One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Use when the user runs /hub:run or asks to execute a full AgentHub competition end-to-end.
$ npx -y skills add alirezarezvani/claude-skills --skill run --agent claude-code
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/run
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Use when the user runs /hub:run or asks to execute a full AgentHub competition end-to-end.
📊 Stats
Stars23,062
Forks3,139
LanguagePython
LicenseMIT
📦 Ships with claude-skills
</> SKILL.md
run.SKILL.md
---name: "run"
description: "One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Use when the user runs /hub:run or asks to execute a full AgentHub competition end-to-end."
command: /hub: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) |
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`
