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Found 23 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.
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".
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.
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 text to detect if it was written by AI. Returns a score from 0-100 with detailed metrics. Use when checking content before publishing or submitting.
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.
Compare two Claude Code resources side-by-side with objective data and recommendations
Diagnose flat dialogue, same-voice characters, and lack of subtext. Use when conversations feel wooden, characters sound alike, or dialogue only does one thing at a time.
Detect AI-generated writing patterns in developer text — docs, docstrings, commit messages, PR descriptions, and code comments. Use when reviewing any text artifact for authenticity and clarity.
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.
Provides Gemini CLI delegation workflows for large-context analysis tasks, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for a specific task such as broad codebase analysis or long-document processing. Triggers on "use gemini", "delegate to gemini", "run gemini cli", "ask gemini", "use gemini for this task".
Get word count, character count, reading time, and text statistics. Quick analysis without questions.