| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 58 | | tagDensity | 0.224 | | leniency | 0.448 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1368 | | totalAiIsmAdverbs | 1 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
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| | 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) | |
| 81.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1368 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "structure" | | 1 | "pristine" | | 2 | "etched" | | 3 | "flickered" |
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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 | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 4 | | totalWords | 1368 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 907 | | uniqueNames | 14 | | maxNameDensity | 1.76 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Okafor" | | discoveredNames | | Camden | 1 | | Young | 1 | | Hendon | 1 | | Quinn | 16 | | Victorian | 1 | | Tube | 1 | | Sergeant | 1 | | Nia | 1 | | Okafor | 14 | | Met | 1 | | Scene | 3 | | Crime | 1 | | Morris | 1 | | TfL | 1 |
| | persons | | 0 | "Camden" | | 1 | "Hendon" | | 2 | "Quinn" | | 3 | "Sergeant" | | 4 | "Nia" | | 5 | "Okafor" | | 6 | "Scene" | | 7 | "Morris" |
| | places | | | globalScore | 0.618 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | 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 | 1368 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 19 | | std | 19.44 | | cv | 1.023 | | sampleLengths | | 0 | 6 | | 1 | 53 | | 2 | 27 | | 3 | 3 | | 4 | 6 | | 5 | 3 | | 6 | 33 | | 7 | 62 | | 8 | 3 | | 9 | 5 | | 10 | 5 | | 11 | 36 | | 12 | 73 | | 13 | 39 | | 14 | 4 | | 15 | 1 | | 16 | 18 | | 17 | 62 | | 18 | 16 | | 19 | 3 | | 20 | 6 | | 21 | 46 | | 22 | 14 | | 23 | 1 | | 24 | 3 | | 25 | 6 | | 26 | 6 | | 27 | 18 | | 28 | 5 | | 29 | 60 | | 30 | 1 | | 31 | 12 | | 32 | 28 | | 33 | 11 | | 34 | 5 | | 35 | 18 | | 36 | 31 | | 37 | 1 | | 38 | 4 | | 39 | 9 | | 40 | 45 | | 41 | 7 | | 42 | 13 | | 43 | 7 | | 44 | 39 | | 45 | 6 | | 46 | 43 | | 47 | 5 | | 48 | 6 | | 49 | 3 |
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| 97.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 93 | | matches | | 0 | "been drawn" | | 1 | "was heard" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 146 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 138 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 911 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.02854006586169045 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003293084522502744 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 9.91 | | std | 9.1 | | cv | 0.918 | | sampleLengths | | 0 | 6 | | 1 | 7 | | 2 | 26 | | 3 | 2 | | 4 | 2 | | 5 | 1 | | 6 | 15 | | 7 | 7 | | 8 | 1 | | 9 | 19 | | 10 | 3 | | 11 | 6 | | 12 | 3 | | 13 | 16 | | 14 | 17 | | 15 | 19 | | 16 | 20 | | 17 | 23 | | 18 | 3 | | 19 | 5 | | 20 | 5 | | 21 | 5 | | 22 | 3 | | 23 | 28 | | 24 | 4 | | 25 | 16 | | 26 | 1 | | 27 | 22 | | 28 | 2 | | 29 | 11 | | 30 | 6 | | 31 | 11 | | 32 | 17 | | 33 | 17 | | 34 | 5 | | 35 | 4 | | 36 | 1 | | 37 | 18 | | 38 | 23 | | 39 | 1 | | 40 | 2 | | 41 | 17 | | 42 | 3 | | 43 | 1 | | 44 | 15 | | 45 | 5 | | 46 | 11 | | 47 | 3 | | 48 | 6 | | 49 | 2 |
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| 89.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5652173913043478 | | totalSentences | 138 | | uniqueOpeners | 78 | |
| 91.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 73 | | matches | | 0 | "*Of course they don't.* Quinn" | | 1 | "Then it swung back." |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 73 | | matches | | 0 | "She stepped over it without" | | 1 | "Her watch caught the torchlight." | | 2 | "She'd been at her desk" | | 3 | "She could see why." | | 4 | "She gestured at the corner" | | 5 | "She didn't touch the hands." | | 6 | "She touched the space just" | | 7 | "She worked the fabric loose" | | 8 | "It swung like it was" | | 9 | "She felt it before she" | | 10 | "She closed her hand around" | | 11 | "She touched it." | | 12 | "Her fingers came away coated" |
| | ratio | 0.178 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 73 | | matches | | 0 | "The ladder had one rung" | | 1 | "Quinn counted them anyway, out" | | 2 | "She stepped over it without" | | 3 | "A constable's torch swung toward" | | 4 | "The constable's torch beam shook" | | 5 | "The smell hit her a" | | 6 | "Her watch caught the torchlight." | | 7 | "She'd been at her desk" | | 8 | "The platform opened ahead." | | 9 | "Scene lights, two of them," | | 10 | "Sacks of something bulbous and" | | 11 | "Candle wax pooled in patterns" | | 12 | "Someone had built a shop" | | 13 | "Detective Sergeant Nia Okafor stood" | | 14 | "Okafor had already decided something." | | 15 | "Okafor turned and pointed at" | | 16 | "The woman lay on her" | | 17 | "The Scene of Crime officers" | | 18 | "She could see why." | | 19 | "Okafor's jaw shifted." |
| | ratio | 0.712 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "Makeshift wooden stalls, maybe a dozen of them, empty, collapsed or folded like a market that had simply picked up and left." | | 1 | "Dressed in a simple grey dress that was too thin for the damp, no coat, no shoes." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "Okafor repeated (repeat)" |
| | dialogueSentences | 58 | | tagDensity | 0.121 | | leniency | 0.241 | | rawRatio | 0.143 | | effectiveRatio | 0.034 | |