hub-run – skill Khác cho agent AI

/hub: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:hub-run --task "Reduce p50 latency" --agents 3 \
  --eval "pytest bench.py --json" --metric p50_ms --direction lower \
  --template optimizer

/hub:hub-run --task "Refactor auth module" --agents 2 --template refactorer

/hub:hub-run --task "Cover untested utils" --agents 3 \
  --eval "pytest --cov=utils --cov-report=json" --metric coverage_pct --direction higher \
  --template test-writer

/hub: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:hub-init with the provided arguments:

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