| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.021 | | leniency | 0.043 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2207 | | 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) | |
| 79.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2207 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "electric" | | 1 | "footfall" | | 2 | "echoed" | | 3 | "silence" | | 4 | "vibrated" | | 5 | "velvet" | | 6 | "tension" | | 7 | "measured" | | 8 | "flicked" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 226 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 226 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 272 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2207 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1973 | | uniqueNames | 19 | | maxNameDensity | 1.47 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 29 | | Greek | 1 | | Street | 3 | | Old | 2 | | Compton | 1 | | Raven | 1 | | Nest | 1 | | Wardour | 1 | | East | 1 | | End | 1 | | Underground | 1 | | Kenley | 1 | | Morris | 3 | | Whitechapel | 1 | | London | 1 | | Spanish | 1 | | Rain | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Morris" | | 5 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "Old" | | 4 | "Compton" | | 5 | "Wardour" | | 6 | "East" | | 7 | "End" | | 8 | "Whitechapel" | | 9 | "London" |
| | globalScore | 0.765 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 126 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.906 | | wordCount | 2207 | | matches | | 0 | "Not one of the lads she’d had under surveillance outside Greek Street, but he had come out of the same meeting" | | 1 | "No prints but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 272 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 125 | | mean | 17.66 | | std | 23.46 | | cv | 1.329 | | sampleLengths | | 0 | 80 | | 1 | 2 | | 2 | 16 | | 3 | 52 | | 4 | 3 | | 5 | 67 | | 6 | 26 | | 7 | 6 | | 8 | 6 | | 9 | 8 | | 10 | 13 | | 11 | 28 | | 12 | 5 | | 13 | 2 | | 14 | 10 | | 15 | 7 | | 16 | 2 | | 17 | 1 | | 18 | 42 | | 19 | 5 | | 20 | 112 | | 21 | 4 | | 22 | 47 | | 23 | 66 | | 24 | 4 | | 25 | 3 | | 26 | 11 | | 27 | 2 | | 28 | 21 | | 29 | 42 | | 30 | 6 | | 31 | 41 | | 32 | 18 | | 33 | 5 | | 34 | 5 | | 35 | 9 | | 36 | 54 | | 37 | 6 | | 38 | 8 | | 39 | 1 | | 40 | 11 | | 41 | 4 | | 42 | 7 | | 43 | 4 | | 44 | 1 | | 45 | 17 | | 46 | 23 | | 47 | 12 | | 48 | 8 | | 49 | 100 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 226 | | matches | | 0 | "been ripped" | | 1 | "been stripped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 347 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 272 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1984 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.022681451612903226 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0020161290322580645 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 272 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 272 | | mean | 8.11 | | std | 6.95 | | cv | 0.857 | | sampleLengths | | 0 | 14 | | 1 | 34 | | 2 | 4 | | 3 | 5 | | 4 | 23 | | 5 | 2 | | 6 | 16 | | 7 | 5 | | 8 | 2 | | 9 | 2 | | 10 | 30 | | 11 | 3 | | 12 | 10 | | 13 | 3 | | 14 | 8 | | 15 | 3 | | 16 | 8 | | 17 | 17 | | 18 | 5 | | 19 | 26 | | 20 | 6 | | 21 | 3 | | 22 | 17 | | 23 | 6 | | 24 | 6 | | 25 | 8 | | 26 | 13 | | 27 | 28 | | 28 | 5 | | 29 | 2 | | 30 | 10 | | 31 | 2 | | 32 | 5 | | 33 | 2 | | 34 | 1 | | 35 | 9 | | 36 | 24 | | 37 | 9 | | 38 | 5 | | 39 | 4 | | 40 | 13 | | 41 | 7 | | 42 | 11 | | 43 | 28 | | 44 | 21 | | 45 | 24 | | 46 | 4 | | 47 | 4 | | 48 | 7 | | 49 | 2 |
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| 67.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.43014705882352944 | | totalSentences | 272 | | uniqueOpeners | 117 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 187 | | matches | | 0 | "Then the cut-throughs between them," | | 1 | "Then he had run." | | 2 | "Instead of the open main" | | 3 | "Then she saw what lay" | | 4 | "Then silence folded over the" | | 5 | "Then her fingers touched the" |
| | ratio | 0.032 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 187 | | matches | | 0 | "Her shoes slapped water." | | 1 | "He skimmed a puddle, braced" | | 2 | "Her sole slid." | | 3 | "She hit the wall with" | | 4 | "Her radio crackled from her" | | 5 | "She snatched the set up" | | 6 | "She knew this district." | | 7 | "She had spent three weeks" | | 8 | "He crossed Wardour Street against" | | 9 | "He had speed, but panic" | | 10 | "His right arm clamped too" | | 11 | "He looked back twice in" | | 12 | "He skidded around a parked" | | 13 | "His accent carried East End" | | 14 | "He made one at the" | | 15 | "He took the stairs three" | | 16 | "She sucked air in through" | | 17 | "Her phone vibrated in her" | | 18 | "She pulled it free." | | 19 | "She stared into the black" |
| | ratio | 0.225 | |
| 85.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 140 | | totalSentences | 187 | | matches | | 0 | "Rain needled off the awnings" | | 1 | "Detective Harlow Quinn cut between" | | 2 | "Her shoes slapped water." | | 3 | "Breath burned in her throat." | | 4 | "The word tore out of" | | 5 | "The runner glanced back once." | | 6 | "That bought suspicion." | | 7 | "The canvas satchel strapped across" | | 8 | "Quinn drove harder." | | 9 | "The city at this hour" | | 10 | "The man knew those teeth." | | 11 | "He skimmed a puddle, braced" | | 12 | "Quinn followed and nearly went" | | 13 | "Her sole slid." | | 14 | "She hit the wall with" | | 15 | "Her radio crackled from her" | | 16 | "She snatched the set up" | | 17 | "The suspect burst from the" | | 18 | "Rain thudded against metal shutters." | | 19 | "A black cab slewed round" |
| | ratio | 0.749 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 187 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 79 | | technicalSentenceCount | 1 | | matches | | 0 | "A woman in a velvet coat laid out rows of glass vials on black felt, each one holding a liquid that caught light like fish scales." |
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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 | 1 | | fancyTags | | | dialogueSentences | 47 | | tagDensity | 0.021 | | leniency | 0.043 | | rawRatio | 1 | | effectiveRatio | 0.043 | |