agent-hub

name	agent-hub
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.
zh_description	用于管理 Agent 能力中心、技能发现、路由和协作工作流。
version	1.0.4
author	seaworld008
source	github:alirezarezvani/claude-skills
source_url
license	MIT
tags	["agent", "ai", "hub"]
created_at	2026-03-27
updated_at	2026-06-24
quality	4
complexity	intermediate

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:init	Create a new collaboration session — task, agent count, eval 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 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: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)

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:

1. Correctness (does it solve the task?)
2. Simplicity (fewer lines changed preferred)
3. 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
npx clawhub@latest 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)

Usage Notes

This supplement is maintained by the repository sync pipeline. It keeps the imported upstream skill usable inside this curated collection when the upstream source is intentionally concise.

Common Patterns

1. Confirm that the user's task matches the skill trigger.
2. Read the relevant project files or user-provided context before acting.
3. Choose the smallest reversible action that advances the task.
4. Run the verification command or manual check that proves the result.
5. Report the outcome, evidence, and any remaining risk.

Boundaries

• Prefer the upstream workflow for Agent Hub; this section only adds local quality guardrails.
• Do not invent project facts when required files, vaults, services, or tools are unavailable.
• Stop and ask for clarification when the next action could overwrite user work, expose private data, or change production state.