| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1387 | | 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) | |
| 31.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1387 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "mechanical" | | 1 | "weight" | | 2 | "shattered" | | 3 | "hulking" | | 4 | "structure" | | 5 | "pounding" | | 6 | "gloom" | | 7 | "pulsed" | | 8 | "silence" | | 9 | "vibrated" | | 10 | "navigated" | | 11 | "constructed" | | 12 | "standard" | | 13 | "chaotic" | | 14 | "chilling" | | 15 | "flawless" | | 16 | "measured" |
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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 | 123 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 123 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 140 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 4 | | totalWords | 1369 | | ratio | 0.003 | | matches | | 0 | "Camden Town – Underground" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.06% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1233 | | uniqueNames | 18 | | maxNameDensity | 1.38 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Kael" | | discoveredNames | | Camden | 3 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 17 | | Metropolitan | 1 | | Police | 1 | | Tomás | 1 | | Herrera | 11 | | Saint | 1 | | Christopher | 1 | | Town | 1 | | Morris | 3 | | London | 2 | | Veil | 1 | | Market | 1 | | Informants | 1 | | Kael | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Market" | | 9 | "Informants" | | 10 | "Kael" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Town" | | 4 | "London" |
| | globalScore | 0.811 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like grinding stones" |
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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 | 1369 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 140 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 24.02 | | std | 19.61 | | cv | 0.816 | | sampleLengths | | 0 | 91 | | 1 | 10 | | 2 | 39 | | 3 | 5 | | 4 | 1 | | 5 | 51 | | 6 | 55 | | 7 | 6 | | 8 | 4 | | 9 | 11 | | 10 | 15 | | 11 | 8 | | 12 | 55 | | 13 | 12 | | 14 | 53 | | 15 | 44 | | 16 | 12 | | 17 | 44 | | 18 | 41 | | 19 | 2 | | 20 | 17 | | 21 | 38 | | 22 | 32 | | 23 | 28 | | 24 | 12 | | 25 | 28 | | 26 | 8 | | 27 | 64 | | 28 | 43 | | 29 | 17 | | 30 | 47 | | 31 | 6 | | 32 | 45 | | 33 | 4 | | 34 | 6 | | 35 | 17 | | 36 | 25 | | 37 | 7 | | 38 | 6 | | 39 | 20 | | 40 | 39 | | 41 | 32 | | 42 | 62 | | 43 | 11 | | 44 | 23 | | 45 | 8 | | 46 | 31 | | 47 | 5 | | 48 | 16 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 123 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 201 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 140 | | ratio | 0.007 | | matches | | 0 | "The faded lettering carved into the stone read: *Camden Town – Underground*." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 814 | | adjectiveStacks | 2 | | stackExamples | | 0 | "sweeping over overturned dumpsters" | | 1 | "heavy, oil-stained canvas" |
| | adverbCount | 8 | | adverbRatio | 0.009828009828009828 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002457002457002457 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 140 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 140 | | mean | 9.78 | | std | 4.73 | | cv | 0.484 | | sampleLengths | | 0 | 10 | | 1 | 23 | | 2 | 11 | | 3 | 13 | | 4 | 11 | | 5 | 15 | | 6 | 2 | | 7 | 3 | | 8 | 3 | | 9 | 10 | | 10 | 18 | | 11 | 7 | | 12 | 14 | | 13 | 5 | | 14 | 1 | | 15 | 4 | | 16 | 17 | | 17 | 11 | | 18 | 19 | | 19 | 6 | | 20 | 13 | | 21 | 11 | | 22 | 9 | | 23 | 16 | | 24 | 6 | | 25 | 4 | | 26 | 11 | | 27 | 15 | | 28 | 8 | | 29 | 13 | | 30 | 15 | | 31 | 8 | | 32 | 19 | | 33 | 12 | | 34 | 5 | | 35 | 10 | | 36 | 11 | | 37 | 17 | | 38 | 10 | | 39 | 5 | | 40 | 5 | | 41 | 5 | | 42 | 5 | | 43 | 8 | | 44 | 16 | | 45 | 12 | | 46 | 4 | | 47 | 9 | | 48 | 18 | | 49 | 13 |
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| 52.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.35 | | totalSentences | 140 | | uniqueOpeners | 49 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 122 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 122 | | matches | | 0 | "Her worn leather watch, strapped" | | 1 | "He banked hard into an" | | 2 | "She scaled the links without" | | 3 | "Her boots slammed into the" | | 4 | "She drew her tactical flashlight," | | 5 | "His voice bounced off the" | | 6 | "He took a sharp left," | | 7 | "She clenched her sharp jaw." | | 8 | "She stepped through the archway," | | 9 | "Her flashlight beam bounced off" | | 10 | "She hopped off the platform" | | 11 | "She followed the curve of" | | 12 | "He stood tall, reaching deep" | | 13 | "She pressed her back against" | | 14 | "He handed the token to" | | 15 | "He melted into the thick" | | 16 | "Her heart hammered a heavy," | | 17 | "She placed her right hand" | | 18 | "He turned his head toward" | | 19 | "She possessed the option to" |
| | ratio | 0.205 | |
| 13.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 109 | | totalSentences | 122 | | matches | | 0 | "Rain lashed against the cracked" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "Drops cascaded from her closely" | | 3 | "Her worn leather watch, strapped" | | 4 | "Quinn maintained her pursuit, her" | | 5 | "Streetlights illuminated his olive skin" | | 6 | "The twenty-nine-year-old moved with relentless," | | 7 | "A silver Saint Christopher medallion" | | 8 | "Herrera ignored the command." | | 9 | "He banked hard into an" | | 10 | "The fabric of his jacket" | | 11 | "A jagged, angry scar running" | | 12 | "Quinn hit the fence seconds" | | 13 | "She scaled the links without" | | 14 | "Her boots slammed into the" | | 15 | "She drew her tactical flashlight," | | 16 | "The blinding white beam cut" | | 17 | "His voice bounced off the" | | 18 | "He took a sharp left," | | 19 | "Quinn rounded the corner, her" |
| | ratio | 0.893 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 122 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |