| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "The lips peeled back [back]" |
| | dialogueSentences | 27 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.167 | | effectiveRatio | 0.074 | |
| 95.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1171 | | totalAiIsmAdverbs | 1 | | 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) | |
| 78.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1171 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "warmth" | | 1 | "scanned" | | 2 | "flickered" | | 3 | "glistening" | | 4 | "weight" |
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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 | 1 | | narrationSentences | 149 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 149 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 174 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1171 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1030 | | uniqueNames | 18 | | maxNameDensity | 0.87 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 3 | | Quinn | 9 | | Victorian | 1 | | London | 1 | | Warren | 2 | | Street | 2 | | Morris | 2 | | Peckham | 1 | | Had | 3 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Metropolitan | 1 | | Police | 1 | | Silas | 5 | | King | 1 | | Cross | 1 |
| | persons | | 0 | "Raven" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Had" | | 4 | "Market" | | 5 | "Silas" | | 6 | "King" | | 7 | "Cross" |
| | places | | 0 | "Nest" | | 1 | "Victorian" | | 2 | "London" | | 3 | "Warren" | | 4 | "Street" | | 5 | "Peckham" |
| | globalScore | 1 | | windowScore | 1 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared behind the bar, polishing a glass that looked clean enough" | | 1 | "quite fire" |
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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 | 1171 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 174 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 17.48 | | std | 15.28 | | cv | 0.874 | | sampleLengths | | 0 | 18 | | 1 | 2 | | 2 | 6 | | 3 | 32 | | 4 | 12 | | 5 | 26 | | 6 | 13 | | 7 | 31 | | 8 | 13 | | 9 | 4 | | 10 | 56 | | 11 | 26 | | 12 | 3 | | 13 | 1 | | 14 | 22 | | 15 | 14 | | 16 | 5 | | 17 | 4 | | 18 | 15 | | 19 | 15 | | 20 | 21 | | 21 | 27 | | 22 | 9 | | 23 | 17 | | 24 | 7 | | 25 | 1 | | 26 | 52 | | 27 | 32 | | 28 | 12 | | 29 | 19 | | 30 | 1 | | 31 | 11 | | 32 | 5 | | 33 | 9 | | 34 | 15 | | 35 | 34 | | 36 | 18 | | 37 | 4 | | 38 | 5 | | 39 | 41 | | 40 | 7 | | 41 | 42 | | 42 | 4 | | 43 | 6 | | 44 | 4 | | 45 | 9 | | 46 | 1 | | 47 | 56 | | 48 | 28 | | 49 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 149 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 180 | | matches | | 0 | "was already descending" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 174 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 166 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.024096385542168676 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 174 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 174 | | mean | 6.72 | | std | 4.91 | | cv | 0.731 | | sampleLengths | | 0 | 3 | | 1 | 15 | | 2 | 2 | | 3 | 3 | | 4 | 3 | | 5 | 5 | | 6 | 8 | | 7 | 12 | | 8 | 7 | | 9 | 12 | | 10 | 2 | | 11 | 8 | | 12 | 4 | | 13 | 6 | | 14 | 6 | | 15 | 4 | | 16 | 9 | | 17 | 3 | | 18 | 3 | | 19 | 13 | | 20 | 2 | | 21 | 3 | | 22 | 7 | | 23 | 8 | | 24 | 3 | | 25 | 1 | | 26 | 1 | | 27 | 4 | | 28 | 15 | | 29 | 13 | | 30 | 14 | | 31 | 10 | | 32 | 4 | | 33 | 13 | | 34 | 6 | | 35 | 7 | | 36 | 3 | | 37 | 1 | | 38 | 7 | | 39 | 5 | | 40 | 10 | | 41 | 10 | | 42 | 4 | | 43 | 5 | | 44 | 4 | | 45 | 10 | | 46 | 5 | | 47 | 4 | | 48 | 6 | | 49 | 2 |
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| 82.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5114942528735632 | | totalSentences | 174 | | uniqueOpeners | 89 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 123 | | matches | | 0 | "Always the same spots." | | 1 | "Just in time to catch" | | 2 | "Then she shoved through." | | 3 | "Then it stepped aside." | | 4 | "Just a wall of rough" | | 5 | "Always check your exits, Quinn." |
| | ratio | 0.049 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 123 | | matches | | 0 | "He didn’t stop." | | 1 | "They never did." | | 2 | "Her boots hammered wet pavement." | | 3 | "She pressed the radio at" | | 4 | "She rounded the corner." | | 5 | "She knew it." | | 6 | "His voice was honey over" | | 7 | "She had her warrant card" | | 8 | "He stepped aside." | | 9 | "Her hand found the torch" | | 10 | "She was already descending." | | 11 | "She flashed the warrant card" | | 12 | "She scanned the ground." | | 13 | "He must have a token." | | 14 | "She thought of Morris." | | 15 | "She always carried it now." | | 16 | "She held it up." | | 17 | "She burst through a gap" | | 18 | "Her torch picked out scratches" | | 19 | "Her hand hovered over the" |
| | ratio | 0.203 | |
| 98.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 123 | | matches | | 0 | "Harlow’s lungs burned." | | 1 | "The figure ahead slipped through" | | 2 | "He didn’t stop." | | 3 | "They never did." | | 4 | "Her boots hammered wet pavement." | | 5 | "Rain slashed sideways between buildings," | | 6 | "Soho’s neon bled into puddles," | | 7 | "She pressed the radio at" | | 8 | "The radio had gone dead" | | 9 | "Quinn had another word for" | | 10 | "She rounded the corner." | | 11 | "The Raven’s Nest." | | 12 | "That distinctive green neon sign" | | 13 | "She knew it." | | 14 | "Quinn pressed her palm flat" | | 15 | "The bar hit her with" | | 16 | "Maps dotted the walls, yellowed" | | 17 | "The few patrons huddled over" | | 18 | "Silas appeared behind the bar," | | 19 | "His voice was honey over" |
| | ratio | 0.724 | |
| 40.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 123 | | matches | | 0 | "To the market, to Silas," |
| | ratio | 0.008 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 4 | | matches | | 0 | "Black-and-white photographs of people who’d been dead a century watched her from dusty frames." | | 1 | "The air that wafted up carried notes of ozone and clove and something darker, something her brain refused to name." | | 2 | "The walls changed from Victorian brick to rough-hewn stone to something that looked organic, like the inside of a throat." | | 3 | "Lanterns hung from arched ceilings, burning with flames that shifted colours as she watched: blue, green, a deep orange that wasn’t quite fire." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "Silas added, conversationally" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 27 | | tagDensity | 0.037 | | leniency | 0.074 | | rawRatio | 1 | | effectiveRatio | 0.074 | |