| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2052 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "quickly" | | 1 | "completely" | | 2 | "suddenly" |
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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) | |
| 46.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2052 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "streaming" | | 1 | "pulse" | | 2 | "weight" | | 3 | "could feel" | | 4 | "loomed" | | 5 | "stomach" | | 6 | "echo" | | 7 | "flickered" | | 8 | "footsteps" | | 9 | "echoed" | | 10 | "gloom" | | 11 | "etched" | | 12 | "hulking" | | 13 | "pounding" | | 14 | "lurched" | | 15 | "velvet" | | 16 | "sense of" | | 17 | "tapestry" | | 18 | "echoing" | | 19 | "chill" |
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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 | 0 | | narrationSentences | 166 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 166 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 171 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2035 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1980 | | uniqueNames | 14 | | maxNameDensity | 0.96 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 2 | | Quinn | 19 | | Raven | 1 | | Nest | 1 | | Tomás | 3 | | Herrera | 14 | | Saint | 3 | | Christopher | 3 | | Morris | 4 | | Oxford | 1 | | Street | 1 | | Tube | 2 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Oxford" | | 3 | "Street" | | 4 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 123 | | glossingSentenceCount | 8 | | matches | | 0 | "smelled like organized crime even if the e" | | 1 | "looked like symbols" | | 2 | "something like burnt sugar" | | 3 | "looked like living tattoos in glass jars" | | 4 | "sounded like stones grinding together" | | 5 | "rom someone so obviously out of her depth" | | 6 | "looked like genuine fear" | | 7 | "felt like she was finally running towar" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.491 | | wordCount | 2035 | | matches | | 0 | "not gang tags but precise, deliberate marks" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 171 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 40.7 | | std | 26.4 | | cv | 0.649 | | sampleLengths | | 0 | 91 | | 1 | 113 | | 2 | 19 | | 3 | 42 | | 4 | 62 | | 5 | 95 | | 6 | 65 | | 7 | 72 | | 8 | 58 | | 9 | 60 | | 10 | 50 | | 11 | 10 | | 12 | 79 | | 13 | 11 | | 14 | 11 | | 15 | 43 | | 16 | 49 | | 17 | 2 | | 18 | 59 | | 19 | 23 | | 20 | 27 | | 21 | 99 | | 22 | 37 | | 23 | 51 | | 24 | 10 | | 25 | 47 | | 26 | 36 | | 27 | 28 | | 28 | 3 | | 29 | 29 | | 30 | 11 | | 31 | 23 | | 32 | 20 | | 33 | 6 | | 34 | 64 | | 35 | 66 | | 36 | 61 | | 37 | 35 | | 38 | 13 | | 39 | 26 | | 40 | 23 | | 41 | 37 | | 42 | 48 | | 43 | 42 | | 44 | 49 | | 45 | 19 | | 46 | 29 | | 47 | 48 | | 48 | 31 | | 49 | 3 |
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| 86.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 166 | | matches | | 0 | "was plastered" | | 1 | "were swallowed" | | 2 | "was supposed" | | 3 | "been wrenched" | | 4 | "being lured" | | 5 | "been ripped" | | 6 | "was supposed" | | 7 | "being watched" | | 8 | "been made" |
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| 47.62% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 350 | | matches | | 0 | "were screaming" | | 1 | "were starting" | | 2 | "was running" | | 3 | "was beginning" | | 4 | "was selling" | | 5 | "was touching" | | 6 | "was still coming" | | 7 | "was finally running" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 2 | | flaggedSentences | 16 | | totalSentences | 171 | | ratio | 0.094 | | matches | | 0 | "She had spent weeks tying him to the clique—off-the-books medical work, whispers of impossible injuries healed overnight, connections that smelled like organized crime even if the evidence refused to behave like any crime she understood." | | 1 | "The streets blurred past—neon reflections writhing in puddles, the smell of wet brick and fried onions from late-night takeaways, the occasional pedestrian leaping aside with curses that followed her like startled birds." | | 2 | "The way the coroner had looked at her when she asked questions—half pity, half warning." | | 3 | "Herrera’s stride was beginning to falter; she saw it in the way his arms pumped less efficiently, the slight hitch in his left leg." | | 4 | "The scar on his forearm must be pulling—old knife wound, her file said." | | 5 | "Emergency lighting flickered somewhere ahead—sickly yellow, like diseased eyes." | | 6 | "Instead she could hear faint sounds drifting up from deeper levels—voices, the clink of glass or metal, a low thrumming that might have been generators or might have been something far stranger." | | 7 | "The tracks had been ripped up years ago; now the space stretched into a cavernous hall that definitely shouldn’t exist." | | 8 | "A figure darted across the far end of the platform—Herrera." | | 9 | "Strange smells wafted up—myrrh, hot copper, something like burnt sugar." | | 10 | "It was a market—rows of stalls carved out of the old platform space, stretching into the distance under vaulted brick ceilings." | | 11 | "That’s what the man at the gate had demanded from the last entrant—a small, yellowed knucklebone etched with tiny runes." | | 12 | "Quinn lifted her chin, letting the detective show in every line of her body—the military bearing, the unflinching stare." | | 13 | "Faces turned—some human, some..." | | 14 | "Something in his expression shifted—recognition, maybe regret." | | 15 | "The market’s sounds pressed in on her—bargaining in dead languages, the wet sound of things moving in jars, a child’s laugh that definitely wasn’t a child’s." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2003 | | adjectiveStacks | 1 | | stackExamples | | 0 | "ahead—sickly yellow, like" |
| | adverbCount | 76 | | adverbRatio | 0.03794308537194209 | | lyAdverbCount | 21 | | lyAdverbRatio | 0.010484273589615577 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 171 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 171 | | mean | 11.9 | | std | 8.15 | | cv | 0.685 | | sampleLengths | | 0 | 19 | | 1 | 26 | | 2 | 19 | | 3 | 27 | | 4 | 16 | | 5 | 26 | | 6 | 6 | | 7 | 35 | | 8 | 12 | | 9 | 18 | | 10 | 4 | | 11 | 7 | | 12 | 5 | | 13 | 3 | | 14 | 13 | | 15 | 29 | | 16 | 3 | | 17 | 11 | | 18 | 16 | | 19 | 32 | | 20 | 15 | | 21 | 15 | | 22 | 3 | | 23 | 21 | | 24 | 12 | | 25 | 3 | | 26 | 3 | | 27 | 23 | | 28 | 15 | | 29 | 5 | | 30 | 7 | | 31 | 15 | | 32 | 16 | | 33 | 7 | | 34 | 15 | | 35 | 15 | | 36 | 24 | | 37 | 13 | | 38 | 1 | | 39 | 4 | | 40 | 13 | | 41 | 6 | | 42 | 19 | | 43 | 3 | | 44 | 3 | | 45 | 7 | | 46 | 7 | | 47 | 16 | | 48 | 23 | | 49 | 9 |
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| 60.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.391812865497076 | | totalSentences | 171 | | uniqueOpeners | 67 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 155 | | matches | | 0 | "Then he ran." | | 1 | "Instead she could hear faint" | | 2 | "Instead it propelled her forward." | | 3 | "Of course she didn’t." | | 4 | "Then a stall owner flung" | | 5 | "Somewhere ahead, Tomás Herrera carried" |
| | ratio | 0.039 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 155 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She had been waiting in" | | 2 | "He moved with the quick," | | 3 | "She had spent weeks tying" | | 4 | "Their eyes met across the" | | 5 | "Her voice cracked through the" | | 6 | "He was fast." | | 7 | "She didn’t slow." | | 8 | "She kept her gaze locked" | | 9 | "Her lungs were starting to" | | 10 | "She could feel it in" | | 11 | "They pounded north, the city" | | 12 | "She took the stairs two" | | 13 | "She hesitated for the first" | | 14 | "Her fingers brushed the grip" | | 15 | "She slipped through the broken" | | 16 | "He glanced back once, face" | | 17 | "Her torch beam caught glimpses" | | 18 | "She killed her torch and" | | 19 | "It was a market—rows of" |
| | ratio | 0.258 | |
| 47.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 128 | | totalSentences | 155 | | matches | | 0 | "Detective Harlow Quinn gripped the" | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "She had been waiting in" | | 3 | "He moved with the quick," | | 4 | "The Saint Christopher medallion around" | | 5 | "Quinn’s pulse kicked up a" | | 6 | "She had spent weeks tying" | | 7 | "Those same instincts had screamed" | | 8 | "Herrera’s head snapped up." | | 9 | "Their eyes met across the" | | 10 | "Quinn exploded after him, boots" | | 11 | "Her voice cracked through the" | | 12 | "He was fast." | | 13 | "Quinn’s legs burned as she" | | 14 | "The streets blurred past—neon reflections" | | 15 | "Herrera cut left into an" | | 16 | "Quinn followed, shoulder clipping the" | | 17 | "She didn’t slow." | | 18 | "The alley spat them out" | | 19 | "Herrera weaved between them like" |
| | ratio | 0.826 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 155 | | matches | (empty) | | ratio | 0 | |
| 51.50% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 86 | | technicalSentenceCount | 11 | | matches | | 0 | "She had spent weeks tying him to the clique—off-the-books medical work, whispers of impossible injuries healed overnight, connections that smelled like organize…" | | 1 | "The streets blurred past—neon reflections writhing in puddles, the smell of wet brick and fried onions from late-night takeaways, the occasional pedestrian leap…" | | 2 | "The beam sliced through the gloom, revealing peeling posters from the 90s, graffiti in languages she didn’t recognize, and a broken turnstile that had been wren…" | | 3 | "Instead she could hear faint sounds drifting up from deeper levels—voices, the clink of glass or metal, a low thrumming that might have been generators or might…" | | 4 | "Lanterns hung from chains, casting a sickly-sweet glow over tables laden with objects that made her skin crawl." | | 5 | "His medallion flashed again as he turned a corner near a stall selling what appeared to be dried herbs that whispered when no one was touching them." | | 6 | "The system had told her to drop it, to stop asking questions about symbols that shouldn’t exist and wounds that shouldn’t heal." | | 7 | "Quinn lifted her chin, letting the detective show in every line of her body—the military bearing, the unflinching stare." | | 8 | "A woman offering glasses of something that showed you your own death." | | 9 | "He glanced back, warm brown eyes wide with something that looked like genuine fear." | | 10 | "The underground market swallowed her completely, and the rain above London kept falling on a world that had suddenly become much larger and far more dangerous t…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 3 | | matches | | 0 | "Her voice cracked, but the words were swallowed by thunder and the distant wail of a siren that had nothing to do with her" | | 1 | "she shouted, voice echoing strangely off the bricks" | | 2 | "he called back, voice carrying oddly through the market’s strange acoustics" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "he called back (call back)" |
| | dialogueSentences | 11 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 1 | | effectiveRatio | 0.364 | |