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For the **current TOC region**, discover page-numbering **regimes** and an
Passed 17 of 20 test cases with the skill, and 8 of 20 without it.
and 9 more in the full report
10 fixed9 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
When writing the notes string for a calibration submission that succeeded after resolving two ambiguous Roman entries, you might be tempted to write a multi-paragraph diagnostic log. What constraint applies to the notes string?
| Checked | Without skill | With skill |
|---|---|---|
| Restricts notes to exactly one short summary sentence | Missed | Passed |
The instructions your model reads when the skill loads.
For the current TOC region, discover page-numbering regimes and an
initial offset for each regime that has usable entries. Submit candidate
offsets via calibration.submit. After submit, a deterministic completion pass
runs the production tail-verify → binary-search → small-step recalibrate loop
(using the same visual page confirmer as production). Only complete segments
are usable for coarse structure; unrecognized pages are treated as no TOC.
link.Inspect every page_number label on the current TOC entries and partition them
into page-numbering regimes (distinct numbering systems / label shapes:
decimal digits, roman numerals, prefixed folio labels, etc.).
For each regime:
link.physical_page, use it as the primary candidate.page_count, then confirm with vision.inspect.pages for that title (start small, expand only if needed):
test cases passed, vs 8 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.