large-document-reader
Split and read long documents chapter-by-chapter for structured analysis
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill large-document-reader --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Large Document Reader Split long documents (books, reports, theses, legal filings, technical manuals) into structured chapters or sections for systematic, chapter-by-chapter reading and analysis within LLM context windows. ## Overview Large Language Models have finite context windows, and even models with 100K+ token limits can lose accuracy on information buried in the middle of very long inputs. Academic researchers frequently work with documents that exceed practical context limits: doctoral theses (200+ pages), government reports, book-length monographs, legal case compilations, and multi-volume technical standards. This skill provides a systematic approach to splitting large documents into semantically meaningful chapters or sections, maintaining cross-references between parts, and reading each section with full comprehension. Rather than naive fixed-size chunking that breaks mid-sentence or mid-argument, this approach respects document structure -- headings, chapter breaks, section markers, and logical boundaries. The result is a structured reading experience where each chapter is analyzed in full context, summaries are maintained across sessions, and the reader can navigat
- Overview
- Document Splitting Strategy
- Hierarchy of Split Points
- Splitting Algorithm
- Structure Detection Patterns
- Structured Reading Workflow
- Phase 1: Survey
- Phase 2: Sequential Deep Reading
- Phase 3: Synthesis
- Cross-Session Persistence
- Format-Specific Handling
- Best Practices
- References
What does the large-document-reader skill do?
Split and read long documents chapter-by-chapter for structured analysis
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill large-document-reader --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.