| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.267 | | leniency | 0.533 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1220 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 87.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1220 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "loomed" | | 1 | "electric" | | 2 | "weight" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 124 | | matches | (empty) | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 124 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | 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 | 1220 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1142 | | uniqueNames | 25 | | maxNameDensity | 0.96 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Morris" | | discoveredNames | | Quinn | 11 | | Soho | 2 | | Herrera | 9 | | Morris | 6 | | Yard | 1 | | Bateman | 1 | | Street | 3 | | Raven | 1 | | Nest | 1 | | Marine | 1 | | Hendon | 1 | | Dominion | 1 | | Tommy | 1 | | London | 2 | | Underground | 1 | | Tuesday | 1 | | March | 1 | | Victorian | 1 | | Hendricks | 1 | | Wapping | 1 | | Three | 1 | | Greek | 1 | | October | 1 | | Tube | 1 | | Eighteen | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Morris" | | 3 | "Raven" | | 4 | "Tommy" |
| | places | | 0 | "Soho" | | 1 | "Bateman" | | 2 | "Street" | | 3 | "Hendon" | | 4 | "Dominion" | | 5 | "London" | | 6 | "March" | | 7 | "Hendricks" | | 8 | "Wapping" | | 9 | "Three" | | 10 | "Greek" | | 11 | "October" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1220 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 134 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 23.46 | | std | 21.38 | | cv | 0.911 | | sampleLengths | | 0 | 9 | | 1 | 55 | | 2 | 23 | | 3 | 5 | | 4 | 6 | | 5 | 26 | | 6 | 11 | | 7 | 64 | | 8 | 62 | | 9 | 12 | | 10 | 17 | | 11 | 13 | | 12 | 5 | | 13 | 22 | | 14 | 63 | | 15 | 22 | | 16 | 2 | | 17 | 54 | | 18 | 4 | | 19 | 34 | | 20 | 9 | | 21 | 37 | | 22 | 8 | | 23 | 3 | | 24 | 31 | | 25 | 43 | | 26 | 9 | | 27 | 5 | | 28 | 54 | | 29 | 8 | | 30 | 7 | | 31 | 14 | | 32 | 57 | | 33 | 4 | | 34 | 21 | | 35 | 7 | | 36 | 50 | | 37 | 3 | | 38 | 4 | | 39 | 47 | | 40 | 6 | | 41 | 32 | | 42 | 8 | | 43 | 66 | | 44 | 3 | | 45 | 22 | | 46 | 84 | | 47 | 19 | | 48 | 12 | | 49 | 14 |
| |
| 99.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 124 | | matches | | 0 | "was gone" | | 1 | "been brewed" |
| |
| 31.65% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 198 | | matches | | 0 | "were beginning" | | 1 | "wasn't running" | | 2 | "was running" | | 3 | "was winning" | | 4 | "was already struggling" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1149 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.024369016536118365 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004351610095735422 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 9.1 | | std | 7.97 | | cv | 0.876 | | sampleLengths | | 0 | 9 | | 1 | 16 | | 2 | 24 | | 3 | 4 | | 4 | 10 | | 5 | 1 | | 6 | 1 | | 7 | 2 | | 8 | 20 | | 9 | 5 | | 10 | 3 | | 11 | 3 | | 12 | 11 | | 13 | 15 | | 14 | 6 | | 15 | 5 | | 16 | 21 | | 17 | 9 | | 18 | 4 | | 19 | 30 | | 20 | 4 | | 21 | 24 | | 22 | 2 | | 23 | 2 | | 24 | 8 | | 25 | 11 | | 26 | 6 | | 27 | 5 | | 28 | 12 | | 29 | 11 | | 30 | 6 | | 31 | 2 | | 32 | 11 | | 33 | 5 | | 34 | 6 | | 35 | 5 | | 36 | 4 | | 37 | 7 | | 38 | 3 | | 39 | 30 | | 40 | 7 | | 41 | 23 | | 42 | 14 | | 43 | 4 | | 44 | 2 | | 45 | 2 | | 46 | 2 | | 47 | 23 | | 48 | 25 | | 49 | 3 |
| |
| 59.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.44029850746268656 | | totalSentences | 134 | | uniqueOpeners | 59 | |
| 64.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 103 | | matches | | 0 | "Actually laughed, over his shoulder," | | 1 | "Then Herrera was gone." |
| | ratio | 0.019 | |
| 91.84% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 103 | | matches | | 0 | "Her boots hammered the wet" | | 1 | "He glanced back once." | | 2 | "She'd memorised that face from" | | 3 | "He didn't stop." | | 4 | "They never did." | | 5 | "She swore and kept running." | | 6 | "She clocked the doorway, logged" | | 7 | "He wasn't running to the" | | 8 | "He was running to ground." | | 9 | "She sucked air that tasted" | | 10 | "He had wind to spare." | | 11 | "She dug deeper." | | 12 | "Her father had been a" | | 13 | "She let Herrera think he" | | 14 | "He wouldn't cross it." | | 15 | "Her nose wrinkled." | | 16 | "She filed it." | | 17 | "She slowed, chest heaving, rain" | | 18 | "She turned on the spot" | | 19 | "Her torch beam found the" |
| | ratio | 0.32 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 103 | | matches | | 0 | "The bastard was fast, Quinn" | | 1 | "Her boots hammered the wet" | | 2 | "He glanced back once." | | 3 | "She'd memorised that face from" | | 4 | "He didn't stop." | | 5 | "They never did." | | 6 | "Quinn thumbed her radio, rain" | | 7 | "Static chewed the reply into" | | 8 | "She swore and kept running." | | 9 | "Herrera cut left onto Bateman" | | 10 | "Quinn vaulted over the man's" | | 11 | "The rain came harder." | | 12 | "Sheets of it now, hissing" | | 13 | "She clocked the doorway, logged" | | 14 | "Herrera hadn't gone in there," | | 15 | "He wasn't running to the" | | 16 | "He was running to ground." | | 17 | "She sucked air that tasted" | | 18 | "He had wind to spare." | | 19 | "A bloody paramedic's lungs." |
| | ratio | 0.67 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 4 | | matches | | 0 | "Herrera cut left onto Bateman Street, shoulder-checking a drunk tourist who went down in a splash of kerb water and complaint." | | 1 | "Found, at their base, a flagstone that sat a quarter-inch proud of its neighbours." | | 2 | "Morris, who'd laughed at superstition, who'd kept his warrant card in his breast pocket and a picture of his kids in his wallet, who'd told her on a rainy Tuesd…" | | 3 | "A sullen orange glow, the kind you got from oil lamps, throwing long shadows up a tiled curve that could only be a Tube tunnel." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.067 | | leniency | 0.133 | | rawRatio | 0 | | effectiveRatio | 0 | |