| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1610 | | totalAiIsmAdverbs | 1 | | 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) | |
| 31.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1610 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "fractured" | | 1 | "jaw clenched" | | 2 | "silence" | | 3 | "flickered" | | 4 | "variables" | | 5 | "echoes" | | 6 | "footsteps" | | 7 | "tracing" | | 8 | "velvet" | | 9 | "echoing" | | 10 | "weight" | | 11 | "scanned" | | 12 | "pulse" | | 13 | "warmth" | | 14 | "familiar" | | 15 | "crystal" | | 16 | "tension" | | 17 | "reminder" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 115 | | matches | (empty) | |
| 93.17% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1610 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.97% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1268 | | uniqueNames | 16 | | maxNameDensity | 1.34 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 17 | | Dean | 1 | | Street | 1 | | Morris | 5 | | Tomás | 14 | | Herrera | 2 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Camden | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Raven" |
| | places | | 0 | "Soho" | | 1 | "Dean" | | 2 | "Street" | | 3 | "London" |
| | globalScore | 0.83 | | windowScore | 0.833 | |
| 12.64% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 5 | | matches | | 0 | "llion swinging visibly against his chest a" | | 1 | "appeared closed early" | | 2 | "looked like glowing vials nestled in velv" | | 3 | "maps that seemed to shift when eyes left them" | | 4 | "appeared beyond the platform rows" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1610 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 136 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 32.86 | | std | 20.33 | | cv | 0.619 | | sampleLengths | | 0 | 69 | | 1 | 11 | | 2 | 75 | | 3 | 48 | | 4 | 17 | | 5 | 66 | | 6 | 69 | | 7 | 35 | | 8 | 72 | | 9 | 52 | | 10 | 89 | | 11 | 28 | | 12 | 32 | | 13 | 22 | | 14 | 29 | | 15 | 6 | | 16 | 21 | | 17 | 15 | | 18 | 27 | | 19 | 47 | | 20 | 18 | | 21 | 39 | | 22 | 62 | | 23 | 19 | | 24 | 37 | | 25 | 22 | | 26 | 16 | | 27 | 14 | | 28 | 44 | | 29 | 9 | | 30 | 16 | | 31 | 23 | | 32 | 25 | | 33 | 10 | | 34 | 28 | | 35 | 32 | | 36 | 26 | | 37 | 5 | | 38 | 17 | | 39 | 38 | | 40 | 20 | | 41 | 23 | | 42 | 29 | | 43 | 63 | | 44 | 23 | | 45 | 16 | | 46 | 32 | | 47 | 56 | | 48 | 18 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 236 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 136 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1275 | | adjectiveStacks | 1 | | stackExamples | | 0 | "tighter under damp fabric." |
| | adverbCount | 31 | | adverbRatio | 0.02431372549019608 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01019607843137255 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 11.84 | | std | 6.33 | | cv | 0.534 | | sampleLengths | | 0 | 22 | | 1 | 11 | | 2 | 19 | | 3 | 17 | | 4 | 11 | | 5 | 13 | | 6 | 7 | | 7 | 24 | | 8 | 10 | | 9 | 21 | | 10 | 16 | | 11 | 11 | | 12 | 21 | | 13 | 11 | | 14 | 6 | | 15 | 17 | | 16 | 22 | | 17 | 27 | | 18 | 5 | | 19 | 6 | | 20 | 15 | | 21 | 8 | | 22 | 11 | | 23 | 24 | | 24 | 14 | | 25 | 18 | | 26 | 3 | | 27 | 6 | | 28 | 23 | | 29 | 12 | | 30 | 18 | | 31 | 13 | | 32 | 9 | | 33 | 7 | | 34 | 15 | | 35 | 8 | | 36 | 13 | | 37 | 15 | | 38 | 10 | | 39 | 14 | | 40 | 12 | | 41 | 22 | | 42 | 13 | | 43 | 3 | | 44 | 17 | | 45 | 11 | | 46 | 13 | | 47 | 19 | | 48 | 9 | | 49 | 13 |
| |
| 64.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.39705882352941174 | | totalSentences | 136 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 109 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 109 | | matches | | 0 | "Her worn leather watch dug" | | 1 | "She caught glimpses of a" | | 2 | "she shouted, her voice cutting" | | 3 | "Her sharp jaw clenched as" | | 4 | "He glanced back, warm brown" | | 5 | "She cleared the wall with" | | 6 | "He veered left, disappearing through" | | 7 | "She knew the bar by" | | 8 | "She pushed the door anyway," | | 9 | "Her suspect threaded past empty" | | 10 | "She drew her radio, considered" | | 11 | "She descended the steps single" | | 12 | "Her breathing remained controlled, but" | | 13 | "She had none." | | 14 | "He spoke urgently to an" | | 15 | "His voice carried across the" | | 16 | "She grabbed his shoulder and" | | 17 | "He touched the medallion at" | | 18 | "She felt the weight of" | | 19 | "She scanned the platform." |
| | ratio | 0.294 | |
| 28.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 109 | | matches | | 0 | "Her worn leather watch dug" | | 1 | "She caught glimpses of a" | | 2 | "Streetlamps cast fractured reflections on" | | 3 | "she shouted, her voice cutting" | | 4 | "The figure ignored the command" | | 5 | "Quinn followed, boots splashing through" | | 6 | "Her sharp jaw clenched as" | | 7 | "This suspect carried the same" | | 8 | "The hood slipped at the" | | 9 | "Quinn registered the man as" | | 10 | "He glanced back, warm brown" | | 11 | "She cleared the wall with" | | 12 | "Tomás pressed onward without reply," | | 13 | "Quinn noted the scar along" | | 14 | "He veered left, disappearing through" | | 15 | "Quinn hesitated at the threshold." | | 16 | "She knew the bar by" | | 17 | "She pushed the door anyway," | | 18 | "Silas the bartender glanced up" | | 19 | "Her suspect threaded past empty" |
| | ratio | 0.862 | |
| 45.87% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 109 | | matches | | 0 | "Before they reached him, the" |
| | ratio | 0.009 | |
| 85.25% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 5 | | matches | | 0 | "He veered left, disappearing through the rear entrance of a nondescript building marked by a distinctive green neon sign that flickered above the front: The Rav…" | | 1 | "Silas the bartender glanced up from wiping a glass, his face tightening, yet he said nothing as she flashed her badge and kept moving." | | 2 | "The hidden back room lay exposed, a low-ceilinged space stacked with crates and an open hatch in the floor that led deeper still." | | 3 | "A supernatural black market operated here, its existence shifting with every full moon according to half-whispered rumors her informants had pieced together." | | 4 | "Morris's last transmission had mentioned an underground exchange, something that defied every forensic report." |
| |
| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "she shouted, her voice cutting through the downpour" |
| |
| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "Quinn continued (continue)" | | 2 | "she demanded (demand)" |
| | dialogueSentences | 27 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.5 | | effectiveRatio | 0.222 | |