| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.37 | | leniency | 0.741 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1032 | | 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) | |
| 17.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1032 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "jaw clenched" | | 1 | "gloom" | | 2 | "reminder" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "scanned" | | 6 | "etched" | | 7 | "echoes" | | 8 | "tension" | | 9 | "raced" | | 10 | "tracing" | | 11 | "silence" | | 12 | "shattered" | | 13 | "trembled" | | 14 | "aligned" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1017 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 5.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 794 | | uniqueNames | 17 | | maxNameDensity | 2.9 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 23 | | Camden | 1 | | Tube | 1 | | Morris | 4 | | Kowalski | 14 | | British | 1 | | Museum | 1 | | Ancient | 1 | | History | 1 | | Oxford | 2 | | Aurora | 1 | | Hale | 1 | | Veil | 2 | | Market | 1 | | Compass | 1 | | Oxford-polished | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Kowalski" | | 4 | "Hale" | | 5 | "Veil" | | 6 | "Market" | | 7 | "Compass" |
| | places | | 0 | "British" | | 1 | "Ancient" | | 2 | "Oxford" | | 3 | "Aurora" |
| | globalScore | 0.052 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 2.95 | | wordCount | 1017 | | matches | | 0 | "not pointing north but twitching toward a shadowed alcove" | | 1 | "not from the body, but to it" | | 2 | "not echoes, but a trap" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 29.91 | | std | 19.93 | | cv | 0.666 | | sampleLengths | | 0 | 70 | | 1 | 56 | | 2 | 24 | | 3 | 10 | | 4 | 62 | | 5 | 60 | | 6 | 17 | | 7 | 6 | | 8 | 14 | | 9 | 28 | | 10 | 62 | | 11 | 9 | | 12 | 43 | | 13 | 8 | | 14 | 37 | | 15 | 64 | | 16 | 14 | | 17 | 42 | | 18 | 54 | | 19 | 20 | | 20 | 46 | | 21 | 48 | | 22 | 14 | | 23 | 10 | | 24 | 36 | | 25 | 15 | | 26 | 12 | | 27 | 12 | | 28 | 38 | | 29 | 10 | | 30 | 28 | | 31 | 11 | | 32 | 8 | | 33 | 29 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 155 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 98 | | ratio | 0.102 | | matches | | 0 | "Flashlight beam cut through the gloom, illuminating stalls that shouldn't exist—twisted herbs dangling from hooks, vials of iridescent liquid glowing without power." | | 1 | "A soft shuffle, then a voice—female, clipped with academic edge." | | 2 | "In the centre, cordoned off with makeshift tape, lay the body—or what passed for one." | | 3 | "And, suspiciously, connected to Aurora Hale's circle—the very clique Quinn had been circling for months." | | 4 | "Quinn recognised it from Morris's old case files—a Veil Compass, attuned to supernatural rifts." | | 5 | "The market hummed with subdued energy—whispers of deals resuming, but tension thick as fog." | | 6 | "Supernatural origins she didn't understand—yet." | | 7 | "Quinn spotted it then—a faint trail of ash leading not from the body, but to it." | | 8 | "Not residue—accelerant?" | | 9 | "The patterns connected— not echoes, but a trap." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 806 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short, Oxford-polished sound." |
| | adverbCount | 19 | | adverbRatio | 0.02357320099255583 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.01240694789081886 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 10.38 | | std | 6.29 | | cv | 0.607 | | sampleLengths | | 0 | 26 | | 1 | 22 | | 2 | 22 | | 3 | 11 | | 4 | 16 | | 5 | 15 | | 6 | 14 | | 7 | 3 | | 8 | 11 | | 9 | 10 | | 10 | 10 | | 11 | 14 | | 12 | 17 | | 13 | 15 | | 14 | 11 | | 15 | 3 | | 16 | 2 | | 17 | 20 | | 18 | 13 | | 19 | 12 | | 20 | 15 | | 21 | 10 | | 22 | 7 | | 23 | 6 | | 24 | 7 | | 25 | 7 | | 26 | 16 | | 27 | 12 | | 28 | 6 | | 29 | 8 | | 30 | 9 | | 31 | 19 | | 32 | 6 | | 33 | 14 | | 34 | 6 | | 35 | 3 | | 36 | 6 | | 37 | 25 | | 38 | 12 | | 39 | 3 | | 40 | 5 | | 41 | 22 | | 42 | 15 | | 43 | 9 | | 44 | 13 | | 45 | 4 | | 46 | 19 | | 47 | 7 | | 48 | 12 | | 49 | 4 |
| |
| 91.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5510204081632653 | | totalSentences | 98 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 76 | | matches | | 0 | "she muttered to herself, military" | | 1 | "Her worn leather watch ticked" | | 2 | "Her green eyes narrowed at" | | 3 | "She adjusted her glasses, a" | | 4 | "She crouched, careful not to" | | 5 | "She pointed to faint markings" | | 6 | "She paced the perimeter, boots" | | 7 | "She knelt again, tracing the" | | 8 | "She picked up a shard" | | 9 | "She held fast, bone token" |
| | ratio | 0.132 | |
| 58.68% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 76 | | matches | | 0 | "Detective Harlow Quinn shoved through" | | 1 | "The bone token in her" | | 2 | "Flashlight beam cut through the" | | 3 | "she muttered to herself, military" | | 4 | "Her worn leather watch ticked" | | 5 | "Footsteps echoed ahead." | | 6 | "Quinn killed her light and" | | 7 | "A soft shuffle, then a" | | 8 | "Quinn emerged into a cavernous" | | 9 | "Lanterns hung from exposed pipes," | | 10 | "A woman knelt beside it," | | 11 | "Eva Kowalski, according to the" | | 12 | "Kowalski straightened, clutching a worn" | | 13 | "Her green eyes narrowed at" | | 14 | "Quinn ignored the jab, circling" | | 15 | "She adjusted her glasses, a" | | 16 | "Quinn's brown eyes scanned the" | | 17 | "The Veil Market, they'd whispered" | | 18 | "The body sprawled near a" | | 19 | "Verdigris patina gleamed under the" |
| | ratio | 0.803 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 76 | | matches | | 0 | "Now this: reports of a" | | 1 | "And, suspiciously, connected to Aurora" |
| | ratio | 0.026 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 3 | | matches | | 0 | "Flashlight beam cut through the gloom, illuminating stalls that shouldn't exist—twisted herbs dangling from hooks, vials of iridescent liquid glowing without po…" | | 1 | "Lanterns hung from exposed pipes, casting flickering shadows over merchants who scattered like rats at her approach." | | 2 | "She paced the perimeter, boots crunching over scattered herbs that smelled of earth and ozone." |
| |
| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "Kowalski shouted, but her voice carried triumph, not fear" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Kowalski shouted (shout)" | | 2 | "Quinn growled (growl)" |
| | dialogueSentences | 27 | | tagDensity | 0.185 | | leniency | 0.37 | | rawRatio | 0.6 | | effectiveRatio | 0.222 | |