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Found 152 Skills
Create, edit, and convert Excel workbooks (.xlsx/.xls) using Syncfusion XlsIO. Supports two modes — generate C# code for the user's project, or execute a temporary CSX script. Use when the user mentions Excel, xlsx, workbook, template markers, Syncfusion XlsIO, or PDF conversion.
Convert PDF files to images with customization options (output format, DPI, pages, scaling, and image quality). Supports one mode — generate C# code for the user's project.
Convert documents to Markdown using markitdown. Use when you need to extract text and convert PDF, Word, PowerPoint, Excel, HTML, CSV, JSON, XML, images (with EXIF/OCR), audio, ZIP archives, YouTube URLs, or EPUBs to Markdown format for LLM processing or text analysis.
Guidance for processing financial documents (invoices, receipts, statements) with OCR and text extraction. This skill should be used when tasks involve extracting data from financial PDFs or images, generating summaries (CSV/JSON), or moving/organizing processed documents. Emphasizes data safety practices to prevent catastrophic data loss.
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.
Generate extractive summaries from long text documents. Control summary length, extract key sentences, and process multiple documents.
Condense long content into short summaries using AI. Use when summarizing meeting notes, condensing articles, creating executive briefs, extracting action items, generating TL;DRs, creating digests from long threads, summarizing customer conversations, or turning lengthy documents into bullet points. Powered by DSPy summarization.
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）。
Recursive Language Model context management for processing documents exceeding context window limits. Enables Claude to match Gemini's 2M token context capability through chunking, sub-LLM delegation, and synthesis.
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.