| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.226 | | leniency | 0.452 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1172 | | 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) | |
| 95.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1172 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 1 | | 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 | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1176 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 974 | | uniqueNames | 16 | | maxNameDensity | 0.92 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Quinn | 9 | | Tomás | 2 | | Herrera | 7 | | Hoxton | 1 | | London | 1 | | Wardour | 1 | | Street | 1 | | Nine | 1 | | Regent | 1 | | Canal | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 | | Procedure | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Procedure" |
| | places | | 0 | "London" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Regent" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1176 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 104 | | matches | | 0 | "had that scar" | | 1 | "seen that spiral" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 23.52 | | std | 23.37 | | cv | 0.994 | | sampleLengths | | 0 | 66 | | 1 | 18 | | 2 | 2 | | 3 | 3 | | 4 | 11 | | 5 | 91 | | 6 | 4 | | 7 | 7 | | 8 | 22 | | 9 | 13 | | 10 | 37 | | 11 | 4 | | 12 | 10 | | 13 | 18 | | 14 | 19 | | 15 | 60 | | 16 | 32 | | 17 | 9 | | 18 | 6 | | 19 | 62 | | 20 | 1 | | 21 | 15 | | 22 | 4 | | 23 | 21 | | 24 | 75 | | 25 | 57 | | 26 | 1 | | 27 | 24 | | 28 | 35 | | 29 | 12 | | 30 | 18 | | 31 | 38 | | 32 | 13 | | 33 | 28 | | 34 | 13 | | 35 | 11 | | 36 | 23 | | 37 | 98 | | 38 | 5 | | 39 | 8 | | 40 | 17 | | 41 | 4 | | 42 | 4 | | 43 | 5 | | 44 | 4 | | 45 | 46 | | 46 | 43 | | 47 | 27 | | 48 | 9 | | 49 | 23 |
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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 | 151 | | matches | (empty) | |
| 87.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 104 | | ratio | 0.019 | | matches | | 0 | "Under the bridge mouths the dark came down like a lid, and his medallion flashed at his open collar — Saint Christopher, patron of travelers — and the man could travel." | | 1 | "Air came up the shaft from below — candlewax, wet stone, something animal — and with it a murmur, a crowd-sound, rising like heat off a road." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 981 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.019367991845056064 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0010193679918450561 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 11.31 | | std | 9.1 | | cv | 0.805 | | sampleLengths | | 0 | 27 | | 1 | 23 | | 2 | 16 | | 3 | 18 | | 4 | 2 | | 5 | 3 | | 6 | 11 | | 7 | 15 | | 8 | 9 | | 9 | 16 | | 10 | 22 | | 11 | 29 | | 12 | 4 | | 13 | 5 | | 14 | 2 | | 15 | 15 | | 16 | 3 | | 17 | 4 | | 18 | 13 | | 19 | 15 | | 20 | 10 | | 21 | 3 | | 22 | 9 | | 23 | 4 | | 24 | 2 | | 25 | 5 | | 26 | 3 | | 27 | 13 | | 28 | 5 | | 29 | 15 | | 30 | 4 | | 31 | 28 | | 32 | 7 | | 33 | 9 | | 34 | 10 | | 35 | 2 | | 36 | 2 | | 37 | 2 | | 38 | 16 | | 39 | 16 | | 40 | 9 | | 41 | 2 | | 42 | 4 | | 43 | 17 | | 44 | 7 | | 45 | 7 | | 46 | 31 | | 47 | 1 | | 48 | 9 | | 49 | 6 |
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| 79.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5 | | totalSentences | 104 | | uniqueOpeners | 52 | |
| 91.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 73 | | matches | | 0 | "Then he ran." | | 1 | "Somewhere, drips kept time." |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 73 | | matches | | 0 | "He tied off the gauze," | | 1 | "he told his patient" | | 2 | "He carried the bag behind" | | 3 | "She set herself in a" | | 4 | "She showed the warrant card" | | 5 | "His voice came back over" | | 6 | "He cut north off Wardour" | | 7 | "His sleeve rode up on" | | 8 | "She had that scar in" | | 9 | "She keyed the radio at" | | 10 | "He dropped down the steps" | | 11 | "Her watch read 11:51 under" | | 12 | "He took the steps up" | | 13 | "He shouldered the gate and" | | 14 | "She had seen that spiral" | | 15 | "He stepped onto the stairs" | | 16 | "He was three steps down," | | 17 | "She pressed the set to" | | 18 | "She put it away." | | 19 | "Her hand found her inside" |
| | ratio | 0.288 | |
| 69.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 73 | | matches | | 0 | "The green neon above the" | | 1 | "Harlow Quinn had nursed the" | | 2 | "He tied off the gauze," | | 3 | "Quinn turned her glass a" | | 4 | "A salve with a luminous" | | 5 | "Herrera packed his bag." | | 6 | "he told his patient" | | 7 | "He carried the bag behind" | | 8 | "The shelf clicked." | | 9 | "The shelf swallowed him." | | 10 | "Quinn left a twenty under" | | 11 | "She set herself in a" | | 12 | "The door opened." | | 13 | "Herrera came out collar-up, bag" | | 14 | "Rain ran off his curls." | | 15 | "She showed the warrant card" | | 16 | "His voice came back over" | | 17 | "He cut north off Wardour" | | 18 | "His sleeve rode up on" | | 19 | "An old knife scar, white" |
| | ratio | 0.781 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 86.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 3 | | matches | | 0 | "Inside, the walls wore old sea maps and black-and-white photographs of men in collars too stiff for the century that had drowned them." | | 1 | "At the corner table, Tomás Herrera bound a man's split knuckles with gauze that glowed a faint pond-green." | | 2 | "He cut north off Wardour Street, through a delivery alley where crates of empties stood in ranks, over a chain-link gate that took her shoulder to rattle loose." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.065 | | leniency | 0.129 | | rawRatio | 0 | | effectiveRatio | 0 | |