2.3万 件 ・ 関連度順
概要と使いどころ
Maxim Banner Design Intelligence — digital banner creation, ad creative, social media banners, hero sections, display advertising, promotional graphics. Activates pre-attentive attribute theory and pattern interruption science for every banner. Powered by Maxim behavioral science layer.
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。
Maxim UI Styling Intelligence — shadcn/ui components, Tailwind CSS, CSS architecture, responsive design, dark mode, accessibility-compliant styling (WCAG 2.1 AA), animation and motion design, theming systems. Activate for component building, layout implementation, theme customization, dark mode, CSS architecture decisions, UI polish. Powered by Maxim behavioral science + Fitts' Law + Color Psychology.
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。
Apply a named behavioral science framework to any task — persuasion, nudge design, habit formation, conversion, influence, negotiation, cognitive bias, choice architecture, decision framing. The framework backbone every other Maxim skill loops into for psychological validation.
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。
Maxim Master Design Intelligence — full-spectrum UI/UX orchestration. Activates all design sub-domain skills in parallel. Use when a task spans multiple design domains simultaneously: product design strategy, interaction design, information architecture, usability heuristics, design critique, cross-platform consistency, design leadership. The master conductor of all Maxim design agents.
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。
Maxim Brand Intelligence — brand identity, voice architecture, visual identity systems, messaging frameworks, brand consistency enforcement. Activate for brand strategy, brand audits, brand guidelines, tone of voice, asset compliance, color palette, typography specs. Powered by Maxim behavioral science layer.
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。
Cross-session memory — store, recall and query project knowledge across sessions. Detects whatever persistent memory is available (MemPalace, claude-mem, or file-only fallback) and uses the best one present.
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。
Generate complete Power Platform custom connector with MCP integration for Copilot Studio - includes schema generation, troubleshooting, and validation
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。
Python JSON parsing best practices covering performance optimization (orjson/msgspec), handling large files (streaming/JSONL), security (injection prevention), and advanced querying (JSONPath/JMESPath).
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。
PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.
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。
Plain-English translation layer for non-technical Copilot CLI users. Translates every approval prompt, error message, and technical output into clear, jargon-free English with color-coded risk indicators.
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。
Comprehensive Power BI data model design review prompt for evaluating model architecture, relationships, and optimization opportunities.
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。
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
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 an integration testing plan for .NET data access artifacts during Oracle-to-PostgreSQL database migrations. Analyzes a single project to identify repositories, DAOs, and service layers that interact with the database, then produces a structured testing plan. Use when planning integration test coverage for a migrated project, identifying which data access methods need tests, or preparing for Oracle-to-PostgreSQL migration validation.
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。
A comprehensive guide for GitHub Copilot to craft immersive, high-performance web experiences with advanced motion, typography, and architectural craftsmanship.
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。
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, inspect datasets, and query the GraphQL API. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.
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。
Comprehensive technology-agnostic prompt generator for documenting end-to-end application workflows. Automatically detects project architecture patterns, technology stacks, and data flow patterns to generate detailed implementation blueprints covering entry points, service layers, data access, error handling, and testing approaches across multiple technologies including .NET, Java/Spring, React, and microservices architectures.
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。
Generate breadboard circuit mockups and visual diagrams using HTML5 Canvas drawing techniques. Use when asked to create circuit layouts, visualize electronic component placements, draw breadboard diagrams, mockup 6502 builds, generate retro computer schematics, or design vintage electronics projects. Supports 555 timers, W65C02S microprocessors, 28C256 EEPROMs, W65C22 VIA chips, 7400-series logic gates, LEDs, resistors, capacitors, switches, buttons, crystals, and wires.
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。
Discovers and integrates third-party APIs using the context-matic MCP server. Uses `fetch_api` to find available API SDKs, `ask` for integration guidance, `model_search` and `endpoint_search` for SDK details. Use when the user asks to integrate a third-party API, add an API client, implement features with an external API, or work with any third-party API or SDK.
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。
Bulk-migrate metadata to GitHub issue fields from two sources: repo labels (e.g. priority labels to a Priority field) and Project V2 fields. Use when users say "migrate my labels to issue fields", "migrate project fields to issue fields", "convert labels to issue fields", "copy project field values to issue fields", or ask about adopting issue fields. Issue fields are org-level typed metadata (single select, text, number, date) that replace label-based workarounds with structured, searchable, cross-repo fields.
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。
Interface for MCP (Model Context Protocol) servers via CLI. Use when you need to interact with external tools, APIs, or data sources through MCP servers, list available MCP servers/tools, or call MCP tools from 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。
Build self-serve acquisition and expansion motions. Use when deciding PLG vs sales-led, optimizing activation, driving freemium conversion, building growth equations, or recognizing when product complexity demands human touch. Includes the parallel test where sales-led won 10x on revenue.
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。
Import existing Azure resources into Terraform using Azure CLI discovery and Azure Verified Modules (AVM). Use when asked to reverse-engineer live Azure infrastructure, generate Infrastructure as Code from existing subscriptions/resource groups/resource IDs, map dependencies, derive exact import addresses from downloaded module source, prevent configuration drift, and produce AVM-based Terraform files ready for validation and planning across any Azure resource type.
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。
Get best practices for JUnit 5 unit testing, including data-driven tests
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。