| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 91 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2058 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 78.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2058 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "weight" | | 1 | "measured" | | 2 | "warmth" | | 3 | "pulse" | | 4 | "silence" | | 5 | "pounding" | | 6 | "familiar" |
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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 | 174 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 174 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 258 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2058 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 107 | | wordCount | 1522 | | uniqueNames | 14 | | maxNameDensity | 2.63 | | worstName | "Aurora" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Prague | 3 | | Aurora | 40 | | Cardiff | 2 | | Eva | 38 | | Carter | 1 | | Evan | 1 | | Come | 2 | | London | 2 | | Berlin | 1 | | Bristol | 1 | | Silas | 11 | | One | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Eva" | | 4 | "Carter" | | 5 | "Evan" | | 6 | "Come" | | 7 | "Silas" | | 8 | "One" |
| | places | | 0 | "Prague" | | 1 | "Cardiff" | | 2 | "London" | | 3 | "Berlin" | | 4 | "Bristol" |
| | globalScore | 0.186 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | 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 | 2058 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 258 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 163 | | mean | 12.63 | | std | 15.65 | | cv | 1.24 | | sampleLengths | | 0 | 9 | | 1 | 41 | | 2 | 9 | | 3 | 10 | | 4 | 110 | | 5 | 14 | | 6 | 12 | | 7 | 21 | | 8 | 54 | | 9 | 49 | | 10 | 3 | | 11 | 50 | | 12 | 4 | | 13 | 1 | | 14 | 9 | | 15 | 1 | | 16 | 41 | | 17 | 33 | | 18 | 4 | | 19 | 6 | | 20 | 3 | | 21 | 3 | | 22 | 3 | | 23 | 10 | | 24 | 7 | | 25 | 4 | | 26 | 13 | | 27 | 7 | | 28 | 2 | | 29 | 1 | | 30 | 16 | | 31 | 9 | | 32 | 25 | | 33 | 7 | | 34 | 16 | | 35 | 11 | | 36 | 4 | | 37 | 3 | | 38 | 25 | | 39 | 4 | | 40 | 4 | | 41 | 3 | | 42 | 2 | | 43 | 19 | | 44 | 9 | | 45 | 20 | | 46 | 3 | | 47 | 2 | | 48 | 3 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 174 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 247 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 258 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1527 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.026850032743942372 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0045841519318926 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 258 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 258 | | mean | 7.98 | | std | 6.79 | | cv | 0.852 | | sampleLengths | | 0 | 9 | | 1 | 6 | | 2 | 10 | | 3 | 25 | | 4 | 9 | | 5 | 7 | | 6 | 3 | | 7 | 9 | | 8 | 7 | | 9 | 18 | | 10 | 13 | | 11 | 12 | | 12 | 22 | | 13 | 29 | | 14 | 14 | | 15 | 12 | | 16 | 21 | | 17 | 39 | | 18 | 15 | | 19 | 3 | | 20 | 3 | | 21 | 12 | | 22 | 3 | | 23 | 3 | | 24 | 7 | | 25 | 18 | | 26 | 3 | | 27 | 5 | | 28 | 3 | | 29 | 7 | | 30 | 28 | | 31 | 7 | | 32 | 4 | | 33 | 1 | | 34 | 9 | | 35 | 1 | | 36 | 5 | | 37 | 8 | | 38 | 10 | | 39 | 18 | | 40 | 3 | | 41 | 13 | | 42 | 4 | | 43 | 3 | | 44 | 10 | | 45 | 4 | | 46 | 6 | | 47 | 3 | | 48 | 3 | | 49 | 3 |
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| 45.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.29069767441860467 | | totalSentences | 258 | | uniqueOpeners | 75 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 156 | | matches | | 0 | "Then the door opened, and" | | 1 | "Then the door closed behind" | | 2 | "Even the way she held" | | 3 | "Then Eva had gone out" | | 4 | "Just enough to take out" |
| | ratio | 0.032 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 156 | | matches | | 0 | "She set the martini upright," | | 1 | "He laughed because he thought" | | 2 | "Her face had thinned around" | | 3 | "His hazel eyes took in" | | 4 | "He gave nothing away." | | 5 | "He never did." | | 6 | "He only limped one measured" | | 7 | "He noticed everything." | | 8 | "He left Aurora a second" | | 9 | "He moved away with that" | | 10 | "She had work after this," | | 11 | "She looked down at the" | | 12 | "Her body knew its business" | | 13 | "Her mother’s voice on the" | | 14 | "I’ve got a sofa and" | | 15 | "Her coat remained buttoned." | | 16 | "Her handbag stayed looped over" | | 17 | "He studied Eva with mild" | | 18 | "He had filed away the" | | 19 | "His silver signet ring caught" |
| | ratio | 0.128 | |
| 59.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 125 | | totalSentences | 156 | | matches | | 0 | "Aurora caught the glass before" | | 1 | "The stem bit into her" | | 2 | "Gin sloshed over her knuckles," | | 3 | "She set the martini upright," | | 4 | "He laughed because he thought" | | 5 | "The Raven’s Nest breathed around" | | 6 | "A saxophone record crackled through" | | 7 | "The walls wore their old" | | 8 | "Men in office shirts loosened" | | 9 | "A pair of tourists had" | | 10 | "Aurora dried her hand on" | | 11 | "Eva stood under the green" | | 12 | "Hair the colour of burnt" | | 13 | "Her face had thinned around" | | 14 | "Eva saw her." | | 15 | "The room did not change." | | 16 | "Glasses still rang." | | 17 | "Someone still fed coins into" | | 18 | "Silas still stood at the" | | 19 | "Eva crossed the room." |
| | ratio | 0.801 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 156 | | matches | (empty) | | ratio | 0 | |
| 75.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 5 | | matches | | 0 | "Even the way she held her handbag looked expensive, guarded, deliberate, as if weight had taught her posture." | | 1 | "Silas still stood at the far end of the bar discussing whisky with a man who had the polished shoes of government and the hands of a bookmaker." | | 2 | "Brendan Carter, barrister, dead of a heart that had spent too many years clenched around disappointment." | | 3 | "Her mother’s voice on the phone had turned thin and formal, as if grief had put on school clothes." | | 4 | "He had filed away the coat, the scar, the expensive ring, the hand never far from the bag, the route to the door, the route to the back room, the two men at the…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 91 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |