- AgentHub: 13 files updated with non-engineering examples (content drafts, research, strategy) — engineering stays primary, cross-domain secondary - AgentHub: 7 slash commands, 5 Python scripts, 3 references, 1 agent, dry_run.py validation (57 checks) - Marketplace: agenthub entry added with cross-domain keywords, engineering POWERFUL updated (25→30), product (12→13), counts synced across all configs - SEO: generate-docs.py now produces keyword-rich <title> tags and meta descriptions using SKILL.md frontmatter — "Claude Code Skills" in site_name propagates to all 276 HTML pages - SEO: per-domain title suffixes (Agent Skill for Codex & OpenClaw, etc.), slug-as-title cleanup, domain label stripping from titles - Broken links: 141→0 warnings — new rewrite_skill_internal_links() converts references/, scripts/, assets/ links to GitHub source URLs; skills/index.md phantom slugs fixed (6 marketing, 7 RA/QM) - Counts synced: 204 skills, 266 tools, 382 refs, 16 agents, 17 commands, 21 plugins — consistent across CLAUDE.md, README.md, docs/index.md, marketplace.json, getting-started.md, mkdocs.yml - Platform sync: Codex 163 skills, Gemini 246 items, OpenClaw compatible Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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title, description
| title | description |
|---|---|
| /hub:init — Create New Session — Agent Skill for Codex & OpenClaw | Create a new AgentHub collaboration session with task, agent count, and evaluation criteria. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw. |
/hub:init — Create New Session
:material-rocket-launch: Engineering - POWERFUL
:material-identifier: `init`
:material-github: Source
Install:
claude /plugin install engineering-advanced-skills
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