| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1252 | | totalAiIsmAdverbs | 1 | | 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) | |
| 4.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1252 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "fluttered" | | 1 | "aligned" | | 2 | "silk" | | 3 | "weight" | | 4 | "shattered" | | 5 | "scanned" | | 6 | "traced" | | 7 | "vibrated" | | 8 | "resonated" | | 9 | "charm" | | 10 | "standard" | | 11 | "gloom" | | 12 | "etched" | | 13 | "echoed" | | 14 | "magnetic" | | 15 | "trembled" | | 16 | "calculated" | | 17 | "silence" | | 18 | "grave" |
| |
| 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 | 239 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 239 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 239 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1252 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 7 | | matches | | 0 | "A crowbar does not dissolve tile, Quinn said." | | 1 | "The victim did not fall, Quinn said." | | 2 | "Watch your step, Miller warned." | | 3 | "Check the pockets, Quinn said." | | 4 | "It is a smuggling ring, Quinn murmured." | | 5 | "Seize everything, Quinn ordered." | | 6 | "Burn the warrants later, Quinn replied." |
| |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1252 | | uniqueNames | 14 | | maxNameDensity | 2.32 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 29 | | Chalk | 1 | | Farm | 1 | | Miller | 10 | | Italian | 1 | | Morris | 2 | | Old | 1 | | English | 1 | | Umbra | 1 | | London | 1 | | Camden | 1 | | Crown | 1 | | Needle | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Morris" | | 4 | "Needle" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Old" | | 3 | "London" |
| | globalScore | 0.342 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | 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 | 1252 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 239 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 32.95 | | std | 24.76 | | cv | 0.752 | | sampleLengths | | 0 | 57 | | 1 | 31 | | 2 | 42 | | 3 | 43 | | 4 | 17 | | 5 | 29 | | 6 | 23 | | 7 | 15 | | 8 | 70 | | 9 | 9 | | 10 | 10 | | 11 | 28 | | 12 | 12 | | 13 | 41 | | 14 | 5 | | 15 | 7 | | 16 | 2 | | 17 | 30 | | 18 | 35 | | 19 | 6 | | 20 | 5 | | 21 | 7 | | 22 | 57 | | 23 | 72 | | 24 | 59 | | 25 | 61 | | 26 | 21 | | 27 | 78 | | 28 | 43 | | 29 | 45 | | 30 | 23 | | 31 | 64 | | 32 | 8 | | 33 | 12 | | 34 | 48 | | 35 | 29 | | 36 | 4 | | 37 | 104 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 239 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 271 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 239 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1253 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Inside lay identical bone" |
| | adverbCount | 24 | | adverbRatio | 0.019154030327214685 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006384676775738229 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 239 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 239 | | mean | 5.24 | | std | 3.04 | | cv | 0.581 | | sampleLengths | | 0 | 13 | | 1 | 8 | | 2 | 8 | | 3 | 7 | | 4 | 16 | | 5 | 5 | | 6 | 13 | | 7 | 7 | | 8 | 5 | | 9 | 6 | | 10 | 4 | | 11 | 7 | | 12 | 5 | | 13 | 11 | | 14 | 15 | | 15 | 7 | | 16 | 3 | | 17 | 6 | | 18 | 3 | | 19 | 7 | | 20 | 8 | | 21 | 4 | | 22 | 5 | | 23 | 8 | | 24 | 4 | | 25 | 5 | | 26 | 4 | | 27 | 11 | | 28 | 6 | | 29 | 8 | | 30 | 8 | | 31 | 7 | | 32 | 4 | | 33 | 4 | | 34 | 2 | | 35 | 2 | | 36 | 5 | | 37 | 6 | | 38 | 2 | | 39 | 6 | | 40 | 6 | | 41 | 5 | | 42 | 5 | | 43 | 12 | | 44 | 8 | | 45 | 5 | | 46 | 3 | | 47 | 2 | | 48 | 2 | | 49 | 6 |
| |
| 98.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.7071129707112971 | | totalSentences | 239 | | uniqueOpeners | 169 | |
| 34.19% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 195 | | matches | | 0 | "Then swung past her shoulder" | | 1 | "Then snapped outward again." |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 195 | | matches | | 0 | "He tapped a pen against" | | 1 | "Her eyes tracked along the" | | 2 | "She adjusted the worn leather" | | 3 | "His lips held a faint" | | 4 | "She brushed a finger along" | | 5 | "She remembered rain slick pavement." | | 6 | "You are talking about ghosts" | | 7 | "She traced the blood trail." | | 8 | "It ended at a hairline" | | 9 | "She pulled a notebook from" | | 10 | "Her inner coat pocket bulged." | | 11 | "He withdrew a bone token," | | 12 | "He dropped it into an" | | 13 | "She stood and walked toward" | | 14 | "She followed the marks upward" | | 15 | "You are trespassing, Miller’s voice" | | 16 | "It is a smuggling ring," | | 17 | "She calculated sightlines." | | 18 | "We require warrants." | | 19 | "She stepped forward." |
| | ratio | 0.113 | |
| 98.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 141 | | totalSentences | 195 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The air tasted of copper" | | 2 | "The abandoned Chalk Farm branch" | | 3 | "Sergeant Miller leaned against a" | | 4 | "He tapped a pen against" | | 5 | "Generator died right after we" | | 6 | "Quinn ignored the excuse." | | 7 | "Her eyes tracked along the" | | 8 | "Scuff marks aligned too perfectly." | | 9 | "Blood smears ran parallel to" | | 10 | "She adjusted the worn leather" | | 11 | "A man lay sprawled near" | | 12 | "Silk shirt torn at the" | | 13 | "His lips held a faint" | | 14 | "Quinn knelt, gloved fingers hovering" | | 15 | "Skin temperature matched stone." | | 16 | "Rigor mortis refused to set." | | 17 | "Crowbar to the temple." | | 18 | "Quinn followed his gaze." | | 19 | "An antique brass walking stick" |
| | ratio | 0.723 | |
| 51.28% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 195 | | matches | | 0 | "Now it held a body." | | 1 | "To find what killed him." |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |