'form' 검색 결과

Create and edit Excel workbooks, analyze spreadsheet data, and visualize results. Preserve existing formatting, use formulas that update when inputs change, and recalculate workbooks to identify formula errors before delivering the finished files.

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• Creating sales summaries with formulas
• Updating existing Excel templates
• Analyzing and visualizing Excel or CSV data

Create, edit, analyze, and format Excel, CSV, and TSV files using Python. Preserves existing spreadsheet styles and formulas, checks references, and supports summaries, charts, and readable layouts for new workbooks.

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• Editing Excel files while preserving formulas and formatting
• Filtering and summarizing CSV or TSV data
• Creating workbooks with formulas

Check and format Python code in Megatron-LM using autoformat.sh and its linting tools. Apply project rules for imports, type hints, documentation, naming, and line length when preparing changes for a pull request.

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• Check code style before opening a pull request
• Run automatic formatting fixes
• Sort imports after editing Python files

Authors, validates, and troubleshoots AWS CloudFormation templates. Covers template authoring with secure defaults, local validation with either cfn-lint or cloudformation-validate, cfn-guard security and compliance checks as a recommended default, account-aware CloudFormation service pre-deployment validation, CloudFormation Express mode for faster deployments, and root-cause diagnosis of failed stacks using CloudFormation events and CloudTrail correlation. Also covers author-time template intelligence with the CloudFormation Language Server and published cloudformation-validate libraries.

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Formats and parses locale-aware numbers, decimals, currencies, percentages, dates, durations, measurements, names, lists, byte counts, and URLs with Foundation FormatStyle and ParseableFormatStyle. Use when replacing legacy Formatter subclasses, designing reusable format APIs, using SwiftUI Text(_:format:), correcting availability, or testing user-facing formatted output across locales.

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Performance scorecard design, post-matter feedback collection, QBR preparation, and firm comparison for in-house legal ops teams evaluating outside counsel. Design a scorecard framework calibrated to your team's maturity level. Produce post-matter feedback forms for supervising attorneys to complete at matter close. Prepare a QBR pack with agenda, data summary, and talking points for structured business reviews. Produce a side-by-side firm comparison for panel decision-making. Trigger on: 'design a performance scorecard', 'how do we evaluate our firms', 'build us a scorecard', 'post-matter feedback form', 'feedback at matter close', 'collect feedback on the firm', 'QBR', 'quarterly business review', 'prep for the firm review', 'what should I raise with the partner', 'compare the firms', 'which firm is performing better', 'rank our panel', 'firm performance data', 'scorecard framework', 'evaluate outside counsel', 'annual firm review', 'how are our firms performing'.

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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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Musical form and structural analysis covering binary, ternary, rondo, sonata, theme-and-variations, and popular song forms. Covers formal archetypes (AB, ABA, ABACA, sonata-allegro), 12-bar blues, arch form, minimalist process forms, strophic and through-composed designs, and formal analysis methodology for both score study and listening. Use when determining the structural plan of a piece, analyzing sectional relationships, or understanding how form shapes musical narrative.

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Tested output format specifications for Claude prompts. Use when the user wants a specific deliverable structure — retrieving, reviewing, customizing, or building output formatting. Trigger on: "give me the Executive Brief format," "show me the output template for," "I need the Decision Matrix structure," "show me available output formats," "build a custom output format for." Provides 10 ready-to-use structures (executive briefs, technical designs, research summaries, implementation plans, decision matrices, competitive analyses, post-mortems, stakeholder updates, strategic memos, process documentation) plus a custom template, with per-section length guidance and format constraints tested against Claude formatting defaults. If unsure which format fits, use rootnode-block-selection first if available. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or choosing reasoning/identity approaches (use rootnode-block-selection if available).

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TanStack Form best practices for type-safe form management, validation, field composition, and submission handling in React. Use when building forms with complex validation, integrating schema libraries (Zod/Valibot/ArkType), composing reusable form components, managing array/dynamic fields, or integrating with meta-frameworks (TanStack Start, Next.js, Remix).

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Use when building or reviewing substantial forms with React Hook Form, Zod schemas, typed validation, dynamic fields, controlled third-party inputs, file upload, multi-step flows, dependent validation, or form performance. Do not use for trivial 1-3 field forms without validation; Chinese triggers include form, form validation, dynamic fields.

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Cross-platform desktop development for macOS and Windows -- platform abstractions, UI convention differences, and multi-platform CI/CD. Activate on: cross-platform app, macOS and Windows, platform differences, DMG installer, MSI installer, NSIS installer, Cmd vs Ctrl, platform abstractions, menu bar differences, native conventions. NOT for: mobile apps, web-only apps, Tauri-specific internals (use rust-tauri-development).

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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. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs 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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Generate a batch of social media video prompts for multiple platforms from a single input. Use this skill whenever the user wants to create videos for multiple social platforms at once, repurpose one video across platforms, or create a social media video campaign. Trigger on phrases like 'social media videos', 'videos for all platforms', 'Instagram and TikTok video', 'social campaign', 'batch of social videos', 'repurpose this for social', 'video for every platform', 'social clips', 'create Reels and Shorts', 'multi-platform video', or any request involving creating short-form video content for multiple social media platforms using Ozor.

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Cross-platform desktop development for macOS and Windows -- platform abstractions, UI convention differences, and multi-platform CI/CD. Activate on: cross-platform app, macOS and Windows, platform differences, DMG installer, MSI installer, NSIS installer, Cmd vs Ctrl, platform abstractions, menu bar differences, native conventions. NOT for: mobile apps, web-only apps, Tauri-specific internals (use rust-tauri-development).

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앱과 분산 시스템의 CPU, 메모리 등 자원 사용을 분석해 처리 지연 원인을 찾습니다. 자원 배분, 부하 분산, 캐시와 알고리즘을 개선하고 변경 전후 성능을 비교합니다.

• 앱의 처리 속도가 느린 원인을 조사합니다
• CPU와 메모리 배분을 검토합니다
• 노드 사이의 부하 분산을 개선합니다

Review CSS animations for properties that cause costly layout updates, then suggest changes using transform and opacity. Check how effects look across screen sizes and interaction states, including support for users who prefer reduced motion.

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• Reviewing animations that stutter
• Replacing position and size animations
• Checking reduced motion support

Create, edit, and clean spreadsheet files, including Excel workbooks and CSV data. Build formulas and formatting with openpyxl, a Python library, then recalculate and validate workbooks to check for formula errors and file problems.

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• Create a workbook with formulas and formatting
• Edit an existing spreadsheet
• Restructure messy CSV or TSV data

Turn business requirements or meeting transcripts into a Power Platform solution design. The skill recommends components, explains how people and data move through the process, and can provide a Mermaid.js diagram to illustrate the proposed architecture.

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• Turning discovery meeting transcripts into a solution design
• Choosing components for employee onboarding
• Designing a Power Apps solution with SQL and approvals

Design and review infrastructure managed with Terraform or OpenTofu. Get guidance on reusable modules, testing choices, automated deployment workflows, and security checks, with approaches suited to different software versions and the cost of running tests.

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• Designing reusable infrastructure modules
• Choosing infrastructure testing methods
• Setting up automated infrastructure deployment

Extract text and tables from PDFs, fill form fields with supplied data, and merge documents. Includes a pdfplumber text extraction example and scripts for inspecting form fields and extracting text.

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• Extracting text from PDF pages
• Retrieving tables from PDF documents
• Filling PDF forms with supplied data

Evaluate threat intelligence platforms, which collect and organize cyber threat information. Define requirements, compare integrations and operating costs, plan practical trials, and use weighted scoring to select a platform and outline its rollout.

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• Preparing requirements for TIP procurement
• Comparing platforms before a TIP migration
• Checking whether an existing TIP meets team needs

Configure AWS Verified Access for VPN-free access to internal applications. Combine user identity and device security checks, write Cedar access policies, and use Terraform and AWS RAM to deploy and share access controls across AWS accounts.

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• Replacing VPN access to internal applications
• Writing policies based on users and device security
• Deploying Verified Access with Terraform

Improve resume layouts with consistent fonts, spacing, section order, and clear headings. The skill recommends formatting that supports quick reading by recruiters and text parsing by applicant tracking systems, with guidance on file formats.

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• Cleaning up a cluttered resume
• Reviewing formatting for applicant tracking systems
• Standardizing fonts, dates, and headings