| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1426 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "angrily" | | 1 | "very" | | 2 | "quickly" | | 3 | "completely" |
| |
| 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) | |
| 36.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1426 | | totalAiIsms | 18 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "rhythmic" | | 1 | "pumping" | | 2 | "weight" | | 3 | "scanning" | | 4 | "gloom" | | 5 | "constructed" | | 6 | "stomach" | | 7 | "tapestry" | | 8 | "scanned" | | 9 | "glint" | | 10 | "silk" | | 11 | "standard" | | 12 | "porcelain" | | 13 | "cacophony" | | 14 | "jaw clenched" | | 15 | "silence" | | 16 | "comforting" | | 17 | "familiar" |
| |
| 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 | 105 | | matches | (empty) | |
| 61.22% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1422 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1415 | | uniqueNames | 16 | | maxNameDensity | 1.06 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 15 | | London | 2 | | Morris | 3 | | Raven | 1 | | Nest | 1 | | Underground | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Herrera | 2 | | Saint | 1 | | Christopher | 1 | | Tomás | 6 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Raven" | | 4 | "Underground" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Veil" | | 3 | "Market" |
| | globalScore | 0.97 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 6 | | matches | | 0 | "looked like they knew too much" | | 1 | "symbols that seemed to shift when she looked at them sideways" | | 2 | "blackness that seemed to swallow the rain before it could hit the rungs" | | 3 | "looked like bleached bone lined a wide th" | | 4 | "skin that seemed to breathe" | | 5 | "felt like a violation of natural law" |
| |
| 59.35% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.406 | | wordCount | 1422 | | matches | | 0 | "not to draw, but to reassure herself of its weight" | | 1 | "not of sewage or damp earth, but of ozone, burnt sugar" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 52.67 | | std | 27.91 | | cv | 0.53 | | sampleLengths | | 0 | 89 | | 1 | 21 | | 2 | 100 | | 3 | 64 | | 4 | 87 | | 5 | 68 | | 6 | 6 | | 7 | 69 | | 8 | 40 | | 9 | 48 | | 10 | 18 | | 11 | 62 | | 12 | 5 | | 13 | 84 | | 14 | 65 | | 15 | 2 | | 16 | 90 | | 17 | 73 | | 18 | 41 | | 19 | 65 | | 20 | 64 | | 21 | 55 | | 22 | 65 | | 23 | 17 | | 24 | 46 | | 25 | 17 | | 26 | 61 |
| |
| 98.58% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 105 | | matches | | 0 | "were paid" | | 1 | "was rusted" |
| |
| 61.11% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 240 | | matches | | 0 | "wasn't heading" | | 1 | "was running" | | 2 | "was looking" | | 3 | "was heading" | | 4 | "was protecting" |
| |
| 61.22% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 105 | | ratio | 0.029 | | matches | | 0 | "Detective Harlow Quinn didn't feel the cold biting through her trench coat; she only felt the burn in her lungs and the rhythmic thud of her boots against the wet asphalt." | | 1 | "They didn't land on her—he was too distracted, too entrenched in this world—but she saw the scar running along his left forearm as he adjusted his sleeve." | | 2 | "He produced a token from his pocket—a small, carved piece of bone—and pressed it into a slot in the doorframe." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 527 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.026565464895635674 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.013282732447817837 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 13.54 | | std | 7.1 | | cv | 0.524 | | sampleLengths | | 0 | 15 | | 1 | 31 | | 2 | 18 | | 3 | 25 | | 4 | 21 | | 5 | 4 | | 6 | 21 | | 7 | 19 | | 8 | 22 | | 9 | 25 | | 10 | 9 | | 11 | 17 | | 12 | 13 | | 13 | 19 | | 14 | 15 | | 15 | 5 | | 16 | 4 | | 17 | 19 | | 18 | 26 | | 19 | 9 | | 20 | 24 | | 21 | 12 | | 22 | 17 | | 23 | 13 | | 24 | 26 | | 25 | 6 | | 26 | 7 | | 27 | 19 | | 28 | 7 | | 29 | 9 | | 30 | 27 | | 31 | 9 | | 32 | 7 | | 33 | 9 | | 34 | 5 | | 35 | 10 | | 36 | 4 | | 37 | 16 | | 38 | 4 | | 39 | 24 | | 40 | 8 | | 41 | 10 | | 42 | 23 | | 43 | 11 | | 44 | 14 | | 45 | 14 | | 46 | 5 | | 47 | 16 | | 48 | 17 | | 49 | 23 |
| |
| 33.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.3047619047619048 | | totalSentences | 105 | | uniqueOpeners | 32 | |
| 64.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 103 | | matches | | 0 | "Dimly lit, walls plastered with" | | 1 | "Just her, her gun, and" |
| | ratio | 0.019 | |
| 68.54% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 103 | | matches | | 0 | "Her bearing was military precision" | | 1 | "She kept her eyes locked" | | 2 | "she shouted, the command lost" | | 3 | "They moved with an unnatural" | | 4 | "She wouldn't let another one" | | 5 | "She took the corner hard," | | 6 | "It buzzed angrily against the" | | 7 | "She knew the place." | | 8 | "It was a known haunt" | | 9 | "They veered left, ducking under" | | 10 | "Her instinct, the one honed" | | 11 | "She had waited then, too." | | 12 | "She had followed protocol while" | | 13 | "She checked her watch." | | 14 | "It was past midnight." | | 15 | "She gripped the cold iron" | | 16 | "She was a human woman" | | 17 | "She scanned the crowd, her" | | 18 | "He wasn't the suspect she" | | 19 | "He stood near a vendor" |
| | ratio | 0.379 | |
| 37.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 103 | | matches | | 0 | "Detective Harlow Quinn didn't feel" | | 1 | "Her bearing was military precision" | | 2 | "She kept her eyes locked" | | 3 | "she shouted, the command lost" | | 4 | "The suspect didn't slow." | | 5 | "They moved with an unnatural" | | 6 | "Quinn's hand drifted to the" | | 7 | "She wouldn't let another one" | | 8 | "She took the corner hard," | | 9 | "The alley emptied onto a" | | 10 | "The suspect was there, twenty" | | 11 | "It buzzed angrily against the" | | 12 | "Quinn picked up the pace." | | 13 | "She knew the place." | | 14 | "It was a known haunt" | | 15 | "They veered left, ducking under" | | 16 | "Quinn skidded to a halt," | | 17 | "The grate was rusted, marked" | | 18 | "The suspect paused, glanced back" | | 19 | "A gust of air rushed" |
| | ratio | 0.845 | |
| 97.09% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 103 | | matches | | 0 | "If the rumors were true," | | 1 | "Before her stretched a cavernous" |
| | ratio | 0.019 | |
| 69.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 7 | | matches | | 0 | "They moved with an unnatural grace, skipping over puddles that should have soaked their trousers, dodging overflowing bins with prescient timing." | | 1 | "Three years ago, her partner had vanished into a case that made no sense on paper, a case with supernatural origins she still couldn't articulate." | | 2 | "Dimly lit, walls plastered with old maps and black-and-white photographs of people who looked like they knew too much." | | 3 | "Before her stretched a cavernous space that defied the geometry of the London Underground." | | 4 | "She fixed it with a glare that had silenced drunkards and hardened criminals alike, and the creature quickly looked away, muttering to its vendor." | | 5 | "She passed a stall selling banned alchemical substances, the jars labeled in scripts that hurt her eyes to read." | | 6 | "The resistance was immense, as if the door itself was alive and pushing back." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |