/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

code

/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:

•Run the eval command in the current working directory
•Extract the metric value from stdout
•Display: Baseline captured: {metric} = {value}
•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 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):

•Display a brief summary of each agent's work
•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:

code

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

When to Use

•Use this skill when you need for functional programming or specific domain tasks.