| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.034 | | leniency | 0.069 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 966 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 966 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "throbbed" | | 1 | "silk" | | 2 | "etched" | | 3 | "scanning" | | 4 | "quivered" | | 5 | "pulsed" | | 6 | "footsteps" | | 7 | "echoed" | | 8 | "traced" | | 9 | "chilled" | | 10 | "echo" | | 11 | "pulse" | | 12 | "quickened" | | 13 | "familiar" | | 14 | "jaw clenched" | | 15 | "flicker" | | 16 | "maw" | | 17 | "charged" |
| |
| 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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 954 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 696 | | uniqueNames | 12 | | maxNameDensity | 2.59 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 18 | | Camden | 2 | | Shade | 2 | | Patel | 7 | | Kowalski | 1 | | Oxford | 1 | | Morris | 3 | | Thames | 1 | | Eva | 12 | | Blood | 3 | | Needle | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Shade" | | 3 | "Patel" | | 4 | "Kowalski" | | 5 | "Morris" | | 6 | "Eva" | | 7 | "Blood" | | 8 | "Needle" |
| | places | | | globalScore | 0.207 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 95.18% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.048 | | wordCount | 954 | | matches | | 0 | "not north, but dead at the artisan's own corpse" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 13.63 | | std | 12.9 | | cv | 0.947 | | sampleLengths | | 0 | 52 | | 1 | 61 | | 2 | 11 | | 3 | 11 | | 4 | 20 | | 5 | 13 | | 6 | 5 | | 7 | 15 | | 8 | 47 | | 9 | 36 | | 10 | 11 | | 11 | 2 | | 12 | 13 | | 13 | 8 | | 14 | 3 | | 15 | 9 | | 16 | 13 | | 17 | 4 | | 18 | 5 | | 19 | 32 | | 20 | 11 | | 21 | 7 | | 22 | 8 | | 23 | 21 | | 24 | 18 | | 25 | 2 | | 26 | 7 | | 27 | 9 | | 28 | 39 | | 29 | 9 | | 30 | 9 | | 31 | 5 | | 32 | 7 | | 33 | 20 | | 34 | 4 | | 35 | 47 | | 36 | 6 | | 37 | 12 | | 38 | 3 | | 39 | 12 | | 40 | 37 | | 41 | 23 | | 42 | 4 | | 43 | 9 | | 44 | 13 | | 45 | 3 | | 46 | 3 | | 47 | 7 | | 48 | 6 | | 49 | 4 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 155 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 133 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 706 | | adjectiveStacks | 1 | | stackExamples | | 0 | "crushed under heavy boots," |
| | adverbCount | 9 | | adverbRatio | 0.012747875354107648 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.00424929178470255 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 7.17 | | std | 4.64 | | cv | 0.647 | | sampleLengths | | 0 | 16 | | 1 | 17 | | 2 | 19 | | 3 | 16 | | 4 | 20 | | 5 | 7 | | 6 | 18 | | 7 | 11 | | 8 | 11 | | 9 | 8 | | 10 | 12 | | 11 | 13 | | 12 | 5 | | 13 | 15 | | 14 | 11 | | 15 | 9 | | 16 | 6 | | 17 | 21 | | 18 | 6 | | 19 | 14 | | 20 | 16 | | 21 | 11 | | 22 | 2 | | 23 | 13 | | 24 | 8 | | 25 | 3 | | 26 | 9 | | 27 | 13 | | 28 | 4 | | 29 | 5 | | 30 | 4 | | 31 | 2 | | 32 | 4 | | 33 | 9 | | 34 | 4 | | 35 | 4 | | 36 | 1 | | 37 | 4 | | 38 | 11 | | 39 | 7 | | 40 | 8 | | 41 | 21 | | 42 | 5 | | 43 | 1 | | 44 | 3 | | 45 | 5 | | 46 | 4 | | 47 | 2 | | 48 | 7 | | 49 | 9 |
| |
| 86.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5263157894736842 | | totalSentences | 133 | | uniqueOpeners | 70 | |
| 36.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 91 | | matches | | | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 91 | | matches | | 0 | "She crouched beside the body" | | 1 | "She pried the artisan's fingers" | | 2 | "She sniffed the pool." | | 3 | "He lumbered off, muttering into" | | 4 | "She uncorked it, silvery motes" | | 5 | "She followed, brass humming in" | | 6 | "She thrust the compass at" | | 7 | "He snatched it, lumbered five" |
| | ratio | 0.088 | |
| 53.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 91 | | matches | | 0 | "Detective Harlow Quinn shoved the" | | 1 | "The bone token throbbed warm" | | 2 | "Torchlight danced across stalls draped" | | 3 | "She crouched beside the body" | | 4 | "The victim, a wiry Shade" | | 5 | "Verdigris patina flaked onto his" | | 6 | "Blood pooled from a single" | | 7 | "DS Patel straightened from snapping" | | 8 | "Quinn's brown eyes narrowed, scanning" | | 9 | "The needle quivered, not north," | | 10 | "Patel shrugged, holstering his camera." | | 11 | "She pried the artisan's fingers" | | 12 | "Sigils pulsed faint under her" | | 13 | "Salt lines crushed under heavy" | | 14 | "Footsteps echoed from a shadowed" | | 15 | "Eva Kowalski emerged, round glasses" | | 16 | "Quinn shot him a glance," | | 17 | "Eva knelt opposite, green eyes" | | 18 | "Patel rolled his eyes." | | 19 | "Quinn traced the gash." |
| | ratio | 0.813 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 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 | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn muttered (mutter)" |
| | dialogueSentences | 29 | | tagDensity | 0.034 | | leniency | 0.069 | | rawRatio | 1 | | effectiveRatio | 0.069 | |