| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1307 | | 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) | |
| 54.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1307 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "scanned" | | 1 | "tracing" | | 2 | "eyebrow" | | 3 | "weight" | | 4 | "traced" | | 5 | "silence" | | 6 | "tension" | | 7 | "trembled" | | 8 | "pulsed" | | 9 | "flicker" |
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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 | 206 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 206 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 206 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1307 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 1307 | | uniqueNames | 5 | | maxNameDensity | 0.69 | | worstName | "You" | | maxWindowNameDensity | 2 | | worstWindowName | "You" | | discoveredNames | | London | 2 | | Cardiff | 3 | | Rory | 4 | | Julian | 3 | | You | 9 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 105 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a man who had spent years hol" |
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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 | 1307 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 206 | | matches | | 0 | "is that I" | | 1 | "learned that certainty" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 29.04 | | std | 30.16 | | cv | 1.038 | | sampleLengths | | 0 | 115 | | 1 | 161 | | 2 | 55 | | 3 | 9 | | 4 | 12 | | 5 | 27 | | 6 | 5 | | 7 | 8 | | 8 | 63 | | 9 | 5 | | 10 | 12 | | 11 | 18 | | 12 | 10 | | 13 | 17 | | 14 | 36 | | 15 | 5 | | 16 | 14 | | 17 | 23 | | 18 | 26 | | 19 | 66 | | 20 | 3 | | 21 | 15 | | 22 | 28 | | 23 | 20 | | 24 | 20 | | 25 | 8 | | 26 | 25 | | 27 | 49 | | 28 | 20 | | 29 | 8 | | 30 | 45 | | 31 | 23 | | 32 | 28 | | 33 | 5 | | 34 | 38 | | 35 | 40 | | 36 | 12 | | 37 | 15 | | 38 | 33 | | 39 | 12 | | 40 | 15 | | 41 | 40 | | 42 | 19 | | 43 | 90 | | 44 | 9 |
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| 98.45% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 206 | | matches | | 0 | "been mended" | | 1 | "were supposed" | | 2 | "was torn" | | 3 | "is gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 268 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 206 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1312 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.01600609756097561 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001524390243902439 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 206 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 206 | | mean | 6.34 | | std | 3.46 | | cv | 0.545 | | sampleLengths | | 0 | 17 | | 1 | 16 | | 2 | 9 | | 3 | 19 | | 4 | 19 | | 5 | 11 | | 6 | 5 | | 7 | 6 | | 8 | 13 | | 9 | 4 | | 10 | 5 | | 11 | 12 | | 12 | 20 | | 13 | 6 | | 14 | 4 | | 15 | 12 | | 16 | 2 | | 17 | 6 | | 18 | 6 | | 19 | 17 | | 20 | 23 | | 21 | 8 | | 22 | 4 | | 23 | 16 | | 24 | 4 | | 25 | 6 | | 26 | 6 | | 27 | 5 | | 28 | 6 | | 29 | 4 | | 30 | 6 | | 31 | 6 | | 32 | 3 | | 33 | 4 | | 34 | 13 | | 35 | 6 | | 36 | 2 | | 37 | 9 | | 38 | 6 | | 39 | 6 | | 40 | 4 | | 41 | 7 | | 42 | 10 | | 43 | 6 | | 44 | 5 | | 45 | 2 | | 46 | 6 | | 47 | 3 | | 48 | 10 | | 49 | 8 |
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| 33.01% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 35 | | diversityRatio | 0.1650485436893204 | | totalSentences | 206 | | uniqueOpeners | 34 | |
| 68.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 196 | | matches | | 0 | "Right after the exams." | | 1 | "Right after everything else." | | 2 | "Then dropped it." | | 3 | "Then across the channel." |
| | ratio | 0.02 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 122 | | totalSentences | 196 | | matches | | 0 | "She shrugged off her coat." | | 1 | "She shook it out and" | | 2 | "She scanned the room." | | 3 | "His back faced the street-facing" | | 4 | "His shoulders slumped inward." | | 5 | "He drank from a glass" | | 6 | "He used to fill rooms" | | 7 | "His knuckles had thickened." | | 8 | "His jaw sat heavy." | | 9 | "He looked down at the" | | 10 | "He did not reach for" | | 11 | "He lifted his head." | | 12 | "His eyes caught the low" | | 13 | "His pupils contracted." | | 14 | "He did not smile." | | 15 | "He shifted his weight and" | | 16 | "You look like you walked" | | 17 | "I should have brought proper" | | 18 | "He watched her hands." | | 19 | "She kept them folded in" |
| | ratio | 0.622 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 184 | | totalSentences | 196 | | matches | | 0 | "The green neon sign bled" | | 1 | "Rory pushed through the heavy" | | 2 | "The bell above the frame" | | 3 | "Maps curling at the edges" | | 4 | "She shrugged off her coat." | | 5 | "The fabric clung to her" | | 6 | "She shook it out and" | | 7 | "She scanned the room." | | 8 | "A couple occupied a corner" | | 9 | "His back faced the street-facing" | | 10 | "His shoulders slumped inward." | | 11 | "He drank from a glass" | | 12 | "The air between them grew" | | 13 | "Julian used to pace the" | | 14 | "He used to fill rooms" | | 15 | "His knuckles had thickened." | | 16 | "A pale line bisected his" | | 17 | "His jaw sat heavy." | | 18 | "He looked down at the" | | 19 | "He did not reach for" |
| | ratio | 0.939 | |
| 25.51% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 196 | | matches | | 0 | "Now his hands lay flat" |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 1 | | matches | | 0 | "He used to fill rooms with arguments, with plans, with the reckless certainty of a young man who believed he could outrun consequence." |
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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 | |