| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1144 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 21.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1144 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "pounding" | | 2 | "fleeting" | | 3 | "stomach" | | 4 | "scanned" | | 5 | "glint" | | 6 | "etched" | | 7 | "echoing" | | 8 | "profound" | | 9 | "familiar" | | 10 | "weight" | | 11 | "footsteps" | | 12 | "throbbed" | | 13 | "pulsed" | | 14 | "constructed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 1135 | | uniqueNames | 11 | | maxNameDensity | 0.7 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 8 | | Raven | 1 | | Nest | 1 | | Camden-bound | 1 | | Tube | 1 | | London | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 3 | | matches | | 0 | "sounded like a ship's bellow in her ear as" | | 1 | "ablade that seemed to drink the light around it" | | 2 | "felt like the edge of that same abyss" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1136 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 56.23% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 81.14 | | std | 28.14 | | cv | 0.347 | | sampleLengths | | 0 | 93 | | 1 | 84 | | 2 | 96 | | 3 | 101 | | 4 | 89 | | 5 | 81 | | 6 | 89 | | 7 | 67 | | 8 | 89 | | 9 | 17 | | 10 | 140 | | 11 | 65 | | 12 | 89 | | 13 | 36 |
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| 85.98% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 91 | | matches | | 0 | "been rusted" | | 1 | "was gone" | | 2 | "was etched" | | 3 | "were lit" | | 4 | "was taken" |
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| 86.36% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 176 | | matches | | 0 | "was losing" | | 1 | "was scrambling" | | 2 | "wasn't losing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 91 | | ratio | 0.011 | | matches | | 0 | "But the image of her partner, Morris, flashed in her mind—his face pale and confused in the moments before he was taken by something she still couldn't explain." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1143 | | adjectiveStacks | 1 | | stackExamples | | 0 | "familiar long, black coat." |
| | adverbCount | 19 | | adverbRatio | 0.016622922134733157 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006124234470691163 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 12.48 | | std | 7.01 | | cv | 0.562 | | sampleLengths | | 0 | 24 | | 1 | 21 | | 2 | 21 | | 3 | 23 | | 4 | 4 | | 5 | 17 | | 6 | 15 | | 7 | 7 | | 8 | 3 | | 9 | 14 | | 10 | 14 | | 11 | 11 | | 12 | 3 | | 13 | 18 | | 14 | 9 | | 15 | 21 | | 16 | 10 | | 17 | 19 | | 18 | 19 | | 19 | 3 | | 20 | 22 | | 21 | 13 | | 22 | 8 | | 23 | 16 | | 24 | 15 | | 25 | 6 | | 26 | 14 | | 27 | 4 | | 28 | 9 | | 29 | 10 | | 30 | 3 | | 31 | 17 | | 32 | 12 | | 33 | 21 | | 34 | 17 | | 35 | 20 | | 36 | 22 | | 37 | 14 | | 38 | 22 | | 39 | 3 | | 40 | 9 | | 41 | 12 | | 42 | 1 | | 43 | 1 | | 44 | 7 | | 45 | 4 | | 46 | 3 | | 47 | 9 | | 48 | 12 | | 49 | 6 |
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| 39.01% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.2967032967032967 | | totalSentences | 91 | | uniqueOpeners | 27 | |
| 38.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 87 | | matches | | | ratio | 0.011 | |
| 36.09% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 87 | | matches | | 0 | "Her gaze remained fixed on" | | 1 | "Her target was inside." | | 2 | "He paused under the awning," | | 3 | "He had a head start," | | 4 | "She hurdled a stack of" | | 5 | "Her movements were economical, honed" | | 6 | "He was fast." | | 7 | "He vaulted a low wall" | | 8 | "She landed with a jolt" | | 9 | "She was on a wider" | | 10 | "She could see the frantic," | | 11 | "He was losing steam." | | 12 | "He cut sharply left, across" | | 13 | "She saw the gap between" | | 14 | "She sprinted, the world a" | | 15 | "She made it, her heart" | | 16 | "Its arched entrance was a" | | 17 | "He fumbled at his pocket," | | 18 | "He was gone." | | 19 | "She shoved against the heavy" |
| | ratio | 0.46 | |
| 28.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 87 | | matches | | 0 | "The rain fell on Soho" | | 1 | "Detective Harlow Quinn sat behind" | | 2 | "Her gaze remained fixed on" | | 3 | "Her target was inside." | | 4 | "The pub door swung open," | | 5 | "A figure emerged, pulling the" | | 6 | "Quinn’s hands tightened on the" | | 7 | "That was him." | | 8 | "He paused under the awning," | | 9 | "Quinn was out of the" | | 10 | "The air was cold and" | | 11 | "He had a head start," | | 12 | "The smell of grease and" | | 13 | "She hurdled a stack of" | | 14 | "Her movements were economical, honed" | | 15 | "He was fast." | | 16 | "He vaulted a low wall" | | 17 | "She landed with a jolt" | | 18 | "She was on a wider" | | 19 | "The suspect weaved through the" |
| | ratio | 0.862 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 88.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 4 | | matches | | 0 | "Quinn was out of the car before the door finished slamming shut, her feet pounding the slick tarmac." | | 1 | "Her movements were economical, honed by years of drills and chases that ended in less forgiving places than this." | | 2 | "She wrenched it wider and slipped through into the darkness, the gate sliding shut behind her with a final, echoing clang that sealed her in." | | 3 | "She stood at the top of a spiral staircase that descended into profound blackness." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |