| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.23% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1326 | | 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) | |
| 81.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1326 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "flickered" | | 1 | "database" | | 2 | "measured" | | 3 | "glint" | | 4 | "footsteps" |
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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 | 97 | | matches | (empty) | |
| 98.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1337 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1275 | | uniqueNames | 19 | | maxNameDensity | 0.78 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 2 | | Harlow | 1 | | Quinn | 10 | | Ford | 1 | | Herrera | 9 | | Chinatown | 1 | | Christopher | 1 | | Morris | 3 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Control | 1 | | Euston | 1 | | Camden | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Christopher" | | 4 | "Morris" | | 5 | "Control" | | 6 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Chinatown" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" | | 6 | "Euston" | | 7 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 69.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like enough" | | 1 | "felt like a formality" |
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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 | 1337 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 34.28 | | std | 28.74 | | cv | 0.838 | | sampleLengths | | 0 | 87 | | 1 | 9 | | 2 | 39 | | 3 | 82 | | 4 | 20 | | 5 | 123 | | 6 | 23 | | 7 | 11 | | 8 | 76 | | 9 | 58 | | 10 | 3 | | 11 | 60 | | 12 | 52 | | 13 | 10 | | 14 | 37 | | 15 | 2 | | 16 | 88 | | 17 | 57 | | 18 | 29 | | 19 | 51 | | 20 | 19 | | 21 | 33 | | 22 | 7 | | 23 | 5 | | 24 | 24 | | 25 | 11 | | 26 | 24 | | 27 | 39 | | 28 | 5 | | 29 | 5 | | 30 | 45 | | 31 | 20 | | 32 | 14 | | 33 | 51 | | 34 | 16 | | 35 | 23 | | 36 | 12 | | 37 | 3 | | 38 | 64 |
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| 94.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 97 | | matches | | 0 | "was gone" | | 1 | "was gone" | | 2 | "being asked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 218 | | matches | | 0 | "was losing" | | 1 | "was shrinking" | | 2 | "were dissolving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 1 | | flaggedSentences | 9 | | totalSentences | 104 | | ratio | 0.087 | | matches | | 0 | "In Soho, a tail was a foot game — crowds, awnings, the reflections in dark shop windows." | | 1 | "And underneath all of it, in the folder she kept at home in a drawer she didn't talk about — the name Morris." | | 2 | "Herrera turned off Charing Cross Road and cut through a housing estate, and her radio squawked once in her pocket — Control, checking in — and she thumbed it silent without breaking stride." | | 3 | "He crossed Euston, ducked through Camden, and the streets began to change — fewer people, more shuttered fronts, the sodium streetlights spaced further apart." | | 4 | "Cracked tiles, peeling signage, a platform lit by lanterns strung on wire, and the platform was — she stopped." | | 5 | "Quinn watched him press something small and pale into the figure's palm — a token, carved bone, worn smooth — and the figure nodded him through into the tunnel beyond." | | 6 | "Then it laughed — a dry, rasping sound with nothing warm in it." | | 7 | "The watch had been her father's; the leather was cracked and dark with eighteen years of sweat and rain, and she'd worn it through every shift since the academy." | | 8 | "Quinn didn't let herself hesitate — hesitation was how people died in doorways — but as she crossed the threshold into the tunnel, following the ghost of footsteps deeper into the earth, she felt the absence on her wrist like a wound, and understood, with the cold clarity of eighteen years of instinct, that she had just traded something she could not buy back." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1272 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.025943396226415096 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0031446540880503146 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 12.86 | | std | 11.81 | | cv | 0.919 | | sampleLengths | | 0 | 21 | | 1 | 18 | | 2 | 48 | | 3 | 2 | | 4 | 7 | | 5 | 25 | | 6 | 4 | | 7 | 10 | | 8 | 29 | | 9 | 2 | | 10 | 41 | | 11 | 10 | | 12 | 20 | | 13 | 5 | | 14 | 17 | | 15 | 22 | | 16 | 14 | | 17 | 17 | | 18 | 2 | | 19 | 3 | | 20 | 43 | | 21 | 23 | | 22 | 11 | | 23 | 3 | | 24 | 33 | | 25 | 2 | | 26 | 2 | | 27 | 21 | | 28 | 15 | | 29 | 24 | | 30 | 20 | | 31 | 14 | | 32 | 3 | | 33 | 23 | | 34 | 31 | | 35 | 6 | | 36 | 6 | | 37 | 7 | | 38 | 12 | | 39 | 27 | | 40 | 5 | | 41 | 5 | | 42 | 5 | | 43 | 23 | | 44 | 2 | | 45 | 7 | | 46 | 2 | | 47 | 10 | | 48 | 19 | | 49 | 3 |
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| 63.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4230769230769231 | | totalSentences | 104 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 86 | | matches | | 0 | "Then he stopped." | | 1 | "Then he was gone, down" | | 2 | "Then she crossed the street." | | 3 | "Then it laughed — a" | | 4 | "Somewhere beneath the fabric, the" |
| | ratio | 0.058 | |
| 80.47% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 86 | | matches | | 0 | "She'd been in the car" | | 1 | "She'd done the boredom." | | 2 | "She knew the shape of" | | 3 | "He checked the alley both" | | 4 | "She didn't take the car." | | 5 | "She kept fifty yards between" | | 6 | "He moved like a man" | | 7 | "She'd read his file enough" | | 8 | "She pushed the thought down" | | 9 | "He crossed Euston, ducked through" | | 10 | "He was moving quicker now," | | 11 | "He crouched behind a construction" | | 12 | "She flattened against the doorframe," | | 13 | "His warm brown eyes passed" | | 14 | "It was full." | | 15 | "She'd read the phrase in" | | 16 | "She checked her watch." | | 17 | "Her radio was dead air." | | 18 | "Her firearm suddenly felt like" | | 19 | "She thought of the locked" |
| | ratio | 0.349 | |
| 70.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 86 | | matches | | 0 | "Rain came down over Soho" | | 1 | "The green sign above the" | | 2 | "Detective Harlow Quinn sat in" | | 3 | "She'd been in the car" | | 4 | "She'd done the boredom." | | 5 | "Tonight, she suspected, was going" | | 6 | "She knew the shape of" | | 7 | "He checked the alley both" | | 8 | "Quinn set the cup down," | | 9 | "She didn't take the car." | | 10 | "She kept fifty yards between" | | 11 | "He moved like a man" | | 12 | "Every block or so, his" | | 13 | "Patron of travellers." | | 14 | "She'd read his file enough" | | 15 | "She pushed the thought down" | | 16 | "The rain thickened." | | 17 | "Herrera turned off Charing Cross" | | 18 | "He crossed Euston, ducked through" | | 19 | "He was moving quicker now," |
| | ratio | 0.779 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 86 | | matches | | 0 | "If she brought Herrera in" | | 1 | "If she followed him, she" | | 2 | "If she walked away now," |
| | ratio | 0.035 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "At eleven fifty-two, the alley door beside the Nest swung open and a figure slipped out, hunched under a thin jacket that was losing its fight with the rain." | | 1 | "Every block or so, his right hand came up to his throat, touching the medallion hanging there." | | 2 | "She'd read his file enough times to have it by heart: former paramedic, struck off for administering treatments to patients who didn't exist in any database she…" | | 3 | "Stalls lined the walls where ticket machines had been, and the crowd moving between them was like no crowd she'd ever policed: coats that shifted colour when th…" | | 4 | "Three years were dissolving into the dark, and Quinn stood in the mouth of a world she didn't understand, in a station that shouldn't exist, being asked for a t…" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "the figure said, as if it could hear her thinking" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |