3,780 件 ・ 関連度順
概要と使いどころ
Formats commit messages using the Conventional Commits specification. Use when creating commits, writing commit messages, or when the user mentions commits, git commits, or commit messages. Ensures commits follow the standard format for automated tooling, changelog generation, and semantic versioning.
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 creating or updating component fixtures for screenshot testing, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, and common pitfalls.
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。
Containerize an ASP.NET .NET Framework project by creating Dockerfile and .dockerfile files customized for the project.
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。
Technology-agnostic blueprint generator for creating comprehensive copilot-instructions.md files that guide GitHub Copilot to produce code consistent with project standards, architecture patterns, and exact technology versions by analyzing existing codebase patterns and avoiding assumptions.
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。
Prompt for creating an Epic Product Requirements Document (PRD) for a new epic. This PRD will be used as input for generating a technical architecture specification.
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。
Prompt for creating the high-level technical architecture for an Epic, based on a Product Requirements Document.
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。
Prompt for creating Product Requirements Documents (PRDs) for new features, based on an Epic.
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。
Prompt for creating detailed feature implementation plans, following Epoch monorepo structure.
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。
INVOKE THIS SKILL when creating, managing, or using annotation configs on Arize (categorical, continuous, freeform), or applying human annotations to project spans via the Python SDK. Configs are the label schema for human feedback on spans and other surfaces in the Arize UI. Triggers: annotation config, label schema, human feedback schema, bulk annotate spans, update_annotations.
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。
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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。
Configures AI agents (GitHub Copilot, Claude Code, Cursor, Codex) to understand a codebase and self-improve as the project evolves. Use when setting up a new project for AI-assisted development, onboarding AI agents to an existing repo, creating AGENTS.md, configuring MCP servers for code navigation, packaging project workflows as agent skills, or establishing self-improvement feedback loops. Covers AGENTS.md authoring, skill packaging, MCP configuration, context management, and living-documentation strategies.
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。
INVOKE THIS SKILL for LLM-as-judge evaluation workflows on Arize: creating/updating evaluators, running evaluations on spans or experiments, tasks, trigger-run, column mapping, and continuous monitoring. Use when the user says: create an evaluator, LLM judge, hallucination/faithfulness/correctness/relevance, run eval, score my spans or experiment, ax tasks, trigger-run, trigger eval, column mapping, continuous monitoring, query filter for evals, evaluator version, or improve an evaluator prompt.
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。
INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.
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 Azure resources used in the app (IaC files and/or resources in a target rg) and optimize costs - creating GitHub issues for identified optimizations.
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。
INVOKE THIS SKILL when creating, running, or analyzing Arize experiments. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI.
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。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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 ACE-Step API to generate music, edit songs, and remix music. Supports text-to-music, lyrics generation, audio continuation, and audio repainting. Use this skill when users mention generating music, creating songs, music production, remix, or audio continuation.
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。
SiYuan Note API for searching, reading, creating, and managing blocks and documents in a self-hosted knowledge base via curl.
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。
Professional assistant for 3D printing, laser engraving, and 3D modeling workflows. Supports generating visual references for sculpting (Forger/ZBrush), creating 3D models via Meshy AI, and optimizing FDM/Resin print settings. Use when designing, fabricating, or troubleshooting physical maker projects.
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, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing a FastMCP server for Claude Code, Cursor, or HTTP deployment.
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。
Develop Burp Suite extensions in Java (Montoya API) or Python (Jython). Supports creating HTTP listeners, scanner checks, intruder payloads, UI tabs, and context menus. Includes project templates, multi-platform setup guides, and 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。
Meta-skill for creating Agent Forge skills under .claude/skills/ with SKILL.md, optional references/ and scripts/, and Bun scaffolds. Use when the user wants to add or author a skill, scaffold a new skill folder, or align skill docs with harness conventions (JSON script output, Beads for tasks).
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。
Scaffold course exercise directories with numbered sections, exercise variants, required readmes, and repo-specific validation. Use when creating exercise stubs, adding a course section, or safely renaming exercises.
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 Beads issue priority (P0–P4, numeric, or named) from urgency, impact, and risk when creating or triaging work.
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。