dsh-skill-pdf-math
A DeepSeek Harness skill for reading
mathematics out of PDFs and formula screenshots without guessing.
Why
Vision models misread formulas in predictable ways: a \mathcal{L} becomes \angle,
a summation subscript j=1 becomes \cdot, a tall bracket turns into [[[.
Most of that is unnecessary, because a LaTeX-produced PDF stores formulas as
positioned text, not images — glyphs, baselines, sizes and font names are all there.
So the skill teaches one rule:
read the text layer first, let formula OCR supply the structure, and cross-check the two.
What it adds
pdf_formula.py — turns a PDF region into readable (approximate) LaTeX by restoring
sub/superscripts from baseline offsets, plus a small TeX cmex10 operator table.--probe — decides whether a page's formulas are extractable text or images.--crosscheck — compares the OCR result against the text layer's letters and their fonts
(cmsy10's L is \mathcal{L}), so a wrong glyph is named instead of merely suspected.--verify — re-recognises with a different crop and resolution; disagreement is reported.
Measured on four numbered equations from a real paper:
| render | mean similarity | exact |
|---|
| 400 dpi | 0.941 | 2/4 |
| 600 dpi | 0.977 | 3/4 |
--check requires the optional rapid_latex_ocr runtime; without it the text-layer
channel still works and the skill says so instead of failing.
Install
dsh plugin --profile <name> add dsh-skill-pdf-math
or install it from the DSH plugin marketplace. The skill registers itself as
pdf-math and is model-invocable.
Notes
- Registration is wrapped in a
try/catch: a broken asset can disable the skill,
never the host application.
- Paths are injected at run time, so the published package contains no machine-specific paths.
License
MIT
