| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1831 | | 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) | |
| 89.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1831 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "scanned" | | 1 | "pulse" | | 2 | "weight" |
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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 | 133 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 133 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 171 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1831 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 80 | | wordCount | 1509 | | uniqueNames | 26 | | maxNameDensity | 1.46 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Harlow | 1 | | Quinn | 22 | | Soho | 1 | | Herrera | 21 | | Saint | 1 | | Christopher | 1 | | Brewer | 1 | | Street | 4 | | Old | 1 | | Compton | 1 | | Charing | 1 | | Cross | 1 | | Road | 2 | | Tottenham | 1 | | Court | 1 | | Underground | 1 | | Northern | 1 | | Morris | 3 | | Camden | 3 | | Town | 1 | | High | 2 | | Spanish | 1 | | Met | 1 | | Rain | 4 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Brewer" | | 2 | "Street" | | 3 | "Old" | | 4 | "Compton" | | 5 | "Charing" | | 6 | "Cross" | | 7 | "Road" | | 8 | "Tottenham" | | 9 | "Court" | | 10 | "Northern" | | 11 | "Camden" | | 12 | "Town" | | 13 | "High" | | 14 | "Spanish" |
| | globalScore | 0.771 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | 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 | 1831 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 171 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 81 | | mean | 22.6 | | std | 23.07 | | cv | 1.021 | | sampleLengths | | 0 | 73 | | 1 | 21 | | 2 | 45 | | 3 | 22 | | 4 | 2 | | 5 | 29 | | 6 | 2 | | 7 | 24 | | 8 | 11 | | 9 | 26 | | 10 | 4 | | 11 | 2 | | 12 | 14 | | 13 | 2 | | 14 | 5 | | 15 | 33 | | 16 | 4 | | 17 | 11 | | 18 | 2 | | 19 | 71 | | 20 | 36 | | 21 | 2 | | 22 | 36 | | 23 | 54 | | 24 | 29 | | 25 | 10 | | 26 | 12 | | 27 | 9 | | 28 | 14 | | 29 | 57 | | 30 | 5 | | 31 | 2 | | 32 | 13 | | 33 | 57 | | 34 | 19 | | 35 | 5 | | 36 | 3 | | 37 | 51 | | 38 | 62 | | 39 | 8 | | 40 | 7 | | 41 | 4 | | 42 | 6 | | 43 | 15 | | 44 | 4 | | 45 | 27 | | 46 | 42 | | 47 | 18 | | 48 | 22 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 260 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 171 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1513 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.009253139458030404 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0013218770654329147 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 171 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 171 | | mean | 10.71 | | std | 6.97 | | cv | 0.651 | | sampleLengths | | 0 | 17 | | 1 | 29 | | 2 | 15 | | 3 | 12 | | 4 | 7 | | 5 | 14 | | 6 | 6 | | 7 | 18 | | 8 | 6 | | 9 | 15 | | 10 | 11 | | 11 | 2 | | 12 | 9 | | 13 | 2 | | 14 | 4 | | 15 | 5 | | 16 | 20 | | 17 | 2 | | 18 | 24 | | 19 | 11 | | 20 | 26 | | 21 | 4 | | 22 | 2 | | 23 | 3 | | 24 | 11 | | 25 | 2 | | 26 | 5 | | 27 | 33 | | 28 | 4 | | 29 | 11 | | 30 | 2 | | 31 | 10 | | 32 | 18 | | 33 | 19 | | 34 | 8 | | 35 | 16 | | 36 | 14 | | 37 | 9 | | 38 | 13 | | 39 | 2 | | 40 | 8 | | 41 | 10 | | 42 | 18 | | 43 | 5 | | 44 | 8 | | 45 | 26 | | 46 | 15 | | 47 | 22 | | 48 | 7 | | 49 | 10 |
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| 54.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3508771929824561 | | totalSentences | 171 | | uniqueOpeners | 60 | |
| 26.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 126 | | matches | | 0 | "Then a voice broke apart" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 126 | | matches | | 0 | "Her worn leather watch showed" | | 1 | "She turned the face from" | | 2 | "His Saint Christopher medallion swung" | | 3 | "His mouth tightened." | | 4 | "He glanced toward the mouth" | | 5 | "Her shoes lost grip on" | | 6 | "She hit the wall with" | | 7 | "She cleared the shards and" | | 8 | "She sprinted around its rear" | | 9 | "He cut left toward Charing" | | 10 | "His scar showed when he" | | 11 | "He reached Tottenham Court Road" | | 12 | "She forced her arm between" | | 13 | "His voice on the last" | | 14 | "He squeezed through the gap" | | 15 | "Her radio gave a burst" | | 16 | "She drew her baton and" | | 17 | "He held out one palm" | | 18 | "Her radio produced a flat" | | 19 | "Her phone showed no signal." |
| | ratio | 0.222 | |
| 3.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 115 | | totalSentences | 126 | | matches | | 0 | "Detective Harlow Quinn stood in" | | 1 | "Cars cut through standing water" | | 2 | "The spray hit her trousers" | | 3 | "Her worn leather watch showed" | | 4 | "She turned the face from" | | 5 | "The door to the Nest" | | 6 | "Tomás Herrera stepped out with" | | 7 | "Rain flattened his short curly" | | 8 | "His Saint Christopher medallion swung" | | 9 | "Quinn crossed the road between" | | 10 | "The taxi driver hit his" | | 11 | "Herrera lowered the phone." | | 12 | "Recognition changed his stance before" | | 13 | "His mouth tightened." | | 14 | "He glanced toward the mouth" | | 15 | "Quinn drove after him through" | | 16 | "Herrera vaulted a stack of" | | 17 | "Quinn kicked a crate aside" | | 18 | "Her shoes lost grip on" | | 19 | "She hit the wall with" |
| | ratio | 0.913 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 126 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn stood in the doorway of the closed bookshop opposite and watched the bar through water that ran from her cropped salt-and-pepper hair and…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |