| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.345 | | leniency | 0.69 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 888 | | totalAiIsmAdverbs | 2 | | 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) | |
| 43.69% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 888 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "traced" | | 3 | "vibrated" | | 4 | "mechanical" | | 5 | "standard" | | 6 | "porcelain" | | 7 | "shimmered" |
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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 | 43 | | matches | (empty) | |
| 76.41% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 43 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 62 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 888 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 67.90% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 609 | | uniqueNames | 12 | | maxNameDensity | 1.64 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 10 | | Quinn | 1 | | Camden | 1 | | Tube | 1 | | Italian | 1 | | Davies | 6 | | Market | 1 | | London | 2 | | Veil | 2 | | Compass | 1 | | District | 1 | | Line | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Davies" |
| | places | | 0 | "London" | | 1 | "District" | | 2 | "Line" |
| | globalScore | 0.679 | | windowScore | 0.833 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 1 | | matches | | 0 | "tasted like ozone and copper" |
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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 | 888 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 62 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 25.37 | | std | 18.93 | | cv | 0.746 | | sampleLengths | | 0 | 25 | | 1 | 11 | | 2 | 37 | | 3 | 6 | | 4 | 49 | | 5 | 45 | | 6 | 34 | | 7 | 8 | | 8 | 22 | | 9 | 64 | | 10 | 11 | | 11 | 11 | | 12 | 3 | | 13 | 76 | | 14 | 9 | | 15 | 33 | | 16 | 25 | | 17 | 40 | | 18 | 6 | | 19 | 12 | | 20 | 19 | | 21 | 13 | | 22 | 41 | | 23 | 6 | | 24 | 8 | | 25 | 51 | | 26 | 10 | | 27 | 11 | | 28 | 56 | | 29 | 15 | | 30 | 45 | | 31 | 19 | | 32 | 3 | | 33 | 23 | | 34 | 41 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 43 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 91 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 62 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 616 | | adjectiveStacks | 1 | | stackExamples | | 0 | "damp, soot-stained tiles" |
| | adverbCount | 19 | | adverbRatio | 0.030844155844155844 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.01948051948051948 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 62 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 62 | | mean | 14.32 | | std | 6.88 | | cv | 0.48 | | sampleLengths | | 0 | 25 | | 1 | 11 | | 2 | 13 | | 3 | 24 | | 4 | 6 | | 5 | 12 | | 6 | 18 | | 7 | 11 | | 8 | 8 | | 9 | 7 | | 10 | 12 | | 11 | 26 | | 12 | 14 | | 13 | 20 | | 14 | 8 | | 15 | 12 | | 16 | 8 | | 17 | 2 | | 18 | 15 | | 19 | 16 | | 20 | 22 | | 21 | 11 | | 22 | 11 | | 23 | 11 | | 24 | 3 | | 25 | 18 | | 26 | 13 | | 27 | 14 | | 28 | 31 | | 29 | 9 | | 30 | 17 | | 31 | 16 | | 32 | 15 | | 33 | 10 | | 34 | 20 | | 35 | 20 | | 36 | 6 | | 37 | 12 | | 38 | 9 | | 39 | 10 | | 40 | 13 | | 41 | 32 | | 42 | 9 | | 43 | 6 | | 44 | 8 | | 45 | 19 | | 46 | 15 | | 47 | 17 | | 48 | 10 | | 49 | 11 |
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| 96.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5967741935483871 | | totalSentences | 62 | | uniqueOpeners | 37 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 41 | | matches | | 0 | "Even the distant dripping of" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 41 | | matches | | 0 | "Her colleague sighed, shifting his" | | 1 | "She pulled the small brass" | | 2 | "She strode past the police" | | 3 | "She climbed aboard the rusted" | | 4 | "She vaulted out the opposite" | | 5 | "She drew her service pistol," |
| | ratio | 0.146 | |
| 8.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 41 | | matches | | 0 | "Detective Harlow Quinn knelt on" | | 1 | "Her colleague sighed, shifting his" | | 2 | "Harlow tapped the edge of" | | 3 | "The dead man wore a" | | 4 | "Veil Market merchants traded in" | | 5 | "Something else festered here beneath" | | 6 | "The air tasted like ozone" | | 7 | "She pulled the small brass" | | 8 | "The verdigris-cased tool rattled slightly" | | 9 | "Davies muttered, eyeing the spinning" | | 10 | "Harlow stood up, her sharp" | | 11 | "The brass casing felt hot" | | 12 | "She strode past the police" | | 13 | "The platform stretched into absolute" | | 14 | "A rusted District Line car" | | 15 | "Harlow called over her shoulder" | | 16 | "She climbed aboard the rusted" | | 17 | "A worn leather satchel sat" | | 18 | "Harlow crouched beside it, nudging" | | 19 | "Davies asked, stepping gingerly onto" |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 41 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 4 | | matches | | 0 | "The verdigris-cased tool rattled slightly in her palm, its needle spinning wildly before snapping hard toward a rusted service door at the end of the platform." | | 1 | "A rusted District Line car sat idling on the center track, its doors gaping open like the jaws of a starved beast." | | 2 | "Harlow slipped through the gap in the fire door, her boots sinking into soft, mossy earth that had no business growing beneath the streets of London." | | 3 | "The air behind the operative shimmered, tearing open to reveal a swirling vortex of ash and violet light that smelled unmistakably of ozone and old blood." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 2 | | matches | | 0 | "Davies crossed, his face hardening" | | 1 | "the figure said, the voice heavily distorted by a localized scrambler" |
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| 81.03% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "Davies muttered (mutter)" | | 1 | "Davies whispered (whisper)" |
| | dialogueSentences | 29 | | tagDensity | 0.172 | | leniency | 0.345 | | rawRatio | 0.4 | | effectiveRatio | 0.138 | |