agenthub

name agenthub description 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 metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo. license MIT metadata {"version":"2.1.2","author":"Alireza Rezvani","category":"engineering","updated":"2026-03-17T00:00:00.000Z"} AgentHub — Multi-Agent Collaboration Spawn N parallel AI agents that compete on the same task. Each agent works in an isolated git worktree. The coordinator evaluates results and merges the winner. Slash Commands Command Description /hub:hub-init Create a new collaboration session — task, agent count, eval criteria /hub:spawn Launch N parallel subagents in isolated worktrees /hub: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 spawning 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" "have agents compete" "parallel optimization" "spawn N agents" "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: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: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) Frontier Detection Frontier = branch tips with no child branches. Equivalent to AgentHub's "leaves" query. python scripts/dag_analyzer.py --frontier --session { id } Immutability The DAG is append-only: Never rebase or force-push agent branches Never delete commits (only branch refs after archival) Every approach preserved via git tags Message Board Location: .agenthub/board/ Channels Channel Writer Reader Purpose dispatch/ Coordinator Agents Task assignments progress/ Agents Coordinator Status updates results/ Agents + Coordinator All Final results + merge summary Post Format --- author: agent-1 timestamp: 2026-03-17T14:30:22Z channel: results parent: null --- ## Result Summary - **Approach** : Replaced O(n²) sort with hash map - **Files changed** : 3 - **Metric** : 142ms (baseline: 180ms, delta: -38ms) - **Confidence** : High — all tests pass Board Rules Append-only: never edit or delete posts Unique filenames: {seq:03d}-{author}-{timestamp}.md YAML frontmatter required on all posts Evaluation Modes Metric-Based Best for: benchmarks, test pass rates, file sizes, response times. python scripts/result_ranker.py --session { id } \ --eval-cmd "pytest bench.py --json" \ --metric p50_ms --direction lower The ranker runs the eval command in each agent's worktree directory and parses the metric from stdout. LLM Judge Best for: code quality, readability, architecture decisions. The coordinator reads each agent's diff ( git diff base...agent-branch ) and ranks by: Correctness (does it solve the task?) Simplicity (fewer lines changed preferred) Quality (clean execution, good structure) Hybrid Run metric first. If top agents are within 10% of each other, use LLM judge to break ties. Session Lifecycle init → running → evaluating → merged → archived (if no winner) State transitions managed by session_manager.py : From To Trigger init running /hub:spawn completes running evaluating All agents return evaluating merged /hub:merge completes evaluating archived No winner / all failed Proactive Triggers The coordinator should act when: Signal Action All agents crashed Post failure summary, suggest retry with different constraints No improvement over baseline Archive session, suggest different approaches Orphan worktrees detected Run session_manager.py --cleanup {id} Session stuck in running Check board for progress, consider timeout Installation # Copy to your Claude Code skills directory cp -r engineering/agenthub ~/.claude/skills/agenthub # Or install via ClawHub clawhub install agenthub Scripts Script Purpose hub_init.py Initialize .agenthub/ structure and session dag_analyzer.py Frontier detection, DAG graph, branch status board_manager.py Message board CRUD (channels, posts, threads) result_ranker.py Rank agents by metric or diff quality session_manager.py Session state machine and cleanup Related Skills autoresearch-agent — Single-agent optimization loop (use AgentHub when you want N agents competing) self-improving-agent — Self-modifying agent (use AgentHub when you want external competition) git-worktree-manager — Git worktree utilities (AgentHub uses worktrees internally)