| 18.18% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 1 | | adverbTags | | 0 | "His voice carried just [just]" |
| | dialogueSentences | 11 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.333 | | effectiveRatio | 0.182 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1412 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 68.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1412 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "glinting" | | 1 | "echoes" | | 2 | "loomed" | | 3 | "tension" | | 4 | "scanned" | | 5 | "etched" | | 6 | "echoed" | | 7 | "shattered" | | 8 | "pulsed" |
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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 | 137 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 137 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 145 | | 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 | 1412 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.90% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1337 | | uniqueNames | 18 | | maxNameDensity | 1.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Quinn | 15 | | Harlow | 1 | | Raven | 2 | | Nest | 3 | | Herrera | 11 | | Saint | 2 | | Christopher | 2 | | Morris | 3 | | Good | 1 | | Camden | 2 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Manchester | 1 | | Spanish | 1 | | Rain | 3 | | One | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Harlow" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Camden" | | 8 | "Market" | | 9 | "Rain" | | 10 | "One" |
| | places | | 0 | "Soho" | | 1 | "Nest" | | 2 | "Manchester" |
| | globalScore | 0.939 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 106 | | glossingSentenceCount | 2 | | matches | | 0 | "runes that seemed to shift when she stared too long" | | 1 | "looked like captured starlight" |
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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 | 1412 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 45.55 | | std | 24.21 | | cv | 0.531 | | sampleLengths | | 0 | 88 | | 1 | 49 | | 2 | 41 | | 3 | 6 | | 4 | 51 | | 5 | 10 | | 6 | 65 | | 7 | 48 | | 8 | 6 | | 9 | 43 | | 10 | 59 | | 11 | 71 | | 12 | 81 | | 13 | 47 | | 14 | 67 | | 15 | 13 | | 16 | 78 | | 17 | 55 | | 18 | 34 | | 19 | 9 | | 20 | 17 | | 21 | 63 | | 22 | 80 | | 23 | 57 | | 24 | 54 | | 25 | 37 | | 26 | 8 | | 27 | 46 | | 28 | 61 | | 29 | 54 | | 30 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 137 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 249 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 145 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1349 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.026686434395848776 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0037064492216456633 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 145 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 145 | | mean | 9.74 | | std | 5.31 | | cv | 0.545 | | sampleLengths | | 0 | 19 | | 1 | 19 | | 2 | 23 | | 3 | 9 | | 4 | 15 | | 5 | 3 | | 6 | 7 | | 7 | 9 | | 8 | 18 | | 9 | 15 | | 10 | 12 | | 11 | 12 | | 12 | 17 | | 13 | 6 | | 14 | 12 | | 15 | 4 | | 16 | 9 | | 17 | 2 | | 18 | 6 | | 19 | 10 | | 20 | 8 | | 21 | 7 | | 22 | 3 | | 23 | 4 | | 24 | 23 | | 25 | 12 | | 26 | 2 | | 27 | 2 | | 28 | 14 | | 29 | 8 | | 30 | 6 | | 31 | 6 | | 32 | 6 | | 33 | 7 | | 34 | 15 | | 35 | 8 | | 36 | 6 | | 37 | 12 | | 38 | 26 | | 39 | 1 | | 40 | 4 | | 41 | 5 | | 42 | 12 | | 43 | 5 | | 44 | 14 | | 45 | 15 | | 46 | 8 | | 47 | 10 | | 48 | 10 | | 49 | 4 |
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| 77.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.47586206896551725 | | totalSentences | 145 | | uniqueOpeners | 69 | |
| 51.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 129 | | matches | | 0 | "Then the bookshelf in the" | | 1 | "Instead he kicked a trash" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 129 | | matches | | 0 | "She gave chase, boots slapping" | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "Her worn leather watch bounced" | | 3 | "His Saint Christopher medallion swung" | | 4 | "He cut left into an" | | 5 | "Her shout bounced off the" | | 6 | "He did not stop." | | 7 | "You lost Morris in shadows" | | 8 | "She pushed the memory down" | | 9 | "He darted between vehicles without" | | 10 | "Her breath streamed behind her" | | 11 | "He threw a look back," | | 12 | "His voice carried just enough" | | 13 | "Her jacket grew heavy, sleeves" | | 14 | "He caught himself on a" | | 15 | "He recovered and sprinted again," | | 16 | "He wrenched one free, the" | | 17 | "Her flashlight beam cut a" | | 18 | "She scanned the top step." | | 19 | "She weighed it in her" |
| | ratio | 0.248 | |
| 56.90% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 129 | | matches | | 0 | "Rain sliced through the night" | | 1 | "Detective Harlow Quinn shoved out" | | 2 | "Tomás Herrera had stepped out," | | 3 | "She gave chase, boots slapping" | | 4 | "Her closely cropped salt-and-pepper hair" | | 5 | "The sharp line of her" | | 6 | "Her worn leather watch bounced" | | 7 | "Herrera's short curly dark brown" | | 8 | "His Saint Christopher medallion swung" | | 9 | "He cut left into an" | | 10 | "Her shout bounced off the" | | 11 | "He did not stop." | | 12 | "Quinn vaulted it, arms spread" | | 13 | "You lost Morris in shadows" | | 14 | "The thought fueled her." | | 15 | "She pushed the memory down" | | 16 | "Treatments for patients who bled" | | 17 | "The clique kept him close" | | 18 | "Herrera burst onto a main" | | 19 | "Headlights smeared across the wet" |
| | ratio | 0.806 | |
| 38.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 129 | | matches | | | ratio | 0.008 | |
| 94.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 4 | | matches | | 0 | "Old maps and black-and-white photographs on the walls inside had drawn her there an hour earlier, hunting leads on shipments that smelled wrong." | | 1 | "Treatments for patients who bled light or whispered in languages that hurt to hear." | | 2 | "Stacks of paper that rearranged their own text while customers read." | | 3 | "One rolled off and shattered at her feet, releasing a puff of violet smoke that smelled of her childhood street in Manchester." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |