| 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 | 1082 | | 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) | |
| 81.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1082 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "pulsed" | | 2 | "flickered" |
| |
| 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 | 286 | | matches | (empty) | |
| 97.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 8 | | hedgeCount | 1 | | narrationSentences | 286 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 286 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 12 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1082 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 1082 | | uniqueNames | 11 | | maxNameDensity | 1.94 | | worstName | "You" | | maxWindowNameDensity | 4 | | worstWindowName | "You" | | discoveredNames | | Quinn | 2 | | Detective | 2 | | Herrera | 2 | | Market | 1 | | Soho | 2 | | Raven | 2 | | Nest | 4 | | Silas | 2 | | Harlow | 10 | | You | 21 | | Tomás | 12 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Harlow" | | 5 | "You" | | 6 | "Tomás" |
| | places | | | globalScore | 0.53 | | 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 | 1082 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 286 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 186 | | mean | 5.82 | | std | 6.22 | | cv | 1.07 | | sampleLengths | | 0 | 58 | | 1 | 19 | | 2 | 1 | | 3 | 8 | | 4 | 5 | | 5 | 11 | | 6 | 21 | | 7 | 33 | | 8 | 4 | | 9 | 8 | | 10 | 23 | | 11 | 1 | | 12 | 1 | | 13 | 4 | | 14 | 2 | | 15 | 4 | | 16 | 2 | | 17 | 2 | | 18 | 3 | | 19 | 8 | | 20 | 8 | | 21 | 4 | | 22 | 4 | | 23 | 20 | | 24 | 4 | | 25 | 2 | | 26 | 3 | | 27 | 2 | | 28 | 3 | | 29 | 11 | | 30 | 6 | | 31 | 5 | | 32 | 3 | | 33 | 1 | | 34 | 13 | | 35 | 5 | | 36 | 5 | | 37 | 2 | | 38 | 6 | | 39 | 13 | | 40 | 2 | | 41 | 1 | | 42 | 6 | | 43 | 4 | | 44 | 4 | | 45 | 4 | | 46 | 4 | | 47 | 7 | | 48 | 2 | | 49 | 1 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 286 | | matches | | 0 | "get eaten" | | 1 | "were gone" | | 2 | "was gone" | | 3 | "was filled" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 268 | | matches | | 0 | "was coming" | | 1 | "was running" | | 2 | "were watching" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 286 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1082 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.015711645101663587 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0018484288354898336 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 286 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 286 | | mean | 3.78 | | std | 1.76 | | cv | 0.466 | | sampleLengths | | 0 | 4 | | 1 | 6 | | 2 | 12 | | 3 | 4 | | 4 | 3 | | 5 | 3 | | 6 | 5 | | 7 | 10 | | 8 | 11 | | 9 | 4 | | 10 | 2 | | 11 | 4 | | 12 | 9 | | 13 | 1 | | 14 | 3 | | 15 | 5 | | 16 | 5 | | 17 | 6 | | 18 | 5 | | 19 | 2 | | 20 | 9 | | 21 | 6 | | 22 | 4 | | 23 | 2 | | 24 | 4 | | 25 | 5 | | 26 | 2 | | 27 | 3 | | 28 | 5 | | 29 | 5 | | 30 | 7 | | 31 | 4 | | 32 | 4 | | 33 | 4 | | 34 | 7 | | 35 | 5 | | 36 | 3 | | 37 | 8 | | 38 | 1 | | 39 | 1 | | 40 | 4 | | 41 | 2 | | 42 | 4 | | 43 | 2 | | 44 | 2 | | 45 | 3 | | 46 | 4 | | 47 | 4 | | 48 | 8 | | 49 | 4 |
| |
| 38.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 34 | | diversityRatio | 0.25524475524475526 | | totalSentences | 286 | | uniqueOpeners | 73 | |
| 45.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 221 | | matches | | 0 | "Then stay behind me." | | 1 | "Just a hole in the" | | 2 | "Only a wall of stone" |
| | ratio | 0.014 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 130 | | totalSentences | 221 | | matches | | 0 | "She tracked the suspect." | | 1 | "He moved fast." | | 2 | "She kept pace." | | 3 | "She followed the scent of" | | 4 | "He cut a corner." | | 5 | "Her leather watch creaked as" | | 6 | "He glanced back." | | 7 | "You can't catch me, Detective." | | 8 | "I don't need to catch" | | 9 | "I need to arrest you." | | 10 | "He dove into the subway" | | 11 | "She reached the stairs." | | 12 | "She hit the bottom." | | 13 | "He held a leather bag." | | 14 | "He looked tired." | | 15 | "He came this way." | | 16 | "You let him pass?" | | 17 | "It isn't your jurisdiction." | | 18 | "It is to me." | | 19 | "He held out a bone" |
| | ratio | 0.588 | |
| 30.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 190 | | totalSentences | 221 | | matches | | 0 | "Harlow Quinn surged through the" | | 1 | "Water streamed down her coat," | | 2 | "She tracked the suspect." | | 3 | "He moved fast." | | 4 | "She kept pace." | | 5 | "Streetlights blurred into yellow streaks." | | 6 | "Soho streets twisted into a" | | 7 | "She followed the scent of" | | 8 | "He cut a corner." | | 9 | "Mud splashed her boots." | | 10 | "Her leather watch creaked as" | | 11 | "He glanced back." | | 12 | "You can't catch me, Detective." | | 13 | "I don't need to catch" | | 14 | "I need to arrest you." | | 15 | "A short, sharp sound that" | | 16 | "He dove into the subway" | | 17 | "The gate was up." | | 18 | "She reached the stairs." | | 19 | "The metal handrail felt slick." |
| | ratio | 0.86 | |
| 67.87% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 221 | | matches | | 0 | "Because I know you." | | 1 | "If I go down." | | 2 | "If I don't." |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 4 | | 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 | |