bsweet101/init
.claude/skills/agenthub/skills/init/SKILL.md
Create a new AgentHub collaboration session with task, agent count, and evaluation criteria.
data-ai
Updated Apr 16, 2026
$ install --global
skillsauthnpx skillsauth add bsweet101/buckstop-rebrand initInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- init
- description:
- Create a new AgentHub collaboration session with task, agent count, and evaluation criteria.
- command:
- /hub:init
/hub:init — Create New Session
Initialize an AgentHub collaboration session. Creates the .agenthub/ directory structure, generates a session ID, and configures evaluation criteria.
Usage
/hub:init # Interactive mode
/hub:init --task "Optimize API" --agents 3 --eval "pytest bench.py" --metric p50_ms --direction lower
/hub:init --task "Refactor auth" --agents 2 # No eval (LLM judge mode)
What It Does
If arguments provided
Pass them to the init script:
python {skill_path}/scripts/hub_init.py \
--task "{task}" --agents {N} \
[--eval "{eval_cmd}"] [--metric {metric}] [--direction {direction}] \
[--base-branch {branch}]
If no arguments (interactive mode)
Collect each parameter:
- Task — What should the agents do? (required)
- Agent count — How many parallel agents? (default: 3)
- Eval command — Command to measure results (optional — skip for LLM judge mode)
- Metric name — What metric to extract from eval output (required if eval command given)
- Direction — Is lower or higher better? (required if metric given)
- Base branch — Branch to fork from (default: current branch)
Output
AgentHub session initialized
Session ID: 20260317-143022
Task: Optimize API response time below 100ms
Agents: 3
Eval: pytest bench.py --json
Metric: p50_ms (lower is better)
Base branch: dev
State: init
Next step: Run /hub:spawn to launch 3 agents
For content or research tasks (no eval command → LLM judge mode):
AgentHub session initialized
Session ID: 20260317-151200
Task: Draft 3 competing taglines for product launch
Agents: 3
Eval: LLM judge (no eval command)
Base branch: dev
State: init
Next step: Run /hub:spawn to launch 3 agents
Baseline Capture
If --eval was provided, capture a baseline measurement after session creation:
- Run the eval command in the current working directory
- Extract the metric value from stdout
- Append
baseline: {value}to.agenthub/sessions/{session-id}/config.yaml - Display:
Baseline captured: {metric} = {value}
This baseline is used by result_ranker.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:spawnto launch agents - Or
/hub:spawn {session-id}if multiple sessions exist
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