| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 123 | | tagDensity | 0.114 | | leniency | 0.228 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2439 | | 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) | |
| 87.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2439 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "charged" | | 1 | "silence" | | 2 | "grave" | | 3 | "scanned" |
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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 | 188 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 188 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 297 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2439 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 30 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 107 | | wordCount | 1624 | | uniqueNames | 12 | | maxNameDensity | 2.4 | | worstName | "Aurora" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 39 | | Cardiff | 2 | | London | 3 | | Rory | 1 | | Silas | 15 | | Raven | 1 | | Nest | 1 | | Eva | 34 | | Evan | 1 | | Two | 3 | | One | 4 | | Changed | 3 |
| | persons | | 0 | "Aurora" | | 1 | "Rory" | | 2 | "Silas" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Eva" | | 6 | "Evan" | | 7 | "One" |
| | places | | | globalScore | 0.299 | | windowScore | 0.167 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 116 | | glossingSentenceCount | 4 | | matches | | 0 | "felt like it knew more than it let on" | | 1 | "felt like it had been waiting" | | 2 | "as if listening through a wall" | | 3 | "not quite between them and not quite not" | | 4 | "not quite not" |
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| 77.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 1.23 | | wordCount | 2439 | | matches | | 0 | "Not Evan, not that night, but the knowledge of him in the city, close enough to cast fear" | | 1 | "not that night, but the knowledge of him in the city, close enough to cast fear" | | 2 | "not into tears, but into something barer" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 297 | | matches | | 0 | "kitchens that smelt" | | 1 | "learned that London" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 200 | | mean | 12.2 | | std | 15.43 | | cv | 1.265 | | sampleLengths | | 0 | 19 | | 1 | 1 | | 2 | 60 | | 3 | 8 | | 4 | 77 | | 5 | 42 | | 6 | 1 | | 7 | 14 | | 8 | 40 | | 9 | 12 | | 10 | 18 | | 11 | 8 | | 12 | 7 | | 13 | 3 | | 14 | 3 | | 15 | 6 | | 16 | 9 | | 17 | 90 | | 18 | 6 | | 19 | 4 | | 20 | 11 | | 21 | 1 | | 22 | 5 | | 23 | 9 | | 24 | 3 | | 25 | 10 | | 26 | 5 | | 27 | 8 | | 28 | 16 | | 29 | 3 | | 30 | 5 | | 31 | 28 | | 32 | 5 | | 33 | 3 | | 34 | 3 | | 35 | 18 | | 36 | 11 | | 37 | 5 | | 38 | 4 | | 39 | 5 | | 40 | 3 | | 41 | 46 | | 42 | 7 | | 43 | 9 | | 44 | 4 | | 45 | 3 | | 46 | 5 | | 47 | 4 | | 48 | 17 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 188 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 281 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 297 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1626 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 57 | | adverbRatio | 0.03505535055350553 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0036900369003690036 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 297 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 297 | | mean | 8.21 | | std | 7.4 | | cv | 0.901 | | sampleLengths | | 0 | 19 | | 1 | 1 | | 2 | 6 | | 3 | 2 | | 4 | 16 | | 5 | 8 | | 6 | 28 | | 7 | 8 | | 8 | 8 | | 9 | 4 | | 10 | 5 | | 11 | 30 | | 12 | 10 | | 13 | 6 | | 14 | 14 | | 15 | 11 | | 16 | 10 | | 17 | 5 | | 18 | 10 | | 19 | 2 | | 20 | 4 | | 21 | 1 | | 22 | 14 | | 23 | 17 | | 24 | 12 | | 25 | 8 | | 26 | 3 | | 27 | 12 | | 28 | 18 | | 29 | 8 | | 30 | 7 | | 31 | 3 | | 32 | 3 | | 33 | 6 | | 34 | 9 | | 35 | 9 | | 36 | 2 | | 37 | 16 | | 38 | 25 | | 39 | 5 | | 40 | 13 | | 41 | 12 | | 42 | 8 | | 43 | 6 | | 44 | 4 | | 45 | 11 | | 46 | 1 | | 47 | 5 | | 48 | 9 | | 49 | 3 |
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| 44.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2558922558922559 | | totalSentences | 297 | | uniqueOpeners | 76 | |
| 59.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 167 | | matches | | 0 | "Then the woman blinked, and" | | 1 | "Just the space between them." | | 2 | "Perhaps she had practised." |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 167 | | matches | | 0 | "She set the tray down" | | 1 | "He took in Aurora’s face," | | 2 | "His signet ring clicked once" | | 3 | "He said nothing." | | 4 | "Her gaze snagged on the" | | 5 | "She had posted them to" | | 6 | "Her small crescent scar showed" | | 7 | "She had once filled every" | | 8 | "She had spoken with both" | | 9 | "She used to swear by" | | 10 | "It made Aurora see the" | | 11 | "He set them down with" | | 12 | "He polished the same glass" | | 13 | "It narrowed to the width" | | 14 | "She said nothing." | | 15 | "Her father had taught her" | | 16 | "She had learned that London" | | 17 | "It taxed it." | | 18 | "He stood with both palms" | | 19 | "She had changed routes after" |
| | ratio | 0.192 | |
| 43.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 139 | | totalSentences | 167 | | matches | | 0 | "The tray tipped in Aurora’s" | | 1 | "Aurora caught it with the" | | 2 | "Beer slopped over her knuckles," | | 3 | "She set the tray down" | | 4 | "The woman had risen halfway" | | 5 | "The face had thinned." | | 6 | "The mouth had gone careful." | | 7 | "The hair, once dyed a" | | 8 | "A cream wool coat hung" | | 9 | "A pearl glinted at each" | | 10 | "Student kitchens that smelt of" | | 11 | "A train ticket to London" | | 12 | "The name left her like" | | 13 | "He took in Aurora’s face," | | 14 | "His signet ring clicked once" | | 15 | "He said nothing." | | 16 | "Eva gave a short laugh" | | 17 | "Aurora wiped her hand on" | | 18 | "Eva looked down into her" | | 19 | "The Raven’s Nest kept its" |
| | ratio | 0.832 | |
| 29.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 167 | | matches | | | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 3 | | matches | | 0 | "The hair, once dyed a fresh new colour every fortnight, sat in a smooth brown bob that looked cut by someone who charged by the hour and by the silence." | | 1 | "Eva looked straight at her then, and there it was, the old nerve, the old reckless honesty that used to make men fall in and women step back." | | 2 | "The taller man stepped in first, broad in the shoulders, coat unbuttoned, face blank in the professional way that meant violence had become administrative." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "Eva murmured (murmur)" | | 1 | "Aurora repeated (repeat)" |
| | dialogueSentences | 123 | | tagDensity | 0.089 | | leniency | 0.179 | | rawRatio | 0.182 | | effectiveRatio | 0.033 | |