| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.367 | | leniency | 0.735 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1082 | | 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) | |
| 53.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1082 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "etched" | | 1 | "standard" | | 2 | "navigate" | | 3 | "traced" | | 4 | "beacon" | | 5 | "gloom" | | 6 | "glistening" | | 7 | "echoed" | | 8 | "vibrated" | | 9 | "fluttered" |
| |
| 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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1082 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 610 | | uniqueNames | 10 | | maxNameDensity | 3.61 | | worstName | "Harlow" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Harlow" | | discoveredNames | | Victorian | 1 | | Harlow | 22 | | Quinn | 1 | | Eva | 8 | | Kowalski | 1 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Shade | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Camden" | | 5 | "Market" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | 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 | 1082 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 21.22 | | std | 15.6 | | cv | 0.735 | | sampleLengths | | 0 | 10 | | 1 | 47 | | 2 | 4 | | 3 | 37 | | 4 | 3 | | 5 | 4 | | 6 | 37 | | 7 | 4 | | 8 | 21 | | 9 | 65 | | 10 | 41 | | 11 | 30 | | 12 | 5 | | 13 | 32 | | 14 | 5 | | 15 | 22 | | 16 | 14 | | 17 | 4 | | 18 | 46 | | 19 | 11 | | 20 | 9 | | 21 | 40 | | 22 | 8 | | 23 | 39 | | 24 | 29 | | 25 | 4 | | 26 | 10 | | 27 | 21 | | 28 | 9 | | 29 | 36 | | 30 | 6 | | 31 | 46 | | 32 | 11 | | 33 | 2 | | 34 | 27 | | 35 | 21 | | 36 | 36 | | 37 | 6 | | 38 | 9 | | 39 | 48 | | 40 | 3 | | 41 | 16 | | 42 | 27 | | 43 | 11 | | 44 | 28 | | 45 | 36 | | 46 | 14 | | 47 | 33 | | 48 | 20 | | 49 | 5 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 83 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 99 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 114 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 613 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy leather-bound tome" |
| | adverbCount | 8 | | adverbRatio | 0.013050570962479609 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004893964110929853 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 9.49 | | std | 6.87 | | cv | 0.724 | | sampleLengths | | 0 | 10 | | 1 | 7 | | 2 | 12 | | 3 | 10 | | 4 | 7 | | 5 | 7 | | 6 | 4 | | 7 | 4 | | 8 | 11 | | 9 | 19 | | 10 | 7 | | 11 | 3 | | 12 | 4 | | 13 | 4 | | 14 | 7 | | 15 | 26 | | 16 | 4 | | 17 | 12 | | 18 | 9 | | 19 | 12 | | 20 | 10 | | 21 | 16 | | 22 | 16 | | 23 | 11 | | 24 | 8 | | 25 | 9 | | 26 | 10 | | 27 | 7 | | 28 | 7 | | 29 | 5 | | 30 | 11 | | 31 | 14 | | 32 | 5 | | 33 | 32 | | 34 | 5 | | 35 | 22 | | 36 | 9 | | 37 | 5 | | 38 | 4 | | 39 | 3 | | 40 | 7 | | 41 | 15 | | 42 | 6 | | 43 | 1 | | 44 | 5 | | 45 | 5 | | 46 | 4 | | 47 | 6 | | 48 | 5 | | 49 | 9 |
| |
| 46.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.3684210526315789 | | totalSentences | 114 | | uniqueOpeners | 42 | |
| 49.02% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 68 | | matches | | 0 | "Just a Shade artisan, and" |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 68 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She checked the worn leather" | | 2 | "She adjusted her round glasses" | | 3 | "She examined the angle of" | | 4 | "She wiped her gloved hands" | | 5 | "She approached the victim and" | | 6 | "She leaned in, her freckled" | | 7 | "She patted down the victim's" | | 8 | "She produced a leather wallet," | | 9 | "She checked the inner breast" | | 10 | "She examined the victim's shoes." | | 11 | "She traced the lines of" | | 12 | "She chewed her lower lip." | | 13 | "She noticed a scuff mark" | | 14 | "She touched the concrete near" | | 15 | "It pointed straight at the" | | 16 | "She took a step back," | | 17 | "They held a long curved" | | 18 | "She levelled the barrel at" | | 19 | "Her stance was wide, her" |
| | ratio | 0.294 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 68 | | matches | | 0 | "Blood pooled in the grooves" | | 1 | "Detective Harlow Quinn knelt beside" | | 2 | "Her closely cropped salt-and-pepper hair" | | 3 | "She checked the worn leather" | | 4 | "The second hand swept past" | | 5 | "Eva Kowalski tucked a curly" | | 6 | "She adjusted her round glasses" | | 7 | "Harlow tilted her head." | | 8 | "She examined the angle of" | | 9 | "Harlow stood up" | | 10 | "She wiped her gloved hands" | | 11 | "The abandoned Tube station beneath" | | 12 | "The old tracks were long" | | 13 | "The flickering emergency lights cast" | | 14 | "Harlow stepped over a puddle" | | 15 | "She approached the victim and" | | 16 | "The stiff fingers clutched a" | | 17 | "The face was etched with" | | 18 | "A patina of verdigris covered" | | 19 | "She leaned in, her freckled" |
| | ratio | 0.926 | |
| 73.53% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 68 | | matches | | 0 | "Now, the market had moved" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 88.78% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 3 | | fancyTags | | 0 | "Harlow noted (note)" | | 1 | "Eva whispered (whisper)" | | 2 | "Harlow shouted (shout)" |
| | dialogueSentences | 49 | | tagDensity | 0.224 | | leniency | 0.449 | | rawRatio | 0.273 | | effectiveRatio | 0.122 | |