| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.778 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1169 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1169 | | totalAiIsms | 37 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | | | 23 | | | 24 | | | 25 | | | 26 | | | 27 | | | 28 | | | 29 | | | 30 | |
| | highlights | | 0 | "cacophony" | | 1 | "fleeting" | | 2 | "flicked" | | 3 | "glint" | | 4 | "footsteps" | | 5 | "echoes" | | 6 | "silence" | | 7 | "depths" | | 8 | "could feel" | | 9 | "maw" | | 10 | "stomach" | | 11 | "charged" | | 12 | "uncanny" | | 13 | "familiar" | | 14 | "glistening" | | 15 | "etching" | | 16 | "scanning" | | 17 | "steeled" | | 18 | "resolve" | | 19 | "quickened" | | 20 | "intricate" | | 21 | "rhythmic" | | 22 | "pounding" | | 23 | "echoing" | | 24 | "pulsed" | | 25 | "tension" | | 26 | "determined" | | 27 | "flickered" | | 28 | "weight" | | 29 | "symphony" | | 30 | "racing" |
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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 | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 74 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 5 | | totalWords | 1150 | | ratio | 0.004 | | matches | | 0 | "No time." | | 1 | "Would she follow?" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 90.37% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 1090 | | uniqueNames | 8 | | maxNameDensity | 1.19 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 13 | | London | 2 | | Veil | 1 | | Market | 1 | | Tomás | 1 | | Herrera | 1 | | Fear | 1 | | Tommy | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Fear" | | 4 | "Tommy" |
| | places | | | globalScore | 0.904 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.87 | | wordCount | 1150 | | matches | | 0 | "not just by the criminal, but by the echoes of her past" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 37.1 | | std | 19.33 | | cv | 0.521 | | sampleLengths | | 0 | 62 | | 1 | 57 | | 2 | 74 | | 3 | 50 | | 4 | 54 | | 5 | 69 | | 6 | 65 | | 7 | 63 | | 8 | 29 | | 9 | 9 | | 10 | 43 | | 11 | 20 | | 12 | 18 | | 13 | 26 | | 14 | 56 | | 15 | 17 | | 16 | 15 | | 17 | 54 | | 18 | 28 | | 19 | 48 | | 20 | 29 | | 21 | 47 | | 22 | 30 | | 23 | 24 | | 24 | 32 | | 25 | 42 | | 26 | 29 | | 27 | 28 | | 28 | 6 | | 29 | 4 | | 30 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 74 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 188 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 2 | | flaggedSentences | 15 | | totalSentences | 78 | | ratio | 0.192 | | matches | | 0 | "Every fleeting glimpse of movement betrayed a darker truth lurking in the London streets—one she would not let slip away again." | | 1 | "She sidestepped and ducked just in time to see a makeshift projectile—a glass bottle—smash against a nearby wall, scattering shards like glimmering daggers." | | 2 | "A flurry of thoughts buzzed around her mind: the rain-soaked streets, the dark figures, the perpetual echoes of her partner’s voice fading away—the whispers of the supernatural, a world she had fought against in silence." | | 3 | "This was no ordinary chase; she could feel the darkness lurking behind the brick walls—the underbelly of London was a dangerous labyrinth." | | 4 | "The narrow alleyway opened into a gaping maw beneath the surface—the Veil Market—an underground world shifting every full moon, hidden beneath the noise of the glossy city above." | | 5 | "Various characters mingled, each one more curious than the last—shapeshifters, witchlings, all trading mysterious wares with a smirk or a nod." | | 6 | "\"Watch it!\" A vendor barked, glaring as she forced her way past, catching a glimpse of strange relics on rickety tables—tattered spellbooks, vials of bubbling liquids, and iron-tipped stakes." | | 7 | "In the corner of her vision, she spotted him—a familiar figure, olive skin glistening in the flickering light." | | 8 | "And then she saw him—the suspect, made of shadows and sin, standing at the edge of a more secluded area, flanked by figures obscured in cloaks." | | 9 | "Her footsteps quickened, drawing her closer to the suspect who met her eyes for a heartbeat—devoid of fear and filled with a sickening grin." | | 10 | "*Would she follow?* A million whispers clamoured in her thoughts, cautioning against the plunge into the unknown—an abyss filled with secrets and horrors she had yet to comprehend." | | 11 | "A lantern swung overhead, illuminating intricate carvings stained against the walls—ancient symbols she recognised from old case files." | | 12 | "Then she saw it—a door adorned with rusted chains and a gnarly, breathing lock." | | 13 | "Beyond that door lay her answers—marked not just by the criminal, but by the echoes of her past." | | 14 | "A gut-wrenching noise emanated from within; the light flickered ominously as the world screamed for her to turn back." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1109 | | adjectiveStacks | 1 | | stackExamples | | 0 | "former paramedic turned underground healer," |
| | adverbCount | 31 | | adverbRatio | 0.027953110910730387 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007213706041478809 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 14.74 | | std | 7.47 | | cv | 0.507 | | sampleLengths | | 0 | 22 | | 1 | 14 | | 2 | 26 | | 3 | 15 | | 4 | 7 | | 5 | 14 | | 6 | 21 | | 7 | 17 | | 8 | 20 | | 9 | 19 | | 10 | 18 | | 11 | 8 | | 12 | 5 | | 13 | 23 | | 14 | 14 | | 15 | 19 | | 16 | 35 | | 17 | 17 | | 18 | 9 | | 19 | 22 | | 20 | 21 | | 21 | 28 | | 22 | 16 | | 23 | 21 | | 24 | 8 | | 25 | 25 | | 26 | 13 | | 27 | 17 | | 28 | 29 | | 29 | 9 | | 30 | 18 | | 31 | 17 | | 32 | 8 | | 33 | 20 | | 34 | 9 | | 35 | 9 | | 36 | 12 | | 37 | 7 | | 38 | 7 | | 39 | 26 | | 40 | 8 | | 41 | 22 | | 42 | 13 | | 43 | 4 | | 44 | 11 | | 45 | 4 | | 46 | 24 | | 47 | 8 | | 48 | 22 | | 49 | 12 |
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| 79.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5 | | totalSentences | 78 | | uniqueOpeners | 39 | |
| 91.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 73 | | matches | | 0 | "Then she saw it—a door" | | 1 | "Instead, she opened the door." |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 73 | | matches | | 0 | "She sprinted, each step a" | | 1 | "They had no idea what" | | 2 | "She sidestepped and ducked just" | | 3 | "she shouted, her voice lost" | | 4 | "It was Tomás Herrera, a" | | 5 | "He glanced up, spotting Quinn" | | 6 | "he said, worry etching into" | | 7 | "he smirked, glancing over his" | | 8 | "She pushed through the bodies," | | 9 | "she bit out, force of" | | 10 | "She could handle this." | | 11 | "Her footsteps quickened, drawing her" | | 12 | "He was part of the" | | 13 | "She shouted, but more than" | | 14 | "They twisted and curled, parting" | | 15 | "she called, lunging toward the" | | 16 | "They pressed deeper, a rhythmic" | | 17 | "She stepped across the threshold," |
| | ratio | 0.247 | |
| 69.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 73 | | matches | | 0 | "Harlow Quinn's voice cut through" | | 1 | "She sprinted, each step a" | | 2 | "The suspect, a shadow cloaked" | | 3 | "Quinn's heart thudded in her" | | 4 | "The haunt of her past" | | 5 | "Every fleeting glimpse of movement" | | 6 | "The alley opened into a" | | 7 | "A flickering neon light from" | | 8 | "Quinn’s gaze flicked to the" | | 9 | "They had no idea what" | | 10 | "a voice pierced through the" | | 11 | "Quinn’s instincts kicked into overdrive." | | 12 | "She sidestepped and ducked just" | | 13 | "The figures scattered, but not" | | 14 | "A flurry of thoughts buzzed" | | 15 | "Quinn hesitated only a beat," | | 16 | "This was no ordinary chase;" | | 17 | "The narrow alleyway opened into" | | 18 | "The dim light of flickering" | | 19 | "Various characters mingled, each one" |
| | ratio | 0.781 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 73 | | matches | | 0 | "However, the figure ahead moved" | | 1 | "Yet, every moment paused, weighted" |
| | ratio | 0.027 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 2 | | matches | | 0 | "*No time.* Quinn pivoted, her footsteps resonating off the bricks, surging after her target who had rounded another corner." | | 1 | "They pressed deeper, a rhythmic pounding echoing from somewhere down the hallway, as though the very heart of the underground pulsed with danger." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 5 | | fancyTags | | 0 | "A vendor barked (bark)" | | 1 | "she shouted (shout)" | | 2 | "She shouted (shout)" | | 3 | "Tommy hissed (hiss)" | | 4 | "Tommy urged (urge)" |
| | dialogueSentences | 18 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.625 | | effectiveRatio | 0.556 | |