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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.

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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.

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Set up eval-based QA for Python LLM applications: instrument the app, build golden datasets, write and run eval tests, and iterate on failures. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.

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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。

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。

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。

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

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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。

Prompt for creating the high-level technical architecture for an Epic, based on a Product Requirements Document.

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Prompt for creating Product Requirements Documents (PRDs) for new features, based on an Epic.

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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。

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

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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)

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Patterns for designing well-structured, maintainable Ansible playbooks with state-based (present/absent) design, play organization, and proper variable scoping.

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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.

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Adopt a virtual Archive AI-native pet at animalhouse.ai. Feeds on reflections. Use the reflect care action to write notes — the Archive grows based on the quality and depth o... Feeding every 12 hours. Uncommon tier creature.

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This skill should be used when the user asks to 'plan my day', 'help me plan today', 'morning planning', 'what should I work on today', 'daily planner', 'evening reflection', 'reflect on my day', 'migrate tasks', 'carry tasks forward', 'rapid log', 'brain dump my tasks', or mentions ADHD-friendly planning, time-blindness, swim lanes, energy-based task management, bullet journal for ADHD, or dopamine menu. Provides a BuJo-style daily planning system with swim lanes, migration, reflection, and dopamine rewards. Part of the ADHD-founder.com ecosystem.

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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

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。

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

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Create HTML-based video compositions, animated title cards, social overlays, captioned talking-head videos, audio-reactive visuals, and shader transitions using HyperFrames. HTML is the source of truth for video. Use when the user wants a rendered MP4/WebM from an HTML composition, wants to animate text/logos/charts over media, needs captions synced to audio, wants TTS narration, or wants to convert a website into a video.

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 refine UI color systems, warmth, semantic color, and color-based hierarchy in designs that are too gray, monochromatic, or missing color meaning. Use when the user mentions dull colors, gray UI, missing warmth, weak semantic color, or a need for stronger color hierarchy.

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。

Triage GitHub issues through gh and a label-based state machine. Use when the user intentionally wants GitHub issue triage, incoming bug or feature review, issue workflow labels, needs-info follow-up, or ready-for-agent briefs.

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.

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You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based engineering practices. Design comprehensive SLO frameworks, establish meaningful SLIs, and create monitoring systems that balance reliability with feature velocity.

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。