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a80eec2267
Round-2 sweep after re-auditing all 15 reported issues against the merged dev: - #885 generalized: the original fix only renamed self-improving-agent's status/review, but three more plugins shipped skills whose bare names shadow Claude Code built-ins. Renamed with the same convention: playwright-pro init/review -> pw-init/pw-review, agenthub init/status -> hub-init/hub-status, autoresearch-agent status/resume -> ar-status/ ar-resume. All command references (/pw: /hub: /ar:), docs, audit records, harness manifests, and mirror trees/indexes updated; the flat mirror namespace no longer collides on 'status'. New scripts/check_skill_names.py gate (wired into ci-quality-gate.yml as blocking) fails CI on any future bare reserved name; rule added to SKILL-AUTHORING-STANDARD.md. - #969 follow-through: five more scripts print box-drawing characters that cannot exist in cp1252 (api_scorecard, api_linter, breaking_change_detector, humanizer_scorer, content_scorer) — same guarded UTF-8 reconfigure applied; all smoke-tested under a forced legacy encoding. Verified: check_skill_names (incl. negative test), check_plugin_json, check_paths, derive_counters, check_dual_publish, smoke_scripts (634/634), 0 broken mirror symlinks. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Qgc6RYXWJPr5oW9DHU7zR4
99 lines
3.3 KiB
Markdown
99 lines
3.3 KiB
Markdown
---
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title: "/hub: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. Use when the user runs /hub:hub-init or asks to start a. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
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---
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# /hub: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/hub-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:hub-init # Interactive mode
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/hub:hub-init --task "Optimize API" --agents 3 --eval "pytest bench.py" --metric p50_ms --direction lower
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/hub: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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