| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 3 | | adverbTags | | 0 | "The word cracked like [like]" | | 1 | "His fingers drummed once [once]" | | 2 | "Her voice echoed strangely [strangely]" |
| | dialogueSentences | 60 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.15 | | effectiveRatio | 0.1 | |
| 79.81% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2229 | | totalAiIsmAdverbs | 9 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "deliberately" | | count | 2 |
| | 4 | | | 5 | |
| | highlights | | 0 | "slowly" | | 1 | "truly" | | 2 | "suddenly" | | 3 | "deliberately" | | 4 | "completely" | | 5 | "sharply" |
| |
| 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) | |
| 41.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2229 | | totalAiIsms | 26 | | 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 | "treacherous" | | 1 | "flickered" | | 2 | "echoing" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "constructed" | | 6 | "velvet" | | 7 | "stomach" | | 8 | "scanned" | | 9 | "quickened" | | 10 | "measured" | | 11 | "unsettled" | | 12 | "raced" | | 13 | "gleaming" | | 14 | "silence" | | 15 | "familiar" | | 16 | "potential" | | 17 | "sanctuary" | | 18 | "glinting" | | 19 | "pulse" | | 20 | "depths" | | 21 | "trembled" |
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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 | 213 | | matches | (empty) | |
| 95.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 5 | | narrationSentences | 213 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 254 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2229 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1768 | | uniqueNames | 17 | | maxNameDensity | 1.02 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 18 | | Soho | 1 | | Raven | 2 | | Nest | 2 | | Herrera | 16 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | London | 2 | | Saint | 3 | | Christopher | 3 | | Black-and-white | 1 | | Seville | 1 | | Morris | 6 | | Spanish | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Black-and-white" | | 3 | "Seville" |
| | globalScore | 0.991 | | windowScore | 1 | |
| 42.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 140 | | glossingSentenceCount | 6 | | matches | | 0 | "light that seemed to writhe" | | 1 | "looked like preserved eyes in glass jars" | | 2 | "sounded like dry leaves scraping concrete" | | 3 | "felt like real leather, soft from handl" | | 4 | "appeared, listed under potential assets" | | 5 | "felt like it burned in her other hand" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.449 | | wordCount | 2229 | | matches | | 0 | "not dissimilar to the one above, throat torn but no blood at the scene" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 254 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 88 | | mean | 25.33 | | std | 16.38 | | cv | 0.647 | | sampleLengths | | 0 | 49 | | 1 | 60 | | 2 | 12 | | 3 | 42 | | 4 | 52 | | 5 | 58 | | 6 | 45 | | 7 | 51 | | 8 | 3 | | 9 | 85 | | 10 | 41 | | 11 | 39 | | 12 | 50 | | 13 | 48 | | 14 | 7 | | 15 | 58 | | 16 | 34 | | 17 | 8 | | 18 | 30 | | 19 | 29 | | 20 | 16 | | 21 | 36 | | 22 | 25 | | 23 | 24 | | 24 | 31 | | 25 | 13 | | 26 | 55 | | 27 | 11 | | 28 | 35 | | 29 | 23 | | 30 | 22 | | 31 | 26 | | 32 | 5 | | 33 | 24 | | 34 | 1 | | 35 | 20 | | 36 | 43 | | 37 | 21 | | 38 | 9 | | 39 | 29 | | 40 | 41 | | 41 | 7 | | 42 | 21 | | 43 | 17 | | 44 | 26 | | 45 | 17 | | 46 | 20 | | 47 | 12 | | 48 | 22 | | 49 | 38 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 213 | | matches | | 0 | "been found" | | 1 | "been drained" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 333 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 254 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 993 | | adjectiveStacks | 1 | | stackExamples | | 0 | "suspect twisted free, boots" |
| | adverbCount | 33 | | adverbRatio | 0.03323262839879154 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008056394763343404 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 254 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 254 | | mean | 8.78 | | std | 5.4 | | cv | 0.615 | | sampleLengths | | 0 | 15 | | 1 | 15 | | 2 | 19 | | 3 | 5 | | 4 | 13 | | 5 | 25 | | 6 | 13 | | 7 | 2 | | 8 | 2 | | 9 | 12 | | 10 | 5 | | 11 | 14 | | 12 | 2 | | 13 | 5 | | 14 | 2 | | 15 | 12 | | 16 | 2 | | 17 | 16 | | 18 | 7 | | 19 | 15 | | 20 | 8 | | 21 | 4 | | 22 | 2 | | 23 | 7 | | 24 | 17 | | 25 | 10 | | 26 | 5 | | 27 | 19 | | 28 | 5 | | 29 | 9 | | 30 | 8 | | 31 | 5 | | 32 | 16 | | 33 | 1 | | 34 | 1 | | 35 | 11 | | 36 | 2 | | 37 | 2 | | 38 | 6 | | 39 | 18 | | 40 | 9 | | 41 | 1 | | 42 | 2 | | 43 | 3 | | 44 | 11 | | 45 | 3 | | 46 | 12 | | 47 | 8 | | 48 | 12 | | 49 | 15 |
| |
| 62.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3937007874015748 | | totalSentences | 254 | | uniqueOpeners | 100 | |
| 87.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 190 | | matches | | 0 | "Too gentle for a suspect." | | 1 | "Only the two of them" | | 2 | "Only the sound of their" | | 3 | "Only seamless wall remained." | | 4 | "Instead she planted her feet," |
| | ratio | 0.026 | |
| 95.79% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 59 | | totalSentences | 190 | | matches | | 0 | "She gave chase without hesitation." | | 1 | "Her worn leather watch dug" | | 2 | "She had his file memorized." | | 3 | "He veered left at the" | | 4 | "She'd dismissed it then." | | 5 | "She followed, descending concrete steps" | | 6 | "Her hand found her torch." | | 7 | "She pushed harder, ignoring the" | | 8 | "She hesitated only a second" | | 9 | "She stepped through." | | 10 | "She'd heard the name in" | | 11 | "Her military bearing kept her" | | 12 | "She scanned the crowd, searching" | | 13 | "She spotted him twenty metres" | | 14 | "He kept glancing back, face" | | 15 | "She met their gazes without" | | 16 | "Her sharp jaw tightened." | | 17 | "She had questions that needed" | | 18 | "She quickened her pace, hand" | | 19 | "She pushed through before it" |
| | ratio | 0.311 | |
| 33.68% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 162 | | totalSentences | 190 | | matches | | 0 | "Detective Harlow Quinn slammed her" | | 1 | "Rain lashed her closely cropped" | | 2 | "The suspect twisted free, boots" | | 3 | "She gave chase without hesitation." | | 4 | "Her worn leather watch dug" | | 5 | "This one moved with unnatural" | | 6 | "The words ripped from her" | | 7 | "The suspect glanced back once." | | 8 | "A flash of olive skin," | | 9 | "She had his file memorized." | | 10 | "Supplier of off-the-books care to" | | 11 | "He veered left at the" | | 12 | "Quinn's lungs burned but her" | | 13 | "Morris's face flickered in her" | | 14 | "She'd dismissed it then." | | 15 | "The door banged open ahead" | | 16 | "She followed, descending concrete steps" | | 17 | "Water dripped from above, each" | | 18 | "Her hand found her torch." | | 19 | "The beam cut a harsh" |
| | ratio | 0.853 | |
| 26.32% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 190 | | matches | | 0 | "Now it pressed against her" |
| | ratio | 0.005 | |
| 80.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 69 | | technicalSentenceCount | 6 | | matches | | 0 | "Supplier of off-the-books care to the sort of people who shouldn't exist." | | 1 | "A small bone token lay discarded near the threshold, carved with symbols that hurt to look at directly." | | 2 | "She scanned the crowd, searching for that distinctive curly hair, the Saint Christopher medallion that would catch the light." | | 3 | "Black-and-white photographs showed people who appeared in multiple images despite wearing different clothes across decades." | | 4 | "Herrera's warm brown eyes held a depth that made her reconsider everything she thought she knew about the man." | | 5 | "The beam revealed movement at the tunnel's end, a shape that defied easy description." |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 2 | | matches | | 0 | "She stepped, boots leaving wet prints on the stone floor" | | 1 | "brown eyes held, steady" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Herrera whispered (whisper)" | | 1 | "she ordered (order)" |
| | dialogueSentences | 60 | | tagDensity | 0.033 | | leniency | 0.067 | | rawRatio | 1 | | effectiveRatio | 0.067 | |