| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1793 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 86.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1793 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanned" | | 1 | "footsteps" | | 2 | "weight" | | 3 | "electric" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 161 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 161 | | filterMatches | | | hedgeMatches | | |
| 99.91% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 195 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 1 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0.005 | | matches | | 0 | "“Quinn. Disused Underground entrance, service alley north of Camden High Street. Suspect has gone below street level. No lighting. No visible staff.”" |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1793 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.71% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1565 | | uniqueNames | 15 | | maxNameDensity | 1.41 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Herrera | 21 | | Camden | 1 | | High | 1 | | Street | 1 | | Quinn | 22 | | Raven | 2 | | Nest | 2 | | Soho | 1 | | Silas | 1 | | Morris | 2 | | Transport | 1 | | London | 1 | | Underground | 1 | | Saint | 2 | | Christopher | 2 |
| | persons | | 0 | "Herrera" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Soho" | | 4 | "London" |
| | globalScore | 0.797 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 120 | | 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 | 1793 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 195 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 113 | | mean | 15.87 | | std | 17.08 | | cv | 1.076 | | sampleLengths | | 0 | 11 | | 1 | 23 | | 2 | 6 | | 3 | 36 | | 4 | 4 | | 5 | 18 | | 6 | 6 | | 7 | 4 | | 8 | 51 | | 9 | 88 | | 10 | 13 | | 11 | 6 | | 12 | 2 | | 13 | 38 | | 14 | 4 | | 15 | 6 | | 16 | 3 | | 17 | 44 | | 18 | 8 | | 19 | 4 | | 20 | 2 | | 21 | 3 | | 22 | 54 | | 23 | 7 | | 24 | 4 | | 25 | 5 | | 26 | 20 | | 27 | 6 | | 28 | 37 | | 29 | 6 | | 30 | 4 | | 31 | 33 | | 32 | 6 | | 33 | 5 | | 34 | 6 | | 35 | 5 | | 36 | 6 | | 37 | 20 | | 38 | 11 | | 39 | 8 | | 40 | 5 | | 41 | 52 | | 42 | 6 | | 43 | 41 | | 44 | 16 | | 45 | 3 | | 46 | 25 | | 47 | 11 | | 48 | 4 | | 49 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 161 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 258 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 195 | | ratio | 0.01 | | matches | | 0 | "The lid flew open; packaging and coffee grounds fanned across the pavement." | | 1 | "The sudden give threw her off balance; her knee struck the tiled edge and her leg went numb to the ankle." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1567 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.019783024888321635 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0006381620931716656 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 195 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 195 | | mean | 9.19 | | std | 5.92 | | cv | 0.644 | | sampleLengths | | 0 | 11 | | 1 | 5 | | 2 | 18 | | 3 | 6 | | 4 | 3 | | 5 | 18 | | 6 | 15 | | 7 | 4 | | 8 | 18 | | 9 | 6 | | 10 | 4 | | 11 | 7 | | 12 | 22 | | 13 | 6 | | 14 | 16 | | 15 | 13 | | 16 | 26 | | 17 | 25 | | 18 | 3 | | 19 | 21 | | 20 | 13 | | 21 | 6 | | 22 | 2 | | 23 | 11 | | 24 | 8 | | 25 | 19 | | 26 | 4 | | 27 | 6 | | 28 | 3 | | 29 | 8 | | 30 | 11 | | 31 | 12 | | 32 | 13 | | 33 | 8 | | 34 | 4 | | 35 | 2 | | 36 | 3 | | 37 | 12 | | 38 | 6 | | 39 | 13 | | 40 | 23 | | 41 | 4 | | 42 | 3 | | 43 | 4 | | 44 | 5 | | 45 | 7 | | 46 | 3 | | 47 | 10 | | 48 | 6 | | 49 | 12 |
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| 54.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3435897435897436 | | totalSentences | 195 | | uniqueOpeners | 67 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 153 | | matches | | 0 | "Then he sprinted between two" | | 1 | "Somewhere above, a siren started" | | 2 | "Perhaps two now." | | 3 | "Just another absence where an" | | 4 | "Somewhere under the platform, metal" |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 153 | | matches | | 0 | "Her radio crackled against her" | | 1 | "She struck its side with" | | 2 | "She had followed because eleven" | | 3 | "She settled into the chase," | | 4 | "He looked back again." | | 5 | "She gained it." | | 6 | "He dragged a wheelie bin" | | 7 | "She caught its handle and" | | 8 | "He ran harder." | | 9 | "She thumbed her radio." | | 10 | "He stumbled on the first" | | 11 | "His left cuff rode up," | | 12 | "He looked at the stairwell," | | 13 | "He tore the coat from" | | 14 | "She pushed herself up and" | | 15 | "He pulled the gate open" | | 16 | "His grip tightened on the" | | 17 | "He turned towards the stairs." | | 18 | "His footsteps continued after the" | | 19 | "She looked at the object" |
| | ratio | 0.261 | |
| 67.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 120 | | totalSentences | 153 | | matches | | 0 | "Tomás Herrera ran with the" | | 1 | "The driver shouted after him." | | 2 | "Quinn left her car blocking" | | 3 | "Herrera glanced back." | | 4 | "Rain streamed from his short," | | 5 | "Quinn went after him." | | 6 | "Her radio crackled against her" | | 7 | "A night bus cut across" | | 8 | "She struck its side with" | | 9 | "Herrera was already across the" | | 10 | "A moped swerved to miss" | | 11 | "She had followed because eleven" | | 12 | "Herrera was different." | | 13 | "A former paramedic with no" | | 14 | "The Raven’s Nest was also" | | 15 | "Herrera dodged into a side" | | 16 | "A group outside a chicken" | | 17 | "Neon stained the rain pink" | | 18 | "She settled into the chase," | | 19 | "He looked back again." |
| | ratio | 0.784 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 153 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 3 | | matches | | 0 | "She had followed because eleven nights outside Silas’s bar had produced nothing but number plates and the names of people who lied for a living." | | 1 | "Herrera passed through it without slowing, his coat turning black across the shoulders." | | 2 | "The air changed from cold mineral damp to something warmer, layered with paraffin, wet wool and a medicinal sharpness that caught at the back of her throat." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 0 | | effectiveRatio | 0 | |