Skillshub agenthub

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.

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/alirezarezvani/claude-skills/agenthub" ~/.claude/skills/comeonoliver-skillshub-agenthub && rm -rf "$T"

manifest: skills/alirezarezvani/claude-skills/agenthub/SKILL.md

source content

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:

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)