| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.109 | | leniency | 0.217 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1845 | | 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) | |
| 91.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1845 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "weight" | | 1 | "pulse" | | 2 | "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 | 166 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 166 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 206 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1845 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.29% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1628 | | uniqueNames | 13 | | maxNameDensity | 2.33 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 38 | | Soho | 2 | | Camden | 2 | | Friday-night | 1 | | Edgware | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Tomás | 8 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Herrera" | | 6 | "Tomás" |
| | places | | 0 | "Detective" | | 1 | "Soho" | | 2 | "Friday-night" | | 3 | "Edgware" |
| | globalScore | 0.333 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 131 | | 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 | 1845 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 206 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 93 | | mean | 19.84 | | std | 18.36 | | cv | 0.925 | | sampleLengths | | 0 | 39 | | 1 | 18 | | 2 | 60 | | 3 | 6 | | 4 | 10 | | 5 | 32 | | 6 | 25 | | 7 | 2 | | 8 | 44 | | 9 | 43 | | 10 | 17 | | 11 | 19 | | 12 | 8 | | 13 | 49 | | 14 | 1 | | 15 | 32 | | 16 | 48 | | 17 | 7 | | 18 | 27 | | 19 | 7 | | 20 | 5 | | 21 | 2 | | 22 | 6 | | 23 | 8 | | 24 | 6 | | 25 | 56 | | 26 | 3 | | 27 | 39 | | 28 | 11 | | 29 | 7 | | 30 | 55 | | 31 | 31 | | 32 | 39 | | 33 | 46 | | 34 | 36 | | 35 | 31 | | 36 | 3 | | 37 | 9 | | 38 | 4 | | 39 | 4 | | 40 | 18 | | 41 | 8 | | 42 | 22 | | 43 | 5 | | 44 | 2 | | 45 | 17 | | 46 | 5 | | 47 | 86 | | 48 | 24 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 166 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 268 | | matches | (empty) | |
| 87.38% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 4 | | flaggedSentences | 4 | | totalSentences | 206 | | ratio | 0.019 | | matches | | 0 | "One woman cried out as he struck her shoulder; another dropped a paper bag that burst at Quinn’s feet." | | 1 | "Several steps bore white deposits where leaks had dried; the rest shone wet." | | 2 | "One promised trains towards Edgware; another had lost every letter but the final N." | | 3 | "The broad woman had reached the tunnel entrance; the suspect was almost there." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1631 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.014714898835070508 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0018393623543838135 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 206 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 206 | | mean | 8.96 | | std | 5.31 | | cv | 0.592 | | sampleLengths | | 0 | 18 | | 1 | 21 | | 2 | 8 | | 3 | 10 | | 4 | 6 | | 5 | 2 | | 6 | 8 | | 7 | 15 | | 8 | 16 | | 9 | 13 | | 10 | 6 | | 11 | 10 | | 12 | 8 | | 13 | 16 | | 14 | 8 | | 15 | 14 | | 16 | 11 | | 17 | 2 | | 18 | 6 | | 19 | 11 | | 20 | 12 | | 21 | 15 | | 22 | 7 | | 23 | 28 | | 24 | 8 | | 25 | 17 | | 26 | 5 | | 27 | 14 | | 28 | 8 | | 29 | 9 | | 30 | 19 | | 31 | 6 | | 32 | 15 | | 33 | 1 | | 34 | 3 | | 35 | 14 | | 36 | 15 | | 37 | 4 | | 38 | 7 | | 39 | 9 | | 40 | 12 | | 41 | 4 | | 42 | 12 | | 43 | 7 | | 44 | 2 | | 45 | 6 | | 46 | 19 | | 47 | 5 | | 48 | 2 | | 49 | 5 |
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| 47.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.2912621359223301 | | totalSentences | 206 | | uniqueOpeners | 60 | |
| 20.70% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 161 | | matches | | 0 | "Then he pushed the token" |
| | ratio | 0.006 | |
| 95.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 161 | | matches | | 0 | "She stood beside a wall" | | 1 | "He had asked the barman" | | 2 | "He hadn’t touched the one" | | 3 | "He wore a grey coat" | | 4 | "She stepped away from the" | | 5 | "It struck Quinn’s knee, hard" | | 6 | "She followed him through it" | | 7 | "He cleared the bonnet with" | | 8 | "She could have called for" | | 9 | "She pulled her radio from" | | 10 | "She caught before a delivery" | | 11 | "He looked back." | | 12 | "He wiped it with his" | | 13 | "His foot slid on a" | | 14 | "He caught the brickwork, and" | | 15 | "She reached for the back" | | 16 | "He twisted free and ducked" | | 17 | "He braced both hands on" | | 18 | "she told him" | | 19 | "His right hand dropped to" |
| | ratio | 0.311 | |
| 22.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 141 | | totalSentences | 161 | | matches | | 0 | "Rainwater streamed from the green" | | 1 | "She stood beside a wall" | | 2 | "He had asked the barman" | | 3 | "He hadn’t touched the one" | | 4 | "Quinn checked her worn leather" | | 5 | "He wore a grey coat" | | 6 | "Quinn had seen both in" | | 7 | "She stepped away from the" | | 8 | "The man drove his elbow" | | 9 | "It struck Quinn’s knee, hard" | | 10 | "She followed him through it" | | 11 | "A cyclist swerved across her" | | 12 | "Quinn caught the bicycle by" | | 13 | "The suspect crossed against the" | | 14 | "A taxi sounded its horn" | | 15 | "He cleared the bonnet with" | | 16 | "Quinn took the crossing behind" | | 17 | "She could have called for" | | 18 | "She pulled her radio from" | | 19 | "Static tore across the reply." |
| | ratio | 0.876 | |
| 31.06% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 161 | | matches | | 0 | "By the time one fought" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 2 | | matches | | 0 | "One woman cried out as he struck her shoulder; another dropped a paper bag that burst at Quinn’s feet." | | 1 | "She kept her hand off the rail, which hung loose from its brackets." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 46 | | tagDensity | 0.087 | | leniency | 0.174 | | rawRatio | 0.25 | | effectiveRatio | 0.043 | |