| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.26% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1336 | | totalAiIsmAdverbs | 1 | | found | | | 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) | |
| 85.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1336 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "churn" | | 1 | "rhythmic" | | 2 | "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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 81 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1346 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1087 | | uniqueNames | 22 | | maxNameDensity | 0.83 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 2 | | Harlow | 1 | | Quinn | 9 | | Tomás | 1 | | Herrera | 6 | | Limehouse | 1 | | Inverness | 2 | | Hawaiian | 1 | | Morris | 4 | | Tuesday | 1 | | Silvertown | 1 | | Alan | 1 | | Selhurst | 1 | | Park | 1 | | Underground | 1 | | Kentish | 1 | | Town | 1 | | Ainsworth | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Alan" | | 6 | "Ainsworth" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Limehouse" | | 4 | "Inverness" | | 5 | "Hawaiian" | | 6 | "Silvertown" | | 7 | "Selhurst" | | 8 | "Park" | | 9 | "Kentish" | | 10 | "Town" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1346 | | matches | (empty) | |
| 97.22% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 96 | | matches | | 0 | "right that day" | | 1 | "knew that tile" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 29.91 | | std | 25.58 | | cv | 0.855 | | sampleLengths | | 0 | 29 | | 1 | 13 | | 2 | 12 | | 3 | 56 | | 4 | 52 | | 5 | 55 | | 6 | 5 | | 7 | 39 | | 8 | 33 | | 9 | 11 | | 10 | 5 | | 11 | 2 | | 12 | 69 | | 13 | 90 | | 14 | 12 | | 15 | 13 | | 16 | 14 | | 17 | 42 | | 18 | 3 | | 19 | 79 | | 20 | 18 | | 21 | 60 | | 22 | 18 | | 23 | 75 | | 24 | 17 | | 25 | 5 | | 26 | 1 | | 27 | 59 | | 28 | 6 | | 29 | 37 | | 30 | 2 | | 31 | 59 | | 32 | 9 | | 33 | 8 | | 34 | 6 | | 35 | 59 | | 36 | 27 | | 37 | 73 | | 38 | 21 | | 39 | 6 | | 40 | 1 | | 41 | 55 | | 42 | 4 | | 43 | 39 | | 44 | 47 |
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| 96.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 81 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 183 | | matches | | 0 | "was going" | | 1 | "was breathing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 96 | | ratio | 0.073 | | matches | | 0 | "That was the professional part of him, she thought — the paramedic part, the part that had learned to move through a crowd without wasting a single motion." | | 1 | "A drunk in a Hawaiian shirt reeled out of a doorway and Herrera caught him by the elbow — actually caught him, actually steadied him — and the delay cost him three seconds." | | 2 | "Quinn's shoes were finished — cheap leather soaking through — and her right sock had gone cold and heavy, and she thought about Morris." | | 3 | "The road ended in a hoarding — blue plywood, planning notices peeling in the rain, a graphic of glass flats that would never get built." | | 4 | "Camden had swallowed a couple of stations back in the day — one under the road junction, sealed since before she was born, the kind of thing that turned up on urban exploration forums and in the mouths of coppers who liked a story." | | 5 | "A low churn of voices, folded and doubled by the tunnel, and under it something rhythmic — a bell, or a hammer on metal." | | 6 | "Below him the tunnel curved away, and in the curve a shape moved — tall, unhurried, carrying a lantern that gave off no smoke — and the churn of voices swelled and dropped like a tide going over shingle." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1081 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.024051803885291396 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0037002775208140612 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 14.02 | | std | 12.24 | | cv | 0.873 | | sampleLengths | | 0 | 29 | | 1 | 13 | | 2 | 8 | | 3 | 4 | | 4 | 4 | | 5 | 28 | | 6 | 24 | | 7 | 2 | | 8 | 32 | | 9 | 5 | | 10 | 5 | | 11 | 8 | | 12 | 22 | | 13 | 33 | | 14 | 5 | | 15 | 10 | | 16 | 29 | | 17 | 5 | | 18 | 28 | | 19 | 11 | | 20 | 5 | | 21 | 2 | | 22 | 15 | | 23 | 16 | | 24 | 14 | | 25 | 24 | | 26 | 8 | | 27 | 9 | | 28 | 51 | | 29 | 4 | | 30 | 18 | | 31 | 7 | | 32 | 5 | | 33 | 13 | | 34 | 14 | | 35 | 3 | | 36 | 25 | | 37 | 11 | | 38 | 3 | | 39 | 3 | | 40 | 6 | | 41 | 22 | | 42 | 3 | | 43 | 4 | | 44 | 44 | | 45 | 18 | | 46 | 3 | | 47 | 4 | | 48 | 24 | | 49 | 10 |
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| 59.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4166666666666667 | | totalSentences | 96 | | uniqueOpeners | 40 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 74 | | matches | | 0 | "Just enough for her to" | | 1 | "Somewhere off to the north" | | 2 | "A lot of people." |
| | ratio | 0.041 | |
| 52.43% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 74 | | matches | | 0 | "She got his name out" | | 1 | "He didn't look back." | | 2 | "He cut left around a" | | 3 | "She kept him in sight." | | 4 | "He turned right into Inverness" | | 5 | "She used the name she'd" | | 6 | "He turned his head then." | | 7 | "he called back" | | 8 | "She always thought about Morris" | | 9 | "She'd gone left and he'd" | | 10 | "She'd written the report." | | 11 | "She'd written it eleven times" | | 12 | "She had not gone right" | | 13 | "She was going right tonight." | | 14 | "He was gone." | | 15 | "She put her torch on" | | 16 | "She knew that tile." | | 17 | "It was people." | | 18 | "Her radio was dead." | | 19 | "Her phone had one bar" |
| | ratio | 0.419 | |
| 54.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 74 | | matches | | 0 | "The rain came sideways down" | | 1 | "She got his name out" | | 2 | "He didn't look back." | | 3 | "That was the professional part" | | 4 | "He cut left around a" | | 5 | "She kept him in sight." | | 6 | "That was all she needed." | | 7 | "He turned right into Inverness" | | 8 | "A drunk in a Hawaiian" | | 9 | "Quinn closed to twenty metres." | | 10 | "She used the name she'd" | | 11 | "He turned his head then." | | 12 | "he called back" | | 13 | "The alley behind Inverness dog-legged" | | 14 | "Bins, scaffolding, a fire door" | | 15 | "Quinn's shoes were finished —" | | 16 | "She always thought about Morris" | | 17 | "She'd gone left and he'd" | | 18 | "She'd written the report." | | 19 | "She'd written it eleven times" |
| | ratio | 0.811 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 20.41% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 6 | | matches | | 0 | "That was the professional part of him, she thought — the paramedic part, the part that had learned to move through a crowd without wasting a single motion." | | 1 | "He cut left around a shuttered kebab shop, and the puddle he hit sent up a sheet of water that caught the streetlight orange." | | 2 | "Eighteen years on the job had given her knees that complained on stairs and a lung capacity she maintained out of pure spite, four mornings a week on the towpat…" | | 3 | "Just enough for her to see his face under the streetlight, wet curls plastered to his forehead, warm brown eyes wide with something that wasn't fear of arrest." | | 4 | "The road ended in a hoarding — blue plywood, planning notices peeling in the rain, a graphic of glass flats that would never get built." | | 5 | "Camden had swallowed a couple of stations back in the day — one under the road junction, sealed since before she was born, the kind of thing that turned up on u…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "she breathed (breathe)" |
| | dialogueSentences | 28 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.25 | | effectiveRatio | 0.071 | |