| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "Herrera's voice dropped so [so]" |
| | dialogueSentences | 31 | | tagDensity | 0.355 | | leniency | 0.71 | | rawRatio | 0.091 | | effectiveRatio | 0.065 | |
| 96.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1268 | | totalAiIsmAdverbs | 1 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
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| | 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1268 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 87 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1272 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.77% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1095 | | uniqueNames | 18 | | maxNameDensity | 1 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Quinn | 11 | | Tomás | 1 | | Herrera | 6 | | Kentish | 1 | | Town | 1 | | Static | 1 | | Saint | 1 | | Christopher | 1 | | Turkish | 1 | | Water | 1 | | London | 1 | | Tube | 1 | | Morris | 1 | | Chingford | 1 | | Harlow | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Static" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Water" | | 7 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Kentish" | | 4 | "Town" | | 5 | "London" |
| | globalScore | 0.998 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | 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 | 1272 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 106 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 24 | | std | 19.47 | | cv | 0.811 | | sampleLengths | | 0 | 26 | | 1 | 43 | | 2 | 6 | | 3 | 2 | | 4 | 33 | | 5 | 35 | | 6 | 10 | | 7 | 50 | | 8 | 10 | | 9 | 48 | | 10 | 41 | | 11 | 5 | | 12 | 35 | | 13 | 39 | | 14 | 42 | | 15 | 6 | | 16 | 4 | | 17 | 41 | | 18 | 1 | | 19 | 1 | | 20 | 28 | | 21 | 25 | | 22 | 8 | | 23 | 35 | | 24 | 5 | | 25 | 4 | | 26 | 45 | | 27 | 13 | | 28 | 59 | | 29 | 9 | | 30 | 39 | | 31 | 6 | | 32 | 47 | | 33 | 8 | | 34 | 63 | | 35 | 25 | | 36 | 10 | | 37 | 86 | | 38 | 15 | | 39 | 1 | | 40 | 39 | | 41 | 3 | | 42 | 5 | | 43 | 2 | | 44 | 19 | | 45 | 47 | | 46 | 8 | | 47 | 23 | | 48 | 44 | | 49 | 27 |
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| 93.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 87 | | matches | | 0 | "was gone" | | 1 | "been chipped" | | 2 | "been trained" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 161 | | matches | | 0 | "wasn't fighting" | | 1 | "was standing" |
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| 61.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 106 | | ratio | 0.028 | | matches | | 0 | "He'd come out of the chemist with a carrier bag hooked on two fingers, hood up, and he'd looked left first — wrong direction for a man heading home to Kentish Town." | | 1 | "She thought about it later — over and over, in the flat grey hours — that the whole thing lasted maybe a second and a half, and in that second and a half she did nothing but breathe." | | 2 | "She'd swept the spilled contents of Herrera's bag off the yard concrete on instinct, the way she'd been trained, evidence first — ampoules, tubing, and something else that had rolled against her boot." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1094 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.022851919561243144 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007312614259597806 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 12 | | std | 11.6 | | cv | 0.967 | | sampleLengths | | 0 | 26 | | 1 | 32 | | 2 | 4 | | 3 | 3 | | 4 | 4 | | 5 | 3 | | 6 | 3 | | 7 | 2 | | 8 | 33 | | 9 | 23 | | 10 | 12 | | 11 | 4 | | 12 | 6 | | 13 | 7 | | 14 | 43 | | 15 | 5 | | 16 | 5 | | 17 | 3 | | 18 | 5 | | 19 | 40 | | 20 | 41 | | 21 | 5 | | 22 | 5 | | 23 | 30 | | 24 | 7 | | 25 | 5 | | 26 | 27 | | 27 | 28 | | 28 | 14 | | 29 | 6 | | 30 | 4 | | 31 | 7 | | 32 | 5 | | 33 | 19 | | 34 | 10 | | 35 | 1 | | 36 | 1 | | 37 | 19 | | 38 | 9 | | 39 | 12 | | 40 | 8 | | 41 | 5 | | 42 | 8 | | 43 | 19 | | 44 | 16 | | 45 | 5 | | 46 | 4 | | 47 | 7 | | 48 | 38 | | 49 | 13 |
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| 88.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5566037735849056 | | totalSentences | 106 | | uniqueOpeners | 59 | |
| 45.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 73 | | matches | | | ratio | 0.014 | |
| 72.05% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 73 | | matches | | 0 | "He'd come out of the" | | 1 | "She corrected herself as she" | | 2 | "He glanced back." | | 3 | "She took it at speed." | | 4 | "He was on the bins." | | 5 | "They came down together in" | | 6 | "His elbow caught her cheekbone." | | 7 | "She had his wrist, the" | | 8 | "He wasn't fighting" | | 9 | "He'd gone loose under her" | | 10 | "She got her cuffs out" | | 11 | "His chest went up and" | | 12 | "She should have snapped the" | | 13 | "She thought about it later" | | 14 | "He went over the wall" | | 15 | "She looked at it, that" | | 16 | "He didn't go to the" | | 17 | "He went to a hoarding," | | 18 | "She went down two flights" | | 19 | "She saw a woman selling" |
| | ratio | 0.37 | |
| 55.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 73 | | matches | | 0 | "Rain came off the awnings" | | 1 | "He'd come out of the" | | 2 | "That was the tell." | | 3 | "The wrongness of it." | | 4 | "The bag swung and clipped" | | 5 | "She corrected herself as she" | | 6 | "Static chewed the reply." | | 7 | "Something about units, something about" | | 8 | "Herrera cut across the road" | | 9 | "A night bus laid on" | | 10 | "He glanced back." | | 11 | "Rain sat in his eyelashes." | | 12 | "The Saint Christopher on its" | | 13 | "Quinn had eighteen years on" | | 14 | "She took it at speed." | | 15 | "He was on the bins." | | 16 | "They came down together in" | | 17 | "His elbow caught her cheekbone." | | 18 | "The carrier bag burst and" | | 19 | "She had his wrist, the" |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 31.25% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 5 | | matches | | 0 | "The bag swung and clipped a bollard and he kept going, trainers slapping the flooded pavement, and Quinn went after him with her coat flapping open and her radi…" | | 1 | "Quinn had eighteen years on the job and knees that told her about all of them, but she'd run this borough as a probationer and she knew that alley dead-ended in…" | | 2 | "She looked at it, that dead plastic brick, and something in the base of her skull went cold in a way that had nothing to do with the weather." | | 3 | "He went to a hoarding, a long stretch of blue ply plastered with fly-posters for club nights that had happened years ago, and he put both hands flat on it and p…" | | 4 | "Quinn spun with her hand going to her hip and found a boy of about sixteen sitting on a milk crate, hood up, holding an umbrella he did not need." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 85.48% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "She corrected (correct)" | | 1 | "she shouted (shout)" |
| | dialogueSentences | 31 | | tagDensity | 0.161 | | leniency | 0.323 | | rawRatio | 0.4 | | effectiveRatio | 0.129 | |