Agent skill · alirezarezvani
hub-run
Use for /hub-run, or to run a whole AgentHub competition end to end: init, baseline, spawn, eval and merge.
What it needs
About 2k tokens when loaded.
What this skill does
/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 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: 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 /agenthub: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 /agenthub:eval with the session ID: If --eval was provided: metric-based ranking with resultranker.py If no --eval: LLM judge mode (coordinator reads diffs and ranks) If baseline was captured, pass --baseline {value} to resultranker.py so deltas are shown. Display the ranked results table. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/hub-run