| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.16 | | leniency | 0.32 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1747 | | 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) | |
| 51.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1747 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | word | "practiced ease" | | count | 1 |
| | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "flickered" | | 1 | "standard" | | 2 | "sense of" | | 3 | "perfect" | | 4 | "loomed" | | 5 | "etched" | | 6 | "database" | | 7 | "silence" | | 8 | "practiced ease" | | 9 | "could feel" | | 10 | "footsteps" | | 11 | "electric" | | 12 | "pulsed" | | 13 | "navigated" | | 14 | "depths" | | 15 | "stomach" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "clenched jaw/fists" | | count | 1 |
|
| | highlights | | 0 | "blood went cold" | | 1 | "clenched my fist" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 198 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 198 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 240 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1747 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1348 | | uniqueNames | 17 | | maxNameDensity | 1.04 | | worstName | "Herrera" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Saint | 1 | | Christopher | 1 | | Soho | 2 | | Chinese | 1 | | Raven | 1 | | Nest | 1 | | Vauxhall | 1 | | Wardour | 1 | | Street | 1 | | Herrera | 14 | | London | 3 | | Tube | 1 | | English | 1 | | Victorian | 1 | | Labrador | 1 | | Morris | 3 | | Like | 3 |
| | persons | | 0 | "Saint" | | 1 | "Christopher" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Labrador" | | 6 | "Morris" | | 7 | "Like" |
| | places | | 0 | "Soho" | | 1 | "Vauxhall" | | 2 | "Wardour" | | 3 | "Street" | | 4 | "London" |
| | globalScore | 0.981 | | windowScore | 0.833 | |
| 9.55% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 5 | | matches | | 0 | "sounded like it was being spoken through w" | | 1 | "something like it, casting the tunnel in a s" | | 2 | "looked like a Victorian market hall, all" | | 3 | "rows that seemed to go on further than the space could possibly allow" | | 4 | "sounded like music" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1747 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 240 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 95 | | mean | 18.39 | | std | 17.45 | | cv | 0.949 | | sampleLengths | | 0 | 12 | | 1 | 46 | | 2 | 22 | | 3 | 6 | | 4 | 43 | | 5 | 63 | | 6 | 3 | | 7 | 42 | | 8 | 2 | | 9 | 8 | | 10 | 30 | | 11 | 5 | | 12 | 5 | | 13 | 27 | | 14 | 66 | | 15 | 5 | | 16 | 2 | | 17 | 72 | | 18 | 34 | | 19 | 9 | | 20 | 31 | | 21 | 5 | | 22 | 40 | | 23 | 18 | | 24 | 17 | | 25 | 4 | | 26 | 11 | | 27 | 14 | | 28 | 13 | | 29 | 4 | | 30 | 18 | | 31 | 20 | | 32 | 8 | | 33 | 3 | | 34 | 5 | | 35 | 53 | | 36 | 4 | | 37 | 26 | | 38 | 1 | | 39 | 10 | | 40 | 6 | | 41 | 37 | | 42 | 17 | | 43 | 36 | | 44 | 32 | | 45 | 3 | | 46 | 8 | | 47 | 22 | | 48 | 6 | | 49 | 36 |
| |
| 99.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 198 | | matches | | 0 | "being spoken" | | 1 | "were shaped" | | 2 | "being negotiated" |
| |
| 38.06% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 247 | | matches | | 0 | "were tearing" | | 1 | "was checking" | | 2 | "was sucking" | | 3 | "was coming" | | 4 | "was chasing" | | 5 | "was selling" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 240 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1353 | | adjectiveStacks | 1 | | stackExamples | | 0 | "sickly blue-green light." |
| | adverbCount | 37 | | adverbRatio | 0.027346637102734665 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.0066518847006651885 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 240 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 240 | | mean | 7.28 | | std | 5.81 | | cv | 0.799 | | sampleLengths | | 0 | 12 | | 1 | 3 | | 2 | 6 | | 3 | 17 | | 4 | 3 | | 5 | 17 | | 6 | 15 | | 7 | 5 | | 8 | 2 | | 9 | 6 | | 10 | 14 | | 11 | 8 | | 12 | 17 | | 13 | 3 | | 14 | 1 | | 15 | 24 | | 16 | 3 | | 17 | 1 | | 18 | 13 | | 19 | 5 | | 20 | 17 | | 21 | 3 | | 22 | 22 | | 23 | 13 | | 24 | 7 | | 25 | 2 | | 26 | 3 | | 27 | 5 | | 28 | 17 | | 29 | 5 | | 30 | 3 | | 31 | 1 | | 32 | 4 | | 33 | 2 | | 34 | 3 | | 35 | 3 | | 36 | 2 | | 37 | 4 | | 38 | 4 | | 39 | 10 | | 40 | 9 | | 41 | 8 | | 42 | 5 | | 43 | 14 | | 44 | 4 | | 45 | 2 | | 46 | 5 | | 47 | 4 | | 48 | 13 | | 49 | 11 |
| |
| 51.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.3541666666666667 | | totalSentences | 240 | | uniqueOpeners | 85 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 159 | | matches | | 0 | "Then he'd seen the car" | | 1 | "Meanwhile I was sucking air" | | 2 | "Then silence again." | | 3 | "Then we descended." | | 4 | "Somewhere in its depths, I" | | 5 | "Then I saw it." |
| | ratio | 0.038 | |
| 84.15% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 54 | | totalSentences | 159 | | matches | | 0 | "I hurdled it." | | 1 | "My knees would hate me" | | 2 | "He was fast." | | 3 | "My shoes had no grip." | | 4 | "I should have worn different" | | 5 | "He cut left past a" | | 6 | "I'd been sitting in the" | | 7 | "He'd looked calm." | | 8 | "I'd trained for that moment," | | 9 | "He'd chosen flight." | | 10 | "He didn't stop." | | 11 | "He didn't even slow down." | | 12 | "My hand found my radio." | | 13 | "I thumbed the button." | | 14 | "I tried again." | | 15 | "I'd made thirty arrests and" | | 16 | "He glanced back at me." | | 17 | "He was checking a watch." | | 18 | "He disappeared down the alley." | | 19 | "My sense of direction was" |
| | ratio | 0.34 | |
| 66.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 125 | | totalSentences | 159 | | matches | | 0 | "Herrera's foot clipped the bin" | | 1 | "I hurdled it." | | 2 | "My knees would hate me" | | 3 | "The Saint Christopher medallion bounced" | | 4 | "He was fast." | | 5 | "The rain came down in" | | 6 | "My shoes had no grip." | | 7 | "I should have worn different" | | 8 | "He cut left past a" | | 9 | "The distinctive green neon of" | | 10 | "I'd been sitting in the" | | 11 | "He'd looked calm." | | 12 | "The recognition had been instant." | | 13 | "I'd trained for that moment," | | 14 | "He'd chosen flight." | | 15 | "That was the lie I" | | 16 | "He didn't stop." | | 17 | "He didn't even slow down." | | 18 | "Herrera shoved through a cluster" | | 19 | "My hand found my radio." |
| | ratio | 0.786 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 159 | | matches | | 0 | "Now we were tearing through" | | 1 | "If the radio worked, which" | | 2 | "If anyone had heard the" | | 3 | "If Herrera had actually committed" |
| | ratio | 0.025 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 2 | | matches | | 0 | "Now we were tearing through Soho at midnight, my lungs burning and my leather watch slapping against my wrist with every stride." | | 1 | "The joints bent at angles that made my stomach turn." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 50 | | tagDensity | 0.04 | | leniency | 0.08 | | rawRatio | 0.5 | | effectiveRatio | 0.04 | |