heavy-file-ingestion-codex

name heavy-file-ingestion-codex description Use in Codex when a user asks to read, analyze, summarize, or extract from a heavyweight file such as PDF, DOCX, PPTX, XLSX, CSV, or TSV. Convert the file into markdown or CSV first with the bundled script, generate a lightweight index, and only spend model tokens on the compressed artifact. author Nate B. Jones version 1.0.0 Heavy File Ingestion For Codex Problem Codex can run local commands and inspect files, so direct ingestion of bulky documents is usually the wrong move. Convert first, index second, reason last. Trigger Conditions The user asks to read or summarize a heavyweight document or spreadsheet The file is large, structured, or expensive enough that raw ingestion is wasteful The task would be better served by markdown, CSV, or a quick file map Process Do not open the raw heavyweight file as your first move if a deterministic conversion path exists. Run the bundled converter from this skill directory: python scripts/convert_heavy_file.py /absolute/path/to/file.ext If the environment is clean and needs packages, prefer: uv run \ --with pdfplumber \ --with python-docx \ --with python-pptx \ --with openpyxl \ python scripts/convert_heavy_file.py /absolute/path/to/file.ext Read index.md first, not the original file. Follow the index recommendation: read_extracted_artifact : inspect the generated markdown or CSV cheap_model_or_stronger_converter : retry with a better deterministic tool or use a cheaper model on the extracted artifact only manual_review : tell the user the deterministic route failed and propose the next cheapest fallback Use expensive model context only after the file has already been compressed into a smaller artifact. Client Rules Keep the main model out of raw PDFs, decks, and spreadsheets whenever possible. Use the generated .ob1/ folder as the working directory for follow-up analysis. For spreadsheets, reason from the CSV per sheet plus the workbook manifest. For presentations, reason from the slide outline before asking for a deeper pass. Bundled References references/open-source-stack.md explains the tool choices and fallback tiers.