| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1371 | | 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) | |
| 19.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1371 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | word | "the last thing" | | count | 1 |
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| | highlights | | 0 | "perfect" | | 1 | "echoed" | | 2 | "synthetic" | | 3 | "traced" | | 4 | "silence" | | 5 | "intricate" | | 6 | "weight" | | 7 | "etched" | | 8 | "grave" | | 9 | "familiar" | | 10 | "flickered" | | 11 | "scanned" | | 12 | "vibrated" | | 13 | "trembled" | | 14 | "shattered" | | 15 | "the last thing" |
| |
| 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 | 216 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 216 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 216 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1371 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 3 | | matches | | 0 | "You are holding evidence, Eva said." | | 1 | "Your cult theory has a flaw, Quinn said." | | 2 | "Step back, Eva said." |
| |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1371 | | uniqueNames | 13 | | maxNameDensity | 1.46 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Eva | 11 | | Kowalski | 1 | | Tube | 1 | | Northern | 1 | | Brighton | 2 | | British | 1 | | Museum | 1 | | Clerkenwell | 1 | | Veil | 1 | | Market | 1 | | Morris | 3 | | Quinn | 20 |
| | persons | | 0 | "Eva" | | 1 | "Kowalski" | | 2 | "Museum" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Quinn" |
| | places | | 0 | "Brighton" | | 1 | "Clerkenwell" |
| | globalScore | 0.771 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | 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 | 1371 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 216 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 34.28 | | std | 20.31 | | cv | 0.592 | | sampleLengths | | 0 | 55 | | 1 | 26 | | 2 | 31 | | 3 | 52 | | 4 | 94 | | 5 | 50 | | 6 | 14 | | 7 | 41 | | 8 | 60 | | 9 | 20 | | 10 | 48 | | 11 | 66 | | 12 | 54 | | 13 | 31 | | 14 | 38 | | 15 | 50 | | 16 | 73 | | 17 | 54 | | 18 | 25 | | 19 | 37 | | 20 | 35 | | 21 | 44 | | 22 | 43 | | 23 | 33 | | 24 | 27 | | 25 | 3 | | 26 | 38 | | 27 | 12 | | 28 | 14 | | 29 | 3 | | 30 | 32 | | 31 | 29 | | 32 | 2 | | 33 | 15 | | 34 | 27 | | 35 | 19 | | 36 | 22 | | 37 | 11 | | 38 | 1 | | 39 | 42 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 216 | | matches | | 0 | "were gone" | | 1 | "get pulled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 236 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 216 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1371 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.019693654266958426 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005105762217359592 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 216 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 216 | | mean | 6.35 | | std | 3.92 | | cv | 0.617 | | sampleLengths | | 0 | 14 | | 1 | 15 | | 2 | 2 | | 3 | 8 | | 4 | 2 | | 5 | 3 | | 6 | 11 | | 7 | 9 | | 8 | 5 | | 9 | 12 | | 10 | 7 | | 11 | 11 | | 12 | 13 | | 13 | 17 | | 14 | 11 | | 15 | 9 | | 16 | 2 | | 17 | 4 | | 18 | 3 | | 19 | 6 | | 20 | 2 | | 21 | 15 | | 22 | 10 | | 23 | 10 | | 24 | 14 | | 25 | 14 | | 26 | 13 | | 27 | 16 | | 28 | 6 | | 29 | 8 | | 30 | 4 | | 31 | 3 | | 32 | 2 | | 33 | 1 | | 34 | 10 | | 35 | 2 | | 36 | 6 | | 37 | 4 | | 38 | 2 | | 39 | 2 | | 40 | 6 | | 41 | 6 | | 42 | 2 | | 43 | 4 | | 44 | 5 | | 45 | 14 | | 46 | 9 | | 47 | 9 | | 48 | 5 | | 49 | 4 |
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| 48.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3333333333333333 | | totalSentences | 216 | | uniqueOpeners | 72 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 185 | | matches | | 0 | "Just a neat, surgical ring" | | 1 | "Then the anchor failed." | | 2 | "Just smooth, undisturbed dust." | | 3 | "Just a perfect, vertical plane" | | 4 | "Then the fall began." |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 185 | | matches | | 0 | "She traced the air above" | | 1 | "She tucked a curl of" | | 2 | "She picked one up." | | 3 | "She rolled it across her" | | 4 | "You are holding evidence, Eva" | | 5 | "Her boots clicked against the" | | 6 | "I am reading it." | | 7 | "Your squatters do not carry" | | 8 | "She opened her satchel." | | 9 | "She pulled out a thick" | | 10 | "I called it a migration" | | 11 | "They do when the tunnel" | | 12 | "Her finger traced a woodcut" | | 13 | "She felt the familiar weight" | | 14 | "She walked back to the" | | 15 | "She knelt again, pressing her" | | 16 | "She moved her hand inward," | | 17 | "Your cult theory has a" | | 18 | "We are standing in yesterday" | | 19 | "She scanned the tracks." |
| | ratio | 0.238 | |
| 54.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 150 | | totalSentences | 185 | | matches | | 0 | "Blood pooled in the shape" | | 1 | "Quinn stepped over the yellow" | | 2 | "The edges of the stain" | | 3 | "Gang initiation, a voice echoed" | | 4 | "The Camden lot have been" | | 5 | "Quinn kept her eyes on" | | 6 | "She traced the air above" | | 7 | "Opioid dealers do not wipe" | | 8 | "Eva Kowalski adjusted her round" | | 9 | "She tucked a curl of" | | 10 | "The uniforms found bone fragments" | | 11 | "This place attracts squatters." | | 12 | "Squatters attract rats." | | 13 | "Rats attract desperate men with" | | 14 | "The abandoned Tube station stretched" | | 15 | "The air tasted of ozone" | | 16 | "A single work light buzzed" | | 17 | "The silence felt heavy, pressed" | | 18 | "Quinn walked to the ticket" | | 19 | "She picked one up." |
| | ratio | 0.811 | |
| 54.05% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 185 | | matches | | 0 | "If the tear opened without" | | 1 | "If that is a residual" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | 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 | |