| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1808 | | 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) | |
| 91.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1808 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "scanned" | | 1 | "pulsed" | | 2 | "silence" |
| |
| 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 | 145 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 145 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 167 | | 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 | 1808 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 87 | | wordCount | 1664 | | uniqueNames | 22 | | maxNameDensity | 1.26 | | worstName | "Herrera" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 3 | | Quinn | 18 | | Metropolitan | 1 | | Police | 1 | | Norfolk | 2 | | Tomás | 2 | | Herrera | 21 | | Saint | 2 | | Christopher | 2 | | Oxford | 1 | | Street | 1 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Tube | 1 | | Camden | 2 | | Lock | 1 | | Morris | 1 | | Rain | 3 | | Harlow | 18 | | Water | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Rain" | | 8 | "Harlow" | | 9 | "Water" |
| | places | | 0 | "Raven" | | 1 | "Norfolk" | | 2 | "Oxford" | | 3 | "Street" | | 4 | "Chalk" | | 5 | "Farm" | | 6 | "Road" | | 7 | "Camden" | | 8 | "Lock" |
| | globalScore | 0.869 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 136 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1808 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 167 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 50.22 | | std | 58.66 | | cv | 1.168 | | sampleLengths | | 0 | 139 | | 1 | 104 | | 2 | 3 | | 3 | 3 | | 4 | 16 | | 5 | 7 | | 6 | 130 | | 7 | 148 | | 8 | 4 | | 9 | 135 | | 10 | 6 | | 11 | 4 | | 12 | 7 | | 13 | 9 | | 14 | 136 | | 15 | 9 | | 16 | 9 | | 17 | 7 | | 18 | 8 | | 19 | 115 | | 20 | 7 | | 21 | 116 | | 22 | 4 | | 23 | 8 | | 24 | 6 | | 25 | 123 | | 26 | 164 | | 27 | 5 | | 28 | 136 | | 29 | 5 | | 30 | 5 | | 31 | 7 | | 32 | 5 | | 33 | 106 | | 34 | 96 | | 35 | 16 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 254 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 167 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1668 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.006594724220623501 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0005995203836930455 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 167 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 167 | | mean | 10.83 | | std | 4.66 | | cv | 0.43 | | sampleLengths | | 0 | 15 | | 1 | 21 | | 2 | 8 | | 3 | 5 | | 4 | 5 | | 5 | 9 | | 6 | 7 | | 7 | 2 | | 8 | 2 | | 9 | 13 | | 10 | 7 | | 11 | 8 | | 12 | 8 | | 13 | 6 | | 14 | 23 | | 15 | 13 | | 16 | 7 | | 17 | 9 | | 18 | 7 | | 19 | 6 | | 20 | 15 | | 21 | 19 | | 22 | 13 | | 23 | 15 | | 24 | 3 | | 25 | 3 | | 26 | 16 | | 27 | 7 | | 28 | 14 | | 29 | 13 | | 30 | 15 | | 31 | 4 | | 32 | 23 | | 33 | 15 | | 34 | 13 | | 35 | 14 | | 36 | 19 | | 37 | 7 | | 38 | 10 | | 39 | 8 | | 40 | 14 | | 41 | 17 | | 42 | 8 | | 43 | 17 | | 44 | 8 | | 45 | 12 | | 46 | 8 | | 47 | 6 | | 48 | 11 | | 49 | 5 |
| |
| 58.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3712574850299401 | | totalSentences | 167 | | uniqueOpeners | 62 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 143 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 143 | | matches | | 0 | "Her cropped salt-and-pepper hair stuck" | | 1 | "Her sharp jaw stayed locked." | | 2 | "Her 5-foot-9 frame sat square" | | 3 | "He tucked the medallion inside" | | 4 | "Her boots found traction on" | | 5 | "She pulled her arm free" | | 6 | "He spat onto the path" | | 7 | "His left forearm flashed under" | | 8 | "He hauled himself over a" | | 9 | "Her jacket caught on a" | | 10 | "She wrenched forward and dropped" | | 11 | "Her knees buckled on slick" | | 12 | "She planted a hand in" | | 13 | "She caught the wall with" | | 14 | "Her worn leather watch strap" | | 15 | "He held the bag high" | | 16 | "Her boots slid half a" | | 17 | "She planted both feet and" | | 18 | "Her sharp jaw tightened until" | | 19 | "She held Herrera's back in" |
| | ratio | 0.21 | |
| 1.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 131 | | totalSentences | 143 | | matches | | 0 | "Harlow Quinn sat in a" | | 1 | "Her cropped salt-and-pepper hair stuck" | | 2 | "Brown eyes held the entrance." | | 3 | "Her sharp jaw stayed locked." | | 4 | "Her 5-foot-9 frame sat square" | | 5 | "Boots planted flat on the" | | 6 | "A Norfolk jacket clung to" | | 7 | "Water dripped from the hem" | | 8 | "A bookshelf shifted half an" | | 9 | "Harlow Quinn marked every detail" | | 10 | "Tomás Herrera stood 5-foot-10 in" | | 11 | "Olive skin shone under the" | | 12 | "A Saint Christopher medallion bounced" | | 13 | "A scar ran along his" | | 14 | "Heat rose in his face" | | 15 | "He tucked the medallion inside" | | 16 | "Harlow Quinn turned the key" | | 17 | "Wipers dragged arcs across the" | | 18 | "The driver took Oxford Street" | | 19 | "Horns blared behind them." |
| | ratio | 0.916 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 143 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 92 | | technicalSentenceCount | 1 | | matches | | 0 | "Harlow Quinn held the wheel with both hands and matched the taxi lane for lane through puddles that tossed spray over the hood." |
| |
| 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 | |