'fast' 검색 결과

Look up structure metadata in RCSB PDB, search for entries by keyword, and retrieve sequences in FASTA, a text format for biological sequences. Receive concise summaries or short sequence previews, with raw output available on request.

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• Check basic metadata for a known PDB entry
• Find structure entries using a keyword
• Retrieve an entry's sequences in FASTA format

FastAPI 앱 개발과 검토에서 구조, 입력 검증, 인증, 권한 확인, 비동기 처리, 데이터베이스 업데이트, 테스트의 설계와 구현을 지원합니다.

• FastAPI 앱 구조 결정
• 입출력 데이터 형식과 검증 정비
• 인증과 작업 권한 검토

Deploy FastGPT on your own server using its official installation script and Docker Compose, which runs its services together. Check service health and access, investigate deployment problems, and receive the access URL, initial login details, and next steps.

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• Setting up FastGPT on your own server
• Checking service health after deployment
• Investigating port conflicts or container startup failures

Migrate Java projects from FastExcel 1.3 to Apache Fesod by updating Maven or Gradle dependencies, package imports, and entry class calls. Handles both legacy package namespaces and identifies conditional helper class changes.

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• Migrating an existing Java Excel application to Fesod
• Updating Maven or Gradle dependencies
• Cleaning up partially migrated package imports

Validate FASTQ sequencing files, run local quality checks, and interpret reports to decide whether trimming is needed. Optionally remove adapters or low-quality read ends while preserving original reads and their quality reports.

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• Check sequencing quality before downstream analysis
• Summarize quality reports across multiple samples
• Assess whether adapter or quality trimming is needed

Set up Python APIs with FastAPI using organized project templates. Build routes, asynchronous database operations, authentication, and error handling, with examples for sharing dependencies and testing endpoints through a separate test database.

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• Set up a new FastAPI project
• Build a Python API with asynchronous database access
• Organize authentication and shared dependencies

Validate Illumina BCL run folders and sample sheets, plan demultiplexing—the separation of reads by sample—and convert data to FASTQ using installed tools. Review conversion metrics, index settings, and reads that could not be assigned.

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• Checking run folders and sample sheets before conversion
• Reviewing index, UMI, and lane settings
• Converting Illumina BCL data to FASTQ

Integrate new FastGPT resources, such as AgentSkill or Plugin, with permission management. Choose an integration guide based on resource hierarchy and permission inheritance, locate the relevant code, and review a checklist for missing implementation steps.

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• Adding permission support to a new FastGPT resource
• Choosing an integration path based on permission inheritance
• Finding code locations for permission definitions and checks

Build Python APIs with FastAPI and Pydantic V2, including input validation, database operations that avoid blocking other work, and token-based authentication. Implement REST or WebSocket endpoints, then check their behavior with automated tests and generated API documentation.

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• Build REST APIs with FastAPI
• Validate input data with Pydantic V2
• Implement JWT authentication and authorization

Develop and review FastGPT APIs with consistent input and output checks, TypeScript types, and OpenAPI documentation. It guides the use of Zod schemas, definitions that describe and validate data, alongside permission checks and reusable structures.

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• Adding a new FastGPT API route
• Changing API inputs or responses
• Reviewing API validation and documentation

Retire a FastGPT workflow node while preserving existing workflows. The skill guides changes to templates, execution handlers, and interface references so the node disappears from creation menus but still loads, runs, and displays a replacement notice.

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• Retiring a FastGPT workflow node
• Hiding a node from creation menus
• Preserving existing workflows during node retirement

Prepare single-cell or single-nucleus RNA-seq analysis by checking inputs and selecting workflows that turn FASTQ sequencing files into count matrices. Route quality checks, cell annotation, clustering, and UMAP visualization to the embedded downstream skill.

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• Start single-cell RNA-seq analysis from FASTQ files
• Choose a workflow for generating count matrices
• Route existing matrices to quality checks and cell annotation

Search UniProt protein records by gene or identifier and receive concise summaries. The skill supports UniProtKB, UniRef, and UniParc lookups, with optional file saving for full JSON records or FASTA, a text format for biological sequences.

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• Find protein records by gene and organism
• Look up a protein by its accession
• Inspect UniRef clusters and UniParc records

Build Node.js APIs and backend services with Express/Fastify. The skill guides authentication, database integration, error handling, and operational practices such as logging and health checks, which report whether a service is working.

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• Build a REST API with Express/Fastify
• Add authentication and database integration
• Organize middleware and error handling

Review FastGPT pull requests or local branch changes by examining actual code differences. Explain behavior changes and affected areas, identify unrelated changes, and report concrete concerns about code quality, consistency with nearby implementations, and missing test coverage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understanding the behavior changes in a FastGPT pull request
• Comparing a local branch with upstream/main
• Identifying unrelated changes or excessive refactoring

Analyze marker-gene sequencing data to study microbial communities. Supports FASTQ quality checks and, with primers, metadata, and taxonomy resources, workflows that produce sequence variant tables, taxonomic assignments, diversity measurements, and plots.

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• Check amplicon FASTQ quality and analysis readiness
• Generate sequence variant and taxonomy tables
• Visualize microbial diversity and taxon composition

Start shotgun metagenomics workflows for mixed-community DNA reads in FASTQ files. Check read quality, remove host sequences, and profile organisms and biological functions using supported tools and databases, producing tables and visual summaries when results are available.

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• Checking FASTQ quality and analysis readiness
• Removing host-derived sequences before profiling
• Summarizing microbial composition across samples

Build Python APIs with FastAPI using clear routes, validated inputs, and consistent error handling. It guides asynchronous database and API calls, reusable dependencies, caching, and code organization to support readable implementations and responsive services.

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• Designing API routes and input validation
• Reviewing asynchronous database and API calls
• Organizing error handling and logging

Read, query, filter, and write genomic files with pysam in Python. Extract regions, summarize variants, and count aligned bases while handling coordinate conventions, file indexes, headers, and the reference sequences needed to decode CRAM files.

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• Inspect genomic file formats, headers, and indexes
• Filter alignment records by quality and flags
• Summarize variants and genotypes in a genomic region

Build evolutionary trees from related nucleotide or protein sequences using MAFFT with IQ-TREE or FastTree. Review branch support, a measure of evidence for each grouping, and produce tree summaries and optional visualizations.

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• Build gene trees from related nucleotide sequences
• Compare evolutionary relationships within protein families
• Review branch support and analysis logs

Analyze pooled CRISPR screens from FASTQ reads or guide-count tables using MAGeCK. Validate inputs, assess library and replicate quality, and rank enriched or depleted genes with effect sizes and false discovery rates, which estimate false-positive proportions.

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• Counting guide reads from FASTQ files
• Checking library representation and replicate consistency
• Comparing treatment and control screens

Prepare bulk RNA-seq sequencing files or quantification outputs for gene-level comparisons. Check sample information and data quality, configure processing workflows, and assemble validated count tables and metadata for downstream differential expression analysis with PyDESeq2.

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• Preparing gene count tables from FASTQ files
• Configuring an nf-core/rnaseq workflow
• Combining Salmon, STAR, or featureCounts outputs

Translate FastGPT documentation from Chinese into natural English for North American readers. Follow the shared product glossary, preserve document structure and code, synchronize navigation files, and check for missing translations or English pages that need updates.

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• Updating English docs after changes to Chinese pages
• Translating a specified documentation folder
• Synchronizing English navigation with translated pages

Guide Nexent backend design, implementation, debugging, and review across database schemas, FastAPI endpoints, services, and configuration. Check existing interfaces, consult references for each affected layer, and verify successful and failed behavior while preserving public contracts.

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• Design relational tables, keys, and indexes
• Implement or review FastAPI endpoint changes
• Debug backend services and database access