2d-games
無料2D game development principles. Sprites, tilemaps, physics, camera.
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。
Turn a decision you can't fully answer into a questionnaire for someone else to fill in.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Turn something the user can't answer alone into a questionnaire: a Markdown document they hand to one person to fill in async, or fill out together over a meeting. The recipient holds knowledge the user lacks; the questionnaire pulls it out of them.
Grill the send, not the subject. Interview the user only about the send, which they can always answer: who it goes to, and what they need back. The questions in the document then target the gap between what the recipient knows and what the user needs.
Who is it going to? Ask, in one exchange, the recipient's role, expertise, and relationship to the user. This fixes the questionnaire's tone and how much context it must carry. Done when you know who the recipient is and what they know that the user doesn't.
What do you need back? Ask, in one exchange, the specific decisions or facts the user can't resolve alone and needs from this person. Done when you have a concrete list of what the user must walk away able to do or decide.
Write the questionnaire. Draft questions aimed at the gap from steps 1–2, following the Document structure below. Write it to to-questionnaire-<slug>.md in the current directory (slug from the topic) and report the path. Done when the file exists and every item the user named in step 2 is covered by a question.
Frame the document as a discovery questionnaire: the user lacks context, the recipient holds it. Order questions most-important-first, since async means you may only get one pass, and group them under ## headings by theme once there are more than a handful. Write it using the template below.
Purpose: why this questionnaire exists and the decision riding on it.
From: <the user>, To: <the recipient>, How your answers will be used: <where they go>
One paragraph orienting a recipient who wasn't in the user's head. Enough to answer well, not a page.
Deadline and rough effort. Partial answers and "I don't know" are useful: flag anything you're unsure of rather than skipping it.
One ## section per theme. Under each, its questions, most-important-first. Every question is one idea, never compound, with an answer stub directly beneath, and a one-line why this matters only where the question could be misread or invite a throwaway answer.
Why this matters: it decides whether we provision for burst traffic now or defer it.
</question-example>
A closing catch-all: anything we didn't ask that we should know?
</questionnaire-template>まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
2D game development principles. Sprites, tilemaps, physics, camera.
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。
3D game development principles. Rendering, shaders, physics, cameras.
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 aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
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 implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.
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 monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
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 building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
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。