Menu
One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
25 advanced engineering agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, release management, platform ops.
Security audit and vulnerability scanner for AI agent skills before installation. Use when: (1) evaluating a skill from an untrusted source, (2) auditing a skill directory or git repo URL for malicious code, (3) pre-install security gate for Claude Code plugins, OpenClaw skills, or Codex skills, (4) scanning Python scripts for dangerous patterns like os.system, eval, subprocess, network exfiltration, (5) detecting prompt injection in SKILL.md files, (6) checking dependency supply chain risks, (7) verifying file system access stays within skill boundaries. Triggers: "audit this skill", "is this skill safe", "scan skill for security", "check skill before install", "skill security check", "skill vulnerability scan".
10 product agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. PM toolkit (RICE), agile PO, product strategist (OKR), UX researcher, UI design system, competitive teardown, landing page generator, SaaS scaffolder, research summarizer. Python tools (stdlib-only).
Reverse-engineer any codebase into a complete Product Requirements Document (PRD). Analyzes routes, components, state management, API integrations, and user interactions to produce business-readable documentation detailed enough for engineers or AI agents to fully reconstruct every page and endpoint. Works with frontend frameworks (React, Vue, Angular, Svelte, Next.js, Nuxt), backend frameworks (NestJS, Django, Express, FastAPI), and fullstack applications. Trigger when users mention: generate PRD, reverse-engineer requirements, code to documentation, extract product specs from code, document page logic, analyze page fields and interactions, create a functional inventory, write requirements from an existing codebase, document API endpoints, or analyze backend routes.
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Read, write, and browse the AgentHub message board for agent coordination.
| name | run |
| description | One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. |
| command | /hub:run |
Run the full AgentHub lifecycle in one command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.
/hub:run --task "Reduce p50 latency" --agents 3 \
--eval "pytest bench.py --json" --metric p50_ms --direction lower \
--template optimizer
/hub:run --task "Refactor auth module" --agents 2 --template refactorer
/hub:run --task "Cover untested utils" --agents 3 \
--eval "pytest --cov=utils --cov-report=json" --metric coverage_pct --direction higher \
--template test-writer
/hub:run --task "Write 3 email subject lines for spring sale campaign" --agents 3 --judge
| Parameter | Required | Description |
|---|---|---|
--task | Yes | Task description for agents |
--agents | No | Number of parallel agents (default: 3) |
--eval | No | Eval command to measure results (skip for LLM judge mode) |
--metric | No | Metric name to extract from eval output (required if --eval given) |
--direction | No | lower or higher — which direction is better (required if --metric given) |
--template | No | Agent template: optimizer, refactorer, test-writer, bug-fixer |
Execute these steps sequentially:
Run /hub:init with the provided arguments:
python {skill_path}/scripts/hub_init.py \
--task "{task}" --agents {N} \
[--eval "{eval_cmd}"] [--metric {metric}] [--direction {direction}]
Display the session ID to the user.
If --eval was provided:
Baseline captured: {metric} = {value}baseline: {value} to .agenthub/sessions/{session-id}/config.yamlIf no --eval was provided, skip this step.
Run /hub:spawn with the session ID.
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.
Launch all agents in a single message with multiple Agent tool calls (true parallelism).
After spawning, inform the user that agents are running. When all agents complete (Agent tool returns results):
Run /hub:eval with the session ID:
--eval was provided: metric-based ranking with result_ranker.py--eval: LLM judge mode (coordinator reads diffs and ranks)If baseline was captured, pass --baseline {value} to result_ranker.py so deltas are shown.
Display the ranked results table.
Present the results to the user and ask for confirmation:
Agent-2 is the winner (128ms, -52ms from baseline).
Merge agent-2's branch? [Y/n]
If confirmed, run /hub:merge. If declined, inform the user they can:
/hub:merge --agent agent-{N} to pick a different winner/hub:eval --judge to re-evaluate with LLM judge--template, agents use the default dispatch prompt from /hub:spawn