| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 53 | | tagDensity | 0.283 | | leniency | 0.566 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1349 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 70.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1349 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "perfect" | | 3 | "etched" | | 4 | "weight" | | 5 | "silence" | | 6 | "database" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1349 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 46.91% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 776 | | uniqueNames | 4 | | maxNameDensity | 2.06 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Tube | 1 | | Harlow | 16 | | Iyer | 14 | | Camden | 1 |
| | persons | | | places | (empty) | | globalScore | 0.469 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1349 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 24.09 | | std | 18.48 | | cv | 0.767 | | sampleLengths | | 0 | 52 | | 1 | 3 | | 2 | 44 | | 3 | 40 | | 4 | 20 | | 5 | 45 | | 6 | 61 | | 7 | 3 | | 8 | 11 | | 9 | 12 | | 10 | 17 | | 11 | 57 | | 12 | 1 | | 13 | 4 | | 14 | 30 | | 15 | 34 | | 16 | 2 | | 17 | 3 | | 18 | 48 | | 19 | 7 | | 20 | 12 | | 21 | 30 | | 22 | 40 | | 23 | 38 | | 24 | 2 | | 25 | 46 | | 26 | 6 | | 27 | 14 | | 28 | 37 | | 29 | 7 | | 30 | 36 | | 31 | 14 | | 32 | 32 | | 33 | 45 | | 34 | 1 | | 35 | 26 | | 36 | 16 | | 37 | 1 | | 38 | 44 | | 39 | 9 | | 40 | 39 | | 41 | 5 | | 42 | 19 | | 43 | 51 | | 44 | 2 | | 45 | 47 | | 46 | 8 | | 47 | 28 | | 48 | 67 | | 49 | 9 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 93 | | matches | | |
| 52.94% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 136 | | matches | | 0 | "was documenting" | | 1 | "was studying" | | 2 | "was preparing" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 132 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 590 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.013559322033898305 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.001694915254237288 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 10.22 | | std | 7.57 | | cv | 0.741 | | sampleLengths | | 0 | 11 | | 1 | 4 | | 2 | 24 | | 3 | 13 | | 4 | 3 | | 5 | 8 | | 6 | 18 | | 7 | 4 | | 8 | 1 | | 9 | 13 | | 10 | 15 | | 11 | 10 | | 12 | 15 | | 13 | 20 | | 14 | 21 | | 15 | 24 | | 16 | 8 | | 17 | 14 | | 18 | 9 | | 19 | 3 | | 20 | 2 | | 21 | 15 | | 22 | 10 | | 23 | 3 | | 24 | 11 | | 25 | 8 | | 26 | 4 | | 27 | 2 | | 28 | 15 | | 29 | 10 | | 30 | 36 | | 31 | 11 | | 32 | 1 | | 33 | 4 | | 34 | 3 | | 35 | 27 | | 36 | 2 | | 37 | 11 | | 38 | 9 | | 39 | 12 | | 40 | 2 | | 41 | 3 | | 42 | 13 | | 43 | 35 | | 44 | 7 | | 45 | 12 | | 46 | 7 | | 47 | 23 | | 48 | 3 | | 49 | 6 |
| |
| 77.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.49242424242424243 | | totalSentences | 132 | | uniqueOpeners | 65 | |
| 80.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 83 | | matches | | 0 | "More symbols on the back," | | 1 | "Somewhere in that black, water" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 83 | | matches | | 0 | "She stepped over the police" | | 1 | "Her breath misted before her" | | 2 | "His eyes stared at the" | | 3 | "His skin had taken on" | | 4 | "She inched closer, her knees" | | 5 | "She stood, brushing dust from" | | 6 | "She pointed to the perfect" | | 7 | "She stepped closer, careful to" | | 8 | "She pulled a pen from" | | 9 | "She was studying the victim's" | | 10 | "It was something else." | | 11 | "She moved around the body," | | 12 | "She coaxed the object free" | | 13 | "She pocketed the compass, her" | | 14 | "She held up a hand" | | 15 | "She watched them work, her" | | 16 | "She looked at Iyer" | | 17 | "Her voice went flat" | | 18 | "She met Iyer's gaze" | | 19 | "She resisted the urge to" |
| | ratio | 0.289 | |
| 32.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 83 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "Something Harlow couldn't name." | | 2 | "She stepped over the police" | | 3 | "Water dripped somewhere in the" | | 4 | "DC Iyer's voice echoed off" | | 5 | "Harlow followed the sound past" | | 6 | "The air grew thicker." | | 7 | "Her breath misted before her" | | 8 | "Iyer stood near the tracks," | | 9 | "A third photographed the surrounding" | | 10 | "Harlow joined Iyer at the" | | 11 | "Iyer consulted his notebook" | | 12 | "Harlow crouched at the edge" | | 13 | "The victim lay on his" | | 14 | "His eyes stared at the" | | 15 | "His skin had taken on" | | 16 | "She inched closer, her knees" | | 17 | "Harlow tilted her head" | | 18 | "She stood, brushing dust from" | | 19 | "She pointed to the perfect" |
| | ratio | 0.855 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "Each one placed with deliberate precision, their edges touching, forming an unbroken ring around the impression of the dead man's shape." |
| |
| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow joined, her sharp jaw tight as she surveyed the scene" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 53 | | tagDensity | 0.019 | | leniency | 0.038 | | rawRatio | 0 | | effectiveRatio | 0 | |