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heavy-file-ingestion-claude-code

Convert heavyweight documents to markdown or CSV before model processing.

Updated Apr 29, 2026

Search Tags:#pdf #claude code #token efficiency #document ingestion #heavy file conversion #csv normalization #index-driven processing

Ready to run

npx skills add https://github.com/Greyborne/OB1-Canobi --skill heavy-file-ingestion-claude-code-greyborne

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: heavy-file-ingestion-claude-code
Source: https://github.com/Greyborne/OB1-Canobi/tree/main/skills/heavy-file-ingestion/variants/claude-code
Command: npx skills add https://github.com/Greyborne/OB1-Canobi --skill heavy-file-ingestion-claude-code-greyborne

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Prevents wasted context and slowdowns when Claude Code is asked to read or extract information from large, complex files like PDFs, spreadsheets, and documents.

Core Features & Use Cases

Deterministic conversion before model processing: Converts heavyweight files into markdown or CSV using the bundled script, then reads a lightweight index.md first.
Token-efficient workflows: Builds a lightweight index and only sends compressed artifacts to the model for analysis, extraction, or summarization.
Smart escalation strategy: Installs missing deterministic conversion dependencies or retries with cheaper/stronger approaches based on what the index indicates.
Practical examples: Normalize a large CSV/TSV into structured CSV artifacts, or map a big PDF/DOCX into markdown for faster downstream analysis.

Quick Start

Use Claude Code and when you’re asked to analyze a heavy file, run the bundled converter on the absolute file path and then work from the generated index.md before reading any converted artifact.

Frequently Asked Questions about heavy-file-ingestion-claude-code

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I read large PDF files in Claude Code without exceeding token limits?▼

To read large PDF files without exceeding token limits, convert the heavyweight documents into lightweight markdown or CSV artifacts first. This approach builds a lightweight index, ensuring only compressed text is sent to the model for extraction instead of raw binaries.

What is the best way to normalize large CSV or TSV files for structured analysis?▼

The best way to normalize large CSV or TSV files for structured analysis is running a deterministic conversion script to map the raw data into structured CSV artifacts. This index-driven workflow reads a generated index before processing the compressed data.

Does Claude Code support direct ingestion of DOCX and XLSX files?▼

Claude Code does not directly ingest raw DOCX and XLSX binaries efficiently. Instead, it uses a bundled converter script to map these heavyweight files into markdown or CSV, creating lightweight summaries that prevent context slowdowns.

Why does loading heavy spreadsheets slow down document processing?▼

Loading heavy spreadsheets slows down document processing because raw binaries consume excessive model context. Converting these files into markdown or CSV first prevents wasted context and maintains processing speed during extraction tasks.

How do I handle missing dependencies when converting heavy documents?▼

To handle missing dependencies when converting heavy documents, the conversion workflow implements a smart escalation strategy that optionally installs missing deterministic converter dependencies or retries with cheaper approaches based on the index.

When should I avoid loading raw binaries into model context?▼

You should avoid loading raw binaries into model context when processing large PDFs, DOCX, PPTX, XLSX, or CSV files. Converting them to markdown or CSV first ensures token-efficient workflows and prevents wasted context.