Agent skill · alirezarezvani
hub-init
Create a new AgentHub collaboration session with task, agent count, and evaluation criteria. Use when the user runs /hub:hub-init or asks to start a multi-agent competition on a task.
What it needs
About 2k tokens when loaded.
What this skill does
/hub:hub-init — Create New Session Initialize an AgentHub collaboration session. Creates the .agenthub/ directory structure, generates a session ID, and configures evaluation criteria. Usage What It Does If arguments provided Pass them to the init script: If no arguments (interactive mode) Collect each parameter: 1. Task — What should the agents do? (required) 2. Agent count — How many parallel agents? (default: 3) 3. Eval command — Command to measure results (optional — skip for LLM judge mode) 4. Metric name — What metric to extract from eval output (required if eval command given) 5. Direction — Is lower or higher better? (required if metric given) 6. Base branch — Branch to fork from (default: current branch) Output For content or research tasks (no eval command → LLM judge mode): Baseline Capture If --eval was provided, capture a baseline measurement after session creation: 1. Run the eval command in the current working directory 2. Extract the metric value from stdout 3. Append baseline: {value} to .agenthub/sessions/{session-id}/config.yaml 4. Display: Baseline captured: {metric} = {value} This baseline is used by resultranker.py --baseline during evaluation to show deltas. If the eval command fails at this stage, warn the user but continue — baseline is optional. After Init Tell the user: Session created with ID {session-id} Baseline metric (if captured) Next step: /hub:spawn to launch agents Or /hub:spawn {session-id} if multiple sessions exist
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/hub-init--88c4fd