| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.261 | | leniency | 0.522 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1118 | | 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) | |
| 82.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1118 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "charged" | | 1 | "footsteps" | | 2 | "flicked" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | 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 | 1118 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 942 | | uniqueNames | 18 | | maxNameDensity | 0.42 | | worstName | "Herrera" | | maxWindowNameDensity | 1 | | worstWindowName | "Herrera" | | discoveredNames | | Herrera | 4 | | Harlow | 1 | | Quinn | 3 | | Raven | 1 | | Nest | 1 | | Greek | 1 | | Street | 1 | | People | 1 | | Seville | 1 | | Left | 1 | | Charing | 1 | | Cross | 1 | | Road | 2 | | Euston | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 | | Camden | 2 |
| | persons | | 0 | "Herrera" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "People" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" | | 9 | "Camden" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Seville" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" | | 6 | "Euston" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 21.11% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.789 | | wordCount | 1118 | | matches | | 0 | "Not interference, not signal fade, but white nothing, the same hiss from a night three years back" | | 1 | "not signal fade, but white nothing, the same hiss from a night three years back" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 26.62 | | std | 22.68 | | cv | 0.852 | | sampleLengths | | 0 | 8 | | 1 | 106 | | 2 | 3 | | 3 | 3 | | 4 | 50 | | 5 | 9 | | 6 | 38 | | 7 | 27 | | 8 | 29 | | 9 | 48 | | 10 | 40 | | 11 | 51 | | 12 | 13 | | 13 | 49 | | 14 | 62 | | 15 | 43 | | 16 | 44 | | 17 | 63 | | 18 | 3 | | 19 | 10 | | 20 | 5 | | 21 | 27 | | 22 | 6 | | 23 | 42 | | 24 | 16 | | 25 | 10 | | 26 | 37 | | 27 | 4 | | 28 | 1 | | 29 | 39 | | 30 | 5 | | 31 | 21 | | 32 | 2 | | 33 | 10 | | 34 | 38 | | 35 | 3 | | 36 | 31 | | 37 | 5 | | 38 | 32 | | 39 | 59 | | 40 | 12 | | 41 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 158 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 97 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 164 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.018292682926829267 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 11.53 | | std | 8.97 | | cv | 0.778 | | sampleLengths | | 0 | 8 | | 1 | 29 | | 2 | 12 | | 3 | 2 | | 4 | 1 | | 5 | 28 | | 6 | 7 | | 7 | 27 | | 8 | 3 | | 9 | 3 | | 10 | 29 | | 11 | 21 | | 12 | 2 | | 13 | 7 | | 14 | 19 | | 15 | 5 | | 16 | 6 | | 17 | 8 | | 18 | 4 | | 19 | 6 | | 20 | 17 | | 21 | 26 | | 22 | 3 | | 23 | 8 | | 24 | 6 | | 25 | 22 | | 26 | 12 | | 27 | 7 | | 28 | 8 | | 29 | 20 | | 30 | 5 | | 31 | 7 | | 32 | 26 | | 33 | 18 | | 34 | 4 | | 35 | 6 | | 36 | 3 | | 37 | 5 | | 38 | 5 | | 39 | 39 | | 40 | 11 | | 41 | 26 | | 42 | 2 | | 43 | 1 | | 44 | 22 | | 45 | 13 | | 46 | 12 | | 47 | 5 | | 48 | 13 | | 49 | 9 |
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| 88.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5625 | | totalSentences | 96 | | uniqueOpeners | 54 | |
| 44.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 75 | | matches | | 0 | "Then everything below went quiet." |
| | ratio | 0.013 | |
| 92.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 75 | | matches | | 0 | "She had shown him the" | | 1 | "He had looked at it" | | 2 | "He ran faster." | | 3 | "She closed on him along" | | 4 | "She took them in her" | | 5 | "He shouted it over his" | | 6 | "He crossed in front of" | | 7 | "He didn't run like a" | | 8 | "He ran like a man" | | 9 | "She dropped and slid under" | | 10 | "He looked back once." | | 11 | "It was arithmetic." | | 12 | "He took it at a" | | 13 | "She ran on sound, on" | | 14 | "Her watch snagged the rail." | | 15 | "She tore the strap free," | | 16 | "He held her gaze, breathing" | | 17 | "He stepped back instead, one" | | 18 | "His hand found the gate" | | 19 | "He stepped through into the" |
| | ratio | 0.32 | |
| 86.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 75 | | matches | | 0 | "Nobody runs like that carrying" | | 1 | "Tomás Herrera cleared the bonnet" | | 2 | "She had shown him the" | | 3 | "He had looked at it" | | 4 | "He ran faster." | | 5 | "She closed on him along" | | 6 | "She took them in her" | | 7 | "Chinatown swallowed him next, steam" | | 8 | "The file said struck off." | | 9 | "People ran harder from their" | | 10 | "He shouted it over his" | | 11 | "A bus laid on its" | | 12 | "He crossed in front of" | | 13 | "The driver offered a word" | | 14 | "He didn't run like a" | | 15 | "He ran like a man" | | 16 | "Every cut came before she" | | 17 | "This was the way home." | | 18 | "Camden threw sodium light across" | | 19 | "She dropped and slid under" |
| | ratio | 0.747 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "He took it at a leap and hit the towpath running, black water sliding along beside him, and a fox that had been at the bin bags melted out of the way, considere…" | | 1 | "Chains slid off it like snakes waking and pooled on the concrete, and he turned on the top step, the little Saint Christopher at his throat throwing back the li…" | | 2 | "At the bottom, lamplight and murmur rose to meet her, a low tide of voices, hundreds of them, haggling, laughing, doing whatever it is people do in a station th…" |
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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 | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 23 | | tagDensity | 0.087 | | leniency | 0.174 | | rawRatio | 0.5 | | effectiveRatio | 0.087 | |