| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1314 | | totalAiIsmAdverbs | 2 | | 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) | |
| 88.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1314 | | totalAiIsms | 3 | | 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 | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 24 | | totalWords | 1327 | | ratio | 0.018 | | matches | | 0 | "oh, you're not meant to be here." | | 1 | "I don't know" | | 2 | "unexplained" | | 3 | "Unexplained." | | 4 | "Camden, hoarding, disused stn, Herrera T., 23:41" | | 5 | "moves" | | 6 | "the Market" | | 7 | "touching her" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1190 | | uniqueNames | 24 | | maxNameDensity | 0.76 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Frith | 1 | | Street | 2 | | Tomás | 1 | | Herrera | 7 | | Soho | 1 | | Bateman | 1 | | Saint | 1 | | Christopher | 1 | | Quinn | 9 | | Charing | 1 | | Cross | 1 | | Road | 2 | | Foyles | 1 | | Tottenham | 1 | | Court | 1 | | Camden | 2 | | Spanish | 1 | | Slow | 1 | | Tube | 1 | | Morris | 3 | | Tuesday | 2 | | Deptford | 1 | | Professional | 1 | | Standards | 1 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Quinn" | | 5 | "Foyles" | | 6 | "Camden" | | 7 | "Slow" | | 8 | "Morris" | | 9 | "Standards" |
| | places | | 0 | "Frith" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Bateman" | | 4 | "Charing" | | 5 | "Cross" | | 6 | "Road" | | 7 | "Tottenham" | | 8 | "Court" | | 9 | "Deptford" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.754 | | wordCount | 1327 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 102 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 34.03 | | std | 23.67 | | cv | 0.696 | | sampleLengths | | 0 | 47 | | 1 | 29 | | 2 | 34 | | 3 | 20 | | 4 | 50 | | 5 | 65 | | 6 | 25 | | 7 | 28 | | 8 | 43 | | 9 | 31 | | 10 | 50 | | 11 | 54 | | 12 | 8 | | 13 | 52 | | 14 | 15 | | 15 | 18 | | 16 | 3 | | 17 | 76 | | 18 | 26 | | 19 | 47 | | 20 | 2 | | 21 | 41 | | 22 | 18 | | 23 | 3 | | 24 | 28 | | 25 | 110 | | 26 | 22 | | 27 | 13 | | 28 | 50 | | 29 | 73 | | 30 | 20 | | 31 | 18 | | 32 | 7 | | 33 | 70 | | 34 | 21 | | 35 | 3 | | 36 | 54 | | 37 | 16 | | 38 | 37 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 191 | | matches | | 0 | "was going" | | 1 | "was standing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 102 | | ratio | 0.088 | | matches | | 0 | "Not the fleeing — anyone ran from police, that was practically a civic tradition in Soho — but the look." | | 1 | "She lost him at a corner, found him again by sound — trainers on standing water, that slap-slap rhythm, unmistakable." | | 2 | "He looked back at her — twenty metres, maybe less — and he was smiling." | | 3 | "Behind it, a set of tiled stairs went into the ground, and the tiles were the wrong colour — that cream and oxblood of a Tube station that hadn't seen a train since her mother was in nappies." | | 4 | "Morris had gone into a stairwell in Deptford ahead of her because he always went in ahead of her, and she'd heard him say — clearly, calmly, in the voice he used for lost children and drunk students — *oh, you're not meant to be here.* Then nothing." | | 5 | "She typed the address into a note anyway — *Camden, hoarding, disused stn, Herrera T., 23:41* — and put it back in her pocket, and understood, doing it, that this was a ritual and not a precaution." | | 6 | "Procedure said wait for units, wait for a structural assessment, wait for a warrant, wait, wait, wait, and by then Herrera would be a rumour and whatever was down those stairs would have moved, because Herrera had said *moves* — no, he hadn't." | | 7 | "Not flickered — pulsed, slow, like something breathing at the bottom of a well." | | 8 | "Not stopped falling — stopped *touching her*, as though the stairwell had drawn a line and the weather respected it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1181 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.0279424216765453 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0059271803556308214 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 13.01 | | std | 11.35 | | cv | 0.873 | | sampleLengths | | 0 | 18 | | 1 | 29 | | 2 | 18 | | 3 | 11 | | 4 | 8 | | 5 | 20 | | 6 | 2 | | 7 | 4 | | 8 | 7 | | 9 | 13 | | 10 | 21 | | 11 | 4 | | 12 | 7 | | 13 | 18 | | 14 | 31 | | 15 | 2 | | 16 | 2 | | 17 | 30 | | 18 | 15 | | 19 | 1 | | 20 | 9 | | 21 | 28 | | 22 | 10 | | 23 | 1 | | 24 | 6 | | 25 | 26 | | 26 | 5 | | 27 | 3 | | 28 | 23 | | 29 | 1 | | 30 | 4 | | 31 | 25 | | 32 | 20 | | 33 | 17 | | 34 | 7 | | 35 | 30 | | 36 | 2 | | 37 | 6 | | 38 | 22 | | 39 | 15 | | 40 | 3 | | 41 | 12 | | 42 | 3 | | 43 | 9 | | 44 | 3 | | 45 | 5 | | 46 | 1 | | 47 | 2 | | 48 | 10 | | 49 | 3 |
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| 63.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4411764705882353 | | totalSentences | 102 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 74 | | matches | | 0 | "Just the throat of it," | | 1 | "Then a smell like hot" | | 2 | "Then a stairwell with nobody" | | 3 | "Somewhere down there a bell" |
| | ratio | 0.054 | |
| 95.68% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 74 | | matches | | 0 | "He caught himself on a" | | 1 | "She shouted it into the" | | 2 | "He went right, past the" | | 3 | "She kept the curly dark" | | 4 | "She'd found him in the" | | 5 | "she said, to nobody" | | 6 | "Her lungs burned." | | 7 | "He was going north." | | 8 | "She lost him at a" | | 9 | "Her hair was flat to" | | 10 | "He stopped at the mouth" | | 11 | "He looked back at her" | | 12 | "She kept walking" | | 13 | "He pulled a sheet of" | | 14 | "He said it gently, and" | | 15 | "He went down." | | 16 | "She stood at the top" | | 17 | "She'd carried that word around" | | 18 | "She typed the address into" | | 19 | "He'd said *the Market*, and" |
| | ratio | 0.311 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 74 | | matches | | 0 | "The rain came sideways down" | | 1 | "Quinn ran through it with" | | 2 | "He caught himself on a" | | 3 | "That was the thing that" | | 4 | "Something closer to apology." | | 5 | "She shouted it into the" | | 6 | "He went right, past the" | | 7 | "Quinn shouldered through them." | | 8 | "Someone screamed at her about" | | 9 | "She kept the curly dark" | | 10 | "She'd found him in the" | | 11 | "The woman had been up" | | 12 | "Quinn had eighteen years of" | | 13 | "Herrera hit Charing Cross Road" | | 14 | "A cyclist bailing into a" | | 15 | "Quinn timed the gap and" | | 16 | "she said, to nobody" | | 17 | "Her lungs burned." | | 18 | "He was going north." | | 19 | "She lost him at a" |
| | ratio | 0.703 | |
| 67.57% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 74 | | matches | | 0 | "By the time she reached" |
| | ratio | 0.014 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 4 | | matches | | 0 | "Not the fleeing — anyone ran from police, that was practically a civic tradition in Soho — but the look." | | 1 | "The leather watch strap on her left wrist had gone slick and cold, and she kept looking at it because she needed something with numbers on it, something that be…" | | 2 | "Quinn wiped her palm on her sodden coat, which achieved nothing at all, and put her hand on the tiled wall." | | 3 | "Not stopped falling — stopped *touching her*, as though the stairwell had drawn a line and the weather respected it." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "He said, and that gentleness landed harder than a threat" |
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| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 18 | | tagDensity | 0.389 | | leniency | 0.778 | | rawRatio | 0.143 | | effectiveRatio | 0.111 | |