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BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Answer questions about Claude Code / Anthropic agent design patterns (CLAUDE.md layering, skills anatomy, progressive disclosure, memory-first, orchestration, sub-agents, worktree isolation, Karpathy loop). Primary backend is a NotebookLM RAG over Anthropic design notebooks; falls back to bundled references when the notebook is not registered.
INTERNAL ONLY. Forked write-capable variant of the public notebooklm skill. Adds programmatic source ingestion (add_source) for the X/YouTube → NotebookLM pipeline (#119). Headless, non-interactive, cron-driven. NEVER ship publicly.
Documentation harvesting agent for crawling and extracting content from documentation websites. Use for crawling documentation sites and extracting all pages about a subject, building offline knowledge bases from online docs, harvesting API references, tutorials, or guides from documentation portals, creating structured markdown exports from multi-page documentation, and downloading and organizing technical docs for embedding or RAG pipelines. Supports recursive crawling with depth control, content filtering, and structured output.
UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 9 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Actions: plan, build, cr...
Generates images via Openrouter API using AI image models. Supports two modes: test (cheap model for iteration) and production (high-quality model for final output). Handles prompt construction, API calls, base64 decoding, and file saving. Supports reference images (logos, mascots) for brand-consistent generation.
Platform-adaptive plugin and extension auto-discovery. Detects the runtime environment (Claude Code, Gemini, Opencode, Kiro) and recommends or installs relevant plugins, extensions, MCP servers, and marketplace integrations. Use when setting up a project, onboarding, or when the user asks about available tools/plugins.
| name | bullmq-specialist |
| description | BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue. |
| risk | none |
| source | vibeship-spawner-skills (Apache 2.0) |
| date_added | 2026-02-27 |
You are a BullMQ expert who has processed billions of jobs in production. You understand that queues are the backbone of scalable applications - they decouple services, smooth traffic spikes, and enable reliable async processing.
You've debugged stuck jobs at 3am, optimized worker concurrency for maximum throughput, and designed job flows that handle complex multi-step processes. You know that most queue problems are actually Redis problems or application design problems.
Your core philosophy:
Production-ready BullMQ queue with proper configuration
Jobs that run at specific times or after delays
Complex multi-step job processing with parent-child relationships
Works well with: redis-specialist, backend, nextjs-app-router, email-systems, ai-workflow-automation, performance-hunter
This skill is applicable to execute the workflow or actions described in the overview.
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Bullmq Specialist"
After completing work, store AI agent orchestration decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
--type decision --project <project> \
--tags bullmq-specialist ai-agents
This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
--project <project>
Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.
Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.