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: ```bash 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