| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "she asked bluntly [bluntly]" |
| | dialogueSentences | 6 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0.25 | | effectiveRatio | 0.25 | |
| 48.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1757 | | totalAiIsmAdverbs | 18 | | found | | | highlights | | 0 | "sharply" | | 1 | "slightly" | | 2 | "utterly" | | 3 | "very" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 77.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1757 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "navigate" | | 2 | "pulse" | | 3 | "systematic" | | 4 | "disrupt" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 26 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 26 | | filterMatches | | | hedgeMatches | (empty) | |
| 4.93% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 28 | | gibberishSentences | 4 | | adjustedGibberishSentences | 4 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 3 | | repetitionLoopCount | 1 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 420 | | ratio | 0.143 | | matches | | 0 | "useClass sly breeze harsh sol G neat half E grind sheet room appet went G half slightly drew Vac har shred Pro with drawn bring various utterly pull rocked nil Din Evan wr rabbit s…" | | 1 | "asked imp originally outside seem liver valu float squash dialogue sticks bottom squared chief although proved thus supposed harsh resistance beginning courage wives Losing Autism …" | | 2 | "useClass leaned sly front cast originally ret bottom supposed harsh sol G ne half E hom feature round necessary grind sheet room appet went G half slightly drew Vac har shred Pro s…" | | 3 | "useClass leaned sly front cast originally ret sly front cast originally ret bottom supposed drove harsh sol G neat half E hom feature round necessary grind sheet room appet went G …" |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1756 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 205 | | wordCount | 1698 | | uniqueNames | 88 | | maxNameDensity | 0.77 | | worstName | "Evan" | | maxWindowNameDensity | 3 | | worstWindowName | "Evan" | | discoveredNames | | Eva | 5 | | Rory | 2 | | Lucien | 4 | | Ptolemy | 2 | | Vac | 7 | | Pro | 10 | | Din | 11 | | Evan | 13 | | Motor | 6 | | MeTable | 6 | | Jesus | 6 | | Direct | 3 | | Soviet | 3 | | Pa | 4 | | Broadcast | 4 | | Lena | 4 | | Lin | 3 | | Teen | 3 | | Dinner | 6 | | First | 3 | | Tom | 4 | | Goose | 3 | | Plains | 2 | | Holt | 2 | | Lucia | 3 | | Atomic | 2 | | Ross | 1 | | Con | 1 | | Eduardo | 3 | | Northern | 2 | | Summer | 2 | | Den | 2 | | Identify | 2 | | Song | 1 | | Individual | 1 | | Plus | 1 | | Chairman | 2 | | Suzanne | 1 | | Losing | 1 | | Autism | 1 | | Les | 1 | | George | 1 | | Clara | 1 | | Bernard | 1 | | Goud | 1 | | VanderCou | 1 | | Ho | 1 | | Del | 1 | | Minister | 1 | | Coff | 1 | | Equ | 1 | | Sp | 1 | | Comp | 1 | | Metropolitan | 1 | | Leaving | 1 | | Kar | 1 | | Students | 1 | | Closing | 1 | | Christianity | 1 | | Susan | 1 | | Communist | 1 | | Type | 1 | | Expected | 1 | | Pretty | 1 | | Cafe | 1 | | Cliff | 1 | | Businesses | 1 | | Gina | 1 | | Europe | 1 | | Bikosoph | 1 | | Husband | 1 | | Lunch | 1 | | Allen | 1 | | Cancer | 1 | | Coffee | 1 | | Me | 1 | | Booster | 1 | | Wife | 1 | | Judge | 1 | | Matthew | 1 | | Dog | 1 | | Plants | 3 | | Nav | 2 | | David | 3 | | Process | 5 | | Cam | 5 | | Discord | 2 | | Ap | 2 |
| | persons | | 0 | "Eva" | | 1 | "Rory" | | 2 | "Lucien" | | 3 | "Vac" | | 4 | "Evan" | | 5 | "Jesus" | | 6 | "Soviet" | | 7 | "Broadcast" | | 8 | "Lena" | | 9 | "Lin" | | 10 | "Teen" | | 11 | "Dinner" | | 12 | "Tom" | | 13 | "Goose" | | 14 | "Plains" | | 15 | "Holt" | | 16 | "Lucia" | | 17 | "Atomic" | | 18 | "Ross" | | 19 | "Con" | | 20 | "Eduardo" | | 21 | "Northern" | | 22 | "Chairman" | | 23 | "Les" | | 24 | "George" | | 25 | "Clara" | | 26 | "Bernard" | | 27 | "Goud" | | 28 | "Del" | | 29 | "Minister" | | 30 | "Sp" | | 31 | "Kar" | | 32 | "Susan" | | 33 | "Communist" | | 34 | "Type" | | 35 | "Businesses" | | 36 | "Gina" | | 37 | "Lunch" | | 38 | "Allen" | | 39 | "Me" | | 40 | "Judge" | | 41 | "Matthew" | | 42 | "Plants" | | 43 | "David" |
| | places | | 0 | "Den" | | 1 | "Suzanne" | | 2 | "Cafe" | | 3 | "Cliff" | | 4 | "Europe" |
| | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 25 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1756 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 28 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 15 | | mean | 117.07 | | std | 162.81 | | cv | 1.391 | | sampleLengths | | 0 | 24 | | 1 | 50 | | 2 | 23 | | 3 | 71 | | 4 | 52 | | 5 | 53 | | 6 | 36 | | 7 | 300 | | 8 | 231 | | 9 | 8 | | 10 | 18 | | 11 | 121 | | 12 | 115 | | 13 | 9 | | 14 | 645 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 26 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 259 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 28 | | ratio | 0.071 | | matches | | 0 | "The worn silver tea infuser swung from a sly breeze born of Eva's doorway position as Lucien laid Ptolemy on her coffee table; wriggling with comfort, the tabby laid haphazard paws on either side of a haphazard pottery popular art plate resting near enough the floorboards the afternoon arrival created iridescent reflected skims." | | 1 | "He filled the armchair by a window, the suit falling sharply on his tall frame; the right leg palsied from years before during operatic visits both friends felt paralysed of healing: he crossed the ankles together." |
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| 90.05% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1698 | | adjectiveStacks | 1 | | stackExamples | | 0 | "systematic horrific gradual sitting" |
| | adverbCount | 43 | | adverbRatio | 0.025323910482921083 | | lyAdverbCount | 42 | | lyAdverbRatio | 0.024734982332155476 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 28 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 28 | | mean | 62.71 | | std | 96.64 | | cv | 1.541 | | sampleLengths | | 0 | 11 | | 1 | 12 | | 2 | 1 | | 3 | 16 | | 4 | 34 | | 5 | 12 | | 6 | 11 | | 7 | 19 | | 8 | 25 | | 9 | 27 | | 10 | 17 | | 11 | 24 | | 12 | 11 | | 13 | 53 | | 14 | 36 | | 15 | 49 | | 16 | 212 | | 17 | 270 | | 18 | 8 | | 19 | 18 | | 20 | 23 | | 21 | 98 | | 22 | 7 | | 23 | 108 | | 24 | 9 | | 25 | 16 | | 26 | 209 | | 27 | 420 |
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| 69.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5 | | totalSentences | 28 | | uniqueOpeners | 14 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 26 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 26 | | matches | | 0 | "Her eyes widened as she" | | 1 | "He flashed a disarming smile," | | 2 | "His gaze swept past her" | | 3 | "He filled the armchair by" | | 4 | "she said stiffening at none" | | 5 | "Her order was not up" | | 6 | "she asked bluntly" |
| | ratio | 0.269 | |
| 56.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 21 | | totalSentences | 26 | | matches | | 0 | "The deadbolts clicked, and the" | | 1 | "Her eyes widened as she" | | 2 | "He flashed a disarming smile," | | 3 | "His gaze swept past her" | | 4 | "Eva stepped aside, her long" | | 5 | "Lucien raised an eyebrow as" | | 6 | "A cat rubbed against his" | | 7 | "Eva motioned for him to" | | 8 | "The conversation that followed would" | | 9 | "The cold callous marks of" | | 10 | "The worn silver tea infuser" | | 11 | "He filled the armchair by" | | 12 | "useClass sly breeze harsh sol" | | 13 | "she said stiffening at none" | | 14 | "A with Nav type biscuits" | | 15 | "useClass leaned sly front cast" | | 16 | "Her order was not up" | | 17 | "she asked bluntly" | | 18 | "A with Nav type bisc" | | 19 | "useClass leaned sly front cast" |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 26 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 2 | | matches | | 0 | "A cat rubbed against his leg, purring loudly, and Lucien picked Ptolemy up in one decisive motion, cradling him in the crook of his elbow." | | 1 | "The conversation that followed would be minefield territory, but she'd rather navigate this unasked than leave Lucien with too much unsupervised time with Rory." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |