| 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 | 1583 | | 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) | |
| 74.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1583 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "vibrated" | | 1 | "pulse" | | 2 | "glinting" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "flickered" | | 6 | "electric" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 171 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 171 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 194 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1583 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1376 | | uniqueNames | 24 | | maxNameDensity | 1.45 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Quinn | 1 | | Tuesday | 1 | | Wardour | 1 | | Street | 4 | | Brewer | 1 | | Harlow | 20 | | Oxford | 1 | | Camden | 3 | | Herrera | 1 | | Tomás | 6 | | Tube | 1 | | High | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | London | 1 | | Morris | 1 | | Rain | 4 | | Water | 4 | | Cold | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Harlow" | | 4 | "Herrera" | | 5 | "Tomás" | | 6 | "Market" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Morris" | | 10 | "Rain" | | 11 | "Water" | | 12 | "Cold" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Brewer" | | 4 | "Oxford" | | 5 | "Camden" | | 6 | "High" | | 7 | "London" |
| | globalScore | 0.773 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | 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 | 1583 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 194 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 71 | | mean | 22.3 | | std | 20.02 | | cv | 0.898 | | sampleLengths | | 0 | 3 | | 1 | 49 | | 2 | 3 | | 3 | 49 | | 4 | 4 | | 5 | 14 | | 6 | 3 | | 7 | 10 | | 8 | 5 | | 9 | 26 | | 10 | 2 | | 11 | 32 | | 12 | 40 | | 13 | 7 | | 14 | 13 | | 15 | 78 | | 16 | 6 | | 17 | 32 | | 18 | 25 | | 19 | 6 | | 20 | 18 | | 21 | 4 | | 22 | 16 | | 23 | 18 | | 24 | 14 | | 25 | 23 | | 26 | 9 | | 27 | 7 | | 28 | 28 | | 29 | 13 | | 30 | 9 | | 31 | 2 | | 32 | 20 | | 33 | 55 | | 34 | 4 | | 35 | 1 | | 36 | 4 | | 37 | 10 | | 38 | 11 | | 39 | 14 | | 40 | 43 | | 41 | 13 | | 42 | 54 | | 43 | 28 | | 44 | 29 | | 45 | 36 | | 46 | 25 | | 47 | 7 | | 48 | 68 | | 49 | 3 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 171 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 232 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 194 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1382 | | adjectiveStacks | 1 | | stackExamples | | 0 | "white under harsh light," |
| | adverbCount | 15 | | adverbRatio | 0.01085383502170767 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 194 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 194 | | mean | 8.16 | | std | 6.48 | | cv | 0.794 | | sampleLengths | | 0 | 3 | | 1 | 11 | | 2 | 17 | | 3 | 21 | | 4 | 3 | | 5 | 5 | | 6 | 11 | | 7 | 15 | | 8 | 4 | | 9 | 4 | | 10 | 10 | | 11 | 4 | | 12 | 7 | | 13 | 7 | | 14 | 3 | | 15 | 7 | | 16 | 3 | | 17 | 5 | | 18 | 4 | | 19 | 5 | | 20 | 17 | | 21 | 2 | | 22 | 11 | | 23 | 4 | | 24 | 8 | | 25 | 5 | | 26 | 4 | | 27 | 16 | | 28 | 2 | | 29 | 22 | | 30 | 7 | | 31 | 4 | | 32 | 3 | | 33 | 6 | | 34 | 6 | | 35 | 3 | | 36 | 6 | | 37 | 27 | | 38 | 5 | | 39 | 18 | | 40 | 4 | | 41 | 9 | | 42 | 3 | | 43 | 2 | | 44 | 1 | | 45 | 6 | | 46 | 6 | | 47 | 6 | | 48 | 12 | | 49 | 2 |
| |
| 45.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3247422680412371 | | totalSentences | 194 | | uniqueOpeners | 63 | |
| 43.57% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 153 | | matches | | 0 | "Then he bolted." | | 1 | "Then another sound." |
| | ratio | 0.013 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 153 | | matches | | 0 | "He moved with purpose, too" | | 1 | "She knew that walk." | | 2 | "She had chased it through" | | 3 | "She crossed the street." | | 4 | "Her feet slapped concrete." | | 5 | "He vaulted a stack of" | | 6 | "Her cropped salt-and-pepper hair plastered" | | 7 | "Her jaw set hard." | | 8 | "He hit Brewer Street at" | | 9 | "He did not answer." | | 10 | "He turned north." | | 11 | "His trainers hissed on wet" | | 12 | "She ignored it." | | 13 | "Her breath burned in her" | | 14 | "He took the cut-through behind" | | 15 | "She closed distance." | | 16 | "He burst out onto Oxford" | | 17 | "He dodged between them with" | | 18 | "He headed north." | | 19 | "Her phone vibrated in her" |
| | ratio | 0.333 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 135 | | totalSentences | 153 | | matches | | 0 | "Water struck the pavement in" | | 1 | "The green neon of The" | | 2 | "Harlow Quinn stood under the" | | 3 | "The door opened." | | 4 | "A figure slipped out, hood" | | 5 | "He moved with purpose, too" | | 6 | "Harlow clocked the gait." | | 7 | "She knew that walk." | | 8 | "She had chased it through" | | 9 | "She crossed the street." | | 10 | "The pavement shone black under" | | 11 | "The wind drove rain into" | | 12 | "The figure froze for half" | | 13 | "Harlow broke into a run." | | 14 | "Her feet slapped concrete." | | 15 | "Water exploded under each stride." | | 16 | "The suspect cut left down" | | 17 | "He vaulted a stack of" | | 18 | "Harlow vaulted after him." | | 19 | "Her cropped salt-and-pepper hair plastered" |
| | ratio | 0.882 | |
| 65.36% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 153 | | matches | | 0 | "If she went back for" | | 1 | "If she went forward without" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |