'fast' 검색 결과

Download raw sequencing reads from NCBI SRA using sra-tools (prefetch, fasterq-dump, vdb-validate) or the ENA mirror. Use when pulling FASTQ for SRR/ERR/DRR accessions, deciding between SRA-direct, ENA mirror, or AWS/GCP cloud mirror (STRIDES), handling --include-technical for 10x and other single-cell records, validating with MD5/vdb-validate, navigating SRR/SRX/SRS/SRP/PRJNA hierarchy, or finding accessions via pysradb. Encodes SRA cloud-egress economics, the fasterq-dump uncompressed-scratch trap, and the --max-size default that silently truncates large prefetches.

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App Store and Google Play submission automator with Fastlane, screenshot automation, metadata management, and TestFlight/internal testing. Activate on: app store submission, Fastlane, TestFlight, Google Play Console, screenshot automation, metadata management, app review, code signing, provisioning profiles. NOT for: CI/CD pipeline setup (use github-actions-pipeline-builder), app architecture (use react-native-architect), analytics (use mobile-analytics-crash-reporting-expert).

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Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns. Use PROACTIVELY for FastAPI development, async optimization, or API architecture.

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Local speech-to-text using faster-whisper. 4-6x faster than OpenAI Whisper with identical accuracy; GPU acceleration enables ~20x realtime transcription. Supports standard and distilled models with word-level timestamps.

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Python 앱을 Windows에 배포하도록 Nuitka 컴파일, 불필요한 파일과 DLL 용량 분석, Inno Setup을 통한 가벼운 설치 프로그램 제작을 지원합니다.

• Python 앱을 Windows용으로 배포하기
• 배포 용량이 큰 의존성 조사하기
• 새 Windows 환경의 실행 오류 조사하기

Use hosted DNA models to predict gene structures, expression levels, and regions that regulate gene activity. Start from a gene symbol, genomic coordinates, or a DNA sequence, without installing model weights or running a local GPU.

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• Finding promoter regions in a DNA sequence
• Predicting splice donor and acceptor sites
• Estimating gene expression for a specified cell type

Convert genomic positions between file conventions, standardize variant representations, and check reference genome or chromosome naming conflicts. The skill helps investigate one-base shifts and compare individual variants against a reference sequence before combining analysis files.

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• Converting coordinates between BED and GFF/GTF
• Standardizing and comparing insertion or deletion records
• Checking reference genome and chromosome naming conflicts

Inspect supported local scientific files before modeling, summarize structure and missing values, and flag possible data leakage or outlier effects. Produces bounded aggregate reports with identifiers obscured and provides a structured analysis report template.

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• Check local CSV or TSV data before modeling
• Audit missing values and overlap between data splits
• Compare outlier influence and transformation sensitivity

Build Python applications that keep other tasks moving while waiting for network, database, or file operations. Guides the choice of asyncio patterns, with timeouts, cancellation, error handling, and testing for asynchronous code.

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• Build an asynchronous web API
• Fetch multiple web pages concurrently
• Develop a WebSocket server or chat system

Plan and build an MCP server, a connection that lets Claude use external tools. Choose hosting, tool structure, authentication, and a framework, then scaffold a server or hand off to specialized packaging and interface skills.

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• Connect a cloud API to Claude
• Choose how to run and distribute an MCP server
• Organize tools for an API with many actions

Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.

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• Adding searchable JSON logs
• Collecting service metrics with Prometheus
• Following requests across multiple services

Choose SGLang Diffusion settings for a specific model, GPU, and memory budget. Compare generation speed, memory use, and output quality to select command-line options, including CPU offloading, multiple GPUs, caching, and reduced precision.

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• Choosing generation settings for a specific model and GPU
• Reducing GPU memory use to fit a memory budget
• Comparing performance settings across multiple GPUs

Build Python applications that keep other tasks moving while waiting for network, file, or database operations. The skill guides asyncio patterns, task cancellation, timeouts, workload limits, and testing for asynchronous APIs, scrapers, and background processing.

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• Building asynchronous web APIs
• Sending concurrent requests in web scrapers
• Designing background tasks and queues

Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.

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• Run a language model within limited GPU memory
• Load a pre-quantized model for text generation
• Quantize and save your own trained model

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

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• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Helps speed up LLM text generation by comparing methods that predict and verify multiple tokens at once. Covers draft model selection, Medusa prediction heads, Lookahead Decoding settings, and deployment examples for inference services.

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• Reducing chatbot response latency
• Comparing methods for faster code generation
• Selecting a small draft model for a larger LLM

Scan API route definitions and generate test files for authentication, input validation, error responses, pagination, uploads, and rate limits. Supports Next.js App Router, Express, FastAPI, and Django REST, with Node and Python test outputs.

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• Creating test scaffolds for a new API
• Adding baseline tests to an existing API
• Checking API behavior against current route definitions

Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.

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• Build an object detection pipeline
• Clean images and convert annotation formats
• Optimize a trained model for deployment

Test an idea, strategy, or investment against 11 future time horizons, from six months to 50 years. Compare scenarios with probability ranges, identify structural risks, and review or update the underlying world models.

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• Testing an investment thesis across time horizons
• Reviewing long-term risks to a business model
• Updating future scenarios after major events

Understand a public GitHub repository through GitDiagram's architecture overview. Get an explanation of its main components, source paths, and connections, plus Mermaid diagram source and an interactive diagram for exploring the code.

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• Getting an overview of an unfamiliar public repository
• Finding source paths for major components
• Understanding connections and data flow

Track machine learning experiments with SwanLab by recording training settings, metrics, and media such as images and audio. Learn to compare runs, inspect training charts, and connect existing training code to local, cloud, or self-hosted dashboards.

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• Comparing experiments with different training settings
• Viewing training metrics in a local dashboard
• Logging sample images and audio alongside metrics

Guides adapting OpenPI robot models to custom datasets and serving them to produce actions from observations. Covers JAX and PyTorch training, checkpoint conversion, and troubleshooting for normalization statistics and GPU memory.

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• Fine-tune OpenPI models on custom robot datasets
• Convert JAX checkpoints to PyTorch
• Serve policies for ALOHA, DROID, or LIBERO

Guides LLM compression through pruning, which removes selected model weights. Covers Wanda and SparseGPT methods without retraining, accuracy comparisons, and structured weight patterns that can accelerate inference on compatible hardware.

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• Trying model compression without retraining
• Comparing accuracy before and after pruning
• Choosing between Wanda and SparseGPT

Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.

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• Run language models with limited GPU memory
• Convert your own model to 4-bit weights
• Compare compression settings for quality and speed