| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 82 | | tagDensity | 0.037 | | leniency | 0.073 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1639 | | 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) | |
| 87.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1639 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "velvet" | | 1 | "measured" | | 2 | "warmth" |
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| 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 | 127 | | matches | (empty) | |
| 97.86% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 127 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 206 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1639 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1123 | | uniqueNames | 9 | | maxNameDensity | 1.51 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Voss" | | discoveredNames | | Harlow | 17 | | Quinn | 1 | | Nadeem | 1 | | Voss | 17 | | Rusk | 3 | | Cinder | 4 | | Madame | 4 | | Morris | 1 | | Blood | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Voss" | | 3 | "Rusk" | | 4 | "Cinder" | | 5 | "Morris" | | 6 | "Blood" |
| | places | | | globalScore | 0.743 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | 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 | 1639 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 206 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 134 | | mean | 12.23 | | std | 14.23 | | cv | 1.164 | | sampleLengths | | 0 | 5 | | 1 | 47 | | 2 | 12 | | 3 | 77 | | 4 | 5 | | 5 | 36 | | 6 | 5 | | 7 | 22 | | 8 | 11 | | 9 | 8 | | 10 | 7 | | 11 | 9 | | 12 | 6 | | 13 | 3 | | 14 | 59 | | 15 | 7 | | 16 | 6 | | 17 | 27 | | 18 | 22 | | 19 | 1 | | 20 | 15 | | 21 | 39 | | 22 | 6 | | 23 | 63 | | 24 | 3 | | 25 | 2 | | 26 | 3 | | 27 | 12 | | 28 | 1 | | 29 | 20 | | 30 | 13 | | 31 | 1 | | 32 | 1 | | 33 | 28 | | 34 | 29 | | 35 | 1 | | 36 | 14 | | 37 | 12 | | 38 | 8 | | 39 | 2 | | 40 | 37 | | 41 | 26 | | 42 | 4 | | 43 | 4 | | 44 | 4 | | 45 | 4 | | 46 | 4 | | 47 | 24 | | 48 | 11 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 172 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 206 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1132 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.01678445229681979 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0008833922261484099 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 206 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 206 | | mean | 7.96 | | std | 5.53 | | cv | 0.696 | | sampleLengths | | 0 | 5 | | 1 | 16 | | 2 | 31 | | 3 | 12 | | 4 | 18 | | 5 | 17 | | 6 | 22 | | 7 | 20 | | 8 | 1 | | 9 | 4 | | 10 | 27 | | 11 | 9 | | 12 | 5 | | 13 | 9 | | 14 | 13 | | 15 | 11 | | 16 | 8 | | 17 | 7 | | 18 | 3 | | 19 | 6 | | 20 | 6 | | 21 | 3 | | 22 | 6 | | 23 | 13 | | 24 | 6 | | 25 | 12 | | 26 | 11 | | 27 | 11 | | 28 | 7 | | 29 | 6 | | 30 | 5 | | 31 | 22 | | 32 | 11 | | 33 | 11 | | 34 | 1 | | 35 | 15 | | 36 | 8 | | 37 | 11 | | 38 | 7 | | 39 | 13 | | 40 | 6 | | 41 | 13 | | 42 | 8 | | 43 | 20 | | 44 | 5 | | 45 | 6 | | 46 | 11 | | 47 | 3 | | 48 | 2 | | 49 | 3 |
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| 62.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.39805825242718446 | | totalSentences | 206 | | uniqueOpeners | 82 | |
| 28.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 118 | | matches | | 0 | "Only a brick wall marked" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 118 | | matches | | 0 | "He held a clear evidence" | | 1 | "Their yellow light slid over" | | 2 | "It tightened jaws, loosened tongues" | | 3 | "They watched her with patient" | | 4 | "It crossed two cracked tiles," | | 5 | "She crouched at the chalk" | | 6 | "His shirt had opened beneath" | | 7 | "His face had gone waxy." | | 8 | "It began in a thin," | | 9 | "She stepped over the chalk," | | 10 | "Her worn leather watch caught" | | 11 | "She knelt by Rusk’s head." | | 12 | "His lips had a faint" | | 13 | "She leaned close enough to" | | 14 | "His fingernails bore grit, but" | | 15 | "She rose and paced towards" | | 16 | "Her movements held the clipped" | | 17 | "She took the evidence bag" | | 18 | "It did not paint both" | | 19 | "Her fingers hovered near the" |
| | ratio | 0.203 | |
| 19.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 118 | | matches | | 0 | "Detective Harlow Quinn halted with" | | 1 | "The constable who had warned" | | 2 | "Harlow lowered her foot behind" | | 3 | "Someone had strung yellow police" | | 4 | "A woman with antlers sold" | | 5 | "A small man in a" | | 6 | "The air held damp brick," | | 7 | "DS Nadeem Voss waited beside" | | 8 | "He held a clear evidence" | | 9 | "Harlow glanced at the bone" | | 10 | "A child’s finger bone, drilled" | | 11 | "Voss’s mouth twitched." | | 12 | "The platform curved away into" | | 13 | "Stalls crowded the edge of" | | 14 | "Their yellow light slid over" | | 15 | "Harlow had spent eighteen years" | | 16 | "It tightened jaws, loosened tongues" | | 17 | "This crowd did none of" | | 18 | "They watched her with patient" | | 19 | "Voss lifted the evidence bag." |
| | ratio | 0.881 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 1 | | matches | | 0 | "The constable who had warned her stood beside a battered enamel sign that read CAMDEN TOWN, though the station beneath it had closed long before anyone in unifo…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she ordered (order)" |
| | dialogueSentences | 82 | | tagDensity | 0.037 | | leniency | 0.073 | | rawRatio | 0.667 | | effectiveRatio | 0.049 | |