2f57ef8948
- 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>
100 lines
3.3 KiB
Markdown
100 lines
3.3 KiB
Markdown
---
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title: "/hub:init — Create New Session — Agent Skill for Codex & OpenClaw"
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description: "Create a new AgentHub collaboration session with task, agent count, and evaluation criteria. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
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---
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# /hub:init — Create New Session
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<div class="page-meta" markdown>
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<span class="meta-badge">:material-rocket-launch: Engineering - POWERFUL</span>
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<span class="meta-badge">:material-identifier: `init`</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/engineering/agenthub/skills/init/SKILL.md">Source</a></span>
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</div>
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<div class="install-banner" markdown>
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<span class="install-label">Install:</span> <code>claude /plugin install engineering-advanced-skills</code>
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</div>
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Initialize an AgentHub collaboration session. Creates the `.agenthub/` directory structure, generates a session ID, and configures evaluation criteria.
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## Usage
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```
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/hub:init # Interactive mode
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/hub:init --task "Optimize API" --agents 3 --eval "pytest bench.py" --metric p50_ms --direction lower
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/hub:init --task "Refactor auth" --agents 2 # No eval (LLM judge mode)
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```
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## What It Does
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### If arguments provided
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Pass them to the init script:
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```bash
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python {skill_path}/scripts/hub_init.py \
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--task "{task}" --agents {N} \
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[--eval "{eval_cmd}"] [--metric {metric}] [--direction {direction}] \
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[--base-branch {branch}]
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```
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### If no arguments (interactive mode)
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Collect each parameter:
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1. **Task** — What should the agents do? (required)
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2. **Agent count** — How many parallel agents? (default: 3)
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3. **Eval command** — Command to measure results (optional — skip for LLM judge mode)
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4. **Metric name** — What metric to extract from eval output (required if eval command given)
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5. **Direction** — Is lower or higher better? (required if metric given)
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6. **Base branch** — Branch to fork from (default: current branch)
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### Output
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```
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AgentHub session initialized
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Session ID: 20260317-143022
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Task: Optimize API response time below 100ms
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Agents: 3
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Eval: pytest bench.py --json
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Metric: p50_ms (lower is better)
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Base branch: dev
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State: init
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Next step: Run /hub:spawn to launch 3 agents
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```
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For content or research tasks (no eval command → LLM judge mode):
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```
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AgentHub session initialized
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Session ID: 20260317-151200
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Task: Draft 3 competing taglines for product launch
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Agents: 3
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Eval: LLM judge (no eval command)
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Base branch: dev
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State: init
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Next step: Run /hub:spawn to launch 3 agents
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```
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## Baseline Capture
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If `--eval` was provided, capture a baseline measurement after session creation:
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1. Run the eval command in the current working directory
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2. Extract the metric value from stdout
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3. Append `baseline: {value}` to `.agenthub/sessions/{session-id}/config.yaml`
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4. Display: `Baseline captured: {metric} = {value}`
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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.
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## After Init
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Tell the user:
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- Session created with ID `{session-id}`
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- Baseline metric (if captured)
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- Next step: `/hub:spawn` to launch agents
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- Or `/hub:spawn {session-id}` if multiple sessions exist
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