Agenthub
/SKILLMulti-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation.
--- name: "agenthub" description: "A multi-agent-collaboration plugin that spawns N parallel subagents competing on the same task via Git worktree isolation. Agents work independently; results are evaluated by a metric or an LLM judge; and the best branch is merged. Use when: the user wants multiple approaches tried in parallel:code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a Gitrepo" license: MIT metadata: version: 2.1.2 author: Alireza Rezvani category: engineering updated: 2026-03-17 --- # AgentHub : Multi-Agent Collaboration Spawn N parallel AI-agentss that compete on the same task. Each agent works in an isolated Git worktree. The coordinator evaluates the results and merges the winner. ## Slash Commands | Command | Description | |---------|-------------| | /hub:init | Create a new collaboration session:task, agent count, evaluation criteria | | /hub:spawn | Launch N parallel subagents in isolated worktrees | | /hub:status | Show DAG state, agent progress, branch status | | /hub:eval | Rank agent results by metric or LLM judge | | /hub:merge | Merge winning branch, archive losers | | /hub:board | Read/write the agent message board | | /hub:run | One-shot lifecycle: init → baseline → spawn → eval → merge | ## Agent Templates When spawned with --template, agents follow a predefined iteration pattern: | Template | Pattern | Use Case | |----------|---------|----------| | optimizer | Edit → eval → keep/discard → repeat x10 | Performance, latency, size | | refactorer | Restructure → test → iterate until green | Code quality, tech debt | | test-writer | Write tests → measure coverage → repeat | Test coverage gaps | | bug-fixer | Reproduce → diagnose → fix → verify | Bug fix approaches | Templates are defined in references/agent-templates.md. ## When This Skill Activates Trigger phrases: - "try multiple approaches" - "haveagentscompete" - "parallel optimization" - "spawn Nagents" - "compare different solutions" - "fan-out" or "tournament" - "generate content variations" - "compare different drafts" - "A/B test copy" - "explore multiple strategies" ## Coordinator Protocol The main Claude Code session is the coordinator. It follows this lifecycle: `` INIT → DISPATCH → MONITOR → EVALUATE → MERGE ` ### 1. Init Run /hub:init to create a session. This generates: - .agenthub/sessions /{session-id}/config.yaml : task config - .agenthub/sessions /{session-id}/state.json : state machine - .agenthub/board / : message board channels ### 2. Dispatch Run /hub:spawn to launch agents. For each agent 1..N: - Post task assignment to .agenthub/board/dispatch / - Spawn via Agent tool with isolation: "worktree" - All agents launched in a single message (parallel) ### 3. Monitor Run /hub:status to check progress: - dag_analyzer.py --status --session {id} shows branch state - Board progress/ channel has agent updates ### 4. Evaluate Run /hub:eval to rank results: - **Metric mode**: run eval command in each worktree, parse numeric result - **Judge mode**: read diffs, coordinator ranks by quality - **Hybrid**: metric first, LLM-judge for ties ### 5. Merge Run /hub:merge to finalize: - git merge --no-ff winner into base branch - Tag losers: git tag hub/archive/{session}/agent-{i} - Clean up worktrees - Post merge summary to board ## Agent Protocol Each subagent receives this prompt pattern: ` You are agent-{i} in hub session {session-id}. Your task: {task description} Instructions: 1. Read your assignment at .agenthub/board/dispatch/{seq}-agent-{i}.md 2. Work in your worktree : make changes, run tests, iterate 3. Commit all changes with descriptive messages 4. Write your result summary to .agenthub/board/results/agent-{i}-result.md 5. Exit when done ` Agents do NOT see each other's work. They do NOT communicate with each other. They only write to the board for the coordinator to read. ## DAG Model ### Branch Naming ` hub/{session-id}/agent-{N}/attempt-{M} ` - Session ID: timestamp-based ( YYYYMMDD-HHMMSS`) - Agent N: sequential (1 to agent -count) - Attempt M: increments on retry (usually 1
