| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.263 | | leniency | 0.526 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 837 | | 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) | |
| 70.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 837 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "etched" | | 1 | "pulse" | | 2 | "scanning" | | 3 | "glint" | | 4 | "tracing" |
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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 | 50 | | matches | (empty) | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 64 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 9 | | totalWords | 832 | | ratio | 0.011 | | matches | | 0 | "TfL Service Only – No Public Access" | | 1 | "come up" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 577 | | uniqueNames | 12 | | maxNameDensity | 2.25 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Town | 1 | | Service | 1 | | Public | 1 | | London | 1 | | Hamilton | 1 | | Quinn | 13 | | Eva | 8 | | Veil | 1 | | Market | 1 | | Daniel | 1 | | Mercer | 2 |
| | persons | | 0 | "Hamilton" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Market" | | 4 | "Daniel" | | 5 | "Mercer" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "London" |
| | globalScore | 0.373 | | windowScore | 0.333 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like they’d been buffed away" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 832 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 64 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 30.81 | | std | 18.93 | | cv | 0.614 | | sampleLengths | | 0 | 74 | | 1 | 83 | | 2 | 38 | | 3 | 24 | | 4 | 36 | | 5 | 33 | | 6 | 36 | | 7 | 18 | | 8 | 20 | | 9 | 7 | | 10 | 48 | | 11 | 32 | | 12 | 15 | | 13 | 16 | | 14 | 34 | | 15 | 27 | | 16 | 12 | | 17 | 33 | | 18 | 36 | | 19 | 22 | | 20 | 66 | | 21 | 15 | | 22 | 45 | | 23 | 9 | | 24 | 16 | | 25 | 27 | | 26 | 10 |
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| 98.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 50 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 106 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 64 | | ratio | 0.078 | | matches | | 0 | "Quinn moved against the trickle of late-night stragglers who didn’t question why a woman in a sharp coat and close-cropped salt-and-pepper hair would walk down the emergency stairs marked *TfL Service Only – No Public Access*." | | 1 | "His watch was missing, though—a silver Hamilton, expensive for an office worker." | | 2 | "The rag marks were too precise—no smears, no drips, just a clean, damp rectangle where the blood should’ve pooled." | | 3 | "From within came the damp stench of old water and something else—ozone and charred bone." | | 4 | "It bore the same sigils Quinn had seen carved into the salt—protection runes, but crossed out with fresh scratches, as if someone had defiled the design in haste." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 588 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.034013605442176874 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.011904761904761904 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 64 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 64 | | mean | 13 | | std | 8.63 | | cv | 0.664 | | sampleLengths | | 0 | 21 | | 1 | 36 | | 2 | 17 | | 3 | 5 | | 4 | 27 | | 5 | 23 | | 6 | 28 | | 7 | 11 | | 8 | 17 | | 9 | 10 | | 10 | 24 | | 11 | 10 | | 12 | 13 | | 13 | 6 | | 14 | 7 | | 15 | 6 | | 16 | 27 | | 17 | 4 | | 18 | 20 | | 19 | 12 | | 20 | 7 | | 21 | 11 | | 22 | 8 | | 23 | 12 | | 24 | 2 | | 25 | 5 | | 26 | 5 | | 27 | 24 | | 28 | 19 | | 29 | 7 | | 30 | 25 | | 31 | 6 | | 32 | 9 | | 33 | 10 | | 34 | 6 | | 35 | 6 | | 36 | 13 | | 37 | 15 | | 38 | 3 | | 39 | 8 | | 40 | 16 | | 41 | 5 | | 42 | 7 | | 43 | 5 | | 44 | 28 | | 45 | 6 | | 46 | 30 | | 47 | 4 | | 48 | 18 | | 49 | 4 |
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| 64.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.40625 | | totalSentences | 64 | | uniqueOpeners | 26 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 47 | | matches | | 0 | "Even the scuffs on the" | | 1 | "Somewhere down there, the Veil" |
| | ratio | 0.043 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 47 | | matches | | 0 | "Her satchel sagged at her" | | 1 | "She tucked a curl behind" | | 2 | "She crouched beside the salt," | | 3 | "His watch was missing, though—a" | | 4 | "She walked to the edge," | | 5 | "She stood, stepped closer, and" | | 6 | "Her fingers closed around something" | | 7 | "She pulled it out: a" | | 8 | "It bore the same sigils" | | 9 | "She stood, brushed dust from" | | 10 | "She tapped the salt with" | | 11 | "She turned toward the tunnel" |
| | ratio | 0.255 | |
| 34.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 47 | | matches | | 0 | "The northern line’s last train" | | 1 | "Quinn moved against the trickle" | | 2 | "The worn leather watch on" | | 3 | "The platform wasn’t abandoned anymore." | | 4 | "Light came from a stripped-down" | | 5 | "Cardboard crates and upturned market" | | 6 | "Eva stood over the body," | | 7 | "Her satchel sagged at her" | | 8 | "She tucked a curl behind" | | 9 | "Quinn said, stepping past her" | | 10 | "She crouched beside the salt," | | 11 | "The residue felt gritty, freshly" | | 12 | "Eva’s pen hovered over her" | | 13 | "Quinn studied the body." | | 14 | "Mercer’s pockets were turned inside" | | 15 | "His watch was missing, though—a" | | 16 | "The soles were clean, no" | | 17 | "Quinn stood, scanning the platform." | | 18 | "Blood traces led from the" | | 19 | "The rag marks were too" |
| | ratio | 0.851 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 3 | | matches | | 0 | "Quinn moved against the trickle of late-night stragglers who didn’t question why a woman in a sharp coat and close-cropped salt-and-pepper hair would walk down …" | | 1 | "Cardboard crates and upturned market stalls cluttered the edges, their contents covered by dusty tarps that shivered even though the air was still." | | 2 | "It bore the same sigils Quinn had seen carved into the salt—protection runes, but crossed out with fresh scratches, as if someone had defiled the design in hast…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.158 | | leniency | 0.316 | | rawRatio | 0 | | effectiveRatio | 0 | |