| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1363 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 63.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1363 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "shattered" | | 1 | "familiar" | | 2 | "depths" | | 3 | "echoes" | | 4 | "vibrated" | | 5 | "rhythmic" | | 6 | "echoed" | | 7 | "weight" | | 8 | "mosaic" | | 9 | "charged" |
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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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 151 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 166 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1354 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.47% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1215 | | uniqueNames | 16 | | maxNameDensity | 1.81 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 22 | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 15 | | Greek | 1 | | Street | 1 | | Fitzrovia | 1 | | Camden | 1 | | London | 2 | | Underground | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "Fitzrovia" | | 4 | "Camden" | | 5 | "London" |
| | globalScore | 0.595 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 112 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like grinding millstones" |
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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 | 1354 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 166 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 20.52 | | std | 12.43 | | cv | 0.606 | | sampleLengths | | 0 | 42 | | 1 | 44 | | 2 | 34 | | 3 | 4 | | 4 | 21 | | 5 | 23 | | 6 | 12 | | 7 | 20 | | 8 | 32 | | 9 | 18 | | 10 | 37 | | 11 | 21 | | 12 | 32 | | 13 | 21 | | 14 | 14 | | 15 | 3 | | 16 | 23 | | 17 | 6 | | 18 | 2 | | 19 | 21 | | 20 | 28 | | 21 | 25 | | 22 | 4 | | 23 | 5 | | 24 | 17 | | 25 | 19 | | 26 | 18 | | 27 | 25 | | 28 | 11 | | 29 | 43 | | 30 | 25 | | 31 | 46 | | 32 | 15 | | 33 | 10 | | 34 | 33 | | 35 | 23 | | 36 | 18 | | 37 | 52 | | 38 | 8 | | 39 | 18 | | 40 | 7 | | 41 | 22 | | 42 | 39 | | 43 | 50 | | 44 | 3 | | 45 | 8 | | 46 | 19 | | 47 | 8 | | 48 | 19 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 151 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 222 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 166 | | ratio | 0.012 | | matches | | 0 | "Beneath the tangled weeds and concrete rubble sat an old, arched stone entry—an abandoned conduit into the sprawling London Underground." | | 1 | "Dried animal parts—claws, wings, and disturbingly human limbs—hung from rusted wire." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1224 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.014705882352941176 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006535947712418301 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 166 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 166 | | mean | 8.16 | | std | 3.93 | | cv | 0.482 | | sampleLengths | | 0 | 5 | | 1 | 19 | | 2 | 6 | | 3 | 10 | | 4 | 2 | | 5 | 9 | | 6 | 7 | | 7 | 11 | | 8 | 17 | | 9 | 8 | | 10 | 4 | | 11 | 16 | | 12 | 6 | | 13 | 4 | | 14 | 2 | | 15 | 8 | | 16 | 11 | | 17 | 5 | | 18 | 7 | | 19 | 11 | | 20 | 12 | | 21 | 11 | | 22 | 9 | | 23 | 8 | | 24 | 8 | | 25 | 5 | | 26 | 11 | | 27 | 9 | | 28 | 9 | | 29 | 5 | | 30 | 7 | | 31 | 4 | | 32 | 4 | | 33 | 8 | | 34 | 2 | | 35 | 7 | | 36 | 9 | | 37 | 12 | | 38 | 7 | | 39 | 12 | | 40 | 4 | | 41 | 9 | | 42 | 6 | | 43 | 9 | | 44 | 3 | | 45 | 3 | | 46 | 4 | | 47 | 10 | | 48 | 3 | | 49 | 6 |
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| 60.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3795180722891566 | | totalSentences | 166 | | uniqueOpeners | 63 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 145 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 145 | | matches | | 0 | "She checked the worn leather" | | 1 | "He pulled the collar of" | | 2 | "Her sharp jaw set." | | 3 | "She squared her shoulders, bringing" | | 4 | "Her military precision demanded absolute" | | 5 | "His warm brown eyes widened" | | 6 | "He spun on his heel" | | 7 | "Her heavy boots crushed discarded" | | 8 | "She kept her eyes locked" | | 9 | "He cast a frantic look" | | 10 | "His short, curly, dark brown" | | 11 | "He pushed a rolling garbage" | | 12 | "Her boots hit the concrete," | | 13 | "She closed the distance." | | 14 | "They burst out of Soho," | | 15 | "He whipped around." | | 16 | "His chest heaved." | | 17 | "She kept her hands empty" | | 18 | "He rubbed the pale scar" | | 19 | "He hurled the top wooden" |
| | ratio | 0.283 | |
| 32.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 124 | | totalSentences | 145 | | matches | | 0 | "Water sheeted off Harlow’s shoulders." | | 1 | "The distinctive green neon sign" | | 2 | "Harlow watched from a darkened" | | 3 | "She checked the worn leather" | | 4 | "The heavy oak door swung" | | 5 | "Tomás Herrera stepped onto the" | | 6 | "He pulled the collar of" | | 7 | "The Saint Christopher medallion swung" | | 8 | "Harlow stepped from the mouth" | | 9 | "Her sharp jaw set." | | 10 | "She squared her shoulders, bringing" | | 11 | "Her military precision demanded absolute" | | 12 | "His warm brown eyes widened" | | 13 | "He spun on his heel" | | 14 | "Harlow broke into a sprint." | | 15 | "Her heavy boots crushed discarded" | | 16 | "Harlow shoved past a stumbling" | | 17 | "She kept her eyes locked" | | 18 | "Tomás hooked a sharp left" | | 19 | "He cast a frantic look" |
| | ratio | 0.855 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 145 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "He clutched his side, dragging his left leg, shoes slipping on wet cobblestones." | | 1 | "Wide shoulders, unnatural posture, hands that hung remarkably far down their thighs." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |