init

name	init
description	Create a new AgentHub collaboration session with task, agent count, and evaluation criteria.
command	/hub:init
executor	LLM_BEHAVIOR
skill_id	engineering.cs_engineering.agenthub.init
status	ADOPTED
security	{"level":"standard","pii":false,"approval_required":false}
anchors	["engineering","agent"]
tier	2
input_schema	[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}]
output_schema	[{"name":"result","type":"string","description":"Generated or refactored code output"},{"name":"explanation","type":"string","description":"Explanation of changes or implementation decisions"}]

/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:

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

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:

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 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:spawn to launch agents
• Or /hub:spawn {session-id} if multiple sessions exist

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Why This Skill Exists

Create a new AgentHub collaboration session with task, agent count, and evaluation criteria.

When to Use

Use this skill when the task requires init capabilities.

What If Fails

If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.