| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1342 | | 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) | |
| 59.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1342 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "shattered" | | 1 | "footfall" | | 2 | "jaw clenched" | | 3 | "electric" | | 4 | "footsteps" | | 5 | "pulse" | | 6 | "echo" | | 7 | "resolved" | | 8 | "raced" | | 9 | "gloom" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 153 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 153 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 153 | | 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 | 1342 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.76% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1340 | | uniqueNames | 14 | | maxNameDensity | 1.04 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Harlow | 1 | | Quinn | 14 | | Met | 1 | | Herrera | 11 | | Saint | 1 | | Christopher | 1 | | Morris | 6 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" | | 6 | "Market" | | 7 | "Raven" |
| | places | | | globalScore | 0.978 | | windowScore | 1 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 5 | | matches | | 0 | "felt like the break" | | 1 | "looked like a trap" | | 2 | "looked like teeth on a string" | | 3 | "quite conversation" | | 4 | "felt like it belonged to the world abov" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1342 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 153 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 49.7 | | std | 28.75 | | cv | 0.579 | | sampleLengths | | 0 | 83 | | 1 | 2 | | 2 | 87 | | 3 | 17 | | 4 | 67 | | 5 | 105 | | 6 | 55 | | 7 | 45 | | 8 | 91 | | 9 | 51 | | 10 | 39 | | 11 | 62 | | 12 | 70 | | 13 | 73 | | 14 | 32 | | 15 | 5 | | 16 | 84 | | 17 | 24 | | 18 | 69 | | 19 | 58 | | 20 | 9 | | 21 | 3 | | 22 | 68 | | 23 | 56 | | 24 | 31 | | 25 | 33 | | 26 | 23 |
| |
| 96.09% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 153 | | matches | | 0 | "were connected" | | 1 | "been peeled" | | 2 | "were covered" | | 3 | "being seen" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 246 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 153 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1353 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 52 | | adverbRatio | 0.038433111603843315 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005173688100517369 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 153 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 153 | | mean | 8.77 | | std | 6.48 | | cv | 0.739 | | sampleLengths | | 0 | 19 | | 1 | 24 | | 2 | 22 | | 3 | 8 | | 4 | 3 | | 5 | 7 | | 6 | 2 | | 7 | 7 | | 8 | 24 | | 9 | 23 | | 10 | 2 | | 11 | 3 | | 12 | 20 | | 13 | 8 | | 14 | 17 | | 15 | 3 | | 16 | 19 | | 17 | 11 | | 18 | 8 | | 19 | 3 | | 20 | 17 | | 21 | 6 | | 22 | 9 | | 23 | 11 | | 24 | 19 | | 25 | 10 | | 26 | 9 | | 27 | 2 | | 28 | 7 | | 29 | 16 | | 30 | 6 | | 31 | 2 | | 32 | 4 | | 33 | 10 | | 34 | 9 | | 35 | 7 | | 36 | 2 | | 37 | 4 | | 38 | 10 | | 39 | 7 | | 40 | 12 | | 41 | 4 | | 42 | 19 | | 43 | 2 | | 44 | 4 | | 45 | 9 | | 46 | 3 | | 47 | 8 | | 48 | 15 | | 49 | 5 |
| |
| 75.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.47058823529411764 | | totalSentences | 153 | | uniqueOpeners | 72 | |
| 76.34% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 131 | | matches | | 0 | "Closely cropped salt-and-pepper hair plastered" | | 1 | "Always the clique." | | 2 | "Then she moved." |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 131 | | matches | | 0 | "He was fast for a" | | 1 | "She’d been circling them for" | | 2 | "Her voice cut through the" | | 3 | "He glanced back once, warm" | | 4 | "Her lungs burned." | | 5 | "She kept her bearing, kept" | | 6 | "He vaulted a low wall" | | 7 | "She took it in one" | | 8 | "She could see the scar" | | 9 | "She hit the opposite pavement," | | 10 | "Her coat was soaked through," | | 11 | "She shrugged it off without" | | 12 | "He was heading north, toward" | | 13 | "She’d walked every inch of" | | 14 | "She could hear him now," | | 15 | "She reached for her radio," | | 16 | "He would be gone in" | | 17 | "He burst onto a wider" | | 18 | "She’d seen the reports." | | 19 | "She reached the top of" |
| | ratio | 0.282 | |
| 89.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 131 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn ran hard," | | 2 | "Brown eyes locked on the" | | 3 | "He was fast for a" | | 4 | "Quinn had been on him" | | 5 | "She’d been circling them for" | | 6 | "Things like the night DS" | | 7 | "Her voice cut through the" | | 8 | "Herrera didn’t stop." | | 9 | "He glanced back once, warm" | | 10 | "Quinn followed, shoulder clipping a" | | 11 | "The alley stank of wet" | | 12 | "Her lungs burned." | | 13 | "She kept her bearing, kept" | | 14 | "He vaulted a low wall" | | 15 | "She took it in one" | | 16 | "Neon from a kebab shop" | | 17 | "Herrera sprinted between them, knocking" | | 18 | "Quinn was three seconds behind," | | 19 | "She could see the scar" |
| | ratio | 0.74 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 131 | | matches | (empty) | | ratio | 0 | |
| 64.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 6 | | matches | | 0 | "The rain came down in sheets, turning the Camden pavement into a black mirror that shattered under every footfall." | | 1 | "Forty-one years old and she still moved with the same military precision that had carried her through eighteen years on the Met." | | 2 | "She’d been circling them for months, certain they were dirty, certain they were connected to things that didn’t add up." | | 3 | "The walls of the old station were covered in things that didn’t belong: maps that shifted when you looked at them, photographs that weren’t black-and-white anym…" | | 4 | "Herrera finished his transaction, slipped a small packet into his jacket, and started moving deeper, toward a tunnel that branched off the main platform." | | 5 | "She kept her eyes on the medallion, the scar, the way he moved like a man who knew these tunnels." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |