2d-games
무료2D game development principles. Sprites, tilemaps, physics, camera.
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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.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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"
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2D game development principles. Sprites, tilemaps, physics, camera.
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3D game development principles. Rendering, shaders, physics, cameras.
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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.
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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.
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Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
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Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
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