| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.556 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1429 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "truly" | | 1 | "softly" | | 2 | "slightly" |
| |
| 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) | |
| 47.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1429 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "fractured" | | 1 | "rhythmic" | | 2 | "silence" | | 3 | "pulsed" | | 4 | "constructed" | | 5 | "kaleidoscope" | | 6 | "velvet" | | 7 | "oppressive" | | 8 | "scanned" | | 9 | "whisper" | | 10 | "could feel" | | 11 | "familiar" | | 12 | "weight" | | 13 | "echoing" |
| |
| 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 | 90 | | matches | (empty) | |
| 79.37% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | 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 | 1426 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1224 | | uniqueNames | 18 | | maxNameDensity | 1.31 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Herrera" | | discoveredNames | | London | 1 | | Soho | 1 | | Harlow | 1 | | Quinn | 16 | | Morris | 5 | | Raven | 1 | | Nest | 1 | | Silas | 1 | | Camden | 2 | | Tomás | 1 | | Herrera | 12 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Saint | 1 | | Christopher | 1 | | Hendon | 1 | | Protocol | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Silas" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Market" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Protocol" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Raven" | | 3 | "Camden" | | 4 | "Hendon" |
| | globalScore | 0.846 | | windowScore | 0.667 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 2 | | matches | | 0 | "fabrics that seemed to shift color when viewed from the corner of the eye" | | 1 | "seemed impossibly far away now" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.701 | | wordCount | 1426 | | matches | | 0 | "Not to a sewer, but to something else" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 99 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 46 | | std | 25.18 | | cv | 0.547 | | sampleLengths | | 0 | 86 | | 1 | 90 | | 2 | 10 | | 3 | 85 | | 4 | 78 | | 5 | 69 | | 6 | 12 | | 7 | 58 | | 8 | 14 | | 9 | 52 | | 10 | 86 | | 11 | 38 | | 12 | 54 | | 13 | 13 | | 14 | 61 | | 15 | 24 | | 16 | 43 | | 17 | 27 | | 18 | 24 | | 19 | 72 | | 20 | 36 | | 21 | 25 | | 22 | 38 | | 23 | 25 | | 24 | 44 | | 25 | 59 | | 26 | 81 | | 27 | 31 | | 28 | 51 | | 29 | 8 | | 30 | 32 |
| |
| 93.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 90 | | matches | | 0 | "were swallowed" | | 1 | "been transformed" | | 2 | "was supposed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 213 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 3 | | flaggedSentences | 5 | | totalSentences | 99 | | ratio | 0.051 | | matches | | 0 | "Detective Harlow Quinn didn't feel the cold biting through her jacket; she felt only the rhythmic thud of her boots and the burning ache in her lungs." | | 1 | "It wasn't just the ozone tang of a coming storm; it was something older, heavier, like the pressure drop before an earthquake." | | 2 | "A vendor sold jars of swirling mist; another displayed rows of teeth that chattered softly in their glass cases." | | 3 | "He was weaving through the throng, his hand clutching something at his throat—a Saint Christopher medallion that caught the dim light." | | 4 | "The civilians—if they could be called that—parted for her with wary glances." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 616 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.025974025974025976 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.012987012987012988 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 14.4 | | std | 7.99 | | cv | 0.555 | | sampleLengths | | 0 | 20 | | 1 | 27 | | 2 | 39 | | 3 | 7 | | 4 | 19 | | 5 | 15 | | 6 | 14 | | 7 | 9 | | 8 | 26 | | 9 | 9 | | 10 | 1 | | 11 | 4 | | 12 | 15 | | 13 | 22 | | 14 | 22 | | 15 | 22 | | 16 | 15 | | 17 | 23 | | 18 | 12 | | 19 | 28 | | 20 | 13 | | 21 | 15 | | 22 | 19 | | 23 | 6 | | 24 | 16 | | 25 | 4 | | 26 | 8 | | 27 | 2 | | 28 | 3 | | 29 | 11 | | 30 | 5 | | 31 | 12 | | 32 | 13 | | 33 | 12 | | 34 | 14 | | 35 | 4 | | 36 | 22 | | 37 | 21 | | 38 | 5 | | 39 | 10 | | 40 | 7 | | 41 | 27 | | 42 | 8 | | 43 | 15 | | 44 | 19 | | 45 | 4 | | 46 | 21 | | 47 | 13 | | 48 | 9 | | 49 | 12 |
| |
| 59.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.40404040404040403 | | totalSentences | 99 | | uniqueOpeners | 40 | |
| 38.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 87 | | matches | | 0 | "Instead of stopping, he dropped" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 87 | | matches | | 0 | "He was fast, moving with" | | 1 | "She checked her watch, the" | | 2 | "He darted left, skidding around" | | 3 | "She knew this part of" | | 4 | "It wasn't just the ozone" | | 5 | "She remembered the files on" | | 6 | "She peered over the rusted" | | 7 | "It was an entrance." | | 8 | "She looked at the shaft," | | 9 | "She spotted Herrera immediately." | | 10 | "He was weaving through the" | | 11 | "He moved with purpose, heading" | | 12 | "They sensed the authority in" | | 13 | "She was flesh and blood" | | 14 | "she shouted, the sound flattening" | | 15 | "He said something to the" | | 16 | "She reached the velvet curtains" | | 17 | "He pulled out a small," | | 18 | "Her eyes scanned the room," | | 19 | "Her grip on the pistol" |
| | ratio | 0.276 | |
| 46.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 87 | | matches | | 0 | "Detective Harlow Quinn didn't feel" | | 1 | "He was fast, moving with" | | 2 | "Quinn adjusted her stride, her" | | 3 | "She checked her watch, the" | | 4 | "Quinn barked, her voice rough," | | 5 | "The figure didn't slow." | | 6 | "He darted left, skidding around" | | 7 | "Quinn followed, her sharp jaw" | | 8 | "She knew this part of" | | 9 | "It wasn't just the ozone" | | 10 | "She remembered the files on" | | 11 | "The unexplained circumstances surrounding Morris's" | | 12 | "This suspect, Tomás Herrera, was" | | 13 | "A former paramedic stripped of" | | 14 | "Herrera reached the mouth of" | | 15 | "Quinn skidded to a halt" | | 16 | "She peered over the rusted" | | 17 | "It was an entrance." | | 18 | "Protocol demanded backup." | | 19 | "Protocol demanded she secure the" |
| | ratio | 0.828 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 13 | | matches | | 0 | "Rain hammered the London pavement, turning the soot-streaked alleyways of Soho into slick mirrors that fractured the neon glow above." | | 1 | "He was fast, moving with the desperate, fluid grace of someone who knew exactly where the shadows pooled thickest." | | 2 | "He darted left, skidding around a dumpster that reeked of rotting cabbage and wet cardboard." | | 3 | "A former paramedic stripped of his license for treating things that shouldn't exist, now running errands for a clique she suspected of trafficking more than jus…" | | 4 | "Below, a faint, sickly green light pulsed, illuminating a graffiti-covered wall that shouldn't have been there." | | 5 | "The ladder ended on a narrow catwalk suspended over a cavernous space that defied the geometry of the Tube station above." | | 6 | "A vendor sold jars of swirling mist; another displayed rows of teeth that chattered softly in their glass cases." | | 7 | "He was weaving through the throng, his hand clutching something at his throat—a Saint Christopher medallion that caught the dim light." | | 8 | "They sensed the authority in her bearing, the rigid line of her shoulders, but also the humanity that made her an anomaly here." | | 9 | "Quinn broke into a run, shoving past a creature that hissed, its breath smelling of formaldehyde." | | 10 | "He pulled out a small, irregular object that looked disturbingly like a knucklebone." | | 11 | "The door groaned, the sound of grinding stone echoing in the small room, and swung inward to reveal a swirl of fog and darkness that smelled of sea salt and old…" | | 12 | "Quinn followed, leaving the safety of the known world behind, her heart hammering a frantic rhythm against her ribs as the iron door slammed shut behind them, s…" |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 5 | | matches | | 0 | "Quinn barked, her voice rough, stripped of pleasantry" | | 1 | "She remembered, the ones marked 'Closed' but never truly solved" | | 2 | "she shouted, the sound flattening against the oppressive humidity of the cavern" | | 3 | "Herrera pleaded, his accent thickening with stress" | | 4 | "Herrera said, his voice dropping to a whisper" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 6 | | fancyTags | | 0 | "Quinn barked (bark)" | | 1 | "she shouted (shout)" | | 2 | "Quinn snapped (snap)" | | 3 | "Herrera pleaded (plead)" | | 4 | "Quinn lied (lie)" | | 5 | "Herrera whispered (whisper)" |
| | dialogueSentences | 18 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.75 | | effectiveRatio | 0.667 | |