| 66.67% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 3 | | adverbTags | | 0 | "he said quietly [quietly]" | | 1 | "Siobhan asked softly [softly]" | | 2 | "Quinn realised aloud [aloud]" |
| | dialogueSentences | 45 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.2 | | effectiveRatio | 0.133 | |
| 86.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2504 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "suddenly" | | 3 | "softly" | | 4 | "quickly" |
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| 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) | |
| 54.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2504 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "streaming" | | 1 | "echo" | | 2 | "electric" | | 3 | "stomach" | | 4 | "constructed" | | 5 | "gleaming" | | 6 | "lilt" | | 7 | "pulse" | | 8 | "echoing" | | 9 | "raced" | | 10 | "flickered" | | 11 | "charged" | | 12 | "crystal" | | 13 | "vibrated" | | 14 | "navigated" | | 15 | "treacherous" | | 16 | "footsteps" | | 17 | "could feel" | | 18 | "intricate" | | 19 | "almost alive" | | 20 | "weight" | | 21 | "flicker" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 180 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 180 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 210 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 2484 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 1 | | matches | | 0 | "Creatures—no, people, she told herself fiercely—moved between the stalls." |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 87 | | wordCount | 2031 | | uniqueNames | 23 | | maxNameDensity | 1.33 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 27 | | Tomás | 15 | | Herrera | 4 | | Saint | 3 | | Christopher | 3 | | Raven | 2 | | Nest | 2 | | Fitzrovia | 1 | | Morris | 5 | | Northern | 1 | | Line | 1 | | Glock | 3 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Underground | 1 | | Seville | 1 | | Siobhan | 8 | | Thought | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Raven" | | 7 | "Nest" | | 8 | "Morris" | | 9 | "Glock" | | 10 | "Underground" | | 11 | "Siobhan" |
| | places | | 0 | "Soho" | | 1 | "Fitzrovia" | | 2 | "Market" | | 3 | "Seville" |
| | globalScore | 0.835 | | windowScore | 0.833 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 130 | | glossingSentenceCount | 5 | | matches | | 0 | "and clipped it visibly to her lapel" | | 1 | "looked like liquid starlight" | | 2 | "he man who had apparently been trying to save" | | 3 | "sounded like Morris laughing the way he ha" | | 4 | "looked like bottled dreams overturned, re" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.805 | | wordCount | 2484 | | matches | | 0 | "not like a criminal caught, but like a man watching someone walk toward a cliff edge" | | 1 | "Not the weak yellow bulbs of the station, but every single lantern in the alcove" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 210 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 81 | | mean | 30.67 | | std | 22.61 | | cv | 0.737 | | sampleLengths | | 0 | 90 | | 1 | 75 | | 2 | 57 | | 3 | 53 | | 4 | 5 | | 5 | 11 | | 6 | 81 | | 7 | 37 | | 8 | 29 | | 9 | 67 | | 10 | 58 | | 11 | 6 | | 12 | 102 | | 13 | 3 | | 14 | 43 | | 15 | 50 | | 16 | 3 | | 17 | 59 | | 18 | 81 | | 19 | 18 | | 20 | 19 | | 21 | 36 | | 22 | 40 | | 23 | 21 | | 24 | 25 | | 25 | 56 | | 26 | 12 | | 27 | 42 | | 28 | 44 | | 29 | 20 | | 30 | 30 | | 31 | 35 | | 32 | 11 | | 33 | 4 | | 34 | 33 | | 35 | 50 | | 36 | 4 | | 37 | 13 | | 38 | 8 | | 39 | 43 | | 40 | 5 | | 41 | 21 | | 42 | 10 | | 43 | 27 | | 44 | 6 | | 45 | 47 | | 46 | 7 | | 47 | 4 | | 48 | 46 | | 49 | 33 |
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| 89.67% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 180 | | matches | | 0 | "was plastered" | | 1 | "been pried" | | 2 | "being swallowed" | | 3 | "been sealed" | | 4 | "been transformed" | | 5 | "were lined" | | 6 | "was hidden" | | 7 | "being offered" | | 8 | "was joined" | | 9 | "being overturned" |
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| 21.27% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 10 | | totalVerbs | 373 | | matches | | 0 | "were getting" | | 1 | "was running" | | 2 | "was carrying" | | 3 | "was beginning" | | 4 | "was chasing" | | 5 | "was looking" | | 6 | "was panicking" | | 7 | "was coming" | | 8 | "was already pulling" | | 9 | "was running" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 20 | | semicolonCount | 1 | | flaggedSentences | 15 | | totalSentences | 210 | | ratio | 0.071 | | matches | | 0 | "The way his shoulders tightened, the way his left arm—scarred from some knife attack he never spoke about—curled protectively against his side." | | 1 | "Three weeks of surveillance had led her here, to the edge of what she was beginning to suspect was far larger—and far stranger—than any organised crime ring she’d ever chased." | | 2 | "The same smell had clung to the crime scene where DS Morris died three years ago—before the body had simply…" | | 3 | "Voices drifted toward her—dozens of them, layered under the drip of water." | | 4 | "Creatures—no, people, she told herself fiercely—moved between the stalls." | | 5 | "One stall keeper—a short man with too many joints in his fingers—snatched a bone token from a customer and tucked it away when he saw her approach." | | 6 | "The hooded woman— Siobhan—stepped forward." | | 7 | "And the way Tomás was looking at her— not like a criminal caught, but like a man watching someone walk toward a cliff edge." | | 8 | "Somewhere in the market proper, someone screamed—a high, keening sound that was quickly cut off." | | 9 | "Quinn caught a glimpse over her shoulder—something tall and wrong, all edges and angles that hurt to perceive, wearing the face of her dead partner like a poorly fitted mask." | | 10 | "It sounded like Morris laughing the way he had the night before he died—right before everything went wrong." | | 11 | "The rain above had found its way down here too; water streamed along the tunnel floor, making every step treacherous." | | 12 | "The market’s chaos had spread here—vendors and customers alike scrambling for exits, clutching strange wares to their chests." | | 13 | "“Maybe. But I’m not letting it take anyone else.” She thought of Morris’s face—distorted, wrong—on that thing." | | 14 | "It wore Morris’s face, but the smile was all wrong—too wide, too many teeth." |
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| 99.29% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2058 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 84 | | adverbRatio | 0.04081632653061224 | | lyAdverbCount | 28 | | lyAdverbRatio | 0.013605442176870748 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 210 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 210 | | mean | 11.83 | | std | 7.67 | | cv | 0.649 | | sampleLengths | | 0 | 18 | | 1 | 23 | | 2 | 19 | | 3 | 30 | | 4 | 30 | | 5 | 4 | | 6 | 3 | | 7 | 6 | | 8 | 22 | | 9 | 6 | | 10 | 4 | | 11 | 27 | | 12 | 30 | | 13 | 13 | | 14 | 4 | | 15 | 7 | | 16 | 24 | | 17 | 5 | | 18 | 5 | | 19 | 8 | | 20 | 3 | | 21 | 7 | | 22 | 13 | | 23 | 11 | | 24 | 4 | | 25 | 10 | | 26 | 3 | | 27 | 20 | | 28 | 1 | | 29 | 12 | | 30 | 3 | | 31 | 8 | | 32 | 24 | | 33 | 2 | | 34 | 16 | | 35 | 13 | | 36 | 4 | | 37 | 24 | | 38 | 7 | | 39 | 17 | | 40 | 15 | | 41 | 21 | | 42 | 12 | | 43 | 9 | | 44 | 16 | | 45 | 6 | | 46 | 7 | | 47 | 21 | | 48 | 20 | | 49 | 9 |
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| 56.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3619047619047619 | | totalSentences | 210 | | uniqueOpeners | 76 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 172 | | matches | | 0 | "Then she swung her legs" | | 1 | "Somewhere in the market proper," | | 2 | "Then she holstered her weapon" | | 3 | "Instead she simply nodded." |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 172 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "He didn’t look back." | | 2 | "He never did." | | 3 | "He was running for the" | | 4 | "She’d been tailing him since" | | 5 | "He was already halfway down." | | 6 | "she shouted, voice cracking against" | | 7 | "She cursed under her breath" | | 8 | "It was something older." | | 9 | "Her stomach tightened." | | 10 | "She shouldn’t follow." | | 11 | "She moved forward, following the" | | 12 | "She’d heard the name in" | | 13 | "His curly dark hair caught" | | 14 | "He glanced over his shoulder" | | 15 | "He ducked beneath a hanging" | | 16 | "She followed, emerging into what" | | 17 | "he said quietly" | | 18 | "His accent still carried the" | | 19 | "She kept the Glock steady" |
| | ratio | 0.256 | |
| 50.12% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 141 | | totalSentences | 172 | | matches | | 0 | "The rain hammered Soho like" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "Her closely cropped salt-and-pepper hair" | | 3 | "The worn leather watch on" | | 4 | "He didn’t look back." | | 5 | "He never did." | | 6 | "The way his shoulders tightened," | | 7 | "He was running for the" | | 8 | "She’d been tailing him since" | | 9 | "Herrera cut left into an" | | 10 | "Quinn followed, boots splashing." | | 11 | "The alley narrowed, forcing her" | | 12 | "A maintenance ladder gleamed wetly." | | 13 | "He was already halfway down." | | 14 | "she shouted, voice cracking against" | | 15 | "The echo of his descent" | | 16 | "She cursed under her breath" | | 17 | "The smell that rose up" | | 18 | "It was something older." | | 19 | "Incense and iron and something" |
| | ratio | 0.82 | |
| 58.14% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 172 | | matches | | 0 | "Before the official report had" | | 1 | "If she lost him here," |
| | ratio | 0.012 | |
| 57.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 92 | | technicalSentenceCount | 11 | | matches | | 0 | "Detective Harlow Quinn kept her stride tight, shoulders squared with the military precision that had carried her through eighteen years on the force." | | 1 | "A single red bulb burned above a torn poster that read *Mind the Gap* in faded letters." | | 2 | "She moved forward, following the faint glow that grew stronger around a bend in the tunnel." | | 3 | "A woman with silver tattoos that moved across her skin like living script haggled over a jar of what looked like liquid starlight." | | 4 | "He glanced over his shoulder once, warm brown eyes meeting hers with something that looked almost like regret." | | 5 | "A single table stood in the centre, covered in bandages and surgical tools that looked far too clean for this place." | | 6 | "She could drag Herrera out at gunpoint and try to fight her way through a rioting black market, or she could listen to the man who had apparently been trying to…" | | 7 | "Quinn caught a glimpse over her shoulder—something tall and wrong, all edges and angles that hurt to perceive, wearing the face of her dead partner like a poorl…" | | 8 | "Tomás moved with the sure-footed grace of someone who had navigated these tunnels many times before." | | 9 | "A stall selling what looked like bottled dreams overturned, releasing a cloud of shimmering dust that made the air taste like childhood summers." | | 10 | "The chase that had begun on rain-soaked streets above now continued in the dark beneath the city." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 3 | | matches | | 0 | "she shouted, voice cracking against the downpour" | | 1 | "she said, voice hard" | | 2 | "she said, voice steady despite the terror clawing up her throat" |
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| 61.11% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 4 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she whispered (whisper)" | | 2 | "she shouted (shout)" | | 3 | "she ordered (order)" |
| | dialogueSentences | 45 | | tagDensity | 0.244 | | leniency | 0.489 | | rawRatio | 0.364 | | effectiveRatio | 0.178 | |