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>
121 lines
4.2 KiB
Markdown
121 lines
4.2 KiB
Markdown
---
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title: "/hub:run — One-Shot Lifecycle — Agent Skill for Codex & OpenClaw"
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description: "One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
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---
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# /hub:run — One-Shot Lifecycle
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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: `run`</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/engineering/agenthub/skills/run/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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Run the full AgentHub lifecycle in one command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.
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## Usage
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```
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/hub:run --task "Reduce p50 latency" --agents 3 \
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--eval "pytest bench.py --json" --metric p50_ms --direction lower \
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--template optimizer
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/hub:run --task "Refactor auth module" --agents 2 --template refactorer
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/hub:run --task "Cover untested utils" --agents 3 \
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--eval "pytest --cov=utils --cov-report=json" --metric coverage_pct --direction higher \
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--template test-writer
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/hub:run --task "Write 3 email subject lines for spring sale campaign" --agents 3 --judge
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```
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## Parameters
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| Parameter | Required | Description |
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|-----------|----------|-------------|
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| `--task` | Yes | Task description for agents |
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| `--agents` | No | Number of parallel agents (default: 3) |
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| `--eval` | No | Eval command to measure results (skip for LLM judge mode) |
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| `--metric` | No | Metric name to extract from eval output (required if `--eval` given) |
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| `--direction` | No | `lower` or `higher` — which direction is better (required if `--metric` given) |
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| `--template` | No | Agent template: `optimizer`, `refactorer`, `test-writer`, `bug-fixer` |
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## What It Does
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Execute these steps sequentially:
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### Step 1: Initialize
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Run `/hub:init` with the provided arguments:
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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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```
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Display the session ID to the user.
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### Step 2: Capture Baseline
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If `--eval` was provided:
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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. Display: `Baseline captured: {metric} = {value}`
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4. Append `baseline: {value}` to `.agenthub/sessions/{session-id}/config.yaml`
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If no `--eval` was provided, skip this step.
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### Step 3: Spawn Agents
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Run `/hub:spawn` with the session ID.
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If `--template` was provided, use the template dispatch prompt from `references/agent-templates.md` instead of the default dispatch prompt. Pass the eval command, metric, and baseline to the template variables.
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Launch all agents in a single message with multiple Agent tool calls (true parallelism).
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### Step 4: Wait and Monitor
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After spawning, inform the user that agents are running. When all agents complete (Agent tool returns results):
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1. Display a brief summary of each agent's work
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2. Proceed to evaluation
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### Step 5: Evaluate
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Run `/hub:eval` with the session ID:
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- If `--eval` was provided: metric-based ranking with `result_ranker.py`
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- If no `--eval`: LLM judge mode (coordinator reads diffs and ranks)
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If baseline was captured, pass `--baseline {value}` to `result_ranker.py` so deltas are shown.
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Display the ranked results table.
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### Step 6: Confirm and Merge
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Present the results to the user and ask for confirmation:
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```
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Agent-2 is the winner (128ms, -52ms from baseline).
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Merge agent-2's branch? [Y/n]
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```
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If confirmed, run `/hub:merge`. If declined, inform the user they can:
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- `/hub:merge --agent agent-{N}` to pick a different winner
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- `/hub:eval --judge` to re-evaluate with LLM judge
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- Inspect branches manually
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## Critical Rules
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- **Sequential execution** — each step depends on the previous
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- **Stop on failure** — if any step fails, report the error and stop
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- **User confirms merge** — never auto-merge without asking
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- **Template is optional** — without `--template`, agents use the default dispatch prompt from `/hub:spawn`
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