| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 37 | | tagDensity | 0.405 | | leniency | 0.811 | | rawRatio | 0.067 | | effectiveRatio | 0.054 | |
| 92.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1362 | | 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) | |
| 81.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1362 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "glistening" | | 1 | "standard" | | 2 | "flickered" |
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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 | 145 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 145 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 167 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1362 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.04% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1120 | | uniqueNames | 24 | | maxNameDensity | 1.34 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Lock | 1 | | London | 1 | | Deptford | 1 | | Tube | 1 | | Transport | 1 | | Museum | 1 | | Herrera | 11 | | Ten | 1 | | Saint | 2 | | Christopher | 2 | | Close | 1 | | Quinn | 15 | | Morris | 3 | | Town | 1 | | Veil | 1 | | Market | 1 | | Dozens | 1 | | Three | 4 |
| | persons | | 0 | "Raven" | | 1 | "Museum" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Close" | | 6 | "Quinn" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Chalk" | | 2 | "Farm" | | 3 | "Road" | | 4 | "London" | | 5 | "Deptford" | | 6 | "Town" | | 7 | "Three" |
| | globalScore | 0.83 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like gravel in a blender" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1362 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 167 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 69 | | mean | 19.74 | | std | 14.19 | | cv | 0.719 | | sampleLengths | | 0 | 24 | | 1 | 1 | | 2 | 22 | | 3 | 53 | | 4 | 5 | | 5 | 42 | | 6 | 23 | | 7 | 3 | | 8 | 40 | | 9 | 33 | | 10 | 32 | | 11 | 3 | | 12 | 51 | | 13 | 14 | | 14 | 17 | | 15 | 9 | | 16 | 19 | | 17 | 36 | | 18 | 14 | | 19 | 36 | | 20 | 5 | | 21 | 13 | | 22 | 21 | | 23 | 3 | | 24 | 45 | | 25 | 23 | | 26 | 34 | | 27 | 9 | | 28 | 11 | | 29 | 11 | | 30 | 31 | | 31 | 33 | | 32 | 43 | | 33 | 7 | | 34 | 19 | | 35 | 12 | | 36 | 12 | | 37 | 7 | | 38 | 8 | | 39 | 27 | | 40 | 5 | | 41 | 38 | | 42 | 8 | | 43 | 29 | | 44 | 11 | | 45 | 49 | | 46 | 3 | | 47 | 39 | | 48 | 5 | | 49 | 23 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 145 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 215 | | matches | | 0 | "was heading" | | 1 | "was running" | | 2 | "were staring" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 167 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1122 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.022281639928698752 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0035650623885918 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 167 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 167 | | mean | 8.16 | | std | 5.73 | | cv | 0.702 | | sampleLengths | | 0 | 6 | | 1 | 18 | | 2 | 1 | | 3 | 4 | | 4 | 10 | | 5 | 3 | | 6 | 2 | | 7 | 3 | | 8 | 4 | | 9 | 17 | | 10 | 12 | | 11 | 20 | | 12 | 5 | | 13 | 8 | | 14 | 14 | | 15 | 11 | | 16 | 2 | | 17 | 7 | | 18 | 1 | | 19 | 4 | | 20 | 14 | | 21 | 4 | | 22 | 3 | | 23 | 12 | | 24 | 2 | | 25 | 7 | | 26 | 10 | | 27 | 2 | | 28 | 7 | | 29 | 3 | | 30 | 10 | | 31 | 4 | | 32 | 1 | | 33 | 15 | | 34 | 6 | | 35 | 3 | | 36 | 4 | | 37 | 19 | | 38 | 3 | | 39 | 7 | | 40 | 10 | | 41 | 18 | | 42 | 6 | | 43 | 10 | | 44 | 11 | | 45 | 3 | | 46 | 3 | | 47 | 6 | | 48 | 8 | | 49 | 9 |
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| 74.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.48502994011976047 | | totalSentences | 167 | | uniqueOpeners | 81 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 121 | | matches | | 0 | "He vaulted a bin, sending" | | 1 | "His blood or someone else's." | | 2 | "She rounded the corner onto" | | 3 | "He was heading for the" | | 4 | "She knew these streets." | | 5 | "He was running blind." | | 6 | "He turned sharp left, disappearing" | | 7 | "She'd seen marks like these" | | 8 | "She kept moving." | | 9 | "His medical bag hung heavy" | | 10 | "She closed the distance between" | | 11 | "She stopped five metres out" | | 12 | "She let the word hang" | | 13 | "He pulled out a token." | | 14 | "he said quietly" | | 15 | "He gestured toward the stairs" | | 16 | "She should radio for backup." | | 17 | "She should cuff him, drag" | | 18 | "he said over his shoulder" | | 19 | "Her training screamed at her" |
| | ratio | 0.256 | |
| 63.31% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 121 | | matches | | 0 | "Quinn's boots hammered the wet" | | 1 | "The man didn't slow." | | 2 | "He vaulted a bin, sending" | | 3 | "A taxi swerved." | | 4 | "Quinn kept running." | | 5 | "Tonight, she'd caught him leaving" | | 6 | "His blood or someone else's." | | 7 | "She rounded the corner onto" | | 8 | "The rain hit harder here," | | 9 | "Herrera's trainers splashed through a" | | 10 | "He was heading for the" | | 11 | "She knew these streets." | | 12 | "He was running blind." | | 13 | "He turned sharp left, disappearing" | | 14 | "Quinn followed, fingers brushing her" | | 15 | "Backup would want explanations she" | | 16 | "The alley narrowed." | | 17 | "Brick walls pressed close, glistening" | | 18 | "Quinn's torch caught it." | | 19 | "She'd seen marks like these" |
| | ratio | 0.793 | |
| 41.32% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 121 | | matches | | 0 | "While she'd spent three years" |
| | ratio | 0.008 | |
| 36.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 7 | | matches | | 0 | "Ahead, the suspect's silhouette carved through the rain, dodging between streetlamps that smeared orange across the slick tarmac." | | 1 | "Three weeks of watching the former paramedic slip through Soho's shadows, patching up people who shouldn't exist." | | 2 | "The kind of thing that would end up in an evidence bag, that would make the CSI team scratch their heads and the CPS drop charges." | | 3 | "The stairs descended into amber light that flickered like candle flames, impossible for an abandoned station." | | 4 | "Vendors hawked goods from wooden carts: bottled light, preserved organs, scrolls that whispered as you passed." | | 5 | "An underground bazaar that moved with the moon, selling things that defied explanation." | | 6 | "Those wrong faces twisted further, and she felt something cold press against her mind, pushing, probing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 95.95% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn warned (warn)" | | 1 | "he hissed (hiss)" |
| | dialogueSentences | 37 | | tagDensity | 0.135 | | leniency | 0.27 | | rawRatio | 0.4 | | effectiveRatio | 0.108 | |