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rikiyanai AI Skills

GitHub ↗

Browse published AI skills maintained by rikiyanai. Compare skill descriptions and follow each source repository for setup instructions.

Author statistics

Indexed skills

7

Repository stars across indexed skills

7

Repository forks

7

Average repository stars per indexed skill

1

Repository stars may repeat across skills from the same repository.

7 skills

Browse skills

GitHub

build-game

rikiyanai

Build the Asciicker game executable using the Mac makefile. Use when building the game, recompiling after changes, or when the user asks to build or compile the game.

GitHub

session-memory

rikiyanai

Manage session memory - load context, summarize sessions, compress old sessions, search memory

GitHub

web-build

rikiyanai

Build the Asciicker game for web using Emscripten, diagnose web build failures, and test WebAssembly output. Leverages specialized MCP agents (Serena, Context7, Chrome DevTools) for deep analysis and debugging.

GitHub

validate-blender

rikiyanai

Validate Blender AKM (Asciicker Mesh) export files, check mesh format, and verify compatibility with the Asciicker engine. Use when exporting from Blender, checking mesh files, or troubleshooting mesh loading.

GitHub

test-grass

rikiyanai

Test the grass terrain implementation in the editor and game. Use when verifying grass/water terrain, testing material changes, or checking terrain visibility. Includes verification steps from TESTING_GUIDE.md.

GitHub

build-editor

rikiyanai

Build the Asciicker map editor (asciiid) using the Mac makefile. Use when building the editor, recompiling after changes, or when the user asks to build or compile the editor.

GitHub

add-material

rikiyanai

Add a new terrain material definition (dirt, stone, sand, etc.) to the Asciicker material system. Use when adding new materials, defining terrain types, or expanding the material palette.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.