Run

# /hub:run — One-Shot Lifecycle Run the full AgentHub lifecycle in one command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner. ## Usage ``` /hub:run --task "Reduce p50 latency" --agents 3 \ --eval "pytest bench.py --json" --metric p50_ms --direction lower \ --template optimizer /hub:run --task "Refactor auth module" --agents 2 --template refactorer /hub:run --task "Cover untested utils" --agents 3 \ --eval "pytest --cov=utils --cov-report=json" --metric coverage_pct --direction higher \ --template test-writer /hub:run --task "Write 3 email subject lines for spring sale campaign" --agents 3 --judge ``` ## Parameters | Parameter | Required | Description | |-----------|----------|-------------| | `--task` | Yes | Task description for agents | | `--agents` | No | Number of parallel agents (default: 3) | | `--eval` | No | Eval command to measure results (skip for LLM judge mode) | | `--metric` | No | Metric name to extract from eval output (required if `--eval` given) | | `--direction` | No | `lower` or `higher` — which direction is better (required if `--metric` given) | | `--template` | No | Agent template: `optimizer`, `refactorer`, `test-writer`, `bug-fixer` | ## What It Does Execute these steps sequentially: ### Step 1: Initialize Run `/hub:init` with the provided arguments: ```bash python {skill_path}/scripts/hub_init.py \ --task "{task}" --agents {N} \ [--eval "{eval_cmd}"] [--metric {metric}] [--direction {direction}] ``` Display the session ID to the user. ### Step 2: Capture Baseline If `--eval` was provided: 1. Run the eval command in the current working directory 2. Extract the metric value from stdout 3. Display: `Baseline captured: {metric} = {value}` 4. Append `baseline: {value}` to `.agenthub/sessions/{session-id}/config.yaml` If no `--eval` was provided, skip this step. ### Step 3: Spawn Agents Run `/hub:spawn` with the session ID. If `--template` was provided, use the template dispatch prompt from `../agenthub/references/agent-templates.md` instead of the default dispatch prompt. Pass the eval command, metric, and baseline to the template variables. Launch all agents in a single message with multiple Agent tool calls (true parallelism). ### Step 4: Wait and Monitor After spawning, inform the user that agents are running. When all agents complete (Agent tool returns results): 1. Display a brief summary of each agent's work 2. Proceed to evaluation ### Step 5: Evaluate Run `/hub:eval` with the session ID: - If `--eval` was provided: metric-based ranking with `result_ranker.py` - If no `--eval`: LLM judge mode (coordinator reads diffs and ranks) If baseline was captured, pass `--baseline {value}` to `result_ranker.py` so deltas are shown. Display the ranked results table. ### Step 6: Confirm and Merge Present the results to the user and ask for confirmation: ``` Agent-2 is the winner (128ms, -52ms from baseline). Merge agent-2's branch? [Y/n] ``` If confirmed, run `/hub:merge`. If declined, inform the user they can: - `/hub:merge --agent agent-{N}` to pick a different winner - `/hub:eval --judge` to re-evaluate with LLM judge - Inspect branches manually ## Critical Rules - **Sequential execution** — each step depends on the previous - **Stop on failure** — if any step fails, report the error and stop - **User confirms merge** — never auto-merge without asking - **Template is optional** — without `--template`, agents use the default dispatch prompt from `/hub:spawn`