init

name	init
description	Create a new AgentHub collaboration session with task, agent count, and evaluation criteria.
command	/hub:init

/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:init                                                    # Interactive mode
/hub:init --task "Optimize API" --agents 3 --eval "pytest bench.py" --metric p50_ms --direction lower
/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