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Found 16 Skills
格式化纯文本或 Markdown 文件，添加 frontmatter、标题、摘要、小标题、加粗、列表和代码块。当用户要求"格式化markdown"、"美化文章"、"添加格式"或改善文章排版时使用。输出到 {filename}-formatted.md。
Expert guidance for natural language processing development using transformers, spaCy, NLTK, and modern NLP techniques.
Use when user requests Chinese terminology conversion, checking, or ensuring terminology - "使用繁體中文", "使用台灣用語", "轉換成台灣用語", "確保都是台灣用語", "統一台灣用語", "改成台灣用語", "用台灣的說法", "簡體轉繁體", "繁體轉簡體", "全部改成繁體", "轉成台灣繁體", check/ensure Taiwan/Hong Kong/China terminology, simplified/traditional conversion, or phonetic transcription (Pinyin/Bopomofo)
An epistemic extraction system that analyzes text to identify its logical structure according to Aristotelian and Objectivist epistemology. Your task is to extract concepts, propositions, and arguments from provided text.
Formats text according to specified style guidelines. A clean example skill with no security issues.
Sample skill for testing the skill-tester validation pipeline. Demonstrates proper skill structure with scripts, references, and assets.
Extract and parse article content from web sources. Retrieves text, metadata, and structured information from articles while preserving formatting and context.
Analyze a complete literary work into a structured Basic Memory knowledge graph. Covers schema design, entity seeding, chapter-by-chapter processing, cross-referencing, validation, and visualization.
Use this skill when building NLP pipelines, implementing text classification, semantic search, embeddings, or summarization. Triggers on text preprocessing, tokenization, embeddings, vector search, named entity recognition, sentiment analysis, text classification, summarization, and any task requiring natural language processing.
Fetch transcripts from YouTube videos for summarization and analysis.
Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
Use when implementing on-device AI with Apple's Foundation Models framework (iOS 26+), building summarization/extraction/classification features, or using @Generable for type-safe structured output.