| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1513 | | 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) | |
| 93.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1513 | | 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 | 0 | | narrationSentences | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 67 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1528 | | ratio | 0 | | matches | (empty) | |
| 62.50% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 1 | | matches | | 0 | "Draft, she told herself." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1339 | | uniqueNames | 16 | | maxNameDensity | 0.45 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Buck | 1 | | Street | 1 | | Chalk | 2 | | Farm | 2 | | Road | 2 | | Quinn | 5 | | Astra | 1 | | Herrera | 6 | | Stables | 1 | | Underground | 1 | | Silence | 1 | | Teeth | 1 | | King | 1 | | Cross | 1 | | Bermondsey | 1 | | Morris | 2 |
| | persons | | 0 | "Buck" | | 1 | "Street" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Underground" | | 5 | "Teeth" | | 6 | "King" | | 7 | "Cross" | | 8 | "Morris" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "Stables" | | 4 | "Bermondsey" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | 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 | 1528 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 25.9 | | std | 24.14 | | cv | 0.932 | | sampleLengths | | 0 | 33 | | 1 | 89 | | 2 | 2 | | 3 | 69 | | 4 | 39 | | 5 | 9 | | 6 | 35 | | 7 | 11 | | 8 | 15 | | 9 | 73 | | 10 | 32 | | 11 | 18 | | 12 | 24 | | 13 | 11 | | 14 | 11 | | 15 | 17 | | 16 | 6 | | 17 | 42 | | 18 | 12 | | 19 | 8 | | 20 | 11 | | 21 | 2 | | 22 | 46 | | 23 | 6 | | 24 | 20 | | 25 | 66 | | 26 | 9 | | 27 | 48 | | 28 | 5 | | 29 | 75 | | 30 | 22 | | 31 | 38 | | 32 | 26 | | 33 | 3 | | 34 | 59 | | 35 | 21 | | 36 | 1 | | 37 | 72 | | 38 | 25 | | 39 | 1 | | 40 | 18 | | 41 | 15 | | 42 | 17 | | 43 | 7 | | 44 | 6 | | 45 | 19 | | 46 | 21 | | 47 | 4 | | 48 | 6 | | 49 | 96 |
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| 98.58% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 105 | | matches | | 0 | "been taught" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 216 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 3 | | flaggedSentences | 13 | | totalSentences | 127 | | ratio | 0.102 | | matches | | 0 | "A minute later the side door of the lockup opened and a man stepped out under the awning — olive skin, dark curls flattened by rain, a nylon holdall over one shoulder." | | 1 | "Under the awning's dead bulb his eyes found hers, and something in them gave up — the look of a man who had run before and knew how it ended." | | 2 | "Quinn ran the way she'd been taught to run — arms in, breath counted in threes, corners taken at the apex — while Herrera ran like the streets owed him money, never checking a junction, choosing without looking." | | 3 | "Herrera hurdled a chain strung between two bollards; she went under it and a stitch settled in under her ribs like a blade finding its groove." | | 4 | "He kicked a stack of crates into the lane behind him; she went through the gap they left rather than around it." | | 5 | "The lane dead-ended at hoarding — plywood warped by years of weather, posters hanging off it in wet curls." | | 6 | "Her torch beam snagged on a shape draped along the wall, and the shape gathered itself — she counted legs and got a number her eye refused — and then it was plastic sheeting again, hanging, dripping, innocent." | | 7 | "Herrera stood at it with one hand raised to knock, and he heard her — she saw it land in his shoulders." | | 8 | "He knocked — three, a pause, two — and fed something through the slot, small and pale as a knuckle bone." | | 9 | "\"Loud coat. Loud questions.\" Teeth clicked — a laugh with the humor removed." | | 10 | "By then Herrera would be a rumor, and the door's owner would be a story, and the question she'd carried for three years — the cellar in Bermondsey, Morris's torch rolling a slow circle on the floor beside him, and nothing else in that room, nothing, she'd written the word eleven times in her own statement — would fold itself shut like a file no one would ever open." | | 11 | "Leather gone soft as an old dog's ear; she'd replaced the keeper twice herself." | | 12 | "\"For the door.\" She set the watch into the palm that slid through the hatch — gray, wide, its knuckles bending half a turn past where knuckles should stop." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1327 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.02110022607385079 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0007535795026375283 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 12.03 | | std | 10.07 | | cv | 0.837 | | sampleLengths | | 0 | 7 | | 1 | 26 | | 2 | 23 | | 3 | 2 | | 4 | 12 | | 5 | 10 | | 6 | 3 | | 7 | 32 | | 8 | 7 | | 9 | 2 | | 10 | 6 | | 11 | 5 | | 12 | 37 | | 13 | 9 | | 14 | 2 | | 15 | 2 | | 16 | 8 | | 17 | 8 | | 18 | 13 | | 19 | 18 | | 20 | 9 | | 21 | 2 | | 22 | 3 | | 23 | 30 | | 24 | 8 | | 25 | 3 | | 26 | 15 | | 27 | 17 | | 28 | 7 | | 29 | 11 | | 30 | 38 | | 31 | 27 | | 32 | 5 | | 33 | 8 | | 34 | 10 | | 35 | 24 | | 36 | 4 | | 37 | 7 | | 38 | 11 | | 39 | 3 | | 40 | 14 | | 41 | 6 | | 42 | 4 | | 43 | 26 | | 44 | 7 | | 45 | 5 | | 46 | 12 | | 47 | 8 | | 48 | 11 | | 49 | 2 |
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| 83.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5196850393700787 | | totalSentences | 127 | | uniqueOpeners | 66 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 96 | | matches | | 0 | "Then he ran." | | 1 | "Then, halfway down the lane," | | 2 | "Then a sound like gravel" |
| | ratio | 0.031 | |
| 65.83% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 96 | | matches | | 0 | "It came down hard and" | | 1 | "She checked the worn leather" | | 2 | "She knew the file by" | | 3 | "His old NHS number had" | | 4 | "She crossed at an angle," | | 5 | "He said it like an" | | 6 | "She gave him three strides," | | 7 | "He went west along Chalk" | | 8 | "She ran on the coin." | | 9 | "She pinned her phone to" | | 10 | "She caught and" | | 11 | "She pocketed the phone." | | 12 | "Her knees kept a ledger" | | 13 | "They'd send the bill tomorrow." | | 14 | "His voice bounced off the" | | 15 | "He burst onto the main" | | 16 | "He threw a pallet against" | | 17 | "He kicked a stack of" | | 18 | "She pulled up short and" | | 19 | "She went through after him." |
| | ratio | 0.385 | |
| 64.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 96 | | matches | | 0 | "It came down hard and" | | 1 | "Harlow Quinn sat in an" | | 2 | "The first two had bought" | | 3 | "She checked the worn leather" | | 4 | "Quarter past midnight." | | 5 | "A minute later the side" | | 6 | "Glass clinked inside it with" | | 7 | "She knew the file by" | | 8 | "His old NHS number had" | | 9 | "The first two had burned" | | 10 | "A coroner's report that read" | | 11 | "Quinn got out of the" | | 12 | "She crossed at an angle," | | 13 | "The holdall swung." | | 14 | "He said it like an" | | 15 | "She gave him three strides," | | 16 | "He went west along Chalk" | | 17 | "Cobbles shone greasy under the" | | 18 | "A fox bolted from under" | | 19 | "Quinn ran the way she'd" |
| | ratio | 0.792 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 84.55% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 4 | | matches | | 0 | "Ahead of him, a kebab shop was shuttering for the night, its owner wrestling the last panel down." | | 1 | "In and out, cold gusts on a rhythm, as if the Underground still dreamed of trains somewhere far below." | | 2 | "She kept the beam moving and her feet moving and did not let the two speeds argue." | | 3 | "It opened on a wall of heat and noise: a hundred voices bargaining over the din, candle smoke, hot metal, a fiddle playing somewhere and something that was not …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.031 | | leniency | 0.063 | | rawRatio | 0 | | effectiveRatio | 0 | |