/hub-init

name: "hub-init"
description: "Create a new AgentHub collaboration session with task, agent count, and evaluation criteria. Use when the user runs /hub:hub-init or asks to start a multi-agent competition on a task."
command: /hub:hub-init

/hub:hub-init — Create New Session

Initialize an AgentHub collaboration session. Creates the `.agenthub/` directory structure, generates a session ID, and configures evaluation criteria.

Usage

/hub:hub-init                                                    # Interactive mode
/hub:hub-init --task "Optimize API" --agents 3 --eval "pytest bench.py" --metric p50_ms --direction lower
/hub:hub-init --task "Refactor auth" --agents 2                  # No eval (LLM judge mode)

What It Does

If arguments provided

Pass them to the init script:

python {skill_path}/scripts/hub_init.py \
  --task "{task}" --agents {N} \
  [--eval "{eval_cmd}"] [--metric {metric}] [--direction {direction}] \
  [--base-branch {branch}]

If no arguments (interactive mode)

Collect each parameter:

1. **Task** — What should the agents do? (required) 2. **Agent count** — How many parallel agents? (default: 3) 3. **Eval command** — Command to measure results (optional — skip for LLM judge mode) 4. **Metric name** — What metric to extract from eval output (required if eval command given) 5. **Direction** — Is lower or higher better? (required if metric given) 6. **Base branch** — Branch to fork from (default: current branch)

Output

AgentHub session initialized
  Session ID: 20260317-143022
  Task: Optimize API response time below 100ms
  Agents: 3
  Eval: pytest bench.py --json
  Metric: p50_ms (lower is better)
  Base branch: dev
  State: init

Next step: Run /hub:spawn to launch 3 agents

For content or research tasks (no eval command → LLM judge mode):

AgentHub session initialized
  Session ID: 20260317-151200
  Task: Draft 3 competing taglines for product launch
  Agents: 3
  Eval: LLM judge (no eval command)
  Base branch: dev
  State: init

Next step: Run /hub:spawn to launch 3 agents

Baseline Capture

If `--eval` was provided, capture a baseline measurement after session creation:

1. Run the eval command in the current working directory 2. Extract the metric value from stdout 3. Append `baseline: {value}` to `.agenthub/sessions/{session-id}/config.yaml` 4. Display: `Baseline captured: {metric} = {value}`

This baseline is used by `result_ranker.py --baseline` during evaluation to show deltas. If the eval command fails at this stage, warn the user but continue — baseline is optional.

After Init

Tell the user:

• Session created with ID `{session-id}`
• Baseline metric (if captured)
• Next step: `/hub:spawn` to launch agents
• Or `/hub:spawn {session-id}` if multiple sessions exist