| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1335 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 88.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1335 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "shattered" | | 1 | "loomed" | | 2 | "calculated" |
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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 | 97 | | matches | (empty) | |
| 98.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1335 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.41% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1236 | | uniqueNames | 10 | | maxNameDensity | 1.05 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | Harlow | 1 | | Quinn | 13 | | High | 1 | | Street | 2 | | Inverness | 1 | | Canal | 1 | | Road | 1 | | Tube | 1 | | Rain | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Canal" | | 3 | "Rain" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Inverness" | | 4 | "Road" |
| | globalScore | 0.974 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | 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 | 1335 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 106 | | matches | | 0 | "fixed that recognition" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 58.04 | | std | 39.6 | | cv | 0.682 | | sampleLengths | | 0 | 95 | | 1 | 2 | | 2 | 76 | | 3 | 69 | | 4 | 22 | | 5 | 72 | | 6 | 85 | | 7 | 8 | | 8 | 20 | | 9 | 86 | | 10 | 86 | | 11 | 99 | | 12 | 63 | | 13 | 11 | | 14 | 17 | | 15 | 85 | | 16 | 62 | | 17 | 13 | | 18 | 144 | | 19 | 11 | | 20 | 120 | | 21 | 20 | | 22 | 69 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 203 | | matches | (empty) | |
| 61.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 3 | | flaggedSentences | 3 | | totalSentences | 106 | | ratio | 0.028 | | matches | | 0 | "Olive skin shone under the rain; dark curls clung to his forehead." | | 1 | "Pain flared sharp and clean; she filed it away." | | 2 | "The man forced a group of three pedestrians apart; one man in a leather jacket yelled after him." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1244 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.023311897106109324 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003215434083601286 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 12.59 | | std | 6.4 | | cv | 0.508 | | sampleLengths | | 0 | 10 | | 1 | 22 | | 2 | 24 | | 3 | 22 | | 4 | 17 | | 5 | 2 | | 6 | 7 | | 7 | 12 | | 8 | 2 | | 9 | 22 | | 10 | 15 | | 11 | 18 | | 12 | 12 | | 13 | 12 | | 14 | 8 | | 15 | 12 | | 16 | 9 | | 17 | 5 | | 18 | 11 | | 19 | 22 | | 20 | 7 | | 21 | 5 | | 22 | 19 | | 23 | 5 | | 24 | 15 | | 25 | 21 | | 26 | 11 | | 27 | 16 | | 28 | 7 | | 29 | 12 | | 30 | 14 | | 31 | 6 | | 32 | 6 | | 33 | 13 | | 34 | 8 | | 35 | 8 | | 36 | 12 | | 37 | 14 | | 38 | 12 | | 39 | 6 | | 40 | 9 | | 41 | 13 | | 42 | 4 | | 43 | 19 | | 44 | 9 | | 45 | 12 | | 46 | 25 | | 47 | 10 | | 48 | 12 | | 49 | 9 |
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| 74.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4716981132075472 | | totalSentences | 106 | | uniqueOpeners | 50 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 96 | | matches | | 0 | "Her boots found purchase on" | | 1 | "He put his head down" | | 2 | "She keyed the radio clipped" | | 3 | "He ducked hard right between" | | 4 | "He ran west along the" | | 5 | "He laughed once, short and" | | 6 | "She fixed that recognition of" | | 7 | "She lost half a second" | | 8 | "They left the main road" | | 9 | "She answered between hard breaths." | | 10 | "She stopped three metres short." | | 11 | "She kept her eyes on" | | 12 | "Her brow knit once, a" | | 13 | "She drew a single slow" | | 14 | "she told the radio" |
| | ratio | 0.156 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 96 | | matches | | 0 | "Rain drummed Camden's night streets" | | 1 | "Water sheeted across the asphalt" | | 2 | "Detective Harlow Quinn rounded the" | | 3 | "The worn leather strap of" | | 4 | "The shout cracked against brick" | | 5 | "The man flinched, then cut" | | 6 | "Her boots found purchase on" | | 7 | "A young couple with plastic" | | 8 | "Grease from a late food" | | 9 | "The alley spat them both" | | 10 | "Posters for forgotten gigs peeled" | | 11 | "The suspect risked a glance" | | 12 | "Olive skin shone under the" | | 13 | "He put his head down" | | 14 | "Quinn closed another three paces." | | 15 | "She keyed the radio clipped" | | 16 | "Static answered first, then a" | | 17 | "The man heard the transmission." | | 18 | "He ducked hard right between" | | 19 | "Quinn vaulted the sliding mess." |
| | ratio | 0.833 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 3 | | matches | | 0 | "Twenty metres ahead the suspect's black hoodie vanished and reappeared between clusters of late-night drinkers who spilled out of a pub doorway." | | 1 | "Her boots found purchase on slick cobbles with the same even cadence that had carried her through eighteen years of metropolitan nights." | | 2 | "In its place rose a mixture of paraffin lamps, dried herbs, hot metal, and a copper note that sat low in the throat." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0 | | effectiveRatio | 0 | |