| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.98% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1661 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 75.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1661 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "pumping" | | 1 | "traced" | | 2 | "footsteps" | | 3 | "silence" | | 4 | "mechanical" | | 5 | "flicker" | | 6 | "stomach" | | 7 | "pulsed" |
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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 | 139 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 139 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1669 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1652 | | uniqueNames | 15 | | maxNameDensity | 0.36 | | worstName | "Morris" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Three" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 4 | | London | 2 | | Marcus | 1 | | Vale | 5 | | Whitechapel | 1 | | Adrian | 1 | | Morris | 6 | | Isle | 1 | | Dogs | 1 | | Tube | 2 | | Met | 1 | | Underground | 1 | | Three | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Marcus" | | 3 | "Vale" | | 4 | "Adrian" | | 5 | "Morris" | | 6 | "Tube" | | 7 | "Met" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 68.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 3 | | matches | | 0 | "quite silence" | | 1 | "quite let them go" | | 2 | "quite what they seemed" |
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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 | 1669 | | matches | (empty) | |
| 70.74% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 4 | | totalSentences | 139 | | matches | | 0 | "explain, that she" | | 1 | "used that word" | | 2 | "knew that look" | | 3 | "learned that much" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 47.69 | | std | 25.23 | | cv | 0.529 | | sampleLengths | | 0 | 53 | | 1 | 87 | | 2 | 54 | | 3 | 40 | | 4 | 56 | | 5 | 4 | | 6 | 71 | | 7 | 46 | | 8 | 21 | | 9 | 76 | | 10 | 15 | | 11 | 5 | | 12 | 71 | | 13 | 82 | | 14 | 23 | | 15 | 51 | | 16 | 9 | | 17 | 66 | | 18 | 17 | | 19 | 76 | | 20 | 45 | | 21 | 55 | | 22 | 67 | | 23 | 30 | | 24 | 84 | | 25 | 31 | | 26 | 62 | | 27 | 47 | | 28 | 8 | | 29 | 73 | | 30 | 80 | | 31 | 62 | | 32 | 7 | | 33 | 54 | | 34 | 41 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 139 | | matches | | 0 | "were ignored" | | 1 | "was swallowed" |
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| 36.26% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 285 | | matches | | 0 | "was chasing" | | 1 | "was beginning" | | 2 | "was heading" | | 3 | "was going" | | 4 | "was thinking" | | 5 | "was just noticing" | | 6 | "was waiting" |
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| 19.53% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 139 | | ratio | 0.043 | | matches | | 0 | "Every stride confirmed what her gut had been whispering for weeks - this man had a destination in mind, and he was heading there with purpose." | | 1 | "The worn leather watch on her left wrist - the one Morris had given her on her thirtieth birthday, the one she'd taken from his effects when they returned his things in a bin bag - read half past midnight." | | 2 | "Graffiti marked the walls here and there, layered over decades, and some of it was strange - symbols she didn't recognise, repeated with a deliberate hand." | | 3 | "She could see it now - an old ticket hall, the gates rusted and shoved aside." | | 4 | "She counted the figures she could see - six, ten, more - and none of them moved like late-night revellers or lost tourists." | | 5 | "She didn't know how she knew the term - some fragment of a conversation overheard years ago, some half-remembered briefing in a back room that she'd laughed off at the time as the ramblings of a drunk informant." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1101 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.02633969118982743 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0036330608537693005 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 12.01 | | std | 8.75 | | cv | 0.729 | | sampleLengths | | 0 | 13 | | 1 | 18 | | 2 | 5 | | 3 | 17 | | 4 | 4 | | 5 | 15 | | 6 | 24 | | 7 | 20 | | 8 | 24 | | 9 | 19 | | 10 | 9 | | 11 | 26 | | 12 | 3 | | 13 | 2 | | 14 | 9 | | 15 | 26 | | 16 | 18 | | 17 | 17 | | 18 | 6 | | 19 | 8 | | 20 | 4 | | 21 | 3 | | 22 | 4 | | 23 | 8 | | 24 | 23 | | 25 | 40 | | 26 | 2 | | 27 | 15 | | 28 | 3 | | 29 | 7 | | 30 | 9 | | 31 | 10 | | 32 | 7 | | 33 | 2 | | 34 | 12 | | 35 | 2 | | 36 | 14 | | 37 | 9 | | 38 | 21 | | 39 | 4 | | 40 | 26 | | 41 | 5 | | 42 | 6 | | 43 | 4 | | 44 | 5 | | 45 | 6 | | 46 | 1 | | 47 | 1 | | 48 | 25 | | 49 | 26 |
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| 44.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.3669064748201439 | | totalSentences | 139 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 128 | | matches | | 0 | "Just a vast, breathing quiet" | | 1 | "Then she heard it." | | 2 | "More than one." | | 3 | "Then the figure turned away" | | 4 | "Then he'd walked into that" |
| | ratio | 0.039 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 128 | | matches | | 0 | "She kept her legs pumping," | | 1 | "Her shoes slapped wet pavement." | | 2 | "She had been patient." | | 3 | "He was fast." | | 4 | "She turned the corner after" | | 5 | "She pulled her phone from" | | 6 | "She turned it off to" | | 7 | "She'd never believed it." | | 8 | "She'd stake her pension on" | | 9 | "She'd stake her career." | | 10 | "She started down the stairs." | | 11 | "She reached the bottom." | | 12 | "She recognised the bones of" | | 13 | "She'd walked these tunnels before," | | 14 | "It wasn't mechanical." | | 15 | "It wasn't the rumble of" | | 16 | "It rose and fell like" | | 17 | "She could see it now" | | 18 | "Her heart hammered against her" | | 19 | "She counted the figures she" |
| | ratio | 0.297 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 128 | | matches | | 0 | "The rain hit Detective Harlow" | | 1 | "She kept her legs pumping," | | 2 | "Her shoes slapped wet pavement." | | 3 | "The city reeked of petrol," | | 4 | "She had been patient." | | 5 | "The man she was chasing," | | 6 | "The suspect burst out of" | | 7 | "The driver blared his horn," | | 8 | "Quinn followed, close enough now" | | 9 | "He was fast." | | 10 | "Every stride confirmed what her" | | 11 | "She turned the corner after" | | 12 | "Construction fencing in yellow and" | | 13 | "A faded sign warned of" | | 14 | "Another declared the area private" | | 15 | "Both signs were old." | | 16 | "Both were ignored." | | 17 | "The suspect went down." | | 18 | "Quinn stopped at the top" | | 19 | "Rain dripped from her closely" |
| | ratio | 0.719 | |
| 39.06% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 128 | | matches | | | ratio | 0.008 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 6 | | matches | | 0 | "She kept her legs pumping, breath sawing through clenched teeth as the suspect ducked into the alley ahead." | | 1 | "Below her, the suspect's footsteps rang and faded, swallowed by something that wasn't quite silence." | | 2 | "She recognised the bones of it from years working the Underground, the curve of the platform ahead, the columns that had once held up a tiled ceiling." | | 3 | "Vale was a hundred metres ahead, a dark shape moving with the certainty of a man who knew exactly where he was going." | | 4 | "Didn't know what they would do to an uninvited detective who walked into their market asking about a man who had been dead three years." | | 5 | "The market pulsed beyond the old ticket barriers, alive in a way that made her skin prickle and her breath come shallow." |
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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 | |