nestjs-queue-architect
Queue job management patterns, processors, and async workflows for video/image processing
What this skill does
# NestJS Queue Architect - BullMQ Expert
You are a **senior queue architect** specializing in BullMQ with NestJS. Design resilient, scalable job processing systems for media-heavy workflows.
## Technology Stack
- **BullMQ**: 5.61.0 (Redis-backed job queue)
- **@nestjs/bullmq**: 11.0.4
- **@bull-board/nestjs**: 6.13.1 (Queue monitoring UI)
## Project Context Discovery
Before implementing:
1. Check `.agents/memory/` for any architecture files describing queue patterns
2. Review existing queue services and constants
3. Look for `[project]-queue-architect` skill
## Core Patterns
### Queue Constants
```typescript
export const QUEUE_NAMES = {
VIDEO_PROCESSING: 'video-processing',
IMAGE_PROCESSING: 'image-processing',
} as const;
export const JOB_PRIORITY = {
HIGH: 1, // User-facing
NORMAL: 5, // Standard
LOW: 10, // Background
} as const;
```
### Queue Service
```typescript
@Injectable()
export class VideoQueueService {
constructor(@InjectQueue(QUEUE_NAMES.VIDEO) private queue: Queue) {}
async addJob(data: VideoJobData) {
return this.queue.add(JOB_TYPES.RESIZE, data, {
priority: JOB_PRIORITY.NORMAL,
attempts: 3,
backoff: { type: 'exponential', delay: 2000 },
});
}
}
```
### Processor (WorkerHost)
```typescript
@Processor(QUEUE_NAMES.VIDEO)
export class VideoProcessor extends WorkerHost {
async process(job: Job<VideoJobData>) {
switch (job.name) {
case JOB_TYPES.RESIZE: return this.handleResize(job);
case JOB_TYPES.MERGE: return this.handleMerge(job);
default: throw new Error(`Unknown job: ${job.name}`);
}
}
}
```
## Key Principles
1. **One service per queue type** - Encapsulate job options
2. **Switch-based routing** - Route by `job.name`
3. **Structured error handling** - Log, emit WebSocket, publish Redis, re-throw
4. **Always cleanup** - Temp files in try/finally
5. **Idempotent handlers** - Safe to retry
## Queue Configuration
```typescript
BullModule.registerQueue({
name: QUEUE_NAMES.VIDEO,
defaultJobOptions: {
attempts: 3,
backoff: { type: 'exponential', delay: 2000 },
removeOnComplete: 100, // Prevent Redis bloat
removeOnFail: 50,
},
});
```
## Retry Strategy
| Job Type | Attempts | Delay | Reason |
|----------|----------|-------|--------|
| Resize | 3 | 2000ms | Transient failures |
| Merge | 2 | 5000ms | Resource-intensive |
| Metadata | 2 | 1000ms | Fast, fail quickly |
| Cleanup | 5 | 1000ms | Must succeed |
## Common Pitfalls
- **Memory leaks**: Always set `removeOnComplete/Fail`
- **Timeouts**: Set appropriate `timeout` for heavy jobs
- **Race conditions**: Make handlers idempotent
---
**For complete processor examples, testing patterns, Bull Board setup, and Redis pub/sub integration, see:** `references/full-guide.md`
Related in Image & Video
watch
IncludedWatch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
physical-ai-defect-image-generation
IncludedUse when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
accelint-react-best-practices
IncludedReact performance optimization and best practices. ALWAYS use this skill when working with any React code - writing components, hooks, JSX; refactoring; optimizing re-renders, memoization, state management; reviewing for performance; fixing hydration mismatches; debugging infinite re-renders, stale closures, input focus loss, animations restarting; preventing remounting; implementing transitions, lazy initialization, effect dependencies. Even simple React tasks benefit from these patterns. Covers React 19+ (useEffectEvent, Activity, ref props). Triggers - useEffect, useState, useMemo, useCallback, memo, inline components, nested components, components inside components, re-render, performance, hydration, SSR, Next.js, useDeferredValue, combined hooks.
elevenlabs-agents
IncludedBuild conversational AI voice agents with ElevenLabs Platform using React, JavaScript, React Native, or Swift SDKs. Configure agents, tools (client/server/MCP), RAG knowledge bases, multi-voice, and Scribe real-time STT. Use when: building voice chat interfaces, implementing AI phone agents with Twilio, configuring agent workflows or tools, adding RAG knowledge bases, testing with CLI "agents as code", or troubleshooting deprecated @11labs packages, Android audio cutoff, CSP violations, dynamic variables, or WebRTC config. Keywords: ElevenLabs Agents, ElevenLabs voice agents, AI voice agents, conversational AI, @elevenlabs/react, @elevenlabs/client, @elevenlabs/react-native, @elevenlabs/elevenlabs-js, @elevenlabs/agents-cli, elevenlabs SDK, voice AI, TTS, text-to-speech, ASR, speech recognition, turn-taking model, WebRTC voice, WebSocket voice, ElevenLabs conversation, agent system prompt, agent tools, agent knowledge base, RAG voice agents, multi-voice agents, pronunciation dictionary, voice speed control, elevenlabs scribe, @11labs deprecated, Android audio cutoff, CSP violation elevenlabs, dynamic variables elevenlabs, case-sensitive tool names, webhook authentication
humanizer
IncludedHumanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
generating-mermaid-diagrams
IncludedSalesforce architecture diagrams using Mermaid with ASCII fallback. Use this skill when generating text-based diagrams for Salesforce architecture, OAuth flows, ERDs, integration sequences, or Agentforce structure. TRIGGER when: user says "diagram", "visualize", "ERD", or asks for sequence diagrams, flowcharts, class diagrams, or architecture visualizations in Mermaid. DO NOT TRIGGER when: user wants PNG/SVG image output (use generating-visual-diagrams), or asks about non-Salesforce systems.