| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.412 | | leniency | 0.824 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1040 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1040 | | totalAiIsms | 27 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | word | "skipped a beat" | | count | 1 |
| | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | |
| | highlights | | 0 | "etched" | | 1 | "furrowed" | | 2 | "reminder" | | 3 | "shattered" | | 4 | "skipped a beat" | | 5 | "pang" | | 6 | "raced" | | 7 | "depths" | | 8 | "silence" | | 9 | "scanning" | | 10 | "racing" | | 11 | "traced" | | 12 | "pulse" | | 13 | "vibrated" | | 14 | "trembled" | | 15 | "resolve" | | 16 | "determined" |
| |
| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 84 | | matches | | 0 | "a pang of guilt" | | 1 | "a surge of determination" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1038 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 73 | | wordCount | 785 | | uniqueNames | 11 | | maxNameDensity | 3.06 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 24 | | Quinn | 2 | | Tube | 1 | | Camden | 2 | | Veil | 7 | | Market | 7 | | Davies | 14 | | Multiple | 1 | | Kowalski | 1 | | Eva | 13 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Davies" | | 4 | "Multiple" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | 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 | 1038 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 78.57% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 25.32 | | std | 10.76 | | cv | 0.425 | | sampleLengths | | 0 | 17 | | 1 | 53 | | 2 | 32 | | 3 | 13 | | 4 | 31 | | 5 | 25 | | 6 | 29 | | 7 | 17 | | 8 | 48 | | 9 | 26 | | 10 | 12 | | 11 | 21 | | 12 | 24 | | 13 | 18 | | 14 | 32 | | 15 | 7 | | 16 | 24 | | 17 | 17 | | 18 | 14 | | 19 | 31 | | 20 | 17 | | 21 | 8 | | 22 | 18 | | 23 | 14 | | 24 | 41 | | 25 | 23 | | 26 | 16 | | 27 | 33 | | 28 | 11 | | 29 | 15 | | 30 | 32 | | 31 | 38 | | 32 | 35 | | 33 | 41 | | 34 | 31 | | 35 | 29 | | 36 | 21 | | 37 | 43 | | 38 | 23 | | 39 | 31 | | 40 | 27 |
| |
| 88.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 84 | | matches | | 0 | "was gone" | | 1 | "was etched" | | 2 | "were filled" | | 3 | "was determined" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 140 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 104 | | ratio | 0.01 | | matches | | 0 | "The air was thick with the scent of copper and something else—something ancient and unsettling." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 787 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, leather-bound journal." |
| | adverbCount | 13 | | adverbRatio | 0.016518424396442185 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008894536213468869 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 9.98 | | std | 4.76 | | cv | 0.477 | | sampleLengths | | 0 | 13 | | 1 | 4 | | 2 | 15 | | 3 | 23 | | 4 | 15 | | 5 | 17 | | 6 | 13 | | 7 | 2 | | 8 | 7 | | 9 | 6 | | 10 | 12 | | 11 | 19 | | 12 | 7 | | 13 | 13 | | 14 | 2 | | 15 | 3 | | 16 | 15 | | 17 | 14 | | 18 | 9 | | 19 | 8 | | 20 | 8 | | 21 | 13 | | 22 | 20 | | 23 | 5 | | 24 | 2 | | 25 | 7 | | 26 | 6 | | 27 | 8 | | 28 | 5 | | 29 | 9 | | 30 | 3 | | 31 | 9 | | 32 | 12 | | 33 | 12 | | 34 | 12 | | 35 | 9 | | 36 | 9 | | 37 | 14 | | 38 | 18 | | 39 | 3 | | 40 | 4 | | 41 | 2 | | 42 | 22 | | 43 | 3 | | 44 | 4 | | 45 | 9 | | 46 | 1 | | 47 | 8 | | 48 | 6 | | 49 | 16 |
| |
| 53.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3557692307692308 | | totalSentences | 104 | | uniqueOpeners | 37 | |
| 43.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 77 | | matches | | | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 77 | | matches | | 0 | "It was from an occult" | | 1 | "He pointed to the heavy" | | 2 | "Her worn leather watch caught" | | 3 | "Her round glasses lay shattered" | | 4 | "she whispered, her voice barely" | | 5 | "She felt a pang of" | | 6 | "She wouldn't have let her" | | 7 | "She felt a surge of" | | 8 | "She owed it to her" | | 9 | "She turned her attention back" | | 10 | "They were a mix of" | | 11 | "She traced her fingers over" | | 12 | "She closed the journal, her" | | 13 | "She would find out who" |
| | ratio | 0.182 | |
| 44.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 77 | | matches | | 0 | "Davies muttered, snapping photos of" | | 1 | "Detective Harlow Quinn crouched beside" | | 2 | "The victim lay sprawled in" | | 3 | "The air was thick with" | | 4 | "Harlow said, picking up a" | | 5 | "It was from an occult" | | 6 | "Davies glanced at her, his" | | 7 | "Harlow pointed to the nearest" | | 8 | "Davies chuckled, but his eyes" | | 9 | "He pointed to the heavy" | | 10 | "Harlow asked, standing up and" | | 11 | "Her worn leather watch caught" | | 12 | "Davies replied, his camera clicking" | | 13 | "Harlow turned her attention back" | | 14 | "A young woman, early twenties," | | 15 | "Her round glasses lay shattered" | | 16 | "Harlow's heart skipped a beat." | | 17 | "she whispered, her voice barely" | | 18 | "She felt a pang of" | | 19 | "Eva had been her best" |
| | ratio | 0.831 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 5 | | matches | | 0 | "Davies replied, his camera clicking away" | | 1 | "she whispered, her voice barely audible" | | 2 | "Harlow said, her voice steady" | | 3 | "Harlow said, her voice firm" | | 4 | "Harlow whispered, her eyes scanning the pages" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 6 | | fancyTags | | 0 | "Davies muttered (mutter)" | | 1 | "she whispered (whisper)" | | 2 | "Harlow murmured (murmur)" | | 3 | "Harlow explained (explain)" | | 4 | "she warned (warn)" | | 5 | "Harlow whispered (whisper)" |
| | dialogueSentences | 34 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0.5 | | effectiveRatio | 0.353 | |