'fast' 검색 결과

Fix Fastly Compute Rust crate `cargo test` failures with "ld: symbol(s) not found" errors for Fastly SDK symbols like `_version_set`, `_body_new`, `_req_send` on native host builds. Use when: (1) `cargo test` on a Fastly Compute crate fails at link time with errors mentioning libfastly and symbols with names like `fastly::http::response::handle::...`, (2) You added a `#[cfg(test)]` module to a file in a Fastly Compute binary crate and the tests never seem to run or fail to link, (3) Existing `#[cfg(test)]` modules in src/main.rs or its sibling modules appear dead despite being present in the source tree, (4) `cargo test --lib` works but `cargo test` fails.

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Automate lab data tasks in Benchling using its Python SDK and REST API. Import and export sequences, inspect sample inventory, and work with electronic lab notebooks and workflow tasks while checking schemas, permissions, and operation results.

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• Importing DNA sequences from FASTA files
• Auditing sample inventory at a storage location
• Exporting sequences for a specific schema to CSV

Process DNA, RNA, and protein sequences with Biopython, convert biological files, and retrieve NCBI records. Supports batch workflows for sequence comparison, searches for similar sequences, protein structure analysis, and evolutionary trees.

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• Convert GenBank files to FASTA
• Calculate sequence length and GC content in batches
• Retrieve sequences and publication records from NCBI

Write unit tests for FastGPT code, covering normal behavior, edge cases, and errors. It guides test placement, limits mocks that replace real dependencies, and checks TypeScript errors and coverage, the share of code exercised by tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding unit tests for individual functions
• Testing edge cases and error handling
• Testing functions that make API requests

Keep FastGPT build and test workflows aligned across GitHub Actions and Forgejo Actions. Adapt registry destinations, credentials, proxy settings, and platform-specific steps so the corresponding workflows support the internal network environment.

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• Synchronizing CI changes across GitHub and Forgejo
• Adapting image builds for an internal network
• Checking pull requests for missing workflow updates

Develop and review FastGPT data migration tasks for automatic system upgrades. Define safe reruns, resume points, progress reporting, and failure handling, while checking task order and whether upgrades must block application startup.

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• Adding a data migration to an automatic upgrade
• Designing batch processing that resumes after interruption
• Reviewing startup blocking and failure policies

Strengthen existing tests with fast-check by checking properties across generated inputs. Find bugs that example tests miss and reduce failing inputs to small, reproducible cases. Property testing is available; the other listed workflows remain planned.

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• Checking parser behavior across generated inputs
• Testing serialization and deserialization round trips
• Finding pure functions covered only by example tests

Plan or run bulk RNA-seq processing from sequencing reads to count matrices, tables of expression counts across samples. Check sample metadata, reference consistency, read mapping, and quantification quality, and produce summaries for downstream analysis.

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• Generate count matrices from FASTQ files
• Check reference files and sample metadata
• Review mapping and quantification quality

Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.

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• Generate expression counts from FASTQ files
• Compare gene expression between sample groups
• Plan quality checks and statistical analysis together

Organize ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag inputs and route them to suitable analysis workflows. Covers initial quality checks and preparation for finding peaks, genomic regions with concentrated signal, and comparing them across samples.

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• Choose an analysis workflow for an epigenomics assay
• Check FASTQ inputs and sample-sheet readiness
• Organize control and biological replicate information

Use when building, extending, or debugging FastMCP v4 (or v3) Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, testing, and v3→v4 migration. Grounded in official FastMCP v4 docs plus locally-verified v3 gotchas — zero speculation.

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FastAPI-specific delta on top of python-foundation:sqlalchemy-patterns: async engine and AsyncSession lifecycle, get_db dependency injection, async-safe lazy loading rules, async Alembic env.py wiring. Used by fastapi-architect (model definitions) and alembic-specialist (column type finalization and migration generation). Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Manage async database sessions with AsyncSession and async_sessionmaker. - Inject sessions into routes via the get_db() dependency. - Apply async-safe lazy loading (lazy="selectin" or "raise"; never sync lazy loads). - Integrate async Alembic env.py for migration autogeneration. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - FastAPI routing and Pydantic schemas — see fastapi-plugin:fastapi-conventions. - Alembic migration execution (that's alembic-specialist's job) — this skill covers definitions.

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Fastify (Node.js 高速 HTTP サーバーフレームワーク, v5) の API リファレンス。 `fastify.register`, encapsulation, `fastify-plugin`, `decorateRequest` / `decorateReply`, `onRequest` / `preHandler` / `onSend` フック, JSON Schema + Ajv, Type Providers (TypeBox / Zod), `fastify.inject`, `FST_ERR_*`, find-my-way ルーティング, fastify-cli, v4→v5 移行。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bring a Remotion branch up to date with the official main branch while preserving local changes and published history. Handle temporary change storage, merges, conflicts, validation, and a normal push when requested.

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• Merge the latest main into a feature branch
• Fast-forward a local main branch
• Update a pull request branch while preserving published history

Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.

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• Validate paired FASTQ files and sample metadata
• Remove primers and denoise reads with DADA2
• Assign taxonomy using a compatible classifier

Work with genomic regions in Polars tables to find overlaps, nearby regions, covered bases, and gaps. Read and write bioinformatics files, query data with SQL, and check coordinate conventions when joining datasets or migrating from bioframe.

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• Finding variants that overlap genomic regions
• Merging regions and measuring covered bases
• Summarizing sequencing read depth

Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.

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• Calculate and compare microbiome diversity
• Align DNA, RNA, or protein sequences
• Build and compare evolutionary trees

Review FastGPT pull requests in stages, starting with the intended change and key execution paths. Check security, performance, tests, and compatibility, then organize findings by severity into a review report and line-level comments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a pull request before merging
• Reasoning through normal, edge, and error cases
• Assessing API and database compatibility risks

Translate selected FastGPT interface text files from Simplified Chinese into every supported target locale. Check product terminology and interface context, preserve JSON structure and placeholders, and validate the translated files for consistency.

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• Translating a selected interface text file into all supported locales
• Updating several translation files together
• Translating every namespace from completed Chinese source text

Turn a written app specification into a runnable starter project using the requested technology stack. Generate implemented features, tests, configuration files, and setup instructions, then check the project for common omissions.

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• Turning a requirements document into a starter project
• Creating a task API with FastAPI and SQLite
• Building a recipe-sharing website with Next.js

Choose a web application stack, generate a starter project, and assess existing code quality. Uses team size, release frequency, audience, and budget to guide recommendations, with reports covering security, complexity, dependencies, and prioritized improvements.

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• Generating a starter project for a web application
• Choosing a technology stack within team and budget constraints
• Reviewing code security and complexity

Writes, reviews, and debugs property-based tests, which generate inputs to check general rules. Helps test conversions, parsers, normalization, and state invariants, assess whether existing assertions are meaningful, and distinguish faulty rules from implementation bugs.

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• Testing encode/decode round trips across generated inputs
• Checking normalization and sorting properties
• Reviewing whether existing property tests assert meaningful rules

Plan or run ATAC-seq analysis to identify accessible DNA regions. Check sequencing quality, align reads, detect peaks, combine evidence across replicate samples, and prepare comparisons between conditions with quality summaries and visualization tracks.

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• Check the quality of ATAC-seq samples
• Detect accessible DNA regions from FASTQ or BAM files
• Build consensus peaks across replicate samples

Predict structures of protein, nucleic acid, and small-molecule complexes with ESMFold2, using single sequences or aligned related sequences. The skill also covers ESMC models for numerical sequence representations, mutation scoring, and predictions of contacts between residues.

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• Predicting complex structures from single sequences
• Assessing designed binders with ESMFold2-Fast
• Predicting structures using aligned related sequences