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Get Started Free →Strip AI writing patterns from text. Checks 24 patterns across 4 categories (structural, lexical, rhetorical, formatting) with academic economics adaptation. This skill should be used on any text that reads too "AI-generated", or as a final pass on drafted sections. Triggers on "humanize", "de-AI", "make it sound natural", or "strip AI patterns".
.claude/skills/brycewang-stanford-humanizer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | 60% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-11 | ✓→✓ | = Same ✓ | 61% | 0% |
| case-20 | ✓→✓ | = Same ✓ | 182% | 0% |
Strip AI writing patterns from academic text while preserving economics content and formal structure.
Input: $ARGUMENTS — path to file to humanize.
Read the target file from $ARGUMENTS. Support .tex, .md, .txt, and .qmd files.
Check all 24 patterns across 4 categories:
For each detected pattern:
\textcite{}, \parencite{}, \label{}, \ref{}, or equation environmentsPresent a summary of changes:
markdown## Humanizer Report: [filename] **Patterns found:** N / 24 **Changes made:** N | # | Pattern | Category | Location | Change | |---|---------|----------|----------|--------| | 1 | Triplet lists | Structural | Section 3, para 2 | Varied list lengths | | 2 | "Delve" | Lexical | Line 47 | Replaced with "examine" | ...
Overwrite the original file with the humanised version (the user can review via git diff).
git checkout to undo.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | pass→pass | 6,543 | 3,487 | -47% | 1 | 1 | 0% | 1,099 | 1,768 | +61% | 0 | 0 | — |
case-20 | pass→pass | 5,505 | 10,065 | +83% | 1 | 1 | 0% | 1,031 | 2,903 | +182% | 0 | 0 | — |
case-01 | fail→fail | 6,079 | 5,206 | -14% | 1 | 1 | 0% | 668 | 1,563 | +134% | 0 | 0 | — |
case-02 | fail→fail | 4,838 | 4,776 | -1% | 1 | 1 | 0% | 937 | 1,353 | +44% | 0 | 0 | — |
case-03 | fail→fail | 5,316 | 3,954 | -26% | 1 | 1 | 0% | 317 | 1,259 | +297% | 0 | 0 | — |
case-04 | pass→pass | 8,013 | 3,596 | -55% | 1 | 1 | 0% | 1,452 | 1,717 | +18% | 0 | 0 | — |
case-05 | pass→pass | 11,077 | 4,723 | -57% | 1 | 1 | 0% | 1,941 | 1,896 | -2% | 0 | 0 | — |
case-06 | pass→pass | 8,710 | 3,886 | -55% | 1 | 1 | 0% | 1,702 | 1,921 | +13% | 0 | 0 | — |
case-07 | pass→pass | 8,237 | 4,941 | -40% | 1 | 1 | 0% | 1,510 | 2,046 | +35% | 0 | 0 | — |
case-08 | pass→pass | 6,414 | 5,689 | -11% | 1 | 1 | 0% | 1,129 | 2,072 | +84% | 0 | 0 | — |
case-09 | pass→pass | 5,830 | 2,815 | -52% | 1 | 1 | 0% | 1,068 | 1,599 | +50% | 0 | 0 | — |
case-10 | pass→pass | 7,172 | 3,158 | -56% | 1 | 1 | 0% | 1,260 | 1,663 | +32% | 0 | 0 | — |
case-12 | pass→pass | 8,285 | 8,579 | +4% | 1 | 1 | 0% | 1,484 | 2,144 | +44% | 0 | 0 | — |
case-13 | fail→fail | 7,568 | 4,768 | -37% | 1 | 1 | 0% | 1,366 | 1,478 | +8% | 0 | 0 | — |
case-14 | fail→pass | 6,529 | 4,444 | -32% | 1 | 1 | 0% | 1,205 | 1,926 | +60% | 0 | 0 | — |
case-15 | fail→pass | 10,072 | 3,527 | -65% | 1 | 1 | 0% | 2,120 | 1,866 | -12% | 0 | 0 | — |
case-16 | pass→pass | 6,083 | 1,464 | -76% | 1 | 1 | 0% | 1,170 | 1,357 | +16% | 0 | 0 | — |
case-17 | fail→pass | 10,128 | 6,237 | -38% | 1 | 1 | 0% | 1,974 | 2,136 | +8% | 0 | 0 | — |
case-18 | pass→pass | 6,533 | 3,433 | -47% | 1 | 1 | 0% | 1,153 | 1,691 | +47% | 0 | 0 | — |
case-19 | pass→pass | 5,887 | 2,526 | -57% | 1 | 1 | 0% | 1,082 | 1,528 | +41% | 0 | 0 | — |
case-21 | fail→fail | 3,972 | 11,872 | +199% | 1 | 1 | 0% | 753 | 3,429 | +355% | 0 | 0 | — |
case-22 | pass→pass | 4,273 | 15,877 | +272% | 1 | 1 | 0% | 819 | 3,916 | +378% | 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 +14 percentage points is the difference between those two pass rates over the 18 comparable cases.
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.