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Found 80 Skills
Implement optimal chunking strategies in RAG systems and document processing pipelines. Use when building retrieval-augmented generation systems, vector databases, or processing large documents that require breaking into semantically meaningful segments for embeddings and search.
Information Question Generator. Given an article, paper, or book, extract its core viewpoints into Q-A pairs — Questions get straight to the point, no textbook-style phrasing; Answers are concise and clear, with formalized conclusions and complete logical chains. As readers follow the Q chain, each Answer drives home a key point, reproducing the author's entire reasoning process. Activate when the user says '问答', 'Q&A', 'QA', '提问', '抽取问题', '/ljg-qa', or shares an article, paper, or book and requests Q-A extraction. This tool triggers when the user wants ideas extracted not as a summary but as a sequence of incisive questions paired with answers. NOT FOR FAQ generation, glossary creation, or comprehension quizzes — this is intellectual scaffolding, not a study aid.
Integrate with Affinda's document AI API to extract structured data from documents (invoices, resumes, receipts, contracts, and custom types). Covers authentication, client libraries (Python, TypeScript), structured outputs with Pydantic models and TypeScript interfaces, webhooks, upload patterns, and the full documentation map. Use when building integrations that parse, classify, or extract data from documents using Affinda.
Build a section-by-section claim–evidence matrix (`outline/claim_evidence_matrix.md`) from the outline and paper notes. **Trigger**: claim–evidence matrix, evidence mapping, 证据矩阵, 主张-证据对齐. **Use when**: 写 prose 之前需要把每个小节的可检验主张与证据来源显式化（outline + paper notes 已就绪）。 **Skip if**: 缺少 `outline/outline.yml` 或 `papers/paper_notes.jsonl`。 **Network**: none. **Guardrail**: bullets-only（NO PROSE）；每个 claim 至少 2 个证据来源（或显式说明例外）。
Convert text with private context or internal dependencies into generic, unbiased expressions. Use for project decontextualization (handoff, open-source prep), methodology abstraction, cross-team sharing, anonymization. Includes path strings and file/folder names as they appear in text.
Converts PDF pages to images and uses vision analysis to extract content including diagrams, charts, and visual elements. Use for PDFs with rich visual content. Requires pdf2image and poppler-utils.
Extract structured information from unstructured text using LLMs with source grounding. Use when extracting entities from documents, medical notes, clinical reports, or any text requiring precise, traceable extraction. Supports Gemini, OpenAI, and local models (Ollama). Includes visualization and long document processing.
Reads PDF files and extracts text content in Markdown format. Handles tables and multi-page documents. Use when needing to read PDF documents. Requires pdfplumber package.
Markdown accessibility rule library covering ambiguous links, anchor validation, emoji handling (remove or translate to English), Mermaid and ASCII diagram replacement templates, heading structure, table descriptions, and severity scoring. Use when auditing or fixing markdown documentation for accessibility.
Guide for implementing Google Gemini API document processing - analyze PDFs with native vision to extract text, images, diagrams, charts, and tables. Use when processing documents, extracting structured data, summarizing PDFs, answering questions about document content, or converting documents to structured formats. (project)
Convert PDF files to editable Word documents using pdf2docx
Translate English or Japanese tech articles and texts into natural, fluent Chinese. Use this skill when the user wants to translate text to Chinese, asks for Chinese translation, mentions "translate to Chinese", "翻译", provides English/Japanese tech content for translation, or wants any text converted into Chinese. Also trigger when the user pastes text and asks to translate it, or references a file to translate into Chinese.