| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 90 | | tagDensity | 0.122 | | leniency | 0.244 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.27% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1829 | | 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) | |
| 86.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1829 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "remarkable" | | 1 | "silence" | | 2 | "flicked" | | 3 | "grave" | | 4 | "trembled" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 157 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 157 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 236 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1829 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.29% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 71 | | wordCount | 1217 | | uniqueNames | 17 | | maxNameDensity | 2.05 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 25 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Callum | 1 | | Venn | 18 | | Met | 1 | | Vale | 3 | | Kowalski | 1 | | Eva | 11 | | Museum | 1 | | Hackney | 1 | | Morris | 1 | | Kentish | 1 | | Town | 1 | | Bexley | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Market" | | 4 | "Venn" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Museum" | | 8 | "Morris" | | 9 | "Bexley" |
| | places | | 0 | "Vale" | | 1 | "Hackney" | | 2 | "Kentish" | | 3 | "Town" |
| | globalScore | 0.473 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.547 | | wordCount | 1829 | | matches | | 0 | "not copper this time, but salt water" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 236 | | matches | | 0 | "insisting that this" | | 1 | "worn that watch" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 159 | | mean | 11.5 | | std | 13.59 | | cv | 1.182 | | sampleLengths | | 0 | 17 | | 1 | 4 | | 2 | 13 | | 3 | 3 | | 4 | 28 | | 5 | 4 | | 6 | 7 | | 7 | 76 | | 8 | 12 | | 9 | 54 | | 10 | 9 | | 11 | 50 | | 12 | 6 | | 13 | 22 | | 14 | 2 | | 15 | 5 | | 16 | 39 | | 17 | 4 | | 18 | 2 | | 19 | 44 | | 20 | 5 | | 21 | 18 | | 22 | 7 | | 23 | 5 | | 24 | 11 | | 25 | 2 | | 26 | 10 | | 27 | 43 | | 28 | 5 | | 29 | 2 | | 30 | 2 | | 31 | 4 | | 32 | 10 | | 33 | 11 | | 34 | 3 | | 35 | 3 | | 36 | 4 | | 37 | 33 | | 38 | 7 | | 39 | 1 | | 40 | 3 | | 41 | 4 | | 42 | 3 | | 43 | 31 | | 44 | 36 | | 45 | 8 | | 46 | 3 | | 47 | 15 | | 48 | 8 | | 49 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 157 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 203 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 236 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1224 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.025326797385620915 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0032679738562091504 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 236 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 236 | | mean | 7.75 | | std | 5.83 | | cv | 0.753 | | sampleLengths | | 0 | 17 | | 1 | 4 | | 2 | 13 | | 3 | 3 | | 4 | 4 | | 5 | 24 | | 6 | 4 | | 7 | 7 | | 8 | 12 | | 9 | 15 | | 10 | 13 | | 11 | 8 | | 12 | 18 | | 13 | 10 | | 14 | 12 | | 15 | 5 | | 16 | 24 | | 17 | 8 | | 18 | 17 | | 19 | 2 | | 20 | 2 | | 21 | 5 | | 22 | 16 | | 23 | 11 | | 24 | 18 | | 25 | 5 | | 26 | 6 | | 27 | 22 | | 28 | 2 | | 29 | 5 | | 30 | 39 | | 31 | 4 | | 32 | 2 | | 33 | 17 | | 34 | 10 | | 35 | 7 | | 36 | 10 | | 37 | 5 | | 38 | 18 | | 39 | 7 | | 40 | 5 | | 41 | 11 | | 42 | 2 | | 43 | 10 | | 44 | 16 | | 45 | 12 | | 46 | 15 | | 47 | 5 | | 48 | 2 | | 49 | 2 |
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| 47.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3177966101694915 | | totalSentences | 236 | | uniqueOpeners | 75 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 132 | | matches | | 0 | "Closely cropped salt-and-pepper hair, sharp" | | 1 | "Somewhere behind the shutters, glass" | | 2 | "Then a second hand joined" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 132 | | matches | | 0 | "He wore an expensive charcoal" | | 1 | "His face had turned waxy" | | 2 | "He had spent most of" | | 3 | "He wore a long butcher’s" | | 4 | "His skin had the grey," | | 5 | "She pulled her hand back." | | 6 | "It looked scrubbed, yet grey" | | 7 | "She crouched again." | | 8 | "They began near the corpse" | | 9 | "Her gaze returned to his" | | 10 | "They stopped at blank tiles." | | 11 | "She ran a finger over" | | 12 | "Its casing carried a thick" | | 13 | "Her curly red hair had" | | 14 | "She clutched a worn leather" | | 15 | "She had expected someone older." | | 16 | "Her green eyes hardened behind" | | 17 | "Her voice cut cleanly through" | | 18 | "She examined his hands." | | 19 | "She looked towards the north" |
| | ratio | 0.227 | |
| 24.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 115 | | totalSentences | 132 | | matches | | 0 | "Detective Harlow Quinn stepped over" | | 1 | "The crime-scene officer froze beside" | | 2 | "Quinn looked at him." | | 3 | "The officer swallowed and raised" | | 4 | "The abandoned station beneath Camden" | | 5 | "Someone had chiselled the signs" | | 6 | "Stalls crowded the old platform," | | 7 | "A fishmonger’s slab displayed teeth" | | 8 | "A woman with a fox" | | 9 | "Incense, rot, damp brick and" | | 10 | "The Veil Market had packed" | | 11 | "Quinn crouched beside the man." | | 12 | "He wore an expensive charcoal" | | 13 | "His face had turned waxy" | | 14 | "A man in a rumpled" | | 15 | "DS Callum Venn wore his" | | 16 | "He had spent most of" | | 17 | "That had earned him silence." | | 18 | "Venn nodded towards the corpse." | | 19 | "Quinn glanced at the narrow" |
| | ratio | 0.871 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 132 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 90 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0.111 | | effectiveRatio | 0.022 | |