| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1008 | | 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) | |
| 90.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1008 | | totalAiIsms | 2 | | 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 | 1 | | narrationSentences | 65 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 66 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 14 | | totalWords | 1013 | | ratio | 0.014 | | matches | | 0 | "talking" | | 1 | "start with what's wrong, not what's there" | | 2 | "probably a trick of the light." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 982 | | uniqueNames | 13 | | maxNameDensity | 0.71 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Waterloo | 3 | | Tomás | 1 | | Herrera | 4 | | Morris | 4 | | Bermondsey | 2 | | Lambeth | 1 | | King | 1 | | College | 1 | | Underground | 1 | | Quinn | 7 |
| | persons | | 0 | "Raven" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "Waterloo" | | 2 | "Bermondsey" | | 3 | "Lambeth" | | 4 | "King" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 1 | | matches | | 0 | "quite read — but the lights were new" |
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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 | 1013 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 66 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 31.66 | | std | 30.28 | | cv | 0.957 | | sampleLengths | | 0 | 25 | | 1 | 10 | | 2 | 82 | | 3 | 35 | | 4 | 47 | | 5 | 3 | | 6 | 77 | | 7 | 7 | | 8 | 58 | | 9 | 6 | | 10 | 9 | | 11 | 8 | | 12 | 41 | | 13 | 2 | | 14 | 58 | | 15 | 6 | | 16 | 73 | | 17 | 10 | | 18 | 14 | | 19 | 30 | | 20 | 15 | | 21 | 109 | | 22 | 7 | | 23 | 74 | | 24 | 8 | | 25 | 9 | | 26 | 94 | | 27 | 16 | | 28 | 15 | | 29 | 46 | | 30 | 9 | | 31 | 10 |
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| 89.07% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 65 | | matches | | 0 | "been — eighteen" | | 1 | "being caught" | | 2 | "been brought" |
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| 30.15% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 157 | | matches | | 0 | "was running" | | 1 | "wasn't running" | | 2 | "was running" | | 3 | "was running" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 66 | | ratio | 0.091 | | matches | | 0 | "She'd only wanted to ask him a few questions about a body found near Waterloo three nights ago — a body with wounds she still couldn't explain to her superintendent — but the moment her eyes landed on him across the bar, something in his face had collapsed, and he was through the door before she'd even moved her hand." | | 1 | "The man ahead of her wasn't Tomás Herrera — she'd confirmed that back inside, when she'd put his face against the photograph on her phone and come up short." | | 2 | "She was faster on the flat, always had been — eighteen years of early morning runs had made her legs reliable things — but he was lighter and he knew these streets better than she did." | | 3 | "Read the street the way Morris had taught her — *start with what's wrong, not what's there* — and her eye caught it almost immediately." | | 4 | "Before that, DS Morris, three years ago, who had gone into a building in Lambeth following a lead she'd given him and had come out — been brought out — with injuries the hospital had photographed and quietly stopped asking about." | | 5 | "The tiles were original — she could tell from the curves and the faded lettering she couldn't quite read — but the lights were new." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 977 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.03582395087001024 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.01023541453428864 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 66 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 66 | | mean | 15.35 | | std | 11.72 | | cv | 0.763 | | sampleLengths | | 0 | 9 | | 1 | 16 | | 2 | 10 | | 3 | 22 | | 4 | 60 | | 5 | 35 | | 6 | 29 | | 7 | 7 | | 8 | 11 | | 9 | 3 | | 10 | 17 | | 11 | 9 | | 12 | 36 | | 13 | 15 | | 14 | 7 | | 15 | 6 | | 16 | 19 | | 17 | 7 | | 18 | 26 | | 19 | 6 | | 20 | 9 | | 21 | 8 | | 22 | 23 | | 23 | 18 | | 24 | 2 | | 25 | 7 | | 26 | 28 | | 27 | 23 | | 28 | 2 | | 29 | 4 | | 30 | 2 | | 31 | 1 | | 32 | 25 | | 33 | 27 | | 34 | 18 | | 35 | 4 | | 36 | 3 | | 37 | 3 | | 38 | 14 | | 39 | 30 | | 40 | 15 | | 41 | 14 | | 42 | 13 | | 43 | 41 | | 44 | 41 | | 45 | 7 | | 46 | 11 | | 47 | 25 | | 48 | 6 | | 49 | 15 |
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| 57.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.42424242424242425 | | totalSentences | 66 | | uniqueOpeners | 28 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 60 | | matches | | 0 | "Then she moved off the" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 60 | | matches | | 0 | "He'd bolted the moment she'd" | | 1 | "She'd only wanted to ask" | | 2 | "He'd been listening to things" | | 3 | "He took a left onto" | | 4 | "She was faster on the" | | 5 | "He moved with a specific," | | 6 | "He swung right, then immediately" | | 7 | "He glanced back once, and" | | 8 | "He wasn't running from the" | | 9 | "He was running from being" | | 10 | "She pulled out her radio" | | 11 | "She looked up for a" | | 12 | "She lowered the radio and" | | 13 | "She stood in the rain" | | 14 | "She'd started pulling at the" | | 15 | "She crouched and went through" | | 16 | "She stepped back against the" | | 17 | "She put the warrant card" |
| | ratio | 0.3 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 60 | | matches | | 0 | "The rain had turned Soho" | | 1 | "Every puddle threw back the" | | 2 | "Quinn kept her eyes on" | | 3 | "He'd bolted the moment she'd" | | 4 | "She'd only wanted to ask" | | 5 | "The man ahead of her" | | 6 | "He'd been listening to things" | | 7 | "That was enough." | | 8 | "He took a left onto" | | 9 | "Quinn went with him, her" | | 10 | "She was faster on the" | | 11 | "He moved with a specific," | | 12 | "That worried her more than" | | 13 | "He swung right, then immediately" | | 14 | "A market stall someone had" | | 15 | "Quinn went round it, lost" | | 16 | "He glanced back once, and" | | 17 | "He wasn't running from the" | | 18 | "He was running from being" | | 19 | "She pulled out her radio" |
| | ratio | 0.75 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 60 | | matches | | 0 | "Now her shoes were soaked" | | 1 | "Before that, another one in" | | 2 | "Before that, DS Morris, three" |
| | ratio | 0.05 | |
| 84.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 3 | | matches | | 0 | "He'd bolted the moment she'd stepped out of the Raven's Nest, which told her everything she needed to know about his conscience." | | 1 | "Now her shoes were soaked through and she was running hard past shuttered restaurants and a supermarket with its lights still blazing, past a group of students …" | | 2 | "The rain intensified without warning, a sharp lateral sheet of it that hit her in the face and turned everything grey and indistinct." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |