| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1369 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "sharply" | | 1 | "very" | | 2 | "tightly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 1.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1369 | | totalAiIsms | 27 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | |
| | highlights | | 0 | "footfall" | | 1 | "rhythmic" | | 2 | "vibrated" | | 3 | "echoed" | | 4 | "gloom" | | 5 | "shattered" | | 6 | "kaleidoscope" | | 7 | "silk" | | 8 | "velvet" | | 9 | "constructed" | | 10 | "cacophony" | | 11 | "flickered" | | 12 | "dancing" | | 13 | "pulsed" | | 14 | "pulse" | | 15 | "standard" | | 16 | "weight" | | 17 | "encounter" | | 18 | "navigating" | | 19 | "crystal" | | 20 | "scanning" | | 21 | "amidst" | | 22 | "silence" |
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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 | 113 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 113 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1368 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.13% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 1253 | | uniqueNames | 7 | | maxNameDensity | 1.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 14 | | Tube | 1 | | Metropolitan | 1 | | Police | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | | globalScore | 0.941 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like grinding stones" |
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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 | 1368 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 25.81 | | std | 19.01 | | cv | 0.736 | | sampleLengths | | 0 | 52 | | 1 | 2 | | 2 | 75 | | 3 | 47 | | 4 | 6 | | 5 | 49 | | 6 | 24 | | 7 | 57 | | 8 | 41 | | 9 | 15 | | 10 | 55 | | 11 | 29 | | 12 | 71 | | 13 | 45 | | 14 | 59 | | 15 | 24 | | 16 | 34 | | 17 | 37 | | 18 | 7 | | 19 | 43 | | 20 | 37 | | 21 | 23 | | 22 | 3 | | 23 | 25 | | 24 | 42 | | 25 | 8 | | 26 | 44 | | 27 | 3 | | 28 | 22 | | 29 | 4 | | 30 | 14 | | 31 | 14 | | 32 | 11 | | 33 | 10 | | 34 | 12 | | 35 | 6 | | 36 | 34 | | 37 | 45 | | 38 | 7 | | 39 | 14 | | 40 | 8 | | 41 | 4 | | 42 | 17 | | 43 | 18 | | 44 | 5 | | 45 | 25 | | 46 | 6 | | 47 | 1 | | 48 | 20 | | 49 | 34 |
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| 99.05% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 113 | | matches | | 0 | "were packed" | | 1 | "were lined" |
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| 73.62% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 211 | | matches | | 0 | "wasn't running" | | 1 | "was stifling" | | 2 | "were watching" | | 3 | "were stretching" |
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| 97.86% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 127 | | ratio | 0.016 | | matches | | 0 | "The smell changed as she descended—the stench of rain and exhaust gave way to something thick, cloying, and metallic." | | 1 | "He didn't look angry; he looked annoyed, as if her presence was a smudge on a clean window." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1259 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, midnight-blue cloth." |
| | adverbCount | 24 | | adverbRatio | 0.019062748212867357 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.010325655281969817 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 10.77 | | std | 5.61 | | cv | 0.521 | | sampleLengths | | 0 | 18 | | 1 | 17 | | 2 | 17 | | 3 | 2 | | 4 | 8 | | 5 | 4 | | 6 | 14 | | 7 | 8 | | 8 | 15 | | 9 | 26 | | 10 | 17 | | 11 | 10 | | 12 | 20 | | 13 | 6 | | 14 | 6 | | 15 | 6 | | 16 | 1 | | 17 | 13 | | 18 | 8 | | 19 | 15 | | 20 | 5 | | 21 | 14 | | 22 | 5 | | 23 | 9 | | 24 | 19 | | 25 | 7 | | 26 | 22 | | 27 | 13 | | 28 | 16 | | 29 | 12 | | 30 | 15 | | 31 | 2 | | 32 | 11 | | 33 | 17 | | 34 | 15 | | 35 | 10 | | 36 | 16 | | 37 | 4 | | 38 | 9 | | 39 | 11 | | 40 | 17 | | 41 | 28 | | 42 | 15 | | 43 | 6 | | 44 | 10 | | 45 | 7 | | 46 | 8 | | 47 | 14 | | 48 | 6 | | 49 | 18 |
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| 35.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.24603174603174602 | | totalSentences | 126 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 110 | | matches | (empty) | | ratio | 0 | |
| 67.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 110 | | matches | | 0 | "He moved with a frantic," | | 1 | "Her military-trained stride ate the" | | 2 | "She pushed past a group" | | 3 | "She checked her worn leather" | | 4 | "She peered into the darkness" | | 5 | "She pried the grate upward." | | 6 | "She slid into the dark." | | 7 | "It smelled of ozone and" | | 8 | "Her flashlight beam cut a" | | 9 | "She pressed her back against" | | 10 | "She stayed low, peering around" | | 11 | "He wasn't running anymore." | | 12 | "He moved with the practiced" | | 13 | "She gripped her radio, then" | | 14 | "She thought of DS Morris," | | 15 | "They would strip her badge" | | 16 | "She watched the suspect approach" | | 17 | "He pressed it into the" | | 18 | "They simply slid a small," | | 19 | "Her boots struck the platform" |
| | ratio | 0.382 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 103 | | totalSentences | 110 | | matches | | 0 | "Rain lashed against the pavement," | | 1 | "Detective Harlow Quinn gripped the" | | 2 | "The command died in the" | | 3 | "The suspect didn't flinch." | | 4 | "He moved with a frantic," | | 5 | "Quinn sprinted, her lungs burning" | | 6 | "Her military-trained stride ate the" | | 7 | "She pushed past a group" | | 8 | "The suspect veered sharply left," | | 9 | "Quinn rounded the corner, her" | | 10 | "The alley ended in a" | | 11 | "The man disappeared through the" | | 12 | "Quinn knelt, her sharp jaw" | | 13 | "She checked her worn leather" | | 14 | "The street was empty, save" | | 15 | "She peered into the darkness" | | 16 | "A faint, rhythmic thrumming vibrated" | | 17 | "She pried the grate upward." | | 18 | "The metal groaned, a harsh" | | 19 | "She slid into the dark." |
| | ratio | 0.936 | |
| 90.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 110 | | matches | | 0 | "To call it in was" | | 1 | "Even from her vantage point," |
| | ratio | 0.018 | |
| 72.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 6 | | matches | | 0 | "The suspect veered sharply left, ducking behind a dumpster that smelled of rotting citrus and wet cardboard." | | 1 | "Her flashlight beam cut a lonely path through the gloom, illuminating patches of moss that glowed with a sickly, pale violet hue." | | 2 | "The air grew warmer here, humming with a low-frequency vibration that set her teeth on edge." | | 3 | "The hum in the air spiked, a dissonant chord that made her skin crawl." | | 4 | "He ducked behind a curtain of hanging beads, disappearing into a cluster of stalls that smelled of old parchment and copper." | | 5 | "The metal was ice-cold, vibrating with a low, rhythmic pulse that mirrored her own heartbeat." |
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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 | |