| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1127 | | 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) | |
| 55.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1127 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "chill" | | 2 | "glint" | | 3 | "unreadable" | | 4 | "gloom" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "shimmered" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 93 | | matches | (empty) | |
| 81.41% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1127 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 1116 | | uniqueNames | 15 | | maxNameDensity | 0.72 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | London | 1 | | Harlow | 8 | | Quinn | 2 | | Raven | 2 | | Nest | 2 | | Soho | 1 | | Tomás | 1 | | Herrera | 1 | | Morris | 4 | | Tube | 1 | | Camden | 1 | | Saint | 1 | | Christopher | 1 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Morris" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like she was finally close to the" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1127 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 95 | | matches | | |
| 58.04% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 66.29 | | std | 23.41 | | cv | 0.353 | | sampleLengths | | 0 | 95 | | 1 | 113 | | 2 | 73 | | 3 | 85 | | 4 | 68 | | 5 | 12 | | 6 | 66 | | 7 | 72 | | 8 | 77 | | 9 | 71 | | 10 | 68 | | 11 | 52 | | 12 | 21 | | 13 | 62 | | 14 | 58 | | 15 | 82 | | 16 | 52 |
| |
| 97.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 93 | | matches | | 0 | "been redacted" | | 1 | "was made" |
| |
| 88.27% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 179 | | matches | | 0 | "was hunting" | | 1 | "was leading" | | 2 | "was waiting" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 95 | | ratio | 0.011 | | matches | | 0 | "The darkness ahead wasn't empty; it was waiting." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1117 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.017905102954341987 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004476275738585497 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 11.86 | | std | 7.58 | | cv | 0.639 | | sampleLengths | | 0 | 29 | | 1 | 21 | | 2 | 19 | | 3 | 26 | | 4 | 13 | | 5 | 3 | | 6 | 9 | | 7 | 21 | | 8 | 31 | | 9 | 15 | | 10 | 21 | | 11 | 20 | | 12 | 9 | | 13 | 19 | | 14 | 5 | | 15 | 11 | | 16 | 2 | | 17 | 7 | | 18 | 12 | | 19 | 12 | | 20 | 6 | | 21 | 3 | | 22 | 18 | | 23 | 34 | | 24 | 8 | | 25 | 15 | | 26 | 5 | | 27 | 7 | | 28 | 15 | | 29 | 18 | | 30 | 10 | | 31 | 2 | | 32 | 3 | | 33 | 16 | | 34 | 4 | | 35 | 25 | | 36 | 18 | | 37 | 9 | | 38 | 5 | | 39 | 6 | | 40 | 11 | | 41 | 9 | | 42 | 5 | | 43 | 20 | | 44 | 7 | | 45 | 7 | | 46 | 4 | | 47 | 5 | | 48 | 4 | | 49 | 15 |
| |
| 33.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2736842105263158 | | totalSentences | 95 | | uniqueOpeners | 26 | |
| 36.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 91 | | matches | | | ratio | 0.011 | |
| 30.99% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 91 | | matches | | 0 | "Her boots struck the wet" | | 1 | "She vaulted it, her breath" | | 2 | "She had been tracking him" | | 3 | "She knew his name was" | | 4 | "She rounded a corner onto" | | 5 | "She caught a glimpse of" | | 6 | "She had seen the look" | | 7 | "She had spent the last" | | 8 | "It was a concrete throat," | | 9 | "He didn't try to open" | | 10 | "He stepped into the shadows" | | 11 | "she shouted, her voice cutting" | | 12 | "He turned, just enough for" | | 13 | "She saw the scar running" | | 14 | "He looked at her, his" | | 15 | "She reached the ticket booth." | | 16 | "She stepped closer, her boots" | | 17 | "It was stale, smelling of" | | 18 | "She could hear him moving" | | 19 | "He was leading her somewhere." |
| | ratio | 0.473 | |
| 36.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 91 | | matches | | 0 | "The rain did not fall" | | 1 | "Detective Harlow Quinn pulled her" | | 2 | "Her boots struck the wet" | | 3 | "Harlow didn't hesitate." | | 4 | "She vaulted it, her breath" | | 5 | "She had been tracking him" | | 6 | "The bar’s distinctive green neon" | | 7 | "She knew his name was" | | 8 | "The file didn't mention the" | | 9 | "She rounded a corner onto" | | 10 | "The streetlights here were fewer," | | 11 | "She caught a glimpse of" | | 12 | "Harlow checked her left wrist." | | 13 | "The worn leather strap of" | | 14 | "Time was a luxury she" | | 15 | "The official report said accidental" | | 16 | "Harlow knew better." | | 17 | "She had seen the look" | | 18 | "She had spent the last" | | 19 | "The suspect reached the entrance" |
| | ratio | 0.846 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 91 | | matches | | 0 | "If she turned back, she" | | 1 | "If she went in, she" | | 2 | "If this was a trap," | | 3 | "If this was a door," |
| | ratio | 0.044 | |
| 68.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 5 | | matches | | 0 | "The rain did not fall in London so much as it hung in the air, a cold, wet shroud that turned the streetlamps into blurred halos of sodium orange." | | 1 | "Her boots struck the wet pavement with a rhythmic, military precision that belied the exhaustion dragging at her shoulders." | | 2 | "It was stale, smelling of ozone and old copper, a scent that triggered a visceral memory of Morris's final case." | | 3 | "It was a threshold, a line between the world of law and the world of things that hunted in the dark." | | 4 | "The tunnel stretched ahead, lined with tiles that shimmered with a wet, unnatural sheen." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "she shouted, her voice cutting through the drumming rain" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 1 | | effectiveRatio | 0.667 | |