| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1482 | | 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) | |
| 49.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1482 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "fractured" | | 1 | "chill" | | 2 | "gleaming" | | 3 | "silence" | | 4 | "navigated" | | 5 | "shattered" | | 6 | "mechanical" | | 7 | "echoed" | | 8 | "tinged" | | 9 | "gloom" | | 10 | "porcelain" | | 11 | "oppressive" | | 12 | "lilt" | | 13 | "jaw clenched" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "jaw clenched" | | 1 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1482 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1482 | | uniqueNames | 22 | | maxNameDensity | 0.88 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 3 | | Harlow | 13 | | Quinn | 1 | | Herrera | 10 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Northern | 1 | | Line | 1 | | Tube | 4 | | Town | 1 | | Metropolitan | 2 | | Police | 2 | | Saint | 1 | | Christopher | 1 | | Veil | 3 | | Market | 3 | | Morris | 4 | | London | 1 | | Spanish | 1 | | Seville | 1 | | July | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Tube" | | 5 | "Police" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Market" | | 9 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Soho" | | 2 | "Town" | | 3 | "London" | | 4 | "Spanish" | | 5 | "Seville" |
| | globalScore | 1 | | windowScore | 1 | |
| 96.24% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 93 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like it led to the maintenance tun" | | 1 | "sounded like the paranoid ramblings of a c" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.675 | | wordCount | 1482 | | matches | | 0 | "not yet fully understand, but she knew they were lethal" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 43.59 | | std | 25.19 | | cv | 0.578 | | sampleLengths | | 0 | 62 | | 1 | 86 | | 2 | 105 | | 3 | 46 | | 4 | 53 | | 5 | 63 | | 6 | 52 | | 7 | 54 | | 8 | 62 | | 9 | 19 | | 10 | 39 | | 11 | 3 | | 12 | 59 | | 13 | 83 | | 14 | 29 | | 15 | 5 | | 16 | 25 | | 17 | 34 | | 18 | 50 | | 19 | 5 | | 20 | 67 | | 21 | 22 | | 22 | 32 | | 23 | 77 | | 24 | 66 | | 25 | 51 | | 26 | 60 | | 27 | 7 | | 28 | 18 | | 29 | 18 | | 30 | 20 | | 31 | 29 | | 32 | 55 | | 33 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 118 | | matches | | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 13 | | totalVerbs | 244 | | matches | | 0 | "was waiting" | | 1 | "was just beginning" | | 2 | "was heading" | | 3 | "was hiding" | | 4 | "were buying" | | 5 | "was running" | | 6 | "was breathing" | | 7 | "was holding" | | 8 | "was already beginning" | | 9 | "was weighing" | | 10 | "was selling" | | 11 | "was buying" | | 12 | "was hunting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 118 | | ratio | 0.008 | | matches | | 0 | "The rain in Camden did not fall in drops; it drove sideways in icy, needle-like sheets, turning the pavement into a slick mirror of fractured streetlights." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1496 | | adjectiveStacks | 2 | | stackExamples | | 0 | "strange, violet-tinged light" | | 1 | "small, lead-lined box" |
| | adverbCount | 35 | | adverbRatio | 0.02339572192513369 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.011363636363636364 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 12.56 | | std | 7.45 | | cv | 0.593 | | sampleLengths | | 0 | 26 | | 1 | 15 | | 2 | 21 | | 3 | 8 | | 4 | 27 | | 5 | 6 | | 6 | 19 | | 7 | 26 | | 8 | 24 | | 9 | 21 | | 10 | 10 | | 11 | 30 | | 12 | 20 | | 13 | 12 | | 14 | 10 | | 15 | 10 | | 16 | 4 | | 17 | 10 | | 18 | 15 | | 19 | 14 | | 20 | 24 | | 21 | 12 | | 22 | 6 | | 23 | 2 | | 24 | 4 | | 25 | 39 | | 26 | 15 | | 27 | 21 | | 28 | 16 | | 29 | 13 | | 30 | 4 | | 31 | 13 | | 32 | 12 | | 33 | 12 | | 34 | 15 | | 35 | 14 | | 36 | 26 | | 37 | 7 | | 38 | 10 | | 39 | 9 | | 40 | 16 | | 41 | 15 | | 42 | 8 | | 43 | 3 | | 44 | 4 | | 45 | 20 | | 46 | 12 | | 47 | 2 | | 48 | 3 | | 49 | 5 |
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| 36.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2796610169491525 | | totalSentences | 118 | | uniqueOpeners | 33 | |
| 29.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 112 | | matches | | 0 | "Officially a structural collapse." |
| | ratio | 0.009 | |
| 62.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 112 | | matches | | 0 | "Her lungs burned with the" | | 1 | "He vaulted a deep puddle," | | 2 | "She wiped the icy rain" | | 3 | "She had been staking out" | | 4 | "She was waiting for the" | | 5 | "She had followed him onto" | | 6 | "He darted down a narrow" | | 7 | "She checked the worn leather" | | 8 | "He was heading toward the" | | 9 | "She did not draw her" | | 10 | "She needed him alive." | | 11 | "She needed to know what" | | 12 | "She ducked under the broken" | | 13 | "She clicked on her penlight," | | 14 | "He was breathing hard, his" | | 15 | "She stepped where he stepped," | | 16 | "He wiped the rain from" | | 17 | "He was holding something small" | | 18 | "She peered around the edge" | | 19 | "He pressed the pale object" |
| | ratio | 0.393 | |
| 26.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 112 | | matches | | 0 | "The rain in Camden did" | | 1 | "Detective Harlow Quinn pumped her" | | 2 | "Her lungs burned with the" | | 3 | "Tomás Herrera was fast for" | | 4 | "He vaulted a deep puddle," | | 5 | "Harlow pushed harder, closing the" | | 6 | "She wiped the icy rain" | | 7 | "She had been staking out" | | 8 | "She was waiting for the" | | 9 | "She had followed him onto" | | 10 | "He darted down a narrow" | | 11 | "Harlow followed, her sharp jaw" | | 12 | "She checked the worn leather" | | 13 | "The streets were dead, save" | | 14 | "Herrera took a sharp left," | | 15 | "He was heading toward the" | | 16 | "The Metropolitan Police had fenced" | | 17 | "Harlow drew her baton, the" | | 18 | "She did not draw her" | | 19 | "She needed him alive." |
| | ratio | 0.866 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 112 | | matches | | 0 | "If she stepped inside, she" | | 1 | "If the clique realized she" | | 2 | "If she let the door" |
| | ratio | 0.027 | |
| 72.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 7 | | matches | | 0 | "At five foot nine, she had a longer stride, but the five-foot-ten suspect had the desperate energy of a man who knew he was being hunted." | | 1 | "She needed to know what the clique was hiding, what they were buying, and why a disgraced medic who had lost his NHS license for administering unauthorized trea…" | | 2 | "She clicked on her penlight, keeping the beam low to avoid giving away her exact position." | | 3 | "He was breathing hard, his chest heaving as he navigated the debris-strewn platform." | | 4 | "She had read the intercepted reports about the Veil Market, the underground black market that moved locations every full moon." | | 5 | "Three years ago, a case that had started with a simple missing persons report and ended in a warehouse smelling of ozone and dried blood." | | 6 | "She thought of the unexplained bruises on his corpse, the frost that had covered his skin in the middle of July." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |