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Found 33 Skills
Use the DRI Text Analysis Method (Data-Rule-Interaction) to perform word-by-word decomposition and domain modeling on natural language requirement descriptions. Reduce unstructured business requirement texts to structured architectural abstractions in three dimensions: Data (D), Rule (R), and Interaction (I), and directly generate conceptual tables usable for system design. It is suitable for requirement analysis, ubiquitous language extraction, text parsing before architecture design, and converting long requirement documents into clear development task decompositions.
Convert documents and files to Markdown using markitdown. Use when converting PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls), HTML, CSV, JSON, XML, images (with EXIF/OCR), audio (with transcription), ZIP archives, YouTube URLs, or EPubs to Markdown format for LLM processing or text analysis.
Analyze story texts, extract main plot points and analyze their dramatic functions. It is suitable for analyzing texts such as novels, script outlines, story synopses, etc., and identifying key turning points and emotional nodes.
Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".
Calculate readability scores (Flesch-Kincaid, Gunning Fog, SMOG) and grade levels for text. Analyze writing complexity and get improvement suggestions.
Coordinate the plot point and dramatic function analysis process, manage text preprocessing, parallel analysis, and result integration. Suitable for scenarios of plot point and dramatic function analysis of long texts, and scenarios requiring structured analysis reports
Resume work from handoff document with context analysis and validation
Computational text analysis for sociology research using R or Python. Guides you through topic models, sentiment analysis, classification, and embeddings with systematic validation. Supports both traditional (LDA, STM) and neural (BERT, BERTopic) methods.
Ancient text restoration, attribution, dating, contextualization, and embedding via Aeneas (Latin) / Ithaca (Ancient Greek). Use when asked to "restore", "attribute", "date", "contextualize", "find parallels", "where was it written", "when was it written", "embed", or "analyze" an ancient text, inscription, or epigraphic document, or when the user mentions "Aeneas", or "Ithaca".
Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
Ask Gemini via the local `gemini` CLI (no MCP). Use when the user says "ask gemini" / "use gemini", wants a second opinion, needs large-context `@path` analysis, sandbox runs, or structured change-mode edits.
Reading companion agent. Accompanies user through any text (books, articles, essays, papers, news) with translation, structural annotation, deep questioning, and cross-domain insights. Detects language, translates English to Chinese (faithfulness-expressiveness-elegance), guides reader to understand the author and encounter real questions. Use when user says '伴读', '陪我读', '读这篇', 'read with me', 'companion read', or shares a text/URL wanting guided reading.