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Get Started Free →Rewrite or edit prose in any language so it reads like edited human writing in the register of books, newspapers and encyclopedias rather than default AI output. A small drafting core — vary every pattern, build density from anchored facts in whole grammar, cap the quotables, put a real writer with real material behind the text, let structure serve content, write each language from inside its idiom and typography, verify what you assert, and aim for the natural distribution of edited prose rathe
.claude/skills/prism-shadow-humanizer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 244% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 87% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 106% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 50% | 0% |
Make prose read like edited human writing: the register of books, quality newspapers and encyclopedia entries. Everything here was derived empirically, across seven rounds of drafting, blind editorial review and revision, documented with counts in reference/case-study.md. The working surface is deliberately small: a writer boxed in by a long checklist produces compliance, not prose — that failure mode is the case study's best-documented finding. Draft with the principles below; diagnose with the catalog afterwards.
If the invocation carries no text and no assignment, ask for one: the draft to edit, or the topic, length, audience and language to write fresh. Settle two things early. The register: books, newspapers and encyclopedias are the default target, while marketing, speeches and reference documentation legitimately bend these rules, so confirm how far to go. And any hard length target: humanizing shrinks text, and gaps are filled with substance, never padding. If the genre needs material nobody has gathered — reportage needs a scene, a person, a quotation — say so and get it, or agree to relabel the piece; never fake the texture.
reference/language-cues.md, which indexes one reference/<lang>-cues.md file per language.Over all seven: aim for the natural distribution of edited prose, not a perfect scorecard. Every rule above, overdriven, mints a new tell — the case study documents four such artifacts (banned openers became cold-open verdicts; scrubbed punctuation became semicolon inflation and rationed warmth; density became telegraph compression; scrubbed abstraction became contrived colloquialism). Keep some looseness, an aside, an unresolved edge.
reference/tells.md and count. The drafting mind cannot see its own tics: in a field test, an author who felt two aphorisms was carrying eight, and five triads survived a no-triads rule. Mechanical counting over impression, catalog over memory; a flagged pattern kept for its quality is still flagged.Before, tells marked: "In today's rapidly evolving AI landscape era opener], skills have emerged as a game-changer hype]. A skill isn't just a document — dash] it's a reusable playbook template contrast] that transforms how agents work. Whether you're automating reports, reviewing code, or managing data triad, reader address], skills unlock consistency, reliability, and scale triad, hype]. The future of agent workflows starts here uplift ending]."
After: "A skill is a document that tells an AI agent how to perform one kind of task. The agent keeps only the document's one-line summary in memory and reads the full text when a matching task arrives, so hundreds of skills can be installed at negligible cost. The format is plain Markdown with a short metadata header, which means anyone who can write instructions can write a skill."
The full tell catalog is in reference/tells.md, per-language surface forms in the reference/<lang>-cues.md files indexed by reference/language-cues.md, and the measured record behind all of it in reference/case-study.md.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 4,298 | 3,891 | -9% | 1 | 1 | 0% | 578 | 1,988 | +244% | 0 | 0 | — |
case-02 | pass→pass | 6,768 | 5,025 | -26% | 1 | 1 | 0% | 878 | 2,174 | +148% | 0 | 0 | — |
case-03 | fail→fail | 10,285 | 16,335 | +59% | 1 | 1 | 0% | 1,309 | 3,552 | +171% | 0 | 0 | — |
case-04 | pass→pass | 20,957 | 11,656 | -44% | 1 | 1 | 0% | 1,872 | 3,263 | +74% | 0 | 0 | — |
case-05 | pass→pass | 7,857 | 5,732 | -27% | 1 | 1 | 0% | 966 | 2,019 | +109% | 0 | 0 | — |
case-06 | fail→pass | 11,293 | 11,052 | -2% | 1 | 1 | 0% | 1,564 | 2,917 | +87% | 0 | 0 | — |
case-07 | fail→pass | 7,795 | 10,062 | +29% | 1 | 1 | 0% | 1,255 | 2,584 | +106% | 0 | 0 | — |
case-08 | fail→pass | 17,914 | 7,041 | -61% | 1 | 1 | 0% | 2,317 | 2,427 | +5% | 0 | 0 | — |
case-09 | pass→pass | 8,743 | 9,017 | +3% | 1 | 1 | 0% | 1,246 | 2,723 | +119% | 0 | 0 | — |
case-10 | fail→fail | 6,905 | 10,399 | +51% | 1 | 1 | 0% | 752 | 2,363 | +214% | 0 | 0 | — |
case-11 | fail→fail | 19,419 | 56,430 | +191% | 1 | 1 | 0% | 2,539 | 5,403 | +113% | 0 | 0 | — |
case-12 | pass→pass | 6,840 | 7,280 | +6% | 1 | 1 | 0% | 1,150 | 2,449 | +113% | 0 | 0 | — |
case-13 | pass→pass | 5,408 | 6,277 | +16% | 1 | 1 | 0% | 836 | 2,485 | +197% | 0 | 0 | — |
case-14 | pass→pass | 9,043 | 9,838 | +9% | 1 | 1 | 0% | 1,363 | 2,887 | +112% | 0 | 0 | — |
case-15 | fail→pass | 11,998 | 18,145 | +51% | 1 | 1 | 0% | 1,701 | 2,544 | +50% | 0 | 0 | — |
case-16 | pass→pass | 38,308 | 14,343 | -63% | 1 | 1 | 0% | 2,132 | 3,288 | +54% | 0 | 0 | — |
case-17 | fail→pass | 14,706 | 6,121 | -58% | 1 | 1 | 0% | 1,381 | 1,904 | +38% | 0 | 0 | — |
case-18 | pass→pass | 11,751 | 14,497 | +23% | 1 | 1 | 0% | 1,701 | 3,112 | +83% | 0 | 0 | — |
case-19 | pass→pass | 14,146 | 9,771 | -31% | 1 | 1 | 0% | 1,886 | 2,693 | +43% | 0 | 0 | — |
case-20 | pass→pass | 11,260 | 8,830 | -22% | 1 | 1 | 0% | 1,460 | 2,485 | +70% | 0 | 0 | — |
case-21 | pass→pass | 12,459 | 9,658 | -22% | 1 | 1 | 0% | 1,883 | 2,662 | +41% | 0 | 0 | — |
case-22 | pass→pass | 22,800 | 10,492 | -54% | 1 | 1 | 0% | 2,042 | 2,895 | +42% | 0 | 0 | — |
case-23 | pass→pass | 7,928 | 20,215 | +155% | 1 | 1 | 0% | 1,172 | 2,635 | +125% | 0 | 0 | — |
case-24 | fail→pass | 13,859 | 5,997 | -57% | 1 | 1 | 0% | 1,669 | 2,111 | +26% | 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. 24 cases were attempted. The headline lift of +29 percentage points is the difference between those two pass rates over the 24 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.