Generated from blueprint.yaml. Do not edit this block by hand.

Executable Interfaces:

Send the required caller (caller skill), interface, version, and arguments; optional dry_run defaults to false. Compact uses ordered positionals plus an option mapping; ordered raw argv uses positionals: [] plus every argv token in list options. Never mix forms.

• pdf-to-markdown._rtx.interface.scripts-check-marker-models — Check whether required Marker/Surya models are downloaded and cached locally.
  • Caller: pdf-to-markdown
  • Version: 1
  • Security level: 0
  • Alternative: owner Arguments JSON (replace labels with actual values). Omit optional positionals and options that are not needed. {"options": {}, "positionals": [], "stdin": null} Required options: []; positional arity: 0..0; stdin: forbidden
• pdf-to-markdown._rtx.interface.scripts-fetch-arxiv-source — Download and extract the LaTeX source tarball for a paper from arXiv.
  • Caller: pdf-to-markdown
  • Version: 1
  • Security level: 2
  • Alternative: owner Arguments JSON (replace labels with actual values). Omit optional positionals and options that are not needed. {"options": {}, "positionals": ["arxiv-id", "output-dir"], "stdin": null} Required options: []; positional arity: 1..2; stdin: forbidden

PDF to Markdown

Overview

Convert a research paper to LLM-readable text. Prefer LaTeX source over PDF conversion — LaTeX preserves math, structure, and cross-references that PDF-to-text destroys.

Step 1 — Find LaTeX source (do this first)

Input required: paper title and authors. arXiv ID if known.

arXiv (check first — highest hit rate for CS/math/econ)

1. No arXiv ID? Search arxiv [title] [authors] via WebSearch to find one.
2. Run the scripts-fetch-arxiv-source interface with <arxiv-id> [<output-dir>].
   • Script downloads arxiv.org/src/<id>, extracts, lists .tex files found.
   • If arXiv returns HTML instead of a tarball, the paper has no source — move on.
3. Root file is usually main.tex; if absent, scan for the file that \begin{document}.
4. Done — hand .tex files to the LLM.

If not on arXiv — check in order

• Author GitHub: WebSearch "[title]" site:github.com — look for a repo with .tex files
• Author personal/institutional page: WebSearch [title] [author] latex source
• OpenReview (openreview.net/search?term=[title]) — Attachments tab, look for source zip
• ACL Anthology (aclanthology.org) — NLP venues; links to arXiv preprint when one exists

If LaTeX source found anywhere: download, extract, done.

Step 2 — PDF fallback via marker_single

If no LaTeX source found, convert the PDF directly.

Before running: check whether models are cached using the scripts-check-marker-models interface. If any models are missing, warn the user: "Running marker will download missing models (~3GB total to ~/.cache/datalab/models/). Proceed?" Do not run marker_single until confirmed.

Standard invocation (good typeset PDF, no extra cost):

marker_single paper.pdf --output_dir ./output --disable_image_extraction

Always specify --output_dir — the default buries output in the anaconda site-packages directory.

Math-heavy paper (ask user before using — slower, better equation/table output):

marker_single paper.pdf \
  --output_dir ./output \
  --disable_image_extraction \
  --llm_service <configured-marker-llm-service> \
  --use_llm \
  --redo_inline_math \
  --highres_image_dpi 300

Requires a configured Marker LLM backend, such as an Anthropic-backed Marker service with ANTHROPIC_API_KEY in the environment or the matching API-key flag. Uses the selected provider API (pay-per-token where applicable).

Other useful flags:

• --page_range 0,5-10 — convert specific pages only
• --output_format json — structured output instead of flat markdown
• --disable_ocr — skip OCR for faster conversion (safe if PDF has selectable text)