| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva Morgan had once [once]" |
| | dialogueSentences | 203 | | tagDensity | 0.074 | | leniency | 0.148 | | rawRatio | 0.067 | | effectiveRatio | 0.01 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2625 | | 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) | |
| 88.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2625 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "pulse" | | 1 | "measured" | | 2 | "traced" | | 3 | "silence" | | 4 | "perfect" |
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| 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 | 184 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 184 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 373 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2625 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 35 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 131 | | wordCount | 1590 | | uniqueNames | 21 | | maxNameDensity | 3.65 | | worstName | "Eva" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Dean | 1 | | Street | 1 | | October | 1 | | Raven | 1 | | Nest | 1 | | Vienna | 1 | | Morgan | 1 | | Aurora | 42 | | Cardiff | 1 | | Eva | 58 | | Berlin | 1 | | Silas | 10 | | Barry | 1 | | Island | 1 | | Llandaff | 1 | | Are | 1 | | Sunday | 1 | | Evan | 1 | | London | 1 | | Rain | 2 | | One | 3 |
| | persons | | 0 | "Morgan" | | 1 | "Aurora" | | 2 | "Eva" | | 3 | "Silas" | | 4 | "Are" | | 5 | "Evan" | | 6 | "London" | | 7 | "Rain" | | 8 | "One" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Raven" | | 3 | "Vienna" | | 4 | "Cardiff" | | 5 | "Berlin" | | 6 | "Barry" | | 7 | "Island" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 118 | | 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 | 2625 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 373 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 285 | | mean | 9.21 | | std | 11.42 | | cv | 1.239 | | sampleLengths | | 0 | 47 | | 1 | 44 | | 2 | 7 | | 3 | 8 | | 4 | 4 | | 5 | 11 | | 6 | 2 | | 7 | 1 | | 8 | 35 | | 9 | 3 | | 10 | 6 | | 11 | 37 | | 12 | 7 | | 13 | 66 | | 14 | 43 | | 15 | 6 | | 16 | 1 | | 17 | 20 | | 18 | 7 | | 19 | 4 | | 20 | 6 | | 21 | 3 | | 22 | 14 | | 23 | 4 | | 24 | 2 | | 25 | 4 | | 26 | 10 | | 27 | 24 | | 28 | 19 | | 29 | 4 | | 30 | 10 | | 31 | 5 | | 32 | 2 | | 33 | 5 | | 34 | 15 | | 35 | 29 | | 36 | 4 | | 37 | 6 | | 38 | 3 | | 39 | 6 | | 40 | 2 | | 41 | 4 | | 42 | 41 | | 43 | 5 | | 44 | 6 | | 45 | 7 | | 46 | 3 | | 47 | 13 | | 48 | 2 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 184 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 282 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 373 | | ratio | 0.003 | | matches | | 0 | "One complained about water in his shoe; another pointed towards the snug beneath a framed map of Vienna." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1090 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.02018348623853211 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009174311926605505 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 373 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 373 | | mean | 7.04 | | std | 5.49 | | cv | 0.78 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 15 | | 3 | 13 | | 4 | 8 | | 5 | 23 | | 6 | 7 | | 7 | 8 | | 8 | 4 | | 9 | 11 | | 10 | 2 | | 11 | 1 | | 12 | 11 | | 13 | 13 | | 14 | 11 | | 15 | 3 | | 16 | 6 | | 17 | 5 | | 18 | 18 | | 19 | 14 | | 20 | 7 | | 21 | 25 | | 22 | 15 | | 23 | 26 | | 24 | 13 | | 25 | 11 | | 26 | 11 | | 27 | 8 | | 28 | 6 | | 29 | 1 | | 30 | 20 | | 31 | 7 | | 32 | 4 | | 33 | 6 | | 34 | 3 | | 35 | 5 | | 36 | 9 | | 37 | 4 | | 38 | 2 | | 39 | 4 | | 40 | 3 | | 41 | 7 | | 42 | 5 | | 43 | 4 | | 44 | 15 | | 45 | 6 | | 46 | 13 | | 47 | 4 | | 48 | 10 | | 49 | 5 |
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| 41.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 30 | | diversityRatio | 0.2520107238605898 | | totalSentences | 373 | | uniqueOpeners | 94 | |
| 83.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 159 | | matches | | 0 | "Then Eva’s phone lit on" | | 1 | "Once, she and Eva had" | | 2 | "Even that had changed." | | 3 | "Just the old measurement: Eva’s" |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 159 | | matches | | 0 | "His silver signet ring clicked" | | 1 | "He had removed his jacket" | | 2 | "Her companions pressed around her." | | 3 | "She had worn chipped green" | | 4 | "Her dark hair ended in" | | 5 | "Her face had not changed" | | 6 | "Her fingers had locked around" | | 7 | "He took the tumbler from" | | 8 | "It vanished when Aurora came" | | 9 | "His gaze touched Eva, measured" | | 10 | "She glanced towards the rain-threaded" | | 11 | "She pulled the cuff down," | | 12 | "She set it down without" | | 13 | "She had always filled silence" | | 14 | "Her fingers closed over the" | | 15 | "He kept his back turned." | | 16 | "She had spent years learning" | | 17 | "Her expression altered, the mouth" | | 18 | "She turned it face down." | | 19 | "They had been fourteen and" |
| | ratio | 0.17 | |
| 44.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 132 | | totalSentences | 159 | | matches | | 0 | "Rain varnished Dean Street and" | | 1 | "Aurora watched it through the" | | 2 | "Each flash of the sign" | | 3 | "Silas sat at the far" | | 4 | "His silver signet ring clicked" | | 5 | "He had removed his jacket" | | 6 | "Aurora told him" | | 7 | "The door opened, dragging wet" | | 8 | "Aurora reached for pint glasses" | | 9 | "A woman near the back" | | 10 | "Her companions pressed around her." | | 11 | "The woman stood among them" | | 12 | "Aurora knew the tilt of" | | 13 | "Eva Morgan had once cut" | | 14 | "She had worn chipped green" | | 15 | "The woman by the door" | | 16 | "Her dark hair ended in" | | 17 | "A gold band sat on" | | 18 | "Time had pared her down" | | 19 | "Her face had not changed" |
| | ratio | 0.83 | |
| 62.89% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 159 | | matches | | 0 | "Now her gaze snagged there" | | 1 | "Now she inspected each sentence" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 2 | | matches | | 0 | "One of Eva’s colleagues cheered as if released from a long captivity." | | 1 | "A bus passed outside, its tyres hissing through standing water." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 3 | | fancyTags | | 0 | "he announced (announce)" | | 1 | "Eva murmured (murmur)" | | 2 | "Eva continued (continue)" |
| | dialogueSentences | 203 | | tagDensity | 0.064 | | leniency | 0.128 | | rawRatio | 0.231 | | effectiveRatio | 0.03 | |