| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 2 | | adverbTags | | 0 | "he said quietly [quietly]" | | 1 | "She advanced slowly [slowly]" |
| | dialogueSentences | 24 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.222 | | effectiveRatio | 0.167 | |
| 89.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1375 | | totalAiIsmAdverbs | 3 | | 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) | |
| 52.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1375 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "flickered" | | 1 | "footsteps" | | 2 | "echoing" | | 3 | "etched" | | 4 | "flicked" | | 5 | "shimmered" | | 6 | "stomach" | | 7 | "velvet" | | 8 | "silk" | | 9 | "raced" | | 10 | "could feel" | | 11 | "weight" |
| |
| 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 | 1 | | narrationSentences | 129 | | matches | | |
| 65.34% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 4 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | | 0 | "began to" | | 1 | "seemed to" | | 2 | "tried to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 143 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1375 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1180 | | uniqueNames | 17 | | maxNameDensity | 1.1 | | worstName | "Tomás" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | North | 1 | | London | 2 | | Morris | 4 | | Spanish | 1 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Tomás | 13 | | Quinn | 11 | | English | 1 | | Seville | 1 |
| | persons | | 0 | "Saint" | | 1 | "Christopher" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Raven" | | 5 | "Market" | | 6 | "Tomás" | | 7 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "North" | | 2 | "London" | | 3 | "Seville" |
| | globalScore | 0.949 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | 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 | 1375 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 143 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 34.38 | | std | 22.33 | | cv | 0.65 | | sampleLengths | | 0 | 35 | | 1 | 5 | | 2 | 57 | | 3 | 65 | | 4 | 45 | | 5 | 12 | | 6 | 27 | | 7 | 64 | | 8 | 55 | | 9 | 59 | | 10 | 61 | | 11 | 12 | | 12 | 44 | | 13 | 21 | | 14 | 2 | | 15 | 61 | | 16 | 11 | | 17 | 63 | | 18 | 12 | | 19 | 66 | | 20 | 61 | | 21 | 17 | | 22 | 70 | | 23 | 9 | | 24 | 47 | | 25 | 29 | | 26 | 22 | | 27 | 45 | | 28 | 6 | | 29 | 34 | | 30 | 13 | | 31 | 66 | | 32 | 41 | | 33 | 44 | | 34 | 39 | | 35 | 5 | | 36 | 29 | | 37 | 4 | | 38 | 8 | | 39 | 9 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 129 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 233 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 143 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1184 | | adjectiveStacks | 1 | | stackExamples | | 0 | "former paramedic turned shadow" |
| | adverbCount | 37 | | adverbRatio | 0.03125 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.008445945945945946 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 143 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 143 | | mean | 9.62 | | std | 5.59 | | cv | 0.581 | | sampleLengths | | 0 | 18 | | 1 | 17 | | 2 | 5 | | 3 | 3 | | 4 | 18 | | 5 | 13 | | 6 | 23 | | 7 | 7 | | 8 | 13 | | 9 | 17 | | 10 | 9 | | 11 | 19 | | 12 | 5 | | 13 | 11 | | 14 | 3 | | 15 | 4 | | 16 | 2 | | 17 | 1 | | 18 | 19 | | 19 | 12 | | 20 | 9 | | 21 | 9 | | 22 | 9 | | 23 | 16 | | 24 | 9 | | 25 | 13 | | 26 | 14 | | 27 | 3 | | 28 | 9 | | 29 | 3 | | 30 | 13 | | 31 | 15 | | 32 | 8 | | 33 | 12 | | 34 | 1 | | 35 | 1 | | 36 | 2 | | 37 | 12 | | 38 | 13 | | 39 | 14 | | 40 | 8 | | 41 | 12 | | 42 | 8 | | 43 | 11 | | 44 | 6 | | 45 | 14 | | 46 | 9 | | 47 | 13 | | 48 | 6 | | 49 | 6 |
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| 70.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.42657342657342656 | | totalSentences | 143 | | uniqueOpeners | 61 | |
| 56.02% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 119 | | matches | | 0 | "Somewhere above, distant sirens wailed," | | 1 | "Further down the platform, something" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 119 | | matches | | 0 | "He did not." | | 1 | "He knew the streets better" | | 2 | "Her intel said he ran" | | 3 | "She still saw the scene" | | 4 | "Her boots slapped through puddles." | | 5 | "She ignored it." | | 6 | "He glanced back once, olive" | | 7 | "he shouted without breaking stride" | | 8 | "His Spanish accent clipped the" | | 9 | "He veered left at the" | | 10 | "She knew what waited if" | | 11 | "He was already halfway down" | | 12 | "She took them three at" | | 13 | "Her watch strap grew slick" | | 14 | "he said quietly" | | 15 | "She advanced slowly, baton extended" | | 16 | "His warm brown eyes flicked" | | 17 | "She'd heard rumours, of course." | | 18 | "She had never seen it." | | 19 | "She thought of the file" |
| | ratio | 0.294 | |
| 23.03% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 119 | | matches | | 0 | "Quinn slammed into a cluster" | | 1 | "The man ahead accelerated, curly" | | 2 | "He did not." | | 3 | "Tomás Herrera vaulted a low" | | 4 | "Quinn followed, badge already in" | | 5 | "He knew the streets better" | | 6 | "A former paramedic turned shadow" | | 7 | "Her intel said he ran" | | 8 | "Things that had taken DS" | | 9 | "She still saw the scene" | | 10 | "Her boots slapped through puddles." | | 11 | "Rain streamed down her closely" | | 12 | "She ignored it." | | 13 | "He glanced back once, olive" | | 14 | "he shouted without breaking stride" | | 15 | "His Spanish accent clipped the" | | 16 | "He veered left at the" | | 17 | "The sign flickered, casting sickly" | | 18 | "Quinn's lungs burned but her" | | 19 | "She knew what waited if" |
| | ratio | 0.874 | |
| 84.03% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 119 | | matches | | 0 | "If she let him vanish" | | 1 | "If she arrested him here," |
| | ratio | 0.017 | |
| 79.08% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn slammed into a cluster of revellers spilling from a Soho doorway, her shoulder connecting with one's back." | | 1 | "Procedure said call for backup, establish a perimeter, wait for specialist support that did not exist." | | 2 | "Others moved with liquid grace that belonged to no human skeleton." | | 3 | "The floating lanterns drifted closer, as though eager for the coming drama." | | 4 | "His sentence carried the clipped rhythm of someone who had learned English in Seville hospitals and London back alleys." |
| |
| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 2 | | matches | | 0 | "She sidestepped, eyes locked on Tomás" | | 1 | "Tomás said, voice low" |
| |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "he shouted (shout)" | | 1 | "it squeaked (squeak)" | | 2 | "he whispered (whisper)" |
| | dialogueSentences | 24 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.5 | | effectiveRatio | 0.25 | |