plain-language
A toolkit for writing clear, accessible, and concise content. Applies Plain Language Movement principles including active voice, sentence shortening, and jargon elimination for improved reader comprehension.
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145 skills found
A toolkit for writing clear, accessible, and concise content. Applies Plain Language Movement principles including active voice, sentence shortening, and jargon elimination for improved reader comprehension.
AI language tutor for personalized learning through conversation, grammar lessons, vocabulary drills, and flashcards. Supports 100+ languages including Spanish, French, Japanese, and Mandarin.
Analyze YouTube videos with automated transcript extraction, AI-powered summarization, Korean translation, and interactive multi-level comprehension quizzes.
End-to-end startup idea validation using S.E.E.D. niche checks, STREAM 6-layer analysis, and Devil's Advocate inversion to generate PRDs.
Supermemory is a long-term memory infrastructure for AI agents, enabling persistent context, user profiles, and semantic RAG across multi-modal knowledge bases.
Evaluate code generation models using BigCode Evaluation Harness. Benchmarks include HumanEval, MBPP, and MultiPL-E with pass@k metrics for multi-language coding models.
Synchronize English README.md with Chinese README_ZH.md, maintaining content parity and structural consistency for bilingual documentation projects.
Generate high-quality Japanese puns (dajare) based on keywords, topics, or situations. Includes rhyme analysis and contextual humor generation.
Automate high-quality screenshot generation for MicroSim visualizations using Chrome headless mode. Ideal for documentation, social media previews, and quality assessment.
Symbol-level code understanding and navigation agent toolkit using LSP for precise code analysis, reference tracking, and surgical refactoring across 30+ programming languages.
Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.
Token-efficient codebase analysis skill for call graphs, semantic search, impact analysis, and data flow. Saves ~95% tokens vs. raw reads.