| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 3 | | adverbTags | | 0 | "Quinn moved then [then]" | | 1 | "Powell said quietly [quietly]" | | 2 | "Eva said quietly [quietly]" |
| | dialogueSentences | 72 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.125 | | effectiveRatio | 0.083 | |
| 87.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2031 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "deliberately" | | count | 1 |
| | 4 | |
| | highlights | | 0 | "slightly" | | 1 | "lazily" | | 2 | "slowly" | | 3 | "deliberately" | | 4 | "very" |
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| 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) | |
| 65.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2031 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "silence" | | 1 | "chill" | | 2 | "traced" | | 3 | "pristine" | | 4 | "glint" | | 5 | "footsteps" | | 6 | "etched" | | 7 | "intricate" | | 8 | "quivered" | | 9 | "scanned" | | 10 | "tension" | | 11 | "gloom" | | 12 | "processed" |
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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 | 164 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 164 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 207 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 1 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0.005 | | matches | | 0 | "\"No obvious signs of struggle. No defensive wounds. Wallet's untouched—credit cards, sixty quid cash, ID. Name's Theo Finch. Works in private equity.\" Powell paused." |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 16 | | totalWords | 2013 | | ratio | 0.008 | | matches | | 0 | "\"It's a market, Quinn. Not a place. It moves. You need a token to see it.\"" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 75 | | wordCount | 1381 | | uniqueNames | 23 | | maxNameDensity | 1.52 | | worstName | "Powell" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Powell" | | discoveredNames | | Harlow | 1 | | Quinn | 16 | | Camden | 1 | | Tube | 1 | | Met | 1 | | Powell | 21 | | Pity | 1 | | London | 3 | | Underground | 2 | | Nineties | 1 | | Oxford | 1 | | Scenes | 2 | | Crime | 2 | | Morris | 2 | | Five | 1 | | Detective | 2 | | Inspector | 2 | | Montague | 7 | | Finch | 2 | | British | 1 | | Museum | 1 | | Kowalski | 1 | | Eva | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Met" | | 3 | "Powell" | | 4 | "Morris" | | 5 | "Montague" | | 6 | "Finch" | | 7 | "Kowalski" | | 8 | "Eva" |
| | places | | 0 | "London" | | 1 | "Oxford" | | 2 | "Scenes" | | 3 | "British" |
| | globalScore | 0.74 | | windowScore | 0.5 | |
| 97.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 95 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like he'd rather be anywhere else" | | 1 | "as if taking inventory" |
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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 | 2013 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 207 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 27.96 | | std | 21.79 | | cv | 0.779 | | sampleLengths | | 0 | 9 | | 1 | 85 | | 2 | 12 | | 3 | 47 | | 4 | 7 | | 5 | 63 | | 6 | 54 | | 7 | 62 | | 8 | 60 | | 9 | 5 | | 10 | 5 | | 11 | 27 | | 12 | 66 | | 13 | 29 | | 14 | 2 | | 15 | 58 | | 16 | 15 | | 17 | 22 | | 18 | 4 | | 19 | 66 | | 20 | 5 | | 21 | 39 | | 22 | 50 | | 23 | 40 | | 24 | 5 | | 25 | 37 | | 26 | 6 | | 27 | 2 | | 28 | 70 | | 29 | 2 | | 30 | 4 | | 31 | 8 | | 32 | 37 | | 33 | 12 | | 34 | 53 | | 35 | 17 | | 36 | 43 | | 37 | 56 | | 38 | 27 | | 39 | 2 | | 40 | 3 | | 41 | 10 | | 42 | 42 | | 43 | 36 | | 44 | 17 | | 45 | 60 | | 46 | 42 | | 47 | 2 | | 48 | 21 | | 49 | 19 |
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| 94.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 164 | | matches | | 0 | "was supposed" | | 1 | "was turned" | | 2 | "were laid" | | 3 | "were gone" | | 4 | "been placed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 222 | | matches | | 0 | "wasn't spinning" | | 1 | "was pointing" |
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| 87.65% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 2 | | flaggedSentences | 4 | | totalSentences | 207 | | ratio | 0.019 | | matches | | 0 | "The Met had cordoned the entrance two blocks back; the usual flashing blues and hi-vis barriers gave way to industrial tape and a single uniform who looked like he'd rather be anywhere else." | | 1 | "But the light also caught something else—a glint on the tracks, halfway between the body and the dead end." | | 2 | "He saw the inconsistencies; she could tell by the tension around his eyes." | | 3 | "Behind her, the station entrance yawned—a square of deeper black against the night." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1392 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.03017241379310345 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.010775862068965518 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 207 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 207 | | mean | 9.72 | | std | 6.93 | | cv | 0.713 | | sampleLengths | | 0 | 9 | | 1 | 23 | | 2 | 33 | | 3 | 5 | | 4 | 24 | | 5 | 4 | | 6 | 8 | | 7 | 14 | | 8 | 12 | | 9 | 15 | | 10 | 6 | | 11 | 7 | | 12 | 10 | | 13 | 2 | | 14 | 2 | | 15 | 17 | | 16 | 7 | | 17 | 6 | | 18 | 19 | | 19 | 22 | | 20 | 4 | | 21 | 13 | | 22 | 7 | | 23 | 8 | | 24 | 13 | | 25 | 5 | | 26 | 24 | | 27 | 20 | | 28 | 3 | | 29 | 15 | | 30 | 4 | | 31 | 17 | | 32 | 21 | | 33 | 5 | | 34 | 5 | | 35 | 8 | | 36 | 19 | | 37 | 17 | | 38 | 5 | | 39 | 19 | | 40 | 5 | | 41 | 6 | | 42 | 14 | | 43 | 25 | | 44 | 4 | | 45 | 2 | | 46 | 45 | | 47 | 13 | | 48 | 2 | | 49 | 13 |
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| 53.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3671497584541063 | | totalSentences | 207 | | uniqueOpeners | 76 | |
| 23.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 144 | | matches | | 0 | "Then she turned and walked" |
| | ratio | 0.007 | |
| 92.22% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 144 | | matches | | 0 | "It didn't bite through your" | | 1 | "Her left wrist itched." | | 2 | "She didn't look at the" | | 3 | "His torch beam cut a" | | 4 | "He ran a hand over" | | 5 | "He led her past the" | | 6 | "It sat upright nearby, a" | | 7 | "He didn't consult his notes." | | 8 | "She stood a metre back," | | 9 | "His head was turned slightly" | | 10 | "she asked, voice flat" | | 11 | "She pointed with her chin" | | 12 | "It ended at the body." | | 13 | "She shone her light directly" | | 14 | "Her wrist itched again." | | 15 | "She ignored it, moving toward" | | 16 | "She knew that smell." | | 17 | "She'd encountered it exactly once" | | 18 | "Her torch beam hit the" | | 19 | "She walked toward it, the" |
| | ratio | 0.319 | |
| 39.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 121 | | totalSentences | 144 | | matches | | 0 | "The conductor's log should have" | | 1 | "That was Harlow Quinn’s first" | | 2 | "The Met had cordoned the" | | 3 | "The cold here was different." | | 4 | "It didn't bite through your" | | 5 | "Her left wrist itched." | | 6 | "She didn't look at the" | | 7 | "DS Powell appeared from behind" | | 8 | "His torch beam cut a" | | 9 | "He ran a hand over" | | 10 | "Powell said, and there was" | | 11 | "He led her past the" | | 12 | "The station was supposed to" | | 13 | "The body lay midway along" | | 14 | "A man, late twenties." | | 15 | "It sat upright nearby, a" | | 16 | "He didn't consult his notes." | | 17 | "Quinn didn't kneel." | | 18 | "She stood a metre back," | | 19 | "The posture was wrong." |
| | ratio | 0.84 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 144 | | matches | (empty) | | ratio | 0 | |
| 64.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 6 | | matches | | 0 | "The Met had cordoned the entrance two blocks back; the usual flashing blues and hi-vis barriers gave way to industrial tape and a single uniform who looked like…" | | 1 | "It didn't bite through your coat so much as settle into the marrow, a damp, earthy chill that smelled of iron and stale electricity." | | 2 | "Well-dressed in a dark wool coat that was too clean for this place." | | 3 | "His eyes scanned the platform ceiling, the pillars, the dark arch of the tunnel, as if taking inventory." | | 4 | "A man from the world of credit cards and private equity who had, for some reason, gone slumming in a place that traded in different currencies altogether." | | 5 | "Eva Kowalski knew about things that didn't make it into evidence bags." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | 0 | "Powell muttered (mutter)" |
| | dialogueSentences | 72 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.125 | | effectiveRatio | 0.028 | |