| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1042 | | 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) | |
| 61.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1042 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "loomed" | | 2 | "standard" | | 3 | "warmth" | | 4 | "unsettled" | | 5 | "unravel" | | 6 | "pulse" | | 7 | "echoing" |
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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 | 68 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 68 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 15 | | totalWords | 1042 | | ratio | 0.014 | | matches | | 0 | "unexplained circumstances" | | 1 | "else" | | 2 | "clack" | | 3 | "Five minutes, Quinn. I'll be right behind you." | | 4 | "Unexplained circumstances." | | 5 | "clack" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 1028 | | uniqueNames | 12 | | maxNameDensity | 0.68 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 7 | | London | 1 | | Bayham | 1 | | Street | 1 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Morris | 2 | | Tube | 2 | | Underground | 1 | | Camden | 2 | | Two | 1 |
| | persons | | | places | | 0 | "London" | | 1 | "Bayham" | | 2 | "Street" | | 3 | "Chalk" | | 4 | "Farm" | | 5 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like and was actively avoiding it" | | 1 | "something like this in" | | 2 | "something like old blood gone to amber" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.96 | | wordCount | 1042 | | matches | | 0 | "not whether she was still chasing him, but whether something *else* was" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 71 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 38.59 | | std | 25.97 | | cv | 0.673 | | sampleLengths | | 0 | 17 | | 1 | 46 | | 2 | 6 | | 3 | 57 | | 4 | 103 | | 5 | 5 | | 6 | 30 | | 7 | 7 | | 8 | 42 | | 9 | 76 | | 10 | 28 | | 11 | 90 | | 12 | 47 | | 13 | 1 | | 14 | 34 | | 15 | 62 | | 16 | 20 | | 17 | 60 | | 18 | 3 | | 19 | 57 | | 20 | 52 | | 21 | 22 | | 22 | 42 | | 23 | 32 | | 24 | 12 | | 25 | 46 | | 26 | 45 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 68 | | matches | | |
| 39.36% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 166 | | matches | | 0 | "was actively avoiding" | | 1 | "wasn't closing" | | 2 | "was still chasing" | | 3 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 71 | | ratio | 0 | | matches | (empty) | |
| 88.50% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1037 | | adjectiveStacks | 1 | | stackExamples | | 0 | "different half-open door," |
| | adverbCount | 48 | | adverbRatio | 0.04628736740597879 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008678881388621022 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 14.68 | | std | 11.66 | | cv | 0.795 | | sampleLengths | | 0 | 13 | | 1 | 4 | | 2 | 2 | | 3 | 22 | | 4 | 19 | | 5 | 1 | | 6 | 2 | | 7 | 6 | | 8 | 9 | | 9 | 23 | | 10 | 25 | | 11 | 38 | | 12 | 15 | | 13 | 11 | | 14 | 2 | | 15 | 23 | | 16 | 14 | | 17 | 5 | | 18 | 22 | | 19 | 4 | | 20 | 1 | | 21 | 3 | | 22 | 7 | | 23 | 2 | | 24 | 40 | | 25 | 15 | | 26 | 3 | | 27 | 20 | | 28 | 11 | | 29 | 27 | | 30 | 2 | | 31 | 7 | | 32 | 19 | | 33 | 36 | | 34 | 32 | | 35 | 3 | | 36 | 19 | | 37 | 3 | | 38 | 10 | | 39 | 34 | | 40 | 1 | | 41 | 6 | | 42 | 28 | | 43 | 5 | | 44 | 37 | | 45 | 20 | | 46 | 3 | | 47 | 2 | | 48 | 15 | | 49 | 12 |
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| 87.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5774647887323944 | | totalSentences | 71 | | uniqueOpeners | 41 | |
| 56.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 59 | | matches | | 0 | "Actually laughed, a bright bark" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 59 | | matches | | 0 | "She didn't slow down." | | 1 | "She'd clocked him leaving a" | | 2 | "They'd written *unexplained circumstances* in" | | 3 | "She wasn't closing this one." | | 4 | "She needed sharp." | | 5 | "Her lungs burned." | | 6 | "She'd seen that look on" | | 7 | "He cut through a gap" | | 8 | "He didn't look back this" | | 9 | "He slid through the gap" | | 10 | "She stood in front of" | | 11 | "I'll be right behind you.*" | | 12 | "He hadn't been." | | 13 | "She pressed her palm flat" | | 14 | "It meant traffic." | | 15 | "She thought of the file" | | 16 | "She put one foot on" |
| | ratio | 0.288 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 59 | | matches | | 0 | "The suspect's trainers slapped through" | | 1 | "She didn't slow down." | | 2 | "That's all she'd given him" | | 3 | "The man in the grey" | | 4 | "Camden at midnight smelled of" | | 5 | "She'd clocked him leaving a" | | 6 | "The kind of things that" | | 7 | "They'd written *unexplained circumstances* in" | | 8 | "She wasn't closing this one." | | 9 | "The hoodie ducked under scaffolding," | | 10 | "The pain sharpened everything." | | 11 | "She needed sharp." | | 12 | "Quinn's boots hit the wet" | | 13 | "Her lungs burned." | | 14 | "She'd seen that look on" | | 15 | "The ones who mentioned things" | | 16 | "He cut through a gap" | | 17 | "A rusted sign, letters missing," | | 18 | "A Tube station." | | 19 | "He didn't look back this" |
| | ratio | 0.678 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 20.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 7 | | matches | | 0 | "She'd clocked him leaving a lock-up off Chalk Farm Road, a package wrapped in oilcloth under one arm, moving with the particular twitchiness of a man who knew e…" | | 1 | "Actually laughed, a bright bark of sound that carried over his shoulder as he vaulted a low fence into a car park she didn't recognise, all skips and abandoned …" | | 2 | "He cut through a gap in a chain-link fence she'd have sworn was too narrow for a grown man, and came out into the mouth of an old service road that ran along a …" | | 3 | "Below, faint but unmistakable, she heard something that had no business existing under Camden at midnight." | | 4 | "A bell, struck once, its note stretching out longer than any bell she'd ever heard, refusing to fade." | | 5 | "*Unexplained circumstances.* Two words that had swallowed a man whole and left her holding his coffee mug and his half-finished paperwork and nothing else." | | 6 | "The air that met her smelled of tallow candles and rust and something sweeter underneath, something like old blood gone to amber." |
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| 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 | |