Results for “production”

Drafts U.S. HACCP plans for food production under FDA or USDA regimes. Triggers on requests involving HACCP plans, food safety plans, hazard analysis, critical control points, FSMA compliance, seafood HACCP (21 CFR 123), or meat/poultry HACCP (9 CFR 417).

Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

Build production-grade Azure Cosmos DB NoSQL services with Python/FastAPI, including client setup with dual authentication, service layer CRUD operations, partition key strategies, parameterized queries, and TDD patterns.

Deploy Llama Guard, NeMo Guardrails, and LLM Guard as runtime input/output scanners to block jailbreaks, prompt injection, and toxic content in production LLM applications.

Manage teams using Andy Grove's principles: production thinking for knowledge work, output indicators, managerial leverage, effective meetings, clean decisions, OKRs, and task-relevant maturity.

Deploy and serve LLMs with high throughput using vLLM's PagedAttention and continuous batching. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism for production inference.

Takes a PRD, reference URL, screenshot, or keywords to produce a DESIGN.md specification, then generates production-ready web code that meets UI/UX, visual, motion, and responsive standards.

Avalia a confiabilidade de uma plataforma inteira, mapeando pontos únicos de falha, calculando error budgets a partir de SLOs, revisando prontidão para produção e analisando resiliência e capacidade.

Audits and iteratively fixes chat-based AI user interfaces against production best practices drawn from Claude, ChatGPT, and modern conversational AI, covering layout, rendering, input, responsiveness, scrolling, streaming, and accessibility.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Read-only audit for the non-functional "20%" AI agents systematically skip: timeouts, retries with backoff+jitter, circuit breakers, idempotency keys, rate limiting, graceful. Use when "is this production-ready", "resilience audit", "will this survive real traffic", "audit retries/timeouts/idempotency", "reliability.

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.

Secure boot and firmware update workflows for Zephyr RTOS. Covers MCUboot integration, production image signing, DFU protocols (MCUmgr), fail-safe rollback mechanisms, and mbedTLS crypto basics. Trigger when implementing over-the-air (OTA) updates, securing the boot process, or managing cryptographic keys.

Takes a content topic from blank page to publish-ready piece through research, drafting, and optimization. Use when you need to execute a blog post, article, or guide end-to-end.

Initializes production-ready logging for a Next.js project using Pino and pino-roll, with file output, log rotation, and request context. Handles server-side and client-side environments, including Vercel/serverless setups.

Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability. Masters log analysis, distributed tracing, Kubernetes debugging, performance optimization, and root cause analysis. Handles production outages, system reliability, and preventive monitoring. Use PROACTIVELY for debugging, incident response, or system troubleshooting.

Systematic debugging workflow for errors and bugs. Use when debugging errors, investigating bugs, troubleshooting issues, or when something isn't working as expected. Integrates Sentry MCP for production error context, Firecrawl for researching fix patterns, and Sequential Thinking for complex multi-step diagnosis.

Create distinctive, production-grade frontend interfaces with high design quality. Use only when invoked by the `design` wrapper skill, OR when working outside a known site (no `~/Sites/<name>/` repo). Otherwise prefer `design`. Generates creative, polished code that avoids generic AI aesthetics.

Expert guidance for Xano, the no-code/low-code backend platform for building APIs, databases, and authentication without writing server code. Helps developers and non-technical builders create production-ready REST APIs with visual function stacks, manage data models, and integrate with frontend frameworks.

Drafts stipulated ESI protocols for federal e-discovery. Produces court-ready agreements governing preservation, collection, processing, review, and production of electronically stored information under FRCP Rules 26, 34, 37 and Sedona Principles. Trigger when formalizing ESI procedures between parties during the discovery phase.

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

Content strategy and operations expert for the Chinese podcast market, with deep expertise in Xiaoyuzhou, Ximalaya, and other major audio platforms, covering show positioning, audio production, audience growth, multi-platform distribution, and monetization to help podcast creators build sticky audio content brands.

Plan and execute rapid game prototypes to validate mechanics, test assumptions, and find the fun before committing to full production. Use when building playable proofs-of-concept. Also trigger for "prototype", "game jam", "proof of concept", "find the fun", "playable demo", or "rapid prototype".

When the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'video pipeline,' 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative.

Generates a complete, production-ready SaaS project boilerplate with Next.js 14+, TypeScript, Tailwind CSS, shadcn/ui, Drizzle ORM, and Stripe, including authentication, database schemas, billing integration, API routes, and a working dashboard.

Detects fail-open insecure defaults (hardcoded secrets, weak auth, permissive security) that allow apps to run insecurely in production. Use when auditing security, reviewing config management, or analyzing environment variable handling.

Designs and iterates production-grade frontend interfaces, covering UX review, visual hierarchy, accessibility, performance, responsive behavior, theming, typography, layout, color, motion, and design systems. Handles websites, landing pages, dashboards, product UI, components, forms, and more.

You're a developer who has built GraphQL APIs at scale. You've seen the N+1 query problem bring down production servers. You've watched clients craft deeply nested queries that took minutes to resolve. You know that GraphQL's power is also its danger.

Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy". (gstack)

Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.