6,431 件 ・ 関連度順
概要と使いどころ
Evaluate and improve AI-generated output with explicit rubrics, reflection loops, and stop conditions. Use when building self-critique workflows, evaluator-optimizer pipelines, or acceptance gates for code, docs, analysis, or plans.
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。
Choose and compare cloud design patterns for distributed systems. Use when reviewing architecture, selecting workload patterns, or mapping reliability, performance, messaging, security, and migration concerns to concrete design options.
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 for explicit undergraduate data-science teaching, assignment guidance, concept clarification, method selection, or output interpretation. Do not activate merely because a task contains data or Python 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。
two-stage review (spec compliance first, then code quality), refactoring, and quality improvement. Use when reviewing code, eliminating code smells, reducing technical debt, refactoring methods, running self-critique loops, or improving maintainability and readability.
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。
Expert en négociation de contrats (terms, SLAs, renewal, upsell, legal review, redlines)
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。
Expert en véhicules autonomes (perception, planning, ROS, simulation, safety)
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。
Expert en optimisation des coûts d'agents IA (sélection modèle, caching, compression prompts, budgeting tokens)
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。
Migrate a .NET 9 project or solution to .NET 10 and resolve all breaking changes. USE FOR: upgrading TargetFramework from net9.0 to net10.0, fixing build errors after updating the .NET 10 SDK, resolving source and behavioral changes in .NET 10 / C# 14 / ASP.NET Core 10 / EF Core 10, updating Dockerfiles for Debian-to-Ubuntu base images, resolving obsoletion warnings (SYSLIB0058-SYSLIB0062), adapting to SDK/NuGet changes (NU1510, PrunePackageReference), migrating System.Linq.Async to built-in AsyncEnumerable, fixing OpenApi v2 API changes, cryptography renames, and C# 14 compiler changes (field keyword, extension keyword, span overloads). DO NOT USE FOR: .NET Framework migrations, upgrading from .NET 8 or earlier (use migrate-dotnet8-to-dotnet9 first), greenfield .NET 10 projects, or cosmetic modernization. LOADS REFERENCES: csharp-compiler, core-libraries, sdk-msbuild (always); aspnet-core, efcore, cryptography, extensions-hosting, serialization-networking, winforms-wpf, containers-interop (selective).
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。
Guide for interpreting ResolveProjectReferences time in MSBuild performance summaries. Only activate in MSBuild/.NET build context. Activate when ResolveProjectReferences appears as the most expensive target and developers are trying to optimize it directly. Explains that the reported time includes wait time for dependent project builds and is misleading. Guides users to focus on task self-time instead. Do not activate for general build performance -- use build-perf-diagnostics 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。
Guide for optimizing MSBuild incremental builds. Only activate in MSBuild/.NET build context. USE FOR: builds slower than expected on subsequent runs, 'nothing changed but it rebuilds anyway', diagnosing why targets re-execute unnecessarily, fixing broken no-op builds. Covers 8 common causes: missing Inputs/Outputs on custom targets, volatile properties in output paths (timestamps/GUIDs), file writes outside tracked Outputs, missing FileWrites registration, glob changes, Visual Studio Fast Up-to-Date Check (FUTDC) issues. Key diagnostic: look for 'Building target completely' vs 'Skipping target' in binlog. DO NOT USE FOR: first-time build slowness (use build-perf-baseline), parallelism issues (use build-parallelism), evaluation-phase slowness (use eval-performance), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, search, target details); falls back to dotnet build /bl, binlog replay with diagnostic verbosity.
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。
Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
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。
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
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。
Elite mobile app image-generation skill for creating premium, app-native screen concepts and flows. Designed for iOS, Android, and cross-platform mobile products. Prioritizes clean hierarchy, comfortably readable text, strong multi-screen consistency, controlled color palettes, non-generic creative direction, textured surfaces, image-led composition, tasteful custom iconography, and clean phone mockup framing. By default, screens should be shown inside a subtle premium iPhone or similar phone mockup with a visible frame, while the main focus stays on the app content itself. This skill generates images only. It does not write 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。
Helps an AI assistant write, structure, and edit articles in the encyclopedic style of Wikipedia — neutral tone, lead section, summary style, inline citations, no peacock/weasel/persuasive language, and the stub→FA quality ladder. Also covers Simple English Wikipedia rules. MADE BY SQERSTERS
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。
Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.
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。
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database 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。
Use when controlling NVIDIA Broadcast through MCP to apply effects, process local media, select devices, or change camera resolution, or when Broadcast is missing or too old to expose the gateway and the user wants it installed; not for Broadcast app settings outside the MCP gateway.
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。
Diagnose your taste gaps across six skill areas. Know what to practice. Use when starting a taste development program, checking progress, or identifying specific areas to improve.
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。
How to use windows-computer-use-mcp effectively — tool selection, focus rules, safety, and mission design.
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。
Consolidate the five tdmcp surveyor reports into one prioritized FEATURE_BACKLOG.md — dedupe cross-surface overlaps, reconcile every candidate against the roadmap (NEW vs planned vs extension), rank by a selectable impact×effort weighting profile into P0/P1/P2 (high-confidence first), and emit per-surface tables + a recommended-next shortlist that hands straight to the build pipeline. Use when a td-synthesizer agent is consolidating surveys during the feature-discovery harness.
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。
Change the agent harness itself (AOS, AO or CBS hooks, gates, memory, permissions, delegation, plugins, bootstrap) the safe way: look up the matching harness pattern, design against its gotchas, build with /startcycle and a live trail, check every harness, open a PR after GO. Use for "change a hook", "fix the go-gate", "harness work", "plugin change".
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。
Bring a new machine to a verified AOS installation: read-only doctor baseline, install after GO, MCP servers wired per harness with the configs backed up first, memB and OpenWiki checks, and a second doctor run for the delta. Use for "set up this machine", "onboard a new computer", "install AOS on this laptop".
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 development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
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。
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
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。