| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1439 | | 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) | |
| 82.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1439 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "measured" | | 2 | "whisper" | | 3 | "flickered" |
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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 | 1 | | narrationSentences | 88 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1450 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 16 | | wordCount | 1420 | | uniqueNames | 9 | | maxNameDensity | 0.42 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Hawley | 1 | | Road | 1 | | Quinn | 6 | | Saint | 2 | | Christopher | 2 | | Camden | 1 | | Blitz | 1 | | Victorian | 1 | | Sunday | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Camden" | | 4 | "Sunday" |
| | places | | 0 | "Hawley" | | 1 | "Road" | | 2 | "Victorian" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | 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 | 1450 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 91 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 41.43 | | std | 34.44 | | cv | 0.831 | | sampleLengths | | 0 | 15 | | 1 | 91 | | 2 | 23 | | 3 | 2 | | 4 | 87 | | 5 | 92 | | 6 | 76 | | 7 | 9 | | 8 | 29 | | 9 | 9 | | 10 | 78 | | 11 | 22 | | 12 | 46 | | 13 | 80 | | 14 | 17 | | 15 | 4 | | 16 | 108 | | 17 | 4 | | 18 | 64 | | 19 | 22 | | 20 | 4 | | 21 | 35 | | 22 | 67 | | 23 | 22 | | 24 | 54 | | 25 | 19 | | 26 | 7 | | 27 | 133 | | 28 | 33 | | 29 | 3 | | 30 | 60 | | 31 | 67 | | 32 | 29 | | 33 | 14 | | 34 | 25 |
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| 93.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 88 | | matches | | 0 | "been sealed" | | 1 | "were gone" | | 2 | "was gone" |
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| 43.51% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 213 | | matches | | 0 | "was protecting" | | 1 | "were filing" | | 2 | "wasn't carrying" | | 3 | "was selling" | | 4 | "was selling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 1 | | flaggedSentences | 9 | | totalSentences | 91 | | ratio | 0.099 | | matches | | 0 | "No glance back, no stumble — just the pivot off the ball of his foot and the burst, and by the time Quinn had cleared the corner he was fifteen metres gone, hood down, his dark curls flattened black with rain, a canvas bag slapping against his hip." | | 1 | "Quinn knew this ground — three weeks of surveillance had taught her the shape of it — but knowing it and running it in the dark were different animals." | | 2 | "She came out ahead of him on the towpath and he saw her and pulled up hard, changed direction, and that was the first time she got a look at his face — the wide, warm brown eyes gone bright and flat with fear, the mouth set." | | 3 | "He was already gone, through a gap between two shuttered units, and she went in after him, and the ground got strange underfoot — gravel, then brick, then steps." | | 4 | "Her feet rang on the treads and then stopped ringing when she reached the bottom, because the bottom was soft — sand, she thought, and then decided she didn't want to know." | | 5 | "People were filing past him into the dark beyond — a woman in a fur coat and rubber boots, two men carrying a crate between them, a girl with a shaved head and a tattoo down her neck." | | 6 | "Her hand went to the inside of her jacket — warrant card, cuffs, the ASP at her belt — and stopped." | | 7 | "Then he looked at her, and something moved behind his eyes — not suspicion exactly; something slower, the way a man looks at a dog walking into a room on its hind legs." | | 8 | "She touched the medallion's shape in her memory — Saint Christopher, the patron of travellers, swinging out of the collar of a man who'd just brought her down here." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1411 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.021970233876683204 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002126151665485471 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 15.93 | | std | 14.28 | | cv | 0.896 | | sampleLengths | | 0 | 15 | | 1 | 13 | | 2 | 48 | | 3 | 30 | | 4 | 21 | | 5 | 2 | | 6 | 2 | | 7 | 4 | | 8 | 26 | | 9 | 57 | | 10 | 12 | | 11 | 29 | | 12 | 37 | | 13 | 14 | | 14 | 47 | | 15 | 8 | | 16 | 9 | | 17 | 12 | | 18 | 4 | | 19 | 5 | | 20 | 29 | | 21 | 6 | | 22 | 3 | | 23 | 54 | | 24 | 24 | | 25 | 12 | | 26 | 10 | | 27 | 12 | | 28 | 12 | | 29 | 7 | | 30 | 3 | | 31 | 12 | | 32 | 10 | | 33 | 54 | | 34 | 5 | | 35 | 11 | | 36 | 14 | | 37 | 3 | | 38 | 4 | | 39 | 16 | | 40 | 32 | | 41 | 41 | | 42 | 6 | | 43 | 3 | | 44 | 10 | | 45 | 4 | | 46 | 38 | | 47 | 12 | | 48 | 5 | | 49 | 9 |
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| 57.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4175824175824176 | | totalSentences | 91 | | uniqueOpeners | 38 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 83 | | matches | | 0 | "Then she went down." | | 1 | "Then he looked at her," | | 2 | "Further down, a woman was" | | 3 | "Then a tarpaulin at the" |
| | ratio | 0.048 | |
| 46.51% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 83 | | matches | | 0 | "She caught him at the" | | 1 | "He ran the way a" | | 2 | "She said it flat, no" | | 3 | "She went after him." | | 4 | "He went up a ramp" | | 5 | "She took the long way," | | 6 | "She came out ahead of" | | 7 | "It was the bag he" | | 8 | "He ran like a man" | | 9 | "He was already gone, through" | | 10 | "She stopped at the top" | | 11 | "She had her radio in" | | 12 | "She tried again." | | 13 | "She still had the file." | | 14 | "She still had the things" | | 15 | "She checked her watch, an" | | 16 | "Her feet rang on the" | | 17 | "He had a brazier down" | | 18 | "He didn't look up." | | 19 | "He barely glanced at it." |
| | ratio | 0.434 | |
| 80.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 83 | | matches | | 0 | "She caught him at the" | | 1 | "He ran the way a" | | 2 | "The Saint Christopher medallion had" | | 3 | "She said it flat, no" | | 4 | "She went after him." | | 5 | "The rain came down in" | | 6 | "Herrera cut left through the" | | 7 | "Quinn knew this ground —" | | 8 | "He went up a ramp" | | 9 | "She took the long way," | | 10 | "She came out ahead of" | | 11 | "It was the bag he" | | 12 | "He ran like a man" | | 13 | "He was already gone, through" | | 14 | "Steps she hadn't known were" | | 15 | "Steps going down." | | 16 | "She stopped at the top" | | 17 | "A rusted sign bolted to" | | 18 | "The rain came off the" | | 19 | "She had her radio in" |
| | ratio | 0.759 | |
| 60.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 83 | | matches | | | ratio | 0.012 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 5 | | matches | | 0 | "The rain came down in sheets off the shop awnings and turned the pavement into a slick, oily skin, and Camden's shutters were all down and padlocked, the night …" | | 1 | "She stopped at the top of them with her hand on the wet rail and her heart going, and she looked down into a stairwell that had no business existing behind a ch…" | | 2 | "A vault opened above her, three times the height of the tunnel behind, a great cathedral arch of Victorian brick with the old track beds still in it, and the be…" | | 3 | "She could climb the stairs and put it in her notebook as an unverified lead and come back with a warrant and a team that would find nothing but sand and a cold …" | | 4 | "She touched the medallion's shape in her memory — Saint Christopher, the patron of travellers, swinging out of the collar of a man who'd just brought her down h…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |