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Get Started Free →Use when `paper-review` needs a canonical manuscript text artifact before claim extraction. **Trigger**: ingest paper, manuscript text, provide paper, paper.md, 输入论文, 导入稿件, 审稿输入. **Use when**: You are running the `paper-review` pipeline and need `output/PAPER.md` before `claims-extractor`.
.claude/skills/willoscar-manuscript-ingest/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -61% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -80% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 48% | 0% |
Transforms a manuscript source file into the canonical text artifact used by paper-review.
One manuscript source from the workspace, typically:
inputs/manuscript.mdinputs/manuscript.txtinputs/manuscript.pdfoutput/PAPER.mdscripts/run.py should:
output/PAPER.mdIt should not summarize, critique, or reformat the manuscript into a review.
The output must preserve:
output/PAPER.md exists| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | pass→pass | 9,643 | 2,696 | -72% | 1 | 1 | 0% | 1,440 | 654 | -55% | 0 | 0 | — |
case-01 | fail→fail | 7,817 | 4,407 | -44% | 1 | 1 | 0% | 1,335 | 404 | -70% | 0 | 0 | — |
case-02 | fail→fail | 32,290 | 4,681 | -86% | 1 | 1 | 0% | 6,179 | 369 | -94% | 0 | 0 | — |
case-03 | fail→fail | 9,253 | 4,650 | -50% | 1 | 1 | 0% | 935 | 361 | -61% | 0 | 0 | — |
case-04 | fail→pass | 8,331 | 2,254 | -73% | 1 | 1 | 0% | 1,282 | 496 | -61% | 0 | 0 | — |
case-05 | pass→pass | 8,714 | 4,099 | -53% | 1 | 1 | 0% | 1,235 | 881 | -29% | 0 | 0 | — |
case-06 | pass→pass | 7,413 | 3,001 | -60% | 1 | 1 | 0% | 1,122 | 677 | -40% | 0 | 0 | — |
case-07 | fail→pass | 12,402 | 1,557 | -87% | 1 | 1 | 0% | 1,963 | 402 | -80% | 0 | 0 | — |
case-08 | pass→pass | 9,428 | 2,208 | -77% | 1 | 1 | 0% | 1,387 | 493 | -64% | 0 | 0 | — |
case-09 | pass→fail | 10,894 | 2,644 | -76% | 1 | 1 | 0% | 1,659 | 599 | -64% | 0 | 0 | — |
case-10 | fail→fail | 10,511 | 3,517 | -67% | 1 | 1 | 0% | 1,759 | 820 | -53% | 0 | 0 | — |
case-11 | fail→fail | 18,090 | 5,912 | -67% | 1 | 1 | 0% | 2,428 | 611 | -75% | 0 | 0 | — |
case-13 | fail→pass | 8,036 | 3,700 | -54% | 1 | 1 | 0% | 1,184 | 840 | -29% | 0 | 0 | — |
case-14 | fail→pass | 9,016 | 5,608 | -38% | 1 | 1 | 0% | 1,171 | 1,043 | -11% | 0 | 0 | — |
case-15 | fail→pass | 16,397 | 17,699 | +8% | 1 | 1 | 0% | 2,263 | 3,359 | +48% | 0 | 0 | — |
case-16 | pass→pass | 9,688 | 3,142 | -68% | 1 | 1 | 0% | 1,460 | 715 | -51% | 0 | 0 | — |
case-17 | fail→pass | 10,550 | 1,440 | -86% | 1 | 1 | 0% | 1,534 | 386 | -75% | 0 | 0 | — |
case-18 | fail→pass | 9,302 | 1,663 | -82% | 1 | 1 | 0% | 1,392 | 484 | -65% | 0 | 0 | — |
case-19 | fail→pass | 9,612 | 1,743 | -82% | 1 | 1 | 0% | 1,390 | 444 | -68% | 0 | 0 | — |
case-20 | pass→pass | 10,255 | 2,663 | -74% | 1 | 1 | 0% | 1,441 | 597 | -59% | 0 | 0 | — |
case-21 | fail→pass | 7,350 | 1,789 | -76% | 1 | 1 | 0% | 1,061 | 459 | -57% | 0 | 0 | — |
case-22 | pass→fail | 9,608 | 3,104 | -68% | 1 | 1 | 0% | 1,418 | 704 | -50% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 18 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +32 percentage points is the difference between those two pass rates over the 18 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.