| 57.14% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "Her tone stopped just [just]" |
| | dialogueSentences | 14 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.25 | | effectiveRatio | 0.143 | |
| 81.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 542 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 35.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 542 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "tinged" | | 1 | "stark" | | 2 | "glinting" | | 3 | "etched" | | 4 | "furrowed" | | 5 | "chill" | | 6 | "glint" |
| |
| 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 | 39 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 39 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 49 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 543 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 1.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 371 | | uniqueNames | 7 | | maxNameDensity | 2.96 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 11 | | Quinn | 1 | | Camden | 1 | | Aurora | 2 | | Eva | 8 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Aurora" | | 3 | "Eva" | | 4 | "Kowalski" |
| | places | (empty) | | globalScore | 0.018 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | 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 | 543 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 49 | | matches | (empty) | |
| 57.13% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 28.58 | | std | 10 | | cv | 0.35 | | sampleLengths | | 0 | 53 | | 1 | 37 | | 2 | 26 | | 3 | 39 | | 4 | 42 | | 5 | 33 | | 6 | 14 | | 7 | 16 | | 8 | 15 | | 9 | 23 | | 10 | 36 | | 11 | 19 | | 12 | 23 | | 13 | 19 | | 14 | 32 | | 15 | 28 | | 16 | 26 | | 17 | 35 | | 18 | 27 |
| |
| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 39 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 59 | | matches | (empty) | |
| 84.55% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 49 | | ratio | 0.02 | | matches | | 0 | "Among the civilian investigators milling about, Harlow spotted her least favorite occult expert - Aurora's bookish childhood friend, Eva Kowalski." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 371 | | adjectiveStacks | 1 | | stackExamples | | 0 | "open nearby, mundane contents" |
| | adverbCount | 11 | | adverbRatio | 0.029649595687331536 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.01078167115902965 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 49 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 49 | | mean | 11.08 | | std | 5.44 | | cv | 0.491 | | sampleLengths | | 0 | 19 | | 1 | 15 | | 2 | 11 | | 3 | 8 | | 4 | 12 | | 5 | 11 | | 6 | 14 | | 7 | 4 | | 8 | 2 | | 9 | 20 | | 10 | 14 | | 11 | 7 | | 12 | 18 | | 13 | 6 | | 14 | 22 | | 15 | 14 | | 16 | 16 | | 17 | 17 | | 18 | 9 | | 19 | 5 | | 20 | 16 | | 21 | 15 | | 22 | 11 | | 23 | 12 | | 24 | 1 | | 25 | 11 | | 26 | 11 | | 27 | 9 | | 28 | 4 | | 29 | 10 | | 30 | 9 | | 31 | 8 | | 32 | 6 | | 33 | 5 | | 34 | 4 | | 35 | 4 | | 36 | 15 | | 37 | 12 | | 38 | 11 | | 39 | 9 | | 40 | 6 | | 41 | 22 | | 42 | 4 | | 43 | 22 | | 44 | 8 | | 45 | 8 | | 46 | 19 | | 47 | 11 | | 48 | 16 |
| |
| 91.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5714285714285714 | | totalSentences | 49 | | uniqueOpeners | 28 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 37 | | matches | | | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 4 | | totalSentences | 37 | | matches | | 0 | "She strode out with military" | | 1 | "Her tone stopped just short" | | 2 | "She swung around to face" | | 3 | "She turned back to Eva," |
| | ratio | 0.108 | |
| 54.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 30 | | totalSentences | 37 | | matches | | 0 | "The rattling lift shuddered open," | | 1 | "She strode out with military" | | 2 | "The air hung thick and" | | 3 | "Pigeon droppings spattered the ground" | | 4 | "Harlow surveyed the scene with" | | 5 | "Graffiti tags and drifts of" | | 6 | "The academic stood off to" | | 7 | "Freckles stood out against her" | | 8 | "Eva clutched her ever-present leather" | | 9 | "Harlow approached, her sharp jaw" | | 10 | "Her tone stopped just short" | | 11 | "Eva shifted, tucking a lock" | | 12 | "Harlow didn't bother hiding her" | | 13 | "The body lay beside the" | | 14 | "A backpack lay split open" | | 15 | "that metal cylinder glinting in" | | 16 | "Harlow pulled on a latex" | | 17 | "A small compass, its brass" | | 18 | "The needle spun erratically." | | 19 | "Eva crouched down beside her" |
| | ratio | 0.811 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 37 | | matches | | | ratio | 0.027 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |