「data write」の検索結果

How to write modern Swift well — modeling with value types, Swift 6 data-race safety and approachable concurrency (@concurrent, main-actor-by-default, actors, task groups), protocols and generics (some vs any), API design, performance and ARC, Swift Testing, macros, and the modern language features agents don't know about yet. Use when writing, reviewing, or migrating Swift, or when a concurrency error, a hang, a data race, a retain cycle, or a performance problem needs fixing.

日本語の概要は準備中です。原文の説明を表示しています。

Builds phased data and system migrations using feature flags, dual writes, backfills, and validation. Includes rollback plans and risk mitigation. Use for "data migration", "system migration", "database migration", or "platform migration".

日本語の概要は準備中です。原文の説明を表示しています。

Read and write data to the agency CRM (Google Sheets via webhook or API)

日本語の概要は準備中です。原文の説明を表示しています。

Redis caching patterns, cache-aside, write-through, TTL strategies, and invalidation. Activate on: caching, Redis, cache invalidation, cache-aside, write-through, TTL, CDN cache, stale-while-revalidate. NOT for: CDN/reverse proxy setup (use api-gateway-reverse-proxy-expert), database query optimization (use data-warehouse-optimizer).

日本語の概要は準備中です。原文の説明を表示しています。

MyBatisを使うJavaのデータ保存・取得処理を設計、レビューします。SQLと結果の対応付け、動的SQLの安全性、トランザクションや検索性能を扱います。

• マッパーとXMLの対応付けの設計
• 動的SQLと入力値の安全性確認
• 複数更新のトランザクション確認

Kotlinのコード作成・レビュー・改修を支援します。nullの扱い、不変データ、非同期処理、型安全な設定記述を見直し、読みやすく保守しやすい構成に整えます。

• Kotlinのnull処理を点検したいとき
• 可変データや深い入れ子を整理したいとき
• コルーチンとFlowの非同期処理設計

Djangoのモデル・画面・APIを、テストから実装する手順で検証します。テスト用データの生成、外部サービスの模擬、検証範囲の確認まで扱うスキル。

• Djangoの機能をテストから作りたいとき
• APIの認証や入力検証を確認したいとき
• 再利用できるテストデータの準備

Scrape web pages using Scrapling with anti-bot bypass (like Cloudflare Turnstile), stealth headless browsing, spiders framework, adaptive scraping, and JavaScript rendering. Use when asked to scrape, crawl, or extract data from websites; web_fetch fails; the site has anti-bot protections; write Python code to scrape/crawl; or write spiders.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts, and strategic insights.

日本語の概要は準備中です。原文の説明を表示しています。

Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.

日本語の概要は準備中です。原文の説明を表示しています。

Performs genomic interval overlap, nearest, merge, coverage, complement and subtraction on Polars DataFrames, and reads or writes BED, VCF, BCF, BAM, CRAM, GFF, GTF, FASTA and FASTQ data. Use for coordinate-aware genomic joins, read-depth analysis, lazy bioinformatics I/O, SQL queries or migration from bioframe.

日本語の概要は準備中です。原文の説明を表示しています。

Inspects and automates microscopy data workflows against OMERO.server with omero-py, BlitzGateway, OMERO CLI, tables, annotations, ROIs, rendering, and documented OMERO.web APIs. Use this skill for scoped OMERO inventory, metadata export, import/export planning, or reviewed write workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Reads, inspects, and writes Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Use for low-level FCS metadata and channel inspection, NumPy event extraction, multi-dataset files, table export, and FCS 3.1 creation; use FlowKit for compensation, cytometry transforms, gating, or FlowJo workspaces.

日本語の概要は準備中です。原文の説明を表示しています。

Converts Excel (.xlsx) workbooks into Markdown so their contents can be accurately analyzed, summarized, searched, or extracted from. Use this skill whenever the user shares, references, or asks about a .xlsx file — even if they don't say "convert" or "markdown" explicitly. This includes requests to "read", "summarize", "review", "extract data from", "compare", "chart", or "analyze" a spreadsheet, workbook, budget, data export, or tracker. Always run the bundled conversion script to produce Markdown first; do not attempt to parse .xlsx content directly or write ad-hoc extraction code. Also use this skill for batch requests involving a whole folder of Excel workbooks. IMPORTANT: When the user references a folder or set of documents containing multiple file types (.pdf, .docx, .xlsx), invoke ALL three sibling skills — convert-pdf-to-md, convert-word-to-md, and convert-excel-to-md — so no file type is silently skipped.

日本語の概要は準備中です。原文の説明を表示しています。

Explore a SigNoz page, map the endpoints, states and query params it has, then write its Storybook page story with control-driven msw mocks that reach every state. Use when asked to create, extend or review a Storybook story for a page under frontend/src/pages, to add controls to an existing page story, or to write defineStoryMocks handlers and mock data for a page.

日本語の概要は準備中です。原文の説明を表示しています。

Build one company memory from a SQL database, a support ticket export and a folder of documents, linked into one graph by cognee, then answer questions that need several of those sources together. Use when someone wants to connect their company's database, tickets and docs, or asks who owns, decided or is handling something across them. Runs locally; reads the sources, writes only to cognee's memory.

日本語の概要は準備中です。原文の説明を表示しています。

Finds grant and solicitation opportunities the organization actually qualifies for, runs a hard go/no-go before anyone starts writing, drafts the application or response from past submissions and real program data, and tracks every deadline and reporting obligation that follows an award. Built for nonprofits, government contractors, and education, and it filters daily feeds like SAM.gov or DIBBS down to the handful worth bidding. Runs on Google Drive or M365 with DocuSign and Calendar deepening it, and works from uploaded past submissions plus web research when nothing is connected. Use this whenever a funding or bidding opportunity is in play — including "find grants we qualify for," "should we bid this," "draft the response to this RFP," "we've got 50 solicitations to filter," "what's due on the federal grant," or "help me write the proposal narrative."

日本語の概要は準備中です。原文の説明を表示しています。

Gets Claude talking to a tool nobody built an official connector for. Works out what the system is, checks the Claude connector directory for an existing connector first, and when there isn't one connects the tool through Zapier — never by hand-building against a raw API — with credentials handled safely and approval gates on anything that writes. This is what turns "my ERP isn't supported" into an afternoon. Use this whenever the owner names a tool that isn't connected — including phrasings like "can you connect to my," "make my tools talk to each other," "there's no API for this," "pull data out of my field service software," "I use ServiceTrade and it's not on the list," or "is there any way to get at this data." Reach for it whenever an unsupported system is blocking something else.

日本語の概要は準備中です。原文の説明を表示しています。

Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.

日本語の概要は準備中です。原文の説明を表示しています。

Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user needs to query, filter, reshape, extract, create, or construct JSON data — including API responses, config files, log output, or any structured data — or when helping the user write or debug JSON transformations.

日本語の概要は準備中です。原文の説明を表示しています。

Use the DBX CLI to inspect DBX-managed database connections, explore schemas, run bounded read-only queries, generate DBML or schema documentation, and open tables in DBX Desktop. Use when the user asks to work with a database configured in DBX from a shell-capable AI agent. Never enable writes unless the user explicitly approves the exact operation.

日本語の概要は準備中です。原文の説明を表示しています。

Remove multi-vendor AI provenance marks: invisible Unicode (Layer A), statistical text watermarks via rewrite (Layer B, always offer), and C2PA/EXIF/XMP/container metadata on PNG/JPEG/WebP/SVG/PDF/DOCX/ODT/HTML/MD/TEX. Covers Claude, Gemini/SynthID-class, OpenAI provenance, and open-LLM sampling marks. Use when the user asks to strip watermarks, remove C2PA/Content Credentials, clean AI metadata, remove invisible Unicode, anti-detect clean AI output, or runs /remove-ai-marks (aliases: /remove-claude-marks).

日本語の概要は準備中です。原文の説明を表示しています。

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

日本語の概要は準備中です。原文の説明を表示しています。