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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AD CS, certificate templates, enrollment rights, EKUs, SAN controls, PKINIT, certificate mapping, and cert-based privilege paths. Use when the user asks about ESC-style abuse, certificate templates, enrollment agents, EKUs, SAN or subject controls, smartcard or PKINIT logon, CA policy, or how an issued cert turns into accepted privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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 authorized reverse engineering of browser extensions (Chrome/Firefox) including manifest analysis, background workers, and extension-based credential or traffic logic recovery.

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。

Systematic code refactoring based on Martin Fowler's methodology. Use when users ask to refactor code, improve code structure, reduce technical debt, clean up legacy code, eliminate code smells, or improve code maintainability. This skill guides through a phased approach with research, planning, and safe incremental 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。

Design a coherent assessment-and-practice lesson using zone-of-proximal-development principles: independent diagnosis, targeted review, supported practice, fresh independent checks, and an evidence-based next step. Use for 习题课（最近发展区）, 最近发展区, 测验习题课, diagnostic review lessons, or guided-to-independent practice. Can also run one practice cycle in chat; do not turn a request for a single exercise or theory explanation into a full lesson.

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。

Evidence-based creation of a reusable personal course-making Skill from the user's own classroom and chat history. Use when the user asks to summarize, analyze, or learn from their past course-making records/history and create a personal, exclusive, or reusable Skill, such as “总结我的做课记录，给我做个专属 Skill” or “从我过去的课程和对话里提炼一个个人技能”.

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 a Playwright test based on a scenario using Playwright MCP

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 ASCII art diagrams using PlantUML text mode. Use when user asks to create ASCII diagrams, text-based diagrams, terminal-friendly diagrams, or mentions plantuml ascii, text diagram, ascii art diagram. Supports: Converting PlantUML diagrams to ASCII art, Creating sequence diagrams, class diagrams, flowcharts in ASCII format, Generating Unicode-enhanced ASCII art with -utxt flag

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。

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。

Generate D2 database diagrams from Entity Framework Core models. USE FOR: EF Core database diagram, Entity Framework Core ERD, DbContext diagram, C# entity relationship diagram, PostgreSQL schema visualization, generate .d2 file from EF Core entities, Fluent API mapping diagram, migrations-based database diagram, table relationships, owned types, many-to-many join tables, indexes and constraints. DO NOT USE FOR: runtime debugging, database migration execution, schema deployment, SQL performance tuning, or draw.io diagrams.

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 skill for building web-based game engines and games using HTML5, Canvas, WebGL, and JavaScript. Use when asked to create games, build game engines, implement game physics, handle collision detection, set up game loops, manage sprites, add game controls, or work with 2D/3D rendering. Covers techniques for platformers, breakout-style games, maze games, tilemaps, audio, multiplayer via WebRTC, and publishing games.

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。

Create and synchronize prompt-based AI agents directly within Azure AI Foundry via REST API, from a local JSON manifest. Unlike scaffolding skills that only generate local code, this skill registers agents in the Foundry service itself — making them immediately available for invocation. Use when the user asks to create agents in Foundry, sync, deploy, register, or push agents to Foundry, update agent instructions, or scaffold the manifest and sync script for a new repository. Triggers: 'create agent in foundry', 'sync foundry agents', 'deploy agents to foundry', 'register agents in foundry', 'push agents', 'create foundry agent manifest', 'scaffold agent sync'.

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。

Set up complete GitHub Copilot configuration for a new project based on technology stack

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。

Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/

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。

Generates a comprehensive and best-practice-oriented .editorconfig file based on project analysis and user preferences.

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。

Convert a text-based document to markdown following instructions from prompt, or if a documented option is passed, follow the instructions for that option.

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 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。

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。

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。

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

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。

Deterministic orchestration graph runtime - declarative DAG pipelines with journal-based crash recovery

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。

Install and configure the OpenViking long-term memory plugin for OpenClaw via natural conversation. Once installed, the plugin automatically captures facts from chats and recalls relevant context before each reply (auto-capture + auto-recall, cross-session). Covers prerequisites, install through OpenClaw's plugin manager (ov-install as backup), wizard-based configuration, slot activation, gateway restart, verification, multi-tenant root keys, multi-instance and uninstall. The user needs no CLI knowledge — the agent runs everything and only asks for a few values. Assumes the OpenViking server is already running; it does NOT install the server. Trigger on: "install OpenViking", "set up memory", "configure memory plugin", "add long-term memory", "semantic memory", "RAG", "帮我装 OpenViking", "配置记忆插件", "安装记忆功能", "接入 OpenViking".

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。

Retrieve and apply OpenViking Experience memories through the Agent runtime's generic OpenViking search and read tools. Use before or during executable, multi-step, or tool-based work such as coding, file or data changes, configuration, deployment, workflow execution, and failure recovery when prior operational guidance could improve reliability. Do not use for casual chat or simple factual questions.

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。