| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1589 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "perfectly" | | 1 | "quickly" | | 2 | "very" | | 3 | "softly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 37.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1589 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | word | "practiced ease" | | count | 1 |
| | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | |
| | highlights | | 0 | "electric" | | 1 | "methodical" | | 2 | "measured" | | 3 | "sanctuary" | | 4 | "silence" | | 5 | "tension" | | 6 | "practiced ease" | | 7 | "footsteps" | | 8 | "crystal" | | 9 | "shattered" | | 10 | "traced" | | 11 | "silk" | | 12 | "weight" | | 13 | "stomach" | | 14 | "comfortable" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "the air was thick with" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 123 | | matches | | |
| 49.94% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 7 | | hedgeCount | 1 | | narrationSentences | 123 | | filterMatches | | 0 | "watch" | | 1 | "look" | | 2 | "know" | | 3 | "see" | | 4 | "realize" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 123 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1589 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 11 | | matches | | 0 | "Rory, he said, his voice barely rising above the hum of the neon sign." | | 1 | "I moved, Rory said." | | 2 | "I will be in the back, Silas said, his voice a low, gravelly baritone carrying quiet authority." | | 3 | "She was the one who convinced me to leave Cardiff, Rory said." | | 4 | "Let him be furious, Rory said, the words slipping out with a sharp edge she quickly reined in." | | 5 | "I took the job at Sterling and Croft, he said quietly." | | 6 | "I see, Rory said." | | 7 | "Because it is easy, he whispered." | | 8 | "We were kids, Thomas, she said softly." | | 9 | "I am getting by, she said." | | 10 | "Take care of yourself, Rory, he said." |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 77 | | wordCount | 1589 | | uniqueNames | 26 | | maxNameDensity | 1.26 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Thomas" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Carter | 1 | | British | 1 | | Empire | 1 | | Soho | 2 | | Silas | 4 | | Blackwood | 1 | | Prague | 1 | | London | 3 | | Cardiff | 4 | | November | 1 | | City | 1 | | French | 1 | | Rory | 20 | | Thomas | 14 | | Canary | 1 | | Wharf | 1 | | Eva | 1 | | Evan | 2 | | Golden | 1 | | Empress | 1 | | Sterling | 2 | | Croft | 2 | | Kensington | 1 | | You | 6 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Silas" | | 4 | "Blackwood" | | 5 | "City" | | 6 | "Rory" | | 7 | "Thomas" | | 8 | "Eva" | | 9 | "Evan" | | 10 | "You" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "London" | | 3 | "Cardiff" | | 4 | "Canary" | | 5 | "Sterling" | | 6 | "Kensington" |
| | globalScore | 0.871 | | windowScore | 0.833 | |
| 77.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like every other junior partner wo" | | 1 | "as if trying to reconcile the woman in the plain black t-shirt and apron, her straight shoulder-length black hair tied back in a messy knot, with the memory he held of her" | | 2 | "felt like a sanctuary" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1589 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 123 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 51.26 | | std | 31.56 | | cv | 0.616 | | sampleLengths | | 0 | 96 | | 1 | 114 | | 2 | 49 | | 3 | 81 | | 4 | 3 | | 5 | 85 | | 6 | 56 | | 7 | 49 | | 8 | 53 | | 9 | 92 | | 10 | 56 | | 11 | 9 | | 12 | 101 | | 13 | 39 | | 14 | 56 | | 15 | 36 | | 16 | 39 | | 17 | 26 | | 18 | 90 | | 19 | 17 | | 20 | 59 | | 21 | 86 | | 22 | 23 | | 23 | 28 | | 24 | 59 | | 25 | 6 | | 26 | 41 | | 27 | 7 | | 28 | 3 | | 29 | 40 | | 30 | 90 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 123 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 289 | | matches | | 0 | "were sitting" | | 1 | "was doing" | | 2 | "was reading" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 123 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1599 | | adjectiveStacks | 2 | | stackExamples | | 0 | "frantic, tear-soaked train" | | 1 | "small, crescent-shaped scar" |
| | adverbCount | 42 | | adverbRatio | 0.02626641651031895 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.008130081300813009 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 123 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 123 | | mean | 12.92 | | std | 8.14 | | cv | 0.63 | | sampleLengths | | 0 | 26 | | 1 | 23 | | 2 | 18 | | 3 | 29 | | 4 | 17 | | 5 | 16 | | 6 | 18 | | 7 | 18 | | 8 | 28 | | 9 | 4 | | 10 | 13 | | 11 | 11 | | 12 | 21 | | 13 | 17 | | 14 | 17 | | 15 | 13 | | 16 | 30 | | 17 | 21 | | 18 | 3 | | 19 | 8 | | 20 | 40 | | 21 | 17 | | 22 | 20 | | 23 | 14 | | 24 | 34 | | 25 | 8 | | 26 | 4 | | 27 | 20 | | 28 | 10 | | 29 | 7 | | 30 | 8 | | 31 | 12 | | 32 | 3 | | 33 | 3 | | 34 | 11 | | 35 | 17 | | 36 | 4 | | 37 | 3 | | 38 | 10 | | 39 | 28 | | 40 | 17 | | 41 | 30 | | 42 | 7 | | 43 | 11 | | 44 | 18 | | 45 | 14 | | 46 | 7 | | 47 | 6 | | 48 | 3 | | 49 | 6 |
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| 34.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3008130081300813 | | totalSentences | 123 | | uniqueOpeners | 37 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 120 | | matches | | 0 | "Instead, she traced the small," | | 1 | "Then why do you do" |
| | ratio | 0.017 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 68 | | totalSentences | 120 | | matches | | 0 | "He moved with a deliberate," | | 1 | "He did not look up," | | 2 | "He was a man who" | | 3 | "It was a commodity she" | | 4 | "He was tall, with sharp" | | 5 | "He looked like every other" | | 6 | "It was Thomas." | | 7 | "He had been a firebrand" | | 8 | "He blinked, as if trying" | | 9 | "I did not know you" | | 10 | "I moved, Rory said." | | 11 | "She kept her voice level," | | 12 | "She untied her apron and" | | 13 | "You look a long way" | | 14 | "He offered a tight, practiced" | | 15 | "It ran late." | | 16 | "I just needed a quiet" | | 17 | "He stepped further into the" | | 18 | "It feels hidden." | | 19 | "He looked at Thomas, then" |
| | ratio | 0.567 | |
| 26.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 120 | | matches | | 0 | "The green neon sign above" | | 1 | "Aurora Carter dragged a damp" | | 2 | "The walls around her were" | | 3 | "He moved with a deliberate," | | 4 | "The dim overhead light caught" | | 5 | "He did not look up," | | 6 | "He was a man who" | | 7 | "Rory appreciated his silence." | | 8 | "It was a commodity she" | | 9 | "The brass bell above the" | | 10 | "A gust of cold November" | | 11 | "Rory tossed her rag into" | | 12 | "The man standing in the" | | 13 | "He was tall, with sharp" | | 14 | "He looked like every other" | | 15 | "It was Thomas." | | 16 | "The last time Rory had" | | 17 | "He had been a firebrand" | | 18 | "Rory, he said, his voice" | | 19 | "He blinked, as if trying" |
| | ratio | 0.867 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 120 | | matches | | 0 | "Now, the man standing before" | | 1 | "Because it is easy, he" | | 2 | "Because once you start climbing" |
| | ratio | 0.025 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 7 | | matches | | 0 | "He moved with a deliberate, measured grace that betrayed the slight limp in his left leg." | | 1 | "He was a man who operated in the periphery, a retired intelligence operative who had traded the shadows of Prague for the quiet sanctuary of a London pub." | | 2 | "He blinked, as if trying to reconcile the woman in the plain black t-shirt and apron, her straight shoulder-length black hair tied back in a messy knot, with th…" | | 3 | "She kept her voice level, relying on the cool-headed detachment that had kept her sane over the past two years." | | 4 | "He looked at Thomas, then at Rory, reading the sudden tension in her shoulders with the practiced ease of a man who had spent his life interpreting micro-expres…" | | 5 | "He gave Rory a brief, reassuring nod, the silver ring flashing as he adjusted his cuff, before turning and walking toward the hidden bookshelf that concealed hi…" | | 6 | "Thomas asked, his eyes dropping to her scar before meeting her bright blue gaze." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |