| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 133 | | tagDensity | 0.18 | | leniency | 0.361 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2180 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 86.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2180 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "silence" | | 1 | "flicked" | | 2 | "standard" | | 3 | "could feel" | | 4 | "unreadable" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 176 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 176 | | filterMatches | | | hedgeMatches | | 0 | "happens to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 285 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2179 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 32 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 117 | | wordCount | 1571 | | uniqueNames | 12 | | maxNameDensity | 2.93 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Carter | 1 | | Aurora | 44 | | London | 1 | | Silas | 13 | | Mercer | 1 | | Eva | 46 | | Cardiff | 3 | | Evan | 1 | | Rory | 1 | | Don | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Aurora" | | 4 | "Silas" | | 5 | "Mercer" | | 6 | "Eva" | | 7 | "Evan" | | 8 | "Rory" |
| | places | | | globalScore | 0.036 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed deeper in the bar’s amber light" | | 1 | "felt like a bridge thrown across a ravi" |
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| 62.32% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 1.377 | | wordCount | 2179 | | matches | | 0 | "not waving but reaching" | | 1 | "not at Rory, not at the girl from Cardiff, but at Aurora, the woman she had failed" | | 2 | "not at the girl from Cardiff, but at Aurora, the woman she had failed" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 285 | | matches | | 0 | "sworn that neither" | | 1 | "shouted that running" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 186 | | mean | 11.72 | | std | 14.01 | | cv | 1.196 | | sampleLengths | | 0 | 44 | | 1 | 46 | | 2 | 6 | | 3 | 4 | | 4 | 6 | | 5 | 8 | | 6 | 19 | | 7 | 20 | | 8 | 2 | | 9 | 6 | | 10 | 15 | | 11 | 6 | | 12 | 2 | | 13 | 43 | | 14 | 70 | | 15 | 17 | | 16 | 16 | | 17 | 2 | | 18 | 74 | | 19 | 2 | | 20 | 6 | | 21 | 3 | | 22 | 21 | | 23 | 4 | | 24 | 12 | | 25 | 51 | | 26 | 16 | | 27 | 3 | | 28 | 15 | | 29 | 3 | | 30 | 7 | | 31 | 12 | | 32 | 6 | | 33 | 7 | | 34 | 3 | | 35 | 47 | | 36 | 14 | | 37 | 46 | | 38 | 3 | | 39 | 11 | | 40 | 4 | | 41 | 3 | | 42 | 6 | | 43 | 4 | | 44 | 4 | | 45 | 9 | | 46 | 1 | | 47 | 8 | | 48 | 9 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 176 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 298 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 285 | | ratio | 0.004 | | matches | | 0 | "Eva looked at her then—not at Rory, not at the girl from Cardiff, but at Aurora, the woman she had failed and who had become someone else anyway." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1575 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 55 | | adverbRatio | 0.03492063492063492 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005079365079365079 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 285 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 285 | | mean | 7.65 | | std | 6.63 | | cv | 0.867 | | sampleLengths | | 0 | 21 | | 1 | 23 | | 2 | 21 | | 3 | 25 | | 4 | 6 | | 5 | 4 | | 6 | 6 | | 7 | 8 | | 8 | 14 | | 9 | 5 | | 10 | 4 | | 11 | 13 | | 12 | 3 | | 13 | 2 | | 14 | 6 | | 15 | 15 | | 16 | 5 | | 17 | 1 | | 18 | 2 | | 19 | 18 | | 20 | 9 | | 21 | 4 | | 22 | 4 | | 23 | 8 | | 24 | 4 | | 25 | 30 | | 26 | 6 | | 27 | 4 | | 28 | 8 | | 29 | 18 | | 30 | 17 | | 31 | 16 | | 32 | 2 | | 33 | 22 | | 34 | 28 | | 35 | 7 | | 36 | 17 | | 37 | 2 | | 38 | 6 | | 39 | 3 | | 40 | 5 | | 41 | 5 | | 42 | 11 | | 43 | 4 | | 44 | 12 | | 45 | 51 | | 46 | 6 | | 47 | 10 | | 48 | 3 | | 49 | 9 |
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| 44.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2631578947368421 | | totalSentences | 285 | | uniqueOpeners | 75 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 149 | | matches | | 0 | "Even the framed photograph of" | | 1 | "Somewhere behind them, a bottle" | | 2 | "Then Eva said," | | 3 | "Instead she said," | | 4 | "Perhaps time had not erased" | | 5 | "Perhaps it had only taught" | | 6 | "Then Eva stepped into the" |
| | ratio | 0.047 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 149 | | matches | | 0 | "He glanced up as she" | | 1 | "His silver signet ring caught" | | 2 | "He moved with his usual" | | 3 | "She had learned, over the" | | 4 | "Her hair, once a riot" | | 5 | "He had a gift for" | | 6 | "he said, pushing the mug" | | 7 | "His gaze flicked toward the" | | 8 | "She placed her hands on" | | 9 | "Her nails were painted black," | | 10 | "It was too strong and" | | 11 | "He placed the glasses beneath" | | 12 | "He understood the word." | | 13 | "He nodded once and moved" | | 14 | "Her own voice becoming small" | | 15 | "She remembered leaving Cardiff with" | | 16 | "She remembered Eva standing on" | | 17 | "She rolled it between her" | | 18 | "She looked at Aurora" | | 19 | "They had no decoration, no" |
| | ratio | 0.195 | |
| 53.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 121 | | totalSentences | 149 | | matches | | 0 | "The green neon above the" | | 1 | "Aurora Carter paused beneath it" | | 2 | "Silas stood behind the counter," | | 3 | "He glanced up as she" | | 4 | "Aurora set the bag on" | | 5 | "Silas took the receipt." | | 6 | "His silver signet ring caught" | | 7 | "A smile touched his beard." | | 8 | "He moved with his usual" | | 9 | "She had learned, over the" | | 10 | "Tonight she watched anyway." | | 11 | "The knee had worsened." | | 12 | "Silas filled the kettle." | | 13 | "The Nest had not changed" | | 14 | "The tables remained scarred." | | 15 | "The brass rail along the" | | 16 | "Aurora was reaching for the" | | 17 | "The sound struck her with" | | 18 | "The woman standing beneath the" | | 19 | "Her hair, once a riot" |
| | ratio | 0.812 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 149 | | matches | (empty) | | ratio | 0 | |
| 72.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 6 | | matches | | 0 | "Silas stood behind the counter, polishing a glass with the patient, unhurried motion of a man who had spent a lifetime making waiting look intentional." | | 1 | "Behind him, old maps covered the walls between black-and-white photographs: streets before the glass towers, faces that had been young when the century was youn…" | | 2 | "The woman standing beneath the green glow was tall and narrow, dressed in a charcoal coat that fell almost to her ankles." | | 3 | "There was a small scar beneath her right eye, pale against skin that had gone almost bloodless." | | 4 | "Aurora looked at the woman’s face, searching for the girl who had stolen biscuits from her mother’s cupboard in Cardiff, who had slept on Aurora’s bedroom floor…" | | 5 | "Childhood accident, her mother always called it, as if a word could make childhood harmless." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 21 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 133 | | tagDensity | 0.158 | | leniency | 0.316 | | rawRatio | 0.048 | | effectiveRatio | 0.015 | |