| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow climbed back [back]" |
| | dialogueSentences | 74 | | tagDensity | 0.216 | | leniency | 0.432 | | rawRatio | 0.063 | | effectiveRatio | 0.027 | |
| 93.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1460 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 86.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1460 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "etched" | | 1 | "quivered" | | 2 | "scanning" | | 3 | "stark" |
| |
| 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 | 120 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 179 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1460 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 6.98% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 874 | | uniqueNames | 9 | | maxNameDensity | 2.86 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 25 | | Quinn | 1 | | London | 1 | | Underground | 1 | | Meera | 1 | | Patel | 17 | | Persian | 1 | | Kowalski | 1 | | Eva | 10 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Meera" | | 3 | "Patel" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | | | globalScore | 0.07 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.685 | | wordCount | 1460 | | matches | | 0 | "not north but southwest, down the eastbound tunnel" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 179 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 80 | | mean | 18.25 | | std | 17.7 | | cv | 0.97 | | sampleLengths | | 0 | 85 | | 1 | 8 | | 2 | 5 | | 3 | 12 | | 4 | 27 | | 5 | 52 | | 6 | 6 | | 7 | 3 | | 8 | 7 | | 9 | 43 | | 10 | 4 | | 11 | 8 | | 12 | 5 | | 13 | 12 | | 14 | 32 | | 15 | 2 | | 16 | 83 | | 17 | 3 | | 18 | 29 | | 19 | 1 | | 20 | 15 | | 21 | 13 | | 22 | 4 | | 23 | 13 | | 24 | 4 | | 25 | 26 | | 26 | 17 | | 27 | 4 | | 28 | 26 | | 29 | 16 | | 30 | 13 | | 31 | 16 | | 32 | 11 | | 33 | 11 | | 34 | 12 | | 35 | 5 | | 36 | 5 | | 37 | 44 | | 38 | 14 | | 39 | 34 | | 40 | 17 | | 41 | 15 | | 42 | 26 | | 43 | 42 | | 44 | 57 | | 45 | 7 | | 46 | 6 | | 47 | 17 | | 48 | 12 | | 49 | 5 |
| |
| 93.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 120 | | matches | | 0 | "been disturbed" | | 1 | "was etched" | | 2 | "been disturbed" | | 3 | "were gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 149 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 179 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 593 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.01517706576728499 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0016863406408094434 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 179 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 179 | | mean | 8.16 | | std | 6.16 | | cv | 0.755 | | sampleLengths | | 0 | 14 | | 1 | 32 | | 2 | 15 | | 3 | 9 | | 4 | 10 | | 5 | 5 | | 6 | 8 | | 7 | 5 | | 8 | 12 | | 9 | 5 | | 10 | 22 | | 11 | 15 | | 12 | 12 | | 13 | 5 | | 14 | 2 | | 15 | 4 | | 16 | 14 | | 17 | 3 | | 18 | 3 | | 19 | 3 | | 20 | 7 | | 21 | 5 | | 22 | 13 | | 23 | 5 | | 24 | 3 | | 25 | 17 | | 26 | 4 | | 27 | 8 | | 28 | 5 | | 29 | 4 | | 30 | 8 | | 31 | 15 | | 32 | 6 | | 33 | 11 | | 34 | 2 | | 35 | 3 | | 36 | 4 | | 37 | 5 | | 38 | 13 | | 39 | 5 | | 40 | 5 | | 41 | 3 | | 42 | 10 | | 43 | 19 | | 44 | 6 | | 45 | 10 | | 46 | 3 | | 47 | 22 | | 48 | 7 | | 49 | 1 |
| |
| 49.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.329608938547486 | | totalSentences | 179 | | uniqueOpeners | 59 | |
| 30.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 110 | | matches | | 0 | "More like burnt honey." |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 110 | | matches | | 0 | "Her shadow stretched long and" | | 1 | "She checked the worn leather" | | 2 | "It came away thick and" | | 3 | "She studied the body." | | 4 | "She leaned closer." | | 5 | "Its casing had a patina" | | 6 | "It pointed not north but" | | 7 | "She stopped at the man's" | | 8 | "She looked up" | | 9 | "It was smooth and warm," | | 10 | "She jumped down." | | 11 | "Her boots landed on the" | | 12 | "She played the beam over" | | 13 | "They ran along the top" | | 14 | "They did not descend." | | 15 | "She touched it with a" | | 16 | "It came away black, but" | | 17 | "She kept her eyes on" | | 18 | "She took a step to" | | 19 | "She stepped right." |
| | ratio | 0.191 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 110 | | matches | | 0 | "The air in the old" | | 1 | "Detective Harlow Quinn ducked beneath" | | 2 | "Her shadow stretched long and" | | 3 | "She checked the worn leather" | | 4 | "DS Meera Patel looked up" | | 5 | "Patel gestured at the body." | | 6 | "Harlow pulled a pair of" | | 7 | "The body lay on a" | | 8 | "Arms folded across the chest." | | 9 | "Hair damp and combed." | | 10 | "The face looked almost peaceful" | | 11 | "Harlow crouched beside the runner." | | 12 | "The platform floor held decades" | | 13 | "The dust had not been" | | 14 | "Patel hugged the clipboard." | | 15 | "Harlow pressed two fingers into" | | 16 | "It came away thick and" | | 17 | "Harlow ignored her." | | 18 | "She studied the body." | | 19 | "The oxfords looked new, but" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 110 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva crouched, her freckles stark under the arc lights" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Harlow pressed (press)" |
| | dialogueSentences | 74 | | tagDensity | 0.041 | | leniency | 0.081 | | rawRatio | 0.333 | | effectiveRatio | 0.027 | |