| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 805 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 6.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 805 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "pulsed" | | 1 | "dance" | | 2 | "jaw clenched" | | 3 | "chaotic" | | 4 | "dancing" | | 5 | "furrowing" | | 6 | "echoing" | | 7 | "cacophony" | | 8 | "oppressive" | | 9 | "silence" | | 10 | "scanning" | | 11 | "etched" | | 12 | "racing" | | 13 | "unreadable" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "jaw clenched" | | 1 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 64 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 64 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 804 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 683 | | uniqueNames | 11 | | maxNameDensity | 0.88 | | worstName | "Thompson" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Thompson" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 4 | | Thompson | 6 | | Veil | 3 | | Market | 2 | | Compass | 1 | | Eva | 1 | | Kowalski | 1 |
| | persons | | 0 | "Tube" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Thompson" | | 4 | "Eva" | | 5 | "Kowalski" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | 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 | 804 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 74 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 38.29 | | std | 23.5 | | cv | 0.614 | | sampleLengths | | 0 | 81 | | 1 | 75 | | 2 | 63 | | 3 | 14 | | 4 | 24 | | 5 | 52 | | 6 | 78 | | 7 | 10 | | 8 | 14 | | 9 | 17 | | 10 | 42 | | 11 | 53 | | 12 | 14 | | 13 | 50 | | 14 | 53 | | 15 | 17 | | 16 | 21 | | 17 | 28 | | 18 | 10 | | 19 | 28 | | 20 | 60 |
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| 83.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 64 | | matches | | 0 | "was posed" | | 1 | "was attuned" | | 2 | "was deserted" | | 3 | "was dressed" |
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| 79.88% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 111 | | matches | | 0 | "was pointing" | | 1 | "was looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 74 | | ratio | 0.014 | | matches | | 0 | "The air was thick with the stench of damp earth and something else—an acrid tang that set her teeth on edge." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 685 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.021897810218978103 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008759124087591242 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 10.86 | | std | 5.91 | | cv | 0.544 | | sampleLengths | | 0 | 29 | | 1 | 21 | | 2 | 17 | | 3 | 14 | | 4 | 20 | | 5 | 19 | | 6 | 21 | | 7 | 15 | | 8 | 13 | | 9 | 10 | | 10 | 22 | | 11 | 8 | | 12 | 5 | | 13 | 5 | | 14 | 10 | | 15 | 4 | | 16 | 5 | | 17 | 19 | | 18 | 15 | | 19 | 15 | | 20 | 4 | | 21 | 9 | | 22 | 5 | | 23 | 4 | | 24 | 8 | | 25 | 15 | | 26 | 10 | | 27 | 3 | | 28 | 14 | | 29 | 15 | | 30 | 13 | | 31 | 7 | | 32 | 3 | | 33 | 7 | | 34 | 7 | | 35 | 9 | | 36 | 8 | | 37 | 15 | | 38 | 5 | | 39 | 9 | | 40 | 13 | | 41 | 20 | | 42 | 3 | | 43 | 13 | | 44 | 17 | | 45 | 10 | | 46 | 4 | | 47 | 10 | | 48 | 12 | | 49 | 6 |
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| 50.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.33783783783783783 | | totalSentences | 74 | | uniqueOpeners | 25 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 62 | | matches | (empty) | | ratio | 0 | |
| 7.10% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 62 | | matches | | 0 | "Her worn leather watch glinted" | | 1 | "She was late, and punctuality" | | 2 | "He held up a evidence" | | 3 | "he greeted her" | | 4 | "It was old, its surface" | | 5 | "She looked back at the" | | 6 | "Her throat was slit, a" | | 7 | "she asked, her voice a" | | 8 | "She noted the lack of" | | 9 | "She leaned in, sniffing." | | 10 | "She'd known her killer." | | 11 | "She stood up, her gaze" | | 12 | "Her eyes landed on a" | | 13 | "She picked it up, turning" | | 14 | "She'd heard whispers of these," | | 15 | "It was attuned to supernatural" | | 16 | "she replied, pocketing it" | | 17 | "She strode towards the tunnel," | | 18 | "She ignored him, her boots" | | 19 | "They emerged into a large" |
| | ratio | 0.532 | |
| 32.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 62 | | matches | | 0 | "The air was thick with" | | 1 | "Her worn leather watch glinted" | | 2 | "She was late, and punctuality" | | 3 | "The crime scene was a" | | 4 | "He held up a evidence" | | 5 | "he greeted her" | | 6 | "Quinn took the bag, her" | | 7 | "It was old, its surface" | | 8 | "She looked back at the" | | 9 | "Her throat was slit, a" | | 10 | "Something about this was off." | | 11 | "she asked, her voice a" | | 12 | "Thompson huffed, adjusting his tie." | | 13 | "Quinn didn't respond, instead walking" | | 14 | "She noted the lack of" | | 15 | "She leaned in, sniffing." | | 16 | "The vic hadn't been afraid." | | 17 | "She'd known her killer." | | 18 | "She stood up, her gaze" | | 19 | "Her eyes landed on a" |
| | ratio | 0.855 | |
| 80.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 62 | | matches | | 0 | "Even if it meant walking" |
| | ratio | 0.016 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "In the belly of Camden, where the Tube's forgotten veins pulsed with a decaying rhythm, Detective Harlow Quinn descended into the abandoned station that was ton…" | | 1 | "The air was thick with the stench of damp earth and something else—an acrid tang that set her teeth on edge." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "she asked, her voice a low rumble" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0 | | effectiveRatio | 0 | |