コーディング規約、フレームワーク、リファクタリング、コードレビュー（1.4万 件）
概要と使いどころ
Visual whiteboard collaboration for Copilot CLI. Creates an interactive whiteboard that opens in your browser — draw, sketch, add sticky notes, then share everything back with Copilot. Copilot sees your drawings and text, and responds with analysis, suggestions, and ideas. Use in Copilot CLI when an idea is easier to sketch than to describe in text.
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。
Interactive onboarding tour for the context-matic MCP server. Walks the user through what the server does, shows all available APIs, lets them pick one to explore, explains it in their project language, demonstrates model_search and endpoint_search live, and ends with a menu of things the user can ask the agent to do. USE FOR: first-time setup; "what can this MCP do?"; "show me the available APIs"; "onboard me"; "how do I use the context-matic server"; "give me a tour". DO NOT USE FOR: actually integrating an API end-to-end (use integrate-context-matic instead).
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 working with Entity Framework Core — modeling entities and relationships, migrations, LINQ query performance, change tracking, N+1 problems, and connection and transaction handling.
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。
Runs an 11-step data cleaning and variable screening pipeline for pre-loan credit risk models: sample analysis, OOS split, missing-rate, IV, PSI, and correlation filters. Use when preparing raw credit data for scorecard or default-prediction modeling.
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。
Makes the agent list the files it needs to see before answering a question about the codebase, instead of guessing. Use when a question depends on code the agent has not read yet, or when earlier answers were confidently wrong about the code.
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 ETL pipeline metadata (Informatica IICS, PowerCenter XML, and similar) into clean dbt models, SQL, and YAML rather than a mechanical one-to-one translation. Use when migrating legacy ETL mappings to dbt.
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。
Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file. Use when designing a new Azure Cosmos DB for NoSQL data model or reviewing partition keys and document shapes.
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。
Creates structured bug reports for defects found during Oracle-to-PostgreSQL migration. Use when documenting behavioral differences between Oracle and PostgreSQL as actionable bug reports with severity, root cause, and remediation steps.
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。
Skill for using the command-line tool pdftk (PDFtk Server) for working with PDF files. Use when asked to merge PDFs, split PDFs, rotate pages, encrypt or decrypt PDFs, fill PDF forms, apply watermarks, stamp overlays, extract metadata, burst documents into pages, repair corrupted PDFs, attach or extract files, or perform any PDF manipulation from the command line.
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。
Explains and designs Microsoft Fabric Lakehouse solutions: Delta tables, the SQL analytics endpoint, shortcuts, and AI functions. Use when building, optimizing, or documenting a Fabric Lakehouse.
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 PostgreSQL logical replication slots retain WAL, become orphaned or invalid after switchover, or must be proven synchronized and usable before failover.
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。
Migrates Oracle PL/SQL stored procedures to PostgreSQL PL/pgSQL. Translates Oracle-specific syntax, preserves method signatures and type-anchored parameters, leverages orafce where appropriate, and applies COLLATE "C" for Oracle-compatible text sorting. Use when converting Oracle stored procedures or functions to PostgreSQL equivalents during a database 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。
Interactive onboarding that learns your communication style, audiences, and data sources to configure personalized status briefings. Paste in examples of updates you already write, answer a few questions, and roundup calibrates itself to your workflow. Use the first time Roundup is used, or to recalibrate its style and sources.
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 working code feels heavier than it needs to be — deeply nested logic, long functions, unclear names, duplication after a merge, or a review that flagged complexity. Reduces complexity while preserving behavior exactly. Not for code you do not yet understand, or code you are about to rewrite.
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。
Reviews and refactors code according to the project's own instruction and guideline files, keeping behavior intact. Use when the user asks for a cleanup pass that must follow repository conventions (for example .github/instructions) rather than generic best practices.
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。
Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review. Use when AI-generated text with factual claims must be checked before it is published or relied on.
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 refining a vague idea through divergent expansion, convergent focus, and assumption stress-testing before planning or implementation.
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。
Document analysis with inline source screenshots. When you ask Copilot to analyze a document, Eyeball generates a Word doc where every factual claim includes a highlighted screenshot from the source material so you can verify it with your own eyes.
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。
Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.
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。
Reverse-engineers and debugs macOS and iOS binaries with Hopper Disassembler and LLDB: ObjC/Swift symbols, dyld loading, framework internals, and crash investigation from disassembly. Use when private API behavior or a runtime crash needs to be understood at the binary level.
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。
Delivers changes incrementally. Use when implementing any feature or change that touches more than one file. Use when you're about to write a large amount of code at once, or when a task feels too big to land in one step.
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。
Profiles macOS and iOS apps with Instruments and xctrace: choosing templates such as Time Profiler, launching binaries with arguments, recording traces, and exporting results. Use when a Mac or iOS app needs CPU, memory, or responsiveness profiling.
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。
Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.
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 writing, editing, or reviewing code, READMEs, docs, commits, changelogs, or any repo content. Pre-2020 human style — terse, opinionated, restrained, with project voice. Blocks the LLM tics (emoji headers, "leverage", "robust", "seamless", "delve", defensive bloat, type-hint overkill, marketing prose, "Whether you're X or Y", verbose error messages) catalogued from cpython, requests, flask, git, ripgrep, express, kubernetes, serde, and the Go stdlib. Triggers on "anti-ai", "human-style", "no slop", "write it like a human", or any time code/docs should look hand-written.
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。