| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said instead [instead]" |
| | dialogueSentences | 55 | | tagDensity | 0.255 | | leniency | 0.509 | | rawRatio | 0.071 | | effectiveRatio | 0.036 | |
| 92.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1290 | | 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1290 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 62 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 62 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1301 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 814 | | uniqueNames | 11 | | maxNameDensity | 0.74 | | worstName | "Eva" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Lucien | 5 | | Moreau | 1 | | Eva | 6 | | Clerkenwell | 1 | | September | 1 | | Brick | 1 | | Lane | 1 | | Rory | 6 | | Ptolemy | 4 | | Quiet | 1 | | London | 1 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Ptolemy" |
| | places | | 0 | "Clerkenwell" | | 1 | "September" | | 2 | "Brick" | | 3 | "Lane" | | 4 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1301 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 21.33 | | std | 21.9 | | cv | 1.027 | | sampleLengths | | 0 | 28 | | 1 | 78 | | 2 | 1 | | 3 | 9 | | 4 | 26 | | 5 | 37 | | 6 | 25 | | 7 | 1 | | 8 | 10 | | 9 | 5 | | 10 | 34 | | 11 | 45 | | 12 | 22 | | 13 | 2 | | 14 | 86 | | 15 | 51 | | 16 | 7 | | 17 | 15 | | 18 | 8 | | 19 | 10 | | 20 | 8 | | 21 | 1 | | 22 | 54 | | 23 | 1 | | 24 | 28 | | 25 | 9 | | 26 | 1 | | 27 | 11 | | 28 | 42 | | 29 | 4 | | 30 | 60 | | 31 | 5 | | 32 | 3 | | 33 | 51 | | 34 | 2 | | 35 | 2 | | 36 | 23 | | 37 | 11 | | 38 | 11 | | 39 | 56 | | 40 | 2 | | 41 | 82 | | 42 | 18 | | 43 | 22 | | 44 | 14 | | 45 | 12 | | 46 | 37 | | 47 | 16 | | 48 | 59 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 62 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 135 | | matches | (empty) | |
| 1.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 1 | | flaggedSentences | 5 | | totalSentences | 101 | | ratio | 0.05 | | matches | | 0 | "His eyes found hers — one amber, one black — and for a long moment the only sounds were the rain ticking against the stairwell window and the curry-house fan droning below." | | 1 | "He stepped over the threshold the way he did everything — as though rooms should be grateful to receive him — and stopped, because there was nowhere to step." | | 2 | "Below, Brick Lane carried on regardless — a night bus sighing at the stop, two men arguing outside the off-licence." | | 3 | "Up close, the amber eye was whisky in lamplight; the black one gave nothing back, and somehow gave back everything." | | 4 | "His gaze dropped — to her mouth, then lower, to the crescent scar on her left wrist where her hand braced against his knee." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 814 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.025798525798525797 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004914004914004914 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 12.88 | | std | 10.93 | | cv | 0.849 | | sampleLengths | | 0 | 28 | | 1 | 5 | | 2 | 12 | | 3 | 18 | | 4 | 11 | | 5 | 32 | | 6 | 1 | | 7 | 9 | | 8 | 18 | | 9 | 8 | | 10 | 2 | | 11 | 35 | | 12 | 5 | | 13 | 20 | | 14 | 1 | | 15 | 10 | | 16 | 5 | | 17 | 34 | | 18 | 45 | | 19 | 15 | | 20 | 7 | | 21 | 2 | | 22 | 29 | | 23 | 25 | | 24 | 12 | | 25 | 20 | | 26 | 33 | | 27 | 7 | | 28 | 11 | | 29 | 7 | | 30 | 7 | | 31 | 8 | | 32 | 8 | | 33 | 10 | | 34 | 8 | | 35 | 1 | | 36 | 11 | | 37 | 43 | | 38 | 1 | | 39 | 24 | | 40 | 4 | | 41 | 7 | | 42 | 2 | | 43 | 1 | | 44 | 11 | | 45 | 42 | | 46 | 4 | | 47 | 28 | | 48 | 32 | | 49 | 5 |
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| 74.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.45544554455445546 | | totalSentences | 101 | | uniqueOpeners | 46 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 37.54% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 57 | | matches | | 0 | "His platinum hair had slipped" | | 1 | "His eyes found hers —" | | 2 | "Her grip tightened on the" | | 3 | "She lifted the chain off" | | 4 | "He stepped over the threshold" | | 5 | "She knew he had never" | | 6 | "She folded her arms" | | 7 | "He turned the wax disc" | | 8 | "His jaw worked" | | 9 | "She crossed the room and" | | 10 | "He spoke to the ivory" | | 11 | "he said instead" | | 12 | "She sat on the edge" | | 13 | "He held still." | | 14 | "She pressed antiseptic to the" | | 15 | "His gaze dropped — to" | | 16 | "His thumb lifted from the" | | 17 | "He didn't touch her." | | 18 | "She kept her voice level," | | 19 | "She pressed a plaster over" |
| | ratio | 0.456 | |
| 21.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 57 | | matches | | 0 | "Rory turned the last deadbolt" | | 1 | "The weather had undone him." | | 2 | "His platinum hair had slipped" | | 3 | "Water dripped from the shoulders" | | 4 | "A cut bled along his" | | 5 | "His eyes found hers —" | | 6 | "Something moved at the corner" | | 7 | "The cat got there first." | | 8 | "Ptolemy slipped between her ankles," | | 9 | "Her grip tightened on the" | | 10 | "She lifted the chain off" | | 11 | "He stepped over the threshold" | | 12 | "Eva's research had colonised every" | | 13 | "A scarf draped over the" | | 14 | "Lucien moved a pile of" | | 15 | "Rory had seen what the" | | 16 | "She knew he had never" | | 17 | "Ptolemy leapt into his lap" | | 18 | "She folded her arms" | | 19 | "Lucien went very still, which" |
| | ratio | 0.877 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 68.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 3 | | matches | | 0 | "On a staircase in Clerkenwell in September, with her heart going like a drum and his hand at the small of her back, she had taught him the pattern and hated her…" | | 1 | "From inside his coat he produced a disc of black wax, no bigger than a two-pound coin, stamped with a sigil her eyes kept sliding off, as though the thing refus…" | | 2 | "He stepped over the threshold the way he did everything — as though rooms should be grateful to receive him — and stopped, because there was nowhere to step." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 55 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0.2 | | effectiveRatio | 0.036 | |