| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 43 | | tagDensity | 0.14 | | leniency | 0.279 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1845 | | 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) | |
| 91.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1845 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "velvet" | | 1 | "throb" | | 2 | "raced" |
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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 | 2 | | narrationSentences | 201 | | matches | | 0 | "e with panic" | | 1 | "t with confusion" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 201 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 239 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1845 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1641 | | uniqueNames | 19 | | maxNameDensity | 1.65 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Greek | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 2 | | Soho | 1 | | Quinn | 27 | | St | 1 | | Morris | 5 | | Christopher | 2 | | Charing | 1 | | Cross | 2 | | Road | 1 | | Underground | 1 | | London | 1 | | Tomás | 14 | | Saint | 2 | | Police | 1 | | King | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Christopher" | | 4 | "Tomás" | | 5 | "Saint" | | 6 | "King" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Raven" | | 3 | "Soho" | | 4 | "St" | | 5 | "Charing" | | 6 | "Road" | | 7 | "London" |
| | globalScore | 0.677 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 124 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.542 | | wordCount | 1845 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 239 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 135 | | mean | 13.67 | | std | 15.6 | | cv | 1.141 | | sampleLengths | | 0 | 20 | | 1 | 2 | | 2 | 31 | | 3 | 43 | | 4 | 16 | | 5 | 7 | | 6 | 5 | | 7 | 68 | | 8 | 29 | | 9 | 2 | | 10 | 69 | | 11 | 9 | | 12 | 13 | | 13 | 2 | | 14 | 40 | | 15 | 32 | | 16 | 3 | | 17 | 28 | | 18 | 7 | | 19 | 2 | | 20 | 1 | | 21 | 9 | | 22 | 4 | | 23 | 42 | | 24 | 6 | | 25 | 47 | | 26 | 2 | | 27 | 6 | | 28 | 47 | | 29 | 9 | | 30 | 3 | | 31 | 5 | | 32 | 29 | | 33 | 3 | | 34 | 4 | | 35 | 1 | | 36 | 13 | | 37 | 4 | | 38 | 2 | | 39 | 4 | | 40 | 41 | | 41 | 5 | | 42 | 7 | | 43 | 18 | | 44 | 3 | | 45 | 8 | | 46 | 26 | | 47 | 44 | | 48 | 7 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 201 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 273 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 239 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 368 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.010869565217391304 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.005434782608695652 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 239 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 239 | | mean | 7.72 | | std | 5.44 | | cv | 0.704 | | sampleLengths | | 0 | 20 | | 1 | 2 | | 2 | 5 | | 3 | 18 | | 4 | 3 | | 5 | 5 | | 6 | 17 | | 7 | 12 | | 8 | 7 | | 9 | 7 | | 10 | 16 | | 11 | 4 | | 12 | 3 | | 13 | 5 | | 14 | 9 | | 15 | 9 | | 16 | 7 | | 17 | 11 | | 18 | 17 | | 19 | 15 | | 20 | 14 | | 21 | 2 | | 22 | 7 | | 23 | 6 | | 24 | 2 | | 25 | 12 | | 26 | 9 | | 27 | 16 | | 28 | 14 | | 29 | 18 | | 30 | 9 | | 31 | 7 | | 32 | 6 | | 33 | 2 | | 34 | 18 | | 35 | 6 | | 36 | 11 | | 37 | 3 | | 38 | 2 | | 39 | 12 | | 40 | 20 | | 41 | 3 | | 42 | 18 | | 43 | 10 | | 44 | 7 | | 45 | 2 | | 46 | 1 | | 47 | 9 | | 48 | 4 | | 49 | 10 |
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| 49.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.34309623430962344 | | totalSentences | 239 | | uniqueOpeners | 82 | |
| 96.34% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 173 | | matches | | 0 | "Only a faint, irregular knock" | | 1 | "Then a voice breathed through" | | 2 | "Somewhere, a violin played three" | | 3 | "Instead, black smoke curled from" | | 4 | "Then a hand pushed through" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 173 | | matches | | 0 | "He did not look back." | | 1 | "He held it too carefully." | | 2 | "It never made it easier." | | 3 | "Her leather watch strap darkened" | | 4 | "He also held three outstanding" | | 5 | "Its driver leaned on the" | | 6 | "Her lungs worked clean and" | | 7 | "He ran like a medic" | | 8 | "He ducked beneath an awning," | | 9 | "He flinched at his name" | | 10 | "He crossed at the lights" | | 11 | "She stopped at the top" | | 12 | "She adjusted the channel." | | 13 | "She had listened to it" | | 14 | "He had called from an" | | 15 | "Her hand tightened around the" | | 16 | "She holstered it and drew" | | 17 | "They wore old gum stains," | | 18 | "It carried incense, hot oil," | | 19 | "He had stopped beside a" |
| | ratio | 0.214 | |
| 61.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 138 | | totalSentences | 173 | | matches | | 0 | "Quinn vaulted the overturned crate" | | 1 | "He did not look back." | | 2 | "A dark coat flapped around" | | 3 | "He held it too carefully." | | 4 | "Rain slapped the pavement and" | | 5 | "Quinn hit the alley at" | | 6 | "A bin bag burst beneath" | | 7 | "It never made it easier." | | 8 | "Her leather watch strap darkened" | | 9 | "A bartender with blood on" | | 10 | "A smashed glass case in" | | 11 | "Quinn had caught a glimpse" | | 12 | "The former paramedic had patched" | | 13 | "He also held three outstanding" | | 14 | "Tomás sprinted across the road" | | 15 | "A black cab shrieked to" | | 16 | "Its driver leaned on the" | | 17 | "Quinn cut between the cab" | | 18 | "Her lungs worked clean and" | | 19 | "Tomás had neither the gait" |
| | ratio | 0.798 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 173 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 2 | | matches | | 0 | "He ran like a medic who had done this before, stepping around puddles, judging corners, saving strength where he could." | | 1 | "Bottles smashed underfoot, releasing clouds of green vapour that crawled close to the floor." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn muttered (mutter)" |
| | dialogueSentences | 43 | | tagDensity | 0.116 | | leniency | 0.233 | | rawRatio | 0.2 | | effectiveRatio | 0.047 | |