'format' 검색 결과

Use the open-source free `CSharpier` formatter for C# and XML. Use when a .NET repo intentionally wants one opinionated formatter instead of a highly configurable `dotnet format`-driven style model. USE FOR: the repo uses or wants CSharpier; the team prefers an opinionated formatter over many configurable style knobs. DO NOT USE FOR: repos that already standardized on dotnet format as the only formatter. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Format or verify trusted .NET projects with the SDK-provided `dotnet format` command. USE FOR: applying or checking `.editorconfig`-driven whitespace, code-style, or analyzer fixes; adding a `--verify-no-changes` CI gate; diagnosing formatter scope or load failures. DO NOT USE FOR: repositories where another formatter exclusively owns the affected files; analyzer policy with no formatter work; mutating files when the user requested only diagnosis.

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数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Date and time formatting for OneKey applications. Use when displaying dates, timestamps, or formatting time in UI components. Always use OneKey utilities instead of native JS date methods. Triggers on date, time, timestamp, formatDate, formatTime, toLocaleDateString, toLocaleString, dateUtils, locale, format, display date, show time, datetime, calendar.

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AI 에이전트의 도구 구성, 입력과 출력 형식, 오류 복구 절차를 설계합니다. 문맥 정보량을 관리하고 완료율, 재시도 횟수, 성공한 작업당 비용으로 개선을 평가합니다.

• 에이전트 도구와 입력 형식을 설계합니다
• 도구 결과와 다음 행동을 명확히 합니다
• 안전한 재시도와 중단 조건을 정합니다

Excel과 CSV를 만들고 편집하며 흐트러진 표 데이터를 정리하고 형식을 변환합니다. 수식 재계산과 오류 확인, 서식 조정까지 거쳐 스프레드시트 파일로 완성합니다.

• 흐트러진 CSV를 표로 정리
• Excel에 수식과 차트 추가
• 작성용 템플릿 제작

Review messages that display counts and check whether each target language gets the correct plural form. Replace simple number-and-text joins or singular/plural branches with Intl.PluralRules or ICU MessageFormat, which select wording according to language-specific rules.

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• Reviewing count messages in multilingual interfaces
• Adding plural forms for Arabic or Russian
• Checking translation files and i18n settings

Create, edit, and clean spreadsheet files, including Excel workbooks and CSV data. Add formulas, formatting, and charts, preserve existing workbook conventions, and recalculate formulas with error checks before delivering the spreadsheet.

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• Clean messy CSV data into a spreadsheet
• Edit an Excel workbook while preserving its formatting
• Create a financial model with recalculating formulas

Create and review CSV geofeeds, files that publish location information for public IP addresses. Check formatting, country and region codes, and privacy concerns, then review suggested improvements in an HTML report.

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• Create a location feed for public IP addresses
• Check CSV formatting and location codes
• Review privacy concerns in published location data

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Extract fields, filter records, and reshape JSON from API responses, configuration files, or logs using Crush’s built-in jq. Get help writing and debugging transformations, constructing new JSON, and formatting output for other commands.

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• Extract selected fields from API responses
• Filter JSON arrays by conditions
• Reshape configuration data or construct JSON

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

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• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

Add formatting, arithmetic, text processing, and translated labels to json-render specifications. This skill explains ready-made directives, or value-processing rules, how to combine them, and how to define custom rules for application-specific transformations.

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• Format dates, currencies, and percentages
• Calculate totals from prices and quantities
• Shorten text and join lists of tags

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Draft board and committee meeting minutes using the format of past minutes. Organize agendas, supplied materials, attendance, resolutions, and votes into a draft, with placeholders and a checklist for attorney review.

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• Drafting board minutes from an agenda and presentation slides
• Matching committee minutes to an established house format
• Checking attendance, quorum, and voting details before review

Build a material contracts disclosure schedule, a contract list attached to a purchase agreement, from diligence findings. Apply the agreement’s inclusion criteria, match its format, flag missing information, and separately track required consents.

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• Selecting contracts that meet the purchase agreement's definition
• Drafting a disclosure schedule from diligence findings
• Flagging missing information and borderline contracts

Inspect game code, data files, live memory, and graphics captures to understand the internals needed for a mod. Choose tools for the game’s technology, document findings, and validate unknown file formats by decoding and re-encoding them.

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• Finding boss behavior or damage calculations
• Investigating an undocumented game file format
• Locating player coordinates in live memory

Find academic papers through Google Scholar and PubMed, retrieve publication details from identifiers, and build BibTeX reference files. Check author and publication information, detect duplicates, and standardize formatting before using references in a manuscript.

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• Finding papers on a research topic
• Converting DOI lists into BibTeX references
• Checking reference details before manuscript submission

Turn regression results, summary statistics, and simulation output into tables and figures for academic papers. Apply consistent number formatting, labels, and accessible visual styles, then produce files ready to include in LaTeX documents.

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• Format regression results as LaTeX tables
• Compare simulation results in a table
• Plot estimates before and after an intervention

Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.

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• Checking missing values and outliers in experimental tables
• Examining sequence lengths and quality scores in FASTQ files
• Inspecting microscopy image dimensions and metadata

Work through information theory problems about channel capacity, the maximum information a channel can carry. Organize channel models, calculate mutual information, and choose formulas or iterative methods to examine capacity and input distributions.

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• Calculate capacity for standard channel models
• Organize mutual information calculations
• Explore input distributions that maximize capacity

Work through natural transformation problems in category theory by checking component maps and compatibility conditions. The skill outlines strategies for natural isomorphisms, composition, and the Yoneda lemma, with references for expressing and checking work in Lean 4.

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• Check the naturality of a transformation
• Examine whether a transformation is a natural isomorphism
• Study composition in functor categories

Work with DICOM, a standard medical imaging format, in Python. Read and edit scan metadata, extract image pixels, anonymize patient information, convert scans to common image formats, and process compressed files or image series.

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• Extracting and editing DICOM metadata
• Anonymizing scans for research sharing
• Converting DICOM images to PNG or JPEG