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Plan backend services and APIs, from service responsibilities and interface definitions to authentication, failure handling, monitoring, and rollout. Produces architecture diagrams, API specifications, and documented tradeoffs based on business and operational requirements.
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Design a database layer from business requirements, access patterns, and expected growth. Compare database technologies, define tables and relationships, and plan indexes, caching, scaling, migrations, backups, and recovery with clear reasons and trade-offs.
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Plan backend systems with clear module boundaries and dependency rules. This skill helps select architecture patterns, organize business logic, and outline migration steps for systems that are easier to maintain and test.
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Create patent line drawings from text or reference images through the PatentFig AI API. Convert existing drawings into scalable vector files for CAD, increase image resolution, and export figures in formats used for patent filing.
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Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.
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Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.
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Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.
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Organize PyTorch training code with LightningModule and Trainer. Follow examples for validation, logging, model saving, and distributed training across multiple GPUs, with callbacks that add monitoring or stop training when progress stalls.
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Design and implement systems that collect, transform, validate, and store data for analysis. Covers scheduled and real-time processing, data warehouses, quality checks, monitoring, and cost tuning using tools such as Apache Spark, dbt, and Airflow.
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Design systems that collect, transform, and store data. Choose between scheduled and continuous processing, generate workflow configurations, validate data quality, and analyze performance using guidance for tools including Python, SQL, Spark, Airflow, dbt, and Kafka.
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Turn system structures, processes, and concepts into dark-themed diagrams. Create architecture diagrams, flowcharts, timelines, and other visual explanations as scalable SVG files, with PNG versions for use as images.
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Build image export features for HTML elements with snapDOM. The skill covers SVG, PNG, JPG, and WebP output, preserving styles and fonts, adjusting dimensions, excluding elements, and troubleshooting missing resources.
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Create pixel art illustrations as SVG files, a scalable image format, for READMEs, documentation, and slides. Build character portraits, conversations, or group scenes, then refine colors, speech bubbles, arrows, and spacing to fit the requested layout.
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Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.
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Guides reinforcement learning, where models learn from rewards, using torchforge and PyTorch. Covers reward design, custom loss functions, and distributed training so researchers can experiment with algorithms while keeping infrastructure concerns separate.
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Organize PyTorch training code with LightningModule and Trainer. This skill guides training, validation, logging, model checkpoint saving, early stopping when progress stalls, and configuration for running the same model across multiple GPUs.
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Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.
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Design and implement a NestJS backend organized into business modules within one deployable application. The skill defines module boundaries, separates data ownership, builds layered code and event communication, and validates isolation through checks and tests.
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Create SVG illustrations, a scalable vector image format, with thick navy outlines, vivid colors, and chunky depth. Combine a cartoon character with overlapping props for product screens and marketing cards, then render, review, and refine the artwork.
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Create SVG illustrations, a scalable vector image format, of people working in airy rooms or terraces. Use thin indigo outlines, mostly white shapes, and restrained color accents, with reusable drawing parts and checks for consistent scenes.
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Add shafts of sunlight to Three.js or similar 3D scenes. Shape rays with roofs and foliage, align them with the sun, and tune rendering effects so foreground surfaces keep their contrast while processing costs remain measurable.
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Design reusable SEO page templates from a niche and data source. Get title patterns, page sections, internal linking rules, structured data for search engines, and requirements that keep each page useful and distinct.
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Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
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Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
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