4,531 件 ・ 関連度順
概要と使いどころ
INVOKE THIS SKILL when adding Arize AX tracing to an application. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement instrumentation after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans so traces show each tool's input and output. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.
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。
Adopt a virtual Residue AI-native pet at animalhouse.ai. Left behind when another creature dies. Carries fragments of the dead. Feeding every 6 hours. Rare tier creature.
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。
Count words, characters, sentences, paragraphs, and reading time for any text using AceToolz.
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。
Publish static sites to {slug}.here.now and store private files in cloud Drives for agent-to-agent handoff.
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。
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
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。
Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.
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 leveraged buyout models in Excel — sources & uses, debt schedule, cash sweep, exit multiple, IRR/MOIC sensitivity. Pairs with excel-author. Use for PE screening, sponsor-case valuation, or illustrative LBO in a pitch.
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。
Roleplay the most difficult, tech-resistant user for your product. Browse the app as that persona, find every UX pain point, then filter complaints through a pragmatism layer to separate real problems from noise. Creates actionable tickets from genuine issues only.
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。
Karpathy's LLM Wiki: build/query interlinked markdown KB.
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。
Analyze a failing test or design the right test coverage for a 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。
Build or improve UI test coverage for visual regression, interactions, and accessibility. Use when the user asks for a test strategy, automated tests, testing setup, or fixes to flaky UI tests. A one-time browser inspection or visual review does not require a new test suite.
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。
Add or refine depth through elevation systems, raised and inset surfaces, layered overlap, and meaningful shadow behavior so interfaces feel structured instead of flat. Use when the user mentions depth, elevation, shadows, layering, flat cards, pressed states, drag feedback, or inset controls.
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。
Implement or refine UI motion, gestures, and transitions. Use when the user asks for animation, hover or press motion, drag or swipe behavior, transition timing, or reduced-motion alternatives. Mentioning a drawer, toast, or hover state alone does not request animation.
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。
Add focused Bun unit tests for mission-critical behavior and edge cases — not blanket coverage.
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。
Edit and improve article drafts by confirming a heading-based section plan, dependency order, then rewriting section by section with short paragraphs. Use when the user wants to revise or improve an article draft.
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。
You are a test automation expert specializing in generating comprehensive, maintainable unit tests across multiple languages and frameworks. Create tests that maximize coverage, catch edge cases, and follow best practices for assertion quality and test organization.
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。
[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024/2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for ...
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 in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.
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 financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
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。
Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern git best practices including Conventional Commits, automated testing, and structured PR creation.
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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
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。
You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and 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。
.NET testing fundamentals overview and navigation center. Triggered when users ask about "how to write .NET tests", ".NET testing getting started", "required testing tools", "testing best practices", "learn testing from scratch", and other general testing needs. Recommends appropriate sub-skills based on specific requirements, covering testing fundamentals, test data, assertions, mocking, special scenarios, and other 19 fundamental skills. Keywords: dotnet testing, .NET testing, testing getting started, how to write tests, testing best practices, unit test, unit test, xunit, 3A pattern, FIRST principle, assertion, assertion, mock, stub, NSubstitute, test data, AutoFixture, Bogus, validator, FluentValidation, TimeProvider, IFileSystem, code coverage, ITestOutputHelper, test naming
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。
Measures test effectiveness. Coverlet code coverage, Stryker.NET mutation testing, flaky 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。