| 66.67% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said again [again]" |
| | dialogueSentences | 15 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.2 | | effectiveRatio | 0.133 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1028 | | 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) | |
| 95.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1028 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 74 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1033 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 891 | | uniqueNames | 17 | | maxNameDensity | 0.67 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Raven | 1 | | Nest | 1 | | Camden | 1 | | Morris | 1 | | Bermondsey | 1 | | Street | 1 | | Asian | 1 | | Dalston | 1 | | Quinn | 6 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Rain | 1 | | Quieter | 1 |
| | persons | | 0 | "Nest" | | 1 | "Morris" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Rain" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "Raven" | | 4 | "Bermondsey" | | 5 | "Street" | | 6 | "Dalston" |
| | globalScore | 1 | | windowScore | 1 | |
| 38.89% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 2 | | matches | | 0 | "something like sulphur and dried lavender fi" | | 1 | "not quite" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.968 | | wordCount | 1033 | | matches | | 0 | "not the static amber she'd first seen but a shifting, restless gold, like torchlight but not quite" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 83 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 29.51 | | std | 26.47 | | cv | 0.897 | | sampleLengths | | 0 | 8 | | 1 | 97 | | 2 | 12 | | 3 | 45 | | 4 | 31 | | 5 | 7 | | 6 | 40 | | 7 | 71 | | 8 | 2 | | 9 | 67 | | 10 | 5 | | 11 | 74 | | 12 | 17 | | 13 | 4 | | 14 | 73 | | 15 | 5 | | 16 | 61 | | 17 | 2 | | 18 | 45 | | 19 | 77 | | 20 | 6 | | 21 | 9 | | 22 | 8 | | 23 | 25 | | 24 | 9 | | 25 | 6 | | 26 | 33 | | 27 | 3 | | 28 | 26 | | 29 | 15 | | 30 | 39 | | 31 | 39 | | 32 | 9 | | 33 | 45 | | 34 | 18 |
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| 86.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 74 | | matches | | 0 | "been aired" | | 1 | "was bricked" | | 2 | "was gone" | | 3 | "being beaten" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 148 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 83 | | ratio | 0.072 | | matches | | 0 | "He was fast — faster than anyone who'd spent the evening hunched over a corner table in the Raven's Nest had any right to be." | | 1 | "Not a signal issue — the thing had simply stopped, the way electronics sometimes stopped around certain people she'd been watching for the past three months." | | 2 | "She landed in a service lane that smelled of old cooking fat and something else beneath it — something metallic and old, like the inside of a church that hadn't been aired in decades." | | 3 | "He stood at a door — or what had been a door once." | | 4 | "The door — the impossible door — sat open at an angle, offering a rectangle of dim amber light from whatever lay behind it." | | 5 | "\"—Quinn, do you copy? Quinn?\" A woman's voice, unfamiliar." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 801 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.0299625468164794 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003745318352059925 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 12.45 | | std | 9.28 | | cv | 0.746 | | sampleLengths | | 0 | 8 | | 1 | 28 | | 2 | 25 | | 3 | 28 | | 4 | 16 | | 5 | 12 | | 6 | 33 | | 7 | 8 | | 8 | 4 | | 9 | 31 | | 10 | 7 | | 11 | 10 | | 12 | 1 | | 13 | 26 | | 14 | 3 | | 15 | 14 | | 16 | 23 | | 17 | 22 | | 18 | 12 | | 19 | 2 | | 20 | 34 | | 21 | 4 | | 22 | 29 | | 23 | 5 | | 24 | 14 | | 25 | 11 | | 26 | 13 | | 27 | 27 | | 28 | 9 | | 29 | 2 | | 30 | 6 | | 31 | 3 | | 32 | 4 | | 33 | 2 | | 34 | 4 | | 35 | 22 | | 36 | 24 | | 37 | 4 | | 38 | 23 | | 39 | 5 | | 40 | 27 | | 41 | 22 | | 42 | 5 | | 43 | 7 | | 44 | 2 | | 45 | 5 | | 46 | 2 | | 47 | 25 | | 48 | 13 | | 49 | 4 |
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| 75.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4819277108433735 | | totalSentences | 83 | | uniqueOpeners | 40 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 66 | | matches | | 0 | "Maybe twenty-five, south Asian, sharp" | | 1 | "Then he was gone." |
| | ratio | 0.03 | |
| 56.36% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 66 | | matches | | 0 | "He was fast — faster" | | 1 | "She'd clocked him forty minutes" | | 2 | "She checked the device with" | | 3 | "Her jaw tightened." | | 4 | "She caught the flash of" | | 5 | "She landed in a service" | | 6 | "She'd smelled that before." | | 7 | "She understood it better now." | | 8 | "He stood at a door" | | 9 | "He wasn't panicked." | | 10 | "He looked almost curious." | | 11 | "She stopped at the threshold." | | 12 | "She had put herself in" | | 13 | "She pulled out her phone." | | 14 | "She photographed the doorway anyway," | | 15 | "It was the noise of" | | 16 | "Her radio crackled alive without" | | 17 | "She pressed the earpiece." | | 18 | "She pulled the earpiece out" | | 19 | "She spun, reaching." |
| | ratio | 0.409 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 66 | | matches | | 0 | "The rain turned the street" | | 1 | "Quinn's boots punched through standing" | | 2 | "He was fast — faster" | | 3 | "She'd clocked him forty minutes" | | 4 | "The moment she'd moved to" | | 5 | "The Camden streets were half-empty" | | 6 | "Headlights dragged orange smears across" | | 7 | "Quinn pressed her earpiece." | | 8 | "A burst of static swallowed" | | 9 | "She checked the device with" | | 10 | "Her jaw tightened." | | 11 | "The suspect cut left without" | | 12 | "She caught the flash of" | | 13 | "A row of shuttered shops," | | 14 | "The man was already at" | | 15 | "She landed in a service" | | 16 | "She'd smelled that before." | | 17 | "The night Morris had died," | | 18 | "She understood it better now." | | 19 | "A set of metal stairs" |
| | ratio | 0.833 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 66 | | matches | | 0 | "Now he was running like" | | 1 | "Before he stepped through, he" |
| | ratio | 0.03 | |
| 56.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 4 | | matches | | 0 | "She'd clocked him forty minutes ago, nursing the same half-pint for two hours while his eyes worked the room like a camera, cataloguing everyone who came and we…" | | 1 | "She landed in a service lane that smelled of old cooking fat and something else beneath it — something metallic and old, like the inside of a church that hadn't…" | | 2 | "Quinn descended the stairs one deliberate step at a time, her hand moving to the firearm at her hip without drawing it." | | 3 | "She photographed the doorway anyway, the stone steps, the brickwork, the lock mechanism that shouldn't have existed on what appeared to be a sealed wall." |
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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 | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0 | | effectiveRatio | 0 | |