| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1237 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 63.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1237 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "shattered" | | 1 | "maw" | | 2 | "footsteps" | | 3 | "electric" | | 4 | "weight" | | 5 | "velvet" | | 6 | "shimmered" | | 7 | "gloom" | | 8 | "traced" |
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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 | 148 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 148 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 153 | | 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 | 1237 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1219 | | uniqueNames | 16 | | maxNameDensity | 0.66 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Metropolitan" | | discoveredNames | | English | 1 | | Bangladeshi | 1 | | Tube | 1 | | Camden | 1 | | Transport | 1 | | Metropolitan | 2 | | Crown | 1 | | Prosecution | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Quinn | 8 | | Three | 3 | | Morris | 4 |
| | persons | | 0 | "Transport" | | 1 | "Market" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Quinn" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "patterns that seemed to squirm when she looked at them directly" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.808 | | wordCount | 1237 | | matches | | 0 | "not scared, but pitying" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 153 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 30.93 | | std | 28.66 | | cv | 0.927 | | sampleLengths | | 0 | 9 | | 1 | 33 | | 2 | 71 | | 3 | 5 | | 4 | 46 | | 5 | 65 | | 6 | 60 | | 7 | 22 | | 8 | 71 | | 9 | 61 | | 10 | 56 | | 11 | 51 | | 12 | 44 | | 13 | 10 | | 14 | 3 | | 15 | 15 | | 16 | 8 | | 17 | 49 | | 18 | 10 | | 19 | 10 | | 20 | 50 | | 21 | 12 | | 22 | 117 | | 23 | 7 | | 24 | 96 | | 25 | 9 | | 26 | 2 | | 27 | 8 | | 28 | 3 | | 29 | 8 | | 30 | 67 | | 31 | 52 | | 32 | 30 | | 33 | 4 | | 34 | 5 | | 35 | 6 | | 36 | 19 | | 37 | 18 | | 38 | 16 | | 39 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 148 | | matches | | 0 | "being struck" | | 1 | "was circled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 190 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 153 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1225 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.02040816326530612 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007346938775510204 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 153 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 153 | | mean | 8.08 | | std | 5.59 | | cv | 0.691 | | sampleLengths | | 0 | 9 | | 1 | 7 | | 2 | 8 | | 3 | 16 | | 4 | 2 | | 5 | 8 | | 6 | 6 | | 7 | 14 | | 8 | 12 | | 9 | 7 | | 10 | 1 | | 11 | 6 | | 12 | 17 | | 13 | 5 | | 14 | 9 | | 15 | 11 | | 16 | 4 | | 17 | 5 | | 18 | 1 | | 19 | 9 | | 20 | 7 | | 21 | 8 | | 22 | 3 | | 23 | 13 | | 24 | 9 | | 25 | 16 | | 26 | 16 | | 27 | 4 | | 28 | 9 | | 29 | 10 | | 30 | 19 | | 31 | 18 | | 32 | 3 | | 33 | 10 | | 34 | 5 | | 35 | 4 | | 36 | 5 | | 37 | 12 | | 38 | 5 | | 39 | 14 | | 40 | 23 | | 41 | 6 | | 42 | 1 | | 43 | 5 | | 44 | 7 | | 45 | 16 | | 46 | 3 | | 47 | 3 | | 48 | 9 | | 49 | 3 |
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| 60.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.39473684210526316 | | totalSentences | 152 | | uniqueOpeners | 60 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 130 | | matches | | 0 | "Then dropped again." | | 1 | "Instead, copper stung her nostrils." | | 2 | "Just a shallow groove carved" | | 3 | "Then he stepped through." |
| | ratio | 0.031 | |
| 93.85% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 130 | | matches | | 0 | "She'd lost visual twice already," | | 1 | "She crouched, ignoring the protest" | | 2 | "She checked the dial out" | | 3 | "She counted his strides." | | 4 | "She'd made him for a" | | 5 | "He crashed into a stack" | | 6 | "She vaulted the debris, her" | | 7 | "Her cropped salt-and-pepper hair plastered" | | 8 | "He scrambled over a chain-link" | | 9 | "She gripped the cold links" | | 10 | "Her sharp jaw caught the" | | 11 | "She landed hard." | | 12 | "Her right foot slipped on" | | 13 | "She recovered and ran." | | 14 | "Her torch beam caught graffiti" | | 15 | "She swept the light across" | | 16 | "She descended after him, one" | | 17 | "Her breath plumed in the" | | 18 | "She stepped over a discarded" | | 19 | "She reached the bottom." |
| | ratio | 0.315 | |
| 75.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 130 | | matches | | 0 | "The hooded figure ducked beneath" | | 1 | "Quinn's torch beam snapped to" | | 2 | "Rain sluiced down the back" | | 3 | "She'd lost visual twice already," | | 4 | "She crouched, ignoring the protest" | | 5 | "The worn leather watch on" | | 6 | "She checked the dial out" | | 7 | "Morris would have called her" | | 8 | "Morris had been dead three" | | 9 | "Quinn slipped beneath the shutter." | | 10 | "The alley beyond stank of" | | 11 | "The courier darted left, his" | | 12 | "She counted his strides." | | 13 | "She'd made him for a" | | 14 | "That made him dangerous in" | | 15 | "He crashed into a stack" | | 16 | "Glass bottles shattered." | | 17 | "She vaulted the debris, her" | | 18 | "Her cropped salt-and-pepper hair plastered" | | 19 | "The suspect cut right, toward" |
| | ratio | 0.769 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 130 | | matches | (empty) | | ratio | 0 | |
| 55.39% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 6 | | matches | | 0 | "A figure on the other side, tall and thin, peered out with eyes that reflected the torch beam like an animal's." | | 1 | "Pressure built against her sole, immense and wrong, as if the hinges resented her interference." | | 2 | "Above the stalls, strings of dried things that might have been herbs hung from the vaulted ceiling, dripping a dark oil onto the shoppers below." | | 3 | "A creature with too many elbows brushed past her shoulder, carrying a wooden crate that whimpered." | | 4 | "A man in a pinstripe suit counted currency that shimmered like heat haze above a summer road." | | 5 | "A nearby stall displayed rows of stoppered hearts that beat wetly against the glass." |
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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 | |