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mindmorass AI Skills

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

Author statistics

Indexed skills

40

Repository stars across indexed skills

80

Repository forks

0

Average repository stars per indexed skill

2

Repository stars may repeat across skills from the same repository.

40 skills

Browse skills

GitHub

podcast-production

mindmorass

Podcast production patterns and workflows. Use when recording podcasts, editing audio, transcribing episodes, generating show notes, RSS feed management, or podcast distribution.

GitHub

aws-patterns

mindmorass

AWS cloud infrastructure patterns and best practices. Use when designing AWS architectures, creating Lambda functions, configuring S3 buckets, setting up EC2 instances, designing VPCs, or implementing any AWS s...

GitHub

video-upload-patterns

mindmorass

Video upload patterns for YouTube, TikTok, and Vimeo. Use when uploading videos to platforms, managing video metadata, scheduling video releases, or handling bulk video uploads.

GitHub

ai-video-generation

mindmorass

AI video generation patterns using Sora, Runway, Pika, and other AI video tools. Use when generating videos from text prompts, image-to-video conversion, AI video editing, or integrating AI video APIs.

GitHub

qdrant-patterns

mindmorass

Store and retrieve documents using Qdrant for RAG workflows. Use for persistent memory, research storage, and semantic search.

GitHub

iconset-maker

mindmorass

Create icon sets for multiple operating systems (macOS, Windows, Linux, iOS, Android, Web). Covers required sizes, formats, and conversion workflows using sips, iconutil, ImageMagick, and rsvg-convert.

GitHub

rag-wrapper

mindmorass

Patterns for wrapping any agent with RAG context from Qdrant. Use to add persistent memory to imported or external agents.

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.