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。
Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.
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。
Create exercise directory structures that pass pnpm ai-hero-cli internal lint, then commit with git commit.
XX-section-name/ inside exercises/ (e.g., 01-retrieval-skill-building)XX.YY-exercise-name/ inside a section (e.g., 01.03-retrieval-with-bm25)XX, exercise number = XX.YYEach exercise needs at least one of these subfolders:
problem/ - student workspace with TODOssolution/ - reference implementationexplainer/ - conceptual material, no TODOsWhen stubbing, default to explainer/ unless the plan specifies otherwise.
Each subfolder (problem/, solution/, explainer/) needs a readme.md that:
When stubbing, create a minimal readme with a title and a description:
# Exercise Title
Description here
If the subfolder has code, it also needs a main.ts (>1 line). But for stubs, a readme-only exercise is fine.
mkdir -p for each pathreadme.md per variant folder with a titlepnpm ai-hero-cli internal lint to validateThe linter (pnpm ai-hero-cli internal lint) checks:
problem/, solution/, explainer/)problem/, explainer/, or explainer.1/ existsreadme.md exists and is non-empty in the primary subfolder.gitkeep filesspeaker-notes.md filespnpm run exercise commands in readmesmain.ts required per subfolder unless it's readme-onlyWhen renumbering or moving exercises:
git mv (not mv) to rename directories - preserves git historyExample:
git mv exercises/01-retrieval/01.03-embeddings exercises/01-retrieval/01.04-embeddings
Given a plan like:
Section 05: Memory Skill Building
- 05.01 Introduction to Memory
- 05.02 Short-term Memory (explainer + problem + solution)
- 05.03 Long-term Memory
Create:
mkdir -p exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer
mkdir -p exercises/05-memory-skill-building/05.02-short-term-memory/{explainer,problem,solution}
mkdir -p exercises/05-memory-skill-building/05.03-long-term-memory/explainer
Then create readme stubs:
exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer/readme.md -> "# Introduction to Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/explainer/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/problem/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/solution/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.03-long-term-memory/explainer/readme.md -> "# Long-term Memory"
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。