| 82.35% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "the man said pleasantly [pleasantly]" |
| | dialogueSentences | 17 | | tagDensity | 0.412 | | leniency | 0.824 | | rawRatio | 0.143 | | effectiveRatio | 0.118 | |
| 96.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1417 | | 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) | |
| 89.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1417 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "wavering" | | 1 | "silence" | | 2 | "warmth" |
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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 | 111 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 111 | | filterMatches | | 0 | "notice" | | 1 | "think" | | 2 | "watch" |
| | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 33 | | totalWords | 1417 | | ratio | 0.023 | | matches | | 0 | "Suspect entered a disused Underground station via a gate that isn't there, guarded by a man who knew I was a police officer without being told." | | 1 | "Forty seconds," | | 2 | "Always forty seconds." | | 3 | "Run," | | 4 | "Don't." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1337 | | uniqueNames | 18 | | maxNameDensity | 0.6 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 1 | | Herrera | 8 | | Saint | 1 | | Christopher | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Met | 1 | | Quinn | 8 | | Underground | 1 | | Detective | 1 | | Sergeant | 1 | | Morris | 3 | | Market | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Quinn" | | 4 | "Sergeant" | | 5 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Chalk" | | 3 | "Farm" | | 4 | "Road" | | 5 | "Market" |
| | globalScore | 1 | | windowScore | 1 | |
| 85.06% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 2 | | matches | | 0 | "something like lightning" | | 1 | "not quite birds" |
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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 | 1417 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 121 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 32.2 | | std | 29.98 | | cv | 0.931 | | sampleLengths | | 0 | 40 | | 1 | 53 | | 2 | 99 | | 3 | 8 | | 4 | 11 | | 5 | 7 | | 6 | 79 | | 7 | 16 | | 8 | 56 | | 9 | 11 | | 10 | 78 | | 11 | 4 | | 12 | 52 | | 13 | 6 | | 14 | 11 | | 15 | 15 | | 16 | 22 | | 17 | 5 | | 18 | 18 | | 19 | 9 | | 20 | 88 | | 21 | 94 | | 22 | 26 | | 23 | 46 | | 24 | 48 | | 25 | 10 | | 26 | 7 | | 27 | 95 | | 28 | 4 | | 29 | 25 | | 30 | 3 | | 31 | 3 | | 32 | 5 | | 33 | 34 | | 34 | 11 | | 35 | 4 | | 36 | 14 | | 37 | 38 | | 38 | 78 | | 39 | 17 | | 40 | 77 | | 41 | 59 | | 42 | 7 | | 43 | 24 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 111 | | matches | | 0 | "been trained" | | 1 | "been closed" | | 2 | "being told" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 221 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 121 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1342 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.027570789865871834 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0037257824143070045 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 11.71 | | std | 8.64 | | cv | 0.738 | | sampleLengths | | 0 | 10 | | 1 | 30 | | 2 | 10 | | 3 | 23 | | 4 | 20 | | 5 | 6 | | 6 | 41 | | 7 | 17 | | 8 | 15 | | 9 | 20 | | 10 | 5 | | 11 | 3 | | 12 | 9 | | 13 | 2 | | 14 | 2 | | 15 | 5 | | 16 | 19 | | 17 | 13 | | 18 | 17 | | 19 | 2 | | 20 | 4 | | 21 | 24 | | 22 | 5 | | 23 | 11 | | 24 | 9 | | 25 | 17 | | 26 | 2 | | 27 | 2 | | 28 | 10 | | 29 | 16 | | 30 | 11 | | 31 | 28 | | 32 | 7 | | 33 | 3 | | 34 | 21 | | 35 | 19 | | 36 | 4 | | 37 | 16 | | 38 | 8 | | 39 | 10 | | 40 | 3 | | 41 | 15 | | 42 | 6 | | 43 | 6 | | 44 | 5 | | 45 | 9 | | 46 | 6 | | 47 | 10 | | 48 | 11 | | 49 | 1 |
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| 68.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4628099173553719 | | totalSentences | 121 | | uniqueOpeners | 56 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 100 | | matches | | 0 | "Twice she saw the gleam" | | 1 | "Then he ran." | | 2 | "Then a figure stepped out" | | 3 | "*Always forty seconds.*" | | 4 | "Somewhere below, a bell rang" | | 5 | "Then she did: forty yards" |
| | ratio | 0.06 | |
| 68.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 100 | | matches | | 0 | "He kept it tucked against" | | 1 | "She had followed him from" | | 2 | "She had sat across from" | | 3 | "It was the look of" | | 4 | "She'd tailed the cab, then" | | 5 | "He hadn't spotted her until" | | 6 | "He'd looked straight at her." | | 7 | "Her voice cracked off the" | | 8 | "She hadn't expected him to." | | 9 | "He cut left down a" | | 10 | "She fixed her attention the" | | 11 | "He vaulted a low wall." | | 12 | "She hit it with her" | | 13 | "It had been closed since" | | 14 | "He was a big man" | | 15 | "She stopped ten feet away," | | 16 | "She hadn't shown him a" | | 17 | "She hadn't said a word." | | 18 | "He said it kindly, the" | | 19 | "She could hear what the" |
| | ratio | 0.38 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 100 | | matches | | 0 | "Rain came down on Camden" | | 1 | "Quinn ran through it with" | | 2 | "Tomás Herrera ran well for" | | 3 | "He kept it tucked against" | | 4 | "She had followed him from" | | 5 | "She had sat across from" | | 6 | "It was the look of" | | 7 | "She'd tailed the cab, then" | | 8 | "He hadn't spotted her until" | | 9 | "He'd looked straight at her." | | 10 | "Her voice cracked off the" | | 11 | "She hadn't expected him to." | | 12 | "He cut left down a" | | 13 | "She fixed her attention the" | | 14 | "A stack of crates." | | 15 | "The pale flash of his" | | 16 | "He vaulted a low wall." | | 17 | "She hit it with her" | | 18 | "It had been closed since" | | 19 | "Herrera slowed at the gates," |
| | ratio | 0.71 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 100 | | matches | | 0 | "If she walked away, the" | | 1 | "If she went down, no" |
| | ratio | 0.02 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 4 | | matches | | 0 | "Quinn ran through it with her coat flapping open and her shoes slapping black water out of the gutters, and she kept her eyes on the man forty yards ahead." | | 1 | "*Suspect entered a disused Underground station via a gate that isn't there, guarded by a man who knew I was a police officer without being told.* She could hear…" | | 2 | "The rain cut off behind her as if a hand had closed over it, and warmth rose up the stairwell, thick with smells she couldn't sort: woodsmoke, hot sugar, wet ir…" | | 3 | "A woman sold vials of something dark from a tray hung round her neck, and a child with eyes too old for her face sat cross-legged beside a cage of birds that we…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.294 | | leniency | 0.588 | | rawRatio | 0 | | effectiveRatio | 0 | |