| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1678 | | 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) | |
| 19.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1678 | | totalAiIsms | 27 | | found | | | highlights | | 0 | "measured" | | 1 | "weight" | | 2 | "gloom" | | 3 | "trembled" | | 4 | "echoed" | | 5 | "scanned" | | 6 | "pulsed" | | 7 | "variables" | | 8 | "standard" | | 9 | "echoing" | | 10 | "silence" | | 11 | "navigated" | | 12 | "calculated" | | 13 | "warmth" | | 14 | "rhythmic" | | 15 | "footsteps" | | 16 | "vibrated" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 313 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 313 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 313 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 15 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1678 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1678 | | uniqueNames | 11 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Quinn | 17 | | Met | 3 | | Tube | 1 | | Market | 2 | | Morris | 4 | | Veil | 2 | | Three | 3 | | You | 13 |
| | persons | | 0 | "Quinn" | | 1 | "Met" | | 2 | "Market" | | 3 | "Morris" | | 4 | "Three" | | 5 | "You" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.993 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 141 | | 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 | 1678 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 313 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 44.16 | | std | 66.05 | | cv | 1.496 | | sampleLengths | | 0 | 360 | | 1 | 11 | | 2 | 33 | | 3 | 3 | | 4 | 48 | | 5 | 3 | | 6 | 12 | | 7 | 23 | | 8 | 59 | | 9 | 9 | | 10 | 55 | | 11 | 4 | | 12 | 6 | | 13 | 120 | | 14 | 4 | | 15 | 26 | | 16 | 95 | | 17 | 7 | | 18 | 33 | | 19 | 40 | | 20 | 4 | | 21 | 101 | | 22 | 1 | | 23 | 11 | | 24 | 1 | | 25 | 24 | | 26 | 1 | | 27 | 76 | | 28 | 8 | | 29 | 51 | | 30 | 5 | | 31 | 19 | | 32 | 126 | | 33 | 5 | | 34 | 27 | | 35 | 6 | | 36 | 98 | | 37 | 163 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 313 | | matches | | 0 | "was buried" | | 1 | "get buried" | | 2 | "was pitted" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 324 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 313 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1681 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.012492563950029744 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.006543723973825104 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 313 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 313 | | mean | 5.36 | | std | 2.67 | | cv | 0.497 | | sampleLengths | | 0 | 10 | | 1 | 10 | | 2 | 9 | | 3 | 5 | | 4 | 10 | | 5 | 7 | | 6 | 12 | | 7 | 7 | | 8 | 7 | | 9 | 4 | | 10 | 7 | | 11 | 15 | | 12 | 5 | | 13 | 11 | | 14 | 7 | | 15 | 1 | | 16 | 7 | | 17 | 6 | | 18 | 6 | | 19 | 6 | | 20 | 7 | | 21 | 2 | | 22 | 7 | | 23 | 9 | | 24 | 8 | | 25 | 6 | | 26 | 10 | | 27 | 4 | | 28 | 2 | | 29 | 6 | | 30 | 5 | | 31 | 7 | | 32 | 7 | | 33 | 5 | | 34 | 12 | | 35 | 6 | | 36 | 6 | | 37 | 6 | | 38 | 5 | | 39 | 3 | | 40 | 6 | | 41 | 6 | | 42 | 3 | | 43 | 4 | | 44 | 2 | | 45 | 6 | | 46 | 6 | | 47 | 2 | | 48 | 3 | | 49 | 12 |
| |
| 36.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 42 | | diversityRatio | 0.2692307692307692 | | totalSentences | 312 | | uniqueOpeners | 84 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 271 | | matches | (empty) | | ratio | 0 | |
| 45.83% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 118 | | totalSentences | 271 | | matches | | 0 | "Her boots struck wet cobblestones." | | 1 | "She kept the target in" | | 2 | "He landed heavy on the" | | 3 | "His jacket glistened with water" | | 4 | "She didn't waste energy on" | | 5 | "She checked her left wrist." | | 6 | "He weaved between abandoned market" | | 7 | "He ducked under a corrugated" | | 8 | "Her salt-and-pepper hair clung to" | | 9 | "She ignored the cold seeping" | | 10 | "Her shoulder checked a wet" | | 11 | "She recovered her balance instantly." | | 12 | "She reached the threshold of" | | 13 | "He faced a steel security" | | 14 | "He reached into his coat." | | 15 | "His fingers trembled." | | 16 | "He pulled out a small" | | 17 | "Her hand moved to her" | | 18 | "She didn't draw her service" | | 19 | "She needed answers." |
| | ratio | 0.435 | |
| 24.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 236 | | totalSentences | 271 | | matches | | 0 | "Rain sheeted down Camden High" | | 1 | "Streetlights bled haloes of sodium" | | 2 | "Harlow Quinn moved through the" | | 3 | "Her boots struck wet cobblestones." | | 4 | "Each step placed weight exactly" | | 5 | "She kept the target in" | | 6 | "The suspect scrambled over a" | | 7 | "He landed heavy on the" | | 8 | "His jacket glistened with water" | | 9 | "Quinn closed the distance." | | 10 | "She didn't waste energy on" | | 11 | "She checked her left wrist." | | 12 | "The worn leather strap of" | | 13 | "The dial glowed faintly in" | | 14 | "The suspect broke into a" | | 15 | "He weaved between abandoned market" | | 16 | "He ducked under a corrugated" | | 17 | "Her salt-and-pepper hair clung to" | | 18 | "A sharp jawline clenched as" | | 19 | "She ignored the cold seeping" |
| | ratio | 0.871 | |
| 73.80% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 271 | | matches | | 0 | "To establish a safe zone." | | 1 | "To call in backup." | | 2 | "To the stall." | | 3 | "To the man who knows" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | 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 | |