw/SKILL.md ~/.openclaw/workspace/skills/clawteam/SKILL.md
- Set OpenClaw exec approvals to 'allowlist' and add the absolute clawteam path to the allowlist (scripts included in the skill). Alternatively run bash scripts/install-openclaw.sh to automate.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/win4r/ClawTeam-OpenClaw.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/openclaw" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Create a 5-agent team to build a web app
After installation, just talk to your OpenClaw agent in natural language — trigger phrases include team, swarm, multi-agent, spawn agents. Example: 'Create a 5-agent team to build a web app' — the agent creates the team, tasks, and workers and starts a background monitoring loop automatically. Manual route: clawteam launch hedge-fund --team fund1 for one-command template launch, or step through team spawn-team → task create → spawn → board serve. Important: keep the default openclaw spawn command; overriding to claude will get agents stuck on interactive permission prompts.
What are this skill's strengths and limitations?
- Agent self-organization: coordination logic runs in the leader agent — no human-written orchestration code
- Real isolation: each agent works on its own git worktree branch, avoiding parallel-edit conflicts, with merge support on completion
- Zero infrastructure: filesystem + tmux only; state is JSON under ~/.clawteam/, no database or message queue needed
- Automatic dependency resolution, task locking against double-claims, and auto-release of stale locks from dead agents
- Multiple monitoring options: terminal kanban, tiled tmux view, Web dashboard, and -- output on all commands
- Spawn safety: pre-validation of commands, automatic rollback on failed spawns, auto-confirmed trust prompts for Claude Code/Codex worktrees
- Installation is multi-step and manual: PATH symlink, exec-approval configuration, and skill file copying
- Strong local-environment dependency: Python 3.10+, git, tmux (Linux/macOS) and at least one CLI agent — not usable in pure cloud-API setups as-is
- board attach requires tmux and is unavailable on native Windows (README suggests board serve or WSL instead)
- No automated test suite or benchmark data appears in SKILL.md or README; some claimed results (e.g., the 8-GPU experiment) are external case studies cited from elsewhere, not verified in this repo
- Switching the spawn backend away from openclaw (e.g., to claude) hits interactive permission prompts unless extra configuration is done
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| ClawTeam Multi-Agent Swarm Coordination this page | 49 · Use with care | ★ 1.5k | 3mo ago | MIT |
| ClawTeam Multi-Agent Coordination | 51 · Use with care | ★ 1.5k | 3mo ago | MIT |
| ClawTeam Multi-Agent Swarm Skill (Hermes Edition) | 48 · Use with care | ★ 1.5k | 3mo ago | MIT |
| ClawTeam Multi-Agent Coordination Skill | 54 · Use with care | ★ 5.5k | 5mo ago | MIT |
| do — Feature Development Orchestrator | 47 · Use with care | ★ 2.8k | 5mo ago | AGPL-3.0 |
The README contrasts ClawTeam with other multi-agent frameworks: those require Docker, cloud APIs, YAML configs, Redis/message queues, are driven by humans writing orchestration code, and are framework-agent-specific. ClawTeam needs only pip install plus one prompt, uses filesystem + tmux as infrastructure, accepts any CLI agent, and isolates via real git worktrees. The upstream HKUDS/ClawTeam defaults to claude and lacks OpenClaw adaptations; this fork is the deeply OpenClaw-integrated version.
How did FollowSkills review this skill?
Docs transparently disclose data locations (~/.clawteam), spawn safety (pre-validation, rollback) and exec-approvals config; but the skill auto-confirms workspace trust prompts, recommends allowlist reconfiguration and uses --force cleanup with broad external effects and no per-action user confirmation — deducted.
SKILL.md command tables are self-consistent with troubleshooting and rollback notes; CI and adapter tests exist, but tests do not cover this skill's key paths (team lifecycle, messaging, merging); failure-feedback quality is asserted only — unverified in static review, deducted.
Clear trigger phrases, scenarios and boundaries (tmux dependency, Windows fallback); multilingual README helps Chinese users; but core dependence on OpenClaw/external CLIs and a GitHub install path, with mainland-China reachability undeclared and non-fit ranges thinly disclosed, deducted.
Well-layered docs, clear versioning (0.3.0+openclaw2), MIT license, explicit upstream attribution; but no changelog, maintenance/update path only implicit in the fork claim, and the npm name-squatting warning reveals naming risk, deducted.
The orchestration value proposition is concrete with a complete workflow, but all effect claims (e.g. the 8-GPU research case via an external repo) are statically unverifiable, with no representative usable-output evidence and thin comparative-benefit data, deducted.
CI workflow plus committed tests (good adapter coverage) exist, but tests do not directly exercise this skill's key paths; effectiveness claims lean on external links and marketing-style narrative without strict fact/inference separation, deducted.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →