| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 786 | | 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 | 786 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | word | "down her spine" | | count | 1 |
| | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "comforting" | | 3 | "determined" | | 4 | "flicker" | | 5 | "quickened" | | 6 | "calculated" | | 7 | "down her spine" | | 8 | "fleeting" | | 9 | "depths" | | 10 | "racing" | | 11 | "shimmered" | | 12 | "sinister" | | 13 | "tapestry" | | 14 | "anticipation" | | 15 | "chilling" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 44 | | matches | | |
| 77.92% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 44 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 45 | | 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 | 0 | | markdownWords | 0 | | totalWords | 792 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 72.08% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 770 | | uniqueNames | 15 | | maxNameDensity | 1.56 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 12 | | Herrera | 11 | | Morris | 2 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Tomás | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Raven" | | 5 | "Market" | | 6 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" |
| | globalScore | 0.721 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 792 | | matches | (empty) | |
| 92.59% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 45 | | matches | | |
| 5.28% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 66 | | std | 11.12 | | cv | 0.168 | | sampleLengths | | 0 | 76 | | 1 | 68 | | 2 | 52 | | 3 | 64 | | 4 | 58 | | 5 | 54 | | 6 | 75 | | 7 | 83 | | 8 | 54 | | 9 | 54 | | 10 | 71 | | 11 | 83 |
| |
| 81.34% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 44 | | matches | | 0 | "was connected" | | 1 | "was determined" | | 2 | "being led" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 118 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 4 | | flaggedSentences | 10 | | totalSentences | 45 | | ratio | 0.222 | | matches | | 0 | "She'd lost her partner, DS Morris, in a case that had left more questions than answers – and an insatiable hunger for the truth." | | 1 | "Quinn's eyes narrowed; where was he headed?" | | 2 | "This wasn't a desperate suspect's frantic attempt to escape; it was calculated, almost...inviting." | | 3 | "Ahead, a door materialized from the shadows – the entrance to the abandoned Tube station beneath Camden." | | 4 | "Her heart rate spiked; this was the stuff of whispered legends, a hidden world she'd only glimpsed in the wakes of bizarre crimes." | | 5 | "She fumbled for her phone, but the screen remained stubbornly dark – no signal." | | 6 | "The market's patrons were a mix of the bizarre and the terrifying – creatures that shouldn't exist, humans with eyes that shimmered like the moon." | | 7 | "As she wove through the crowd, a hand brushed against hers, depositing a small, cool object into her palm – a bone token, identical to Herrera's." | | 8 | "This was no longer a simple pursuit; she'd stumbled into a realm where the rules were writ in shadow and blood." | | 9 | "Quinn's heart pounded in counterpoint, her thoughts reduced to a single, chilling certainty: she was being led deeper into the heart of the unknown, further from the world she understood – and the only way out might be through the very darkness itself." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 218 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.03211009174311927 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.009174311926605505 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 45 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 45 | | mean | 17.6 | | std | 7.79 | | cv | 0.442 | | sampleLengths | | 0 | 21 | | 1 | 15 | | 2 | 11 | | 3 | 29 | | 4 | 19 | | 5 | 12 | | 6 | 24 | | 7 | 13 | | 8 | 17 | | 9 | 12 | | 10 | 16 | | 11 | 7 | | 12 | 11 | | 13 | 12 | | 14 | 19 | | 15 | 22 | | 16 | 6 | | 17 | 13 | | 18 | 4 | | 19 | 13 | | 20 | 22 | | 21 | 17 | | 22 | 14 | | 23 | 23 | | 24 | 15 | | 25 | 17 | | 26 | 8 | | 27 | 20 | | 28 | 15 | | 29 | 16 | | 30 | 17 | | 31 | 14 | | 32 | 36 | | 33 | 8 | | 34 | 25 | | 35 | 21 | | 36 | 26 | | 37 | 28 | | 38 | 6 | | 39 | 21 | | 40 | 14 | | 41 | 30 | | 42 | 21 | | 43 | 19 | | 44 | 43 |
| |
| 81.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.4888888888888889 | | totalSentences | 45 | | uniqueOpeners | 22 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 44 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 44 | | matches | | 0 | "She'd lost her partner, DS" | | 1 | "She quickened her pace, just" | | 2 | "Her heart rate spiked; this" | | 3 | "She fumbled for her phone," | | 4 | "She kept her gaze locked" |
| | ratio | 0.114 | |
| 62.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 35 | | totalSentences | 44 | | matches | | 0 | "Rain lashed down on the" | | 1 | "Detective Harlow Quinn squinted through" | | 2 | "Tomás Herrera, her suspect, had" | | 3 | "Quinn's military-honed bearing propelled her" | | 4 | "She'd lost her partner, DS" | | 5 | "Tonight, she was determined to" | | 6 | "Herrera vanished around a corner," | | 7 | "Water dripped from her eyebrows" | | 8 | "The suspect's dark form reappeared," | | 9 | "Quinn's eyes narrowed; where was" | | 10 | "A flicker of green neon" | | 11 | "Quinn's gut told her Herrera" | | 12 | "She quickened her pace, just" | | 13 | "The alley's darkness swallowed him" | | 14 | "Quinn hesitated, her hand resting" | | 15 | "Something didn't add up." | | 16 | "This wasn't a desperate suspect's" | | 17 | "A shiver danced down her" | | 18 | "The Veil Market's rumored location," | | 19 | "Her heart rate spiked; this" |
| | ratio | 0.795 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 44 | | matches | | 0 | "Yet, she pressed on, driven" |
| | ratio | 0.023 | |
| 65.64% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 4 | | matches | | 0 | "She'd lost her partner, DS Morris, in a case that had left more questions than answers – and an insatiable hunger for the truth." | | 1 | "The market's patrons were a mix of the bizarre and the terrifying – creatures that shouldn't exist, humans with eyes that shimmered like the moon." | | 2 | "She kept her gaze locked on Herrera's retreating back, her hand resting on the pepper spray, now a woefully inadequate comfort." | | 3 | "The violin's lament soared, entwining with the anticipation that hung heavy as the mist that shrouded the city above." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "raspy voice whispered (whisper)" |
| | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |