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Searches academic literature via arXiv, Semantic Scholar, and open-access PDFs.
Passed 15 of 19 test cases with the skill, and 11 of 19 without it.
and 1 more in the full report
4 fixed13 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
If the primary markdown conversion tool is unavailable when parsing a 50-page research paper PDF, how should page chunking be configured when falling back to a document reading tool?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies reading the document in 20-page increments starting with pages 1-20. | Missed | Passed |
The instructions your model reads when the skill loads.
Skill(tome:discourse))Skill(tome:code-search))Search arXiv, Semantic Scholar, and open-access sources.
After acquiring a paper URL or local file path, convert the PDF to markdown for better extraction quality.
Apply the leyline:document-conversion protocol:
Try markitdown first: call the MCP convert_to_markdown
tool with the PDF URL or file:// path. This produces
structured markdown preserving tables, equations, figures,
and section hierarchy.
Fall back to Read tool if markitdown is unavailable:
pages: "1-20" for the first chunkpages: "21-40" for longer paperstest cases passed, vs 11 / 19 without
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