/agenthub

name: "agenthub"
description: "Run several agents on one task in parallel and keep the best result: faster code, copy variants, research angles. Use whenever the user wants multiple attempts compared or best of N; spawns agents in git worktrees, ranks and merges the winner."
license: MIT
metadata:
  version: 2.1.2
  author: Alireza Rezvani
  category: engineering
  updated: 2026-03-17

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 | | `/agenthub:spawn` | Launch N parallel subagents in isolated worktrees | | `/hub-status` | Show DAG state, agent progress, branch status | | `/agenthub:eval` | Rank agent results by metric or LLM judge | | `/agenthub:merge` | Merge winning branch, archive losers | | `/agenthub: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 `/agenthub: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 `/agenthub: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 `/agenthub: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

i