| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 962 | | 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) | |
| 48.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 962 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "electric" | | 1 | "loomed" | | 2 | "shattered" | | 3 | "gloom" | | 4 | "etched" | | 5 | "footsteps" | | 6 | "echoing" | | 7 | "methodical" | | 8 | "maw" | | 9 | "glinting" |
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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 | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 79 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 959 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 753 | | uniqueNames | 6 | | maxNameDensity | 1.59 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 12 | | Camden | 1 | | Davies | 5 | | Eva | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Davies" | | 3 | "Eva" |
| | places | (empty) | | globalScore | 0.703 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 2.086 | | wordCount | 959 | | matches | | 0 | "Not the usual rot and decay of an abandoned Tube station, but a sharp, coppery tang layered over something else" | | 1 | "Not the black grime of the station, but something finer" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 99 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 25.24 | | std | 19.74 | | cv | 0.782 | | sampleLengths | | 0 | 68 | | 1 | 19 | | 2 | 8 | | 3 | 43 | | 4 | 52 | | 5 | 14 | | 6 | 15 | | 7 | 26 | | 8 | 3 | | 9 | 3 | | 10 | 48 | | 11 | 5 | | 12 | 39 | | 13 | 3 | | 14 | 48 | | 15 | 6 | | 16 | 3 | | 17 | 39 | | 18 | 11 | | 19 | 42 | | 20 | 17 | | 21 | 3 | | 22 | 57 | | 23 | 52 | | 24 | 50 | | 25 | 27 | | 26 | 7 | | 27 | 10 | | 28 | 10 | | 29 | 2 | | 30 | 31 | | 31 | 32 | | 32 | 7 | | 33 | 46 | | 34 | 60 | | 35 | 11 | | 36 | 11 | | 37 | 31 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 79 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 112 | | matches | (empty) | |
| 56.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 99 | | ratio | 0.03 | | matches | | 0 | "Not the usual rot and decay of an abandoned Tube station, but a sharp, coppery tang layered over something else—something ancient and electric, like the air before a thunderstorm." | | 1 | "She counted the footprints in the dust—half a dozen sets, all police issue, plus one heavy boot print." | | 2 | "The air emanated from it in a slow, steady exhale—warm and humid and reeking of that same ozone smell." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 757 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.031704095112285335 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0066050198150594455 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 9.69 | | std | 7.33 | | cv | 0.756 | | sampleLengths | | 0 | 4 | | 1 | 29 | | 2 | 15 | | 3 | 20 | | 4 | 19 | | 5 | 8 | | 6 | 7 | | 7 | 16 | | 8 | 5 | | 9 | 2 | | 10 | 13 | | 11 | 13 | | 12 | 13 | | 13 | 26 | | 14 | 8 | | 15 | 6 | | 16 | 15 | | 17 | 4 | | 18 | 9 | | 19 | 1 | | 20 | 12 | | 21 | 3 | | 22 | 2 | | 23 | 1 | | 24 | 22 | | 25 | 10 | | 26 | 1 | | 27 | 2 | | 28 | 13 | | 29 | 5 | | 30 | 17 | | 31 | 18 | | 32 | 4 | | 33 | 2 | | 34 | 1 | | 35 | 14 | | 36 | 18 | | 37 | 2 | | 38 | 3 | | 39 | 11 | | 40 | 6 | | 41 | 3 | | 42 | 13 | | 43 | 12 | | 44 | 14 | | 45 | 5 | | 46 | 6 | | 47 | 8 | | 48 | 34 | | 49 | 2 |
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| 68.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.45454545454545453 | | totalSentences | 99 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 64 | | matches | | 0 | "Alongside the copper, she caught" | | 1 | "Somewhere deep in the darkness," |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 64 | | matches | | 0 | "His throat gaped wide, but" | | 1 | "He looked up as Quinn" | | 2 | "She crouched down, her torch" | | 3 | "She pointed her torch at" | | 4 | "She counted the footprints in" | | 5 | "She knelt beside the dead" | | 6 | "She checked the man’s hands" | | 7 | "She scraped a sample into" | | 8 | "Her watch ticked softly on" | | 9 | "She pulled out her phone," | | 10 | "She hung up before Eva" | | 11 | "She pulled it free." | | 12 | "She slipped the token into" | | 13 | "she whispered to the dead" |
| | ratio | 0.219 | |
| 53.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 64 | | matches | | 0 | "The smell hit first." | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The escalator shaft loomed before" | | 3 | "A uniformed constable met her" | | 4 | "Quinn followed, her boots crunching" | | 5 | "The stillness here felt wrong." | | 6 | "The air pressure had changed," | | 7 | "The victim lay sprawled across" | | 8 | "A man, mid-thirties, dressed in" | | 9 | "His throat gaped wide, but" | | 10 | "Constable Davies crouched nearby, scribbling" | | 11 | "He looked up as Quinn" | | 12 | "Quinn circled the body." | | 13 | "The tear in the tweed" | | 14 | "She crouched down, her torch" | | 15 | "She pointed her torch at" | | 16 | "The fingers were curled, but" | | 17 | "Quinn stood, her gaze tracking" | | 18 | "She counted the footprints in" | | 19 | "Quinn walked the length of" |
| | ratio | 0.813 | |
| 78.13% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 64 | | matches | | 0 | "Whoever had done this knew" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 2 | | matches | | 0 | "The air pressure had changed, as if the station itself held its breath." | | 1 | "Somewhere deep in the darkness, she heard the faint, distant clatter of a train that hadn’t run in forty years." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn stood, her gaze tracking the scatter of debris" |
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| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 26 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 1 | | effectiveRatio | 0.154 | |