| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.053 | | leniency | 0.105 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1324 | | 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) | |
| 92.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1324 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 128 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 128 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 146 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1324 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.74% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1172 | | uniqueNames | 17 | | maxNameDensity | 1.37 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 16 | | Herrera | 13 | | Christopher | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Morris | 2 | | Vauxhall | 1 | | Tube | 1 | | Camden | 2 | | Town | 1 | | London | 1 | | Rain | 3 | | Eighteen | 3 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Rain" |
| | places | | 0 | "Christopher" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "Vauxhall" | | 5 | "Camden" | | 6 | "Town" | | 7 | "London" |
| | globalScore | 0.817 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | 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 | 1324 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 146 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 22.83 | | std | 19.69 | | cv | 0.863 | | sampleLengths | | 0 | 79 | | 1 | 51 | | 2 | 19 | | 3 | 5 | | 4 | 31 | | 5 | 6 | | 6 | 45 | | 7 | 46 | | 8 | 45 | | 9 | 31 | | 10 | 73 | | 11 | 47 | | 12 | 17 | | 13 | 26 | | 14 | 25 | | 15 | 27 | | 16 | 51 | | 17 | 14 | | 18 | 46 | | 19 | 36 | | 20 | 12 | | 21 | 3 | | 22 | 8 | | 23 | 5 | | 24 | 2 | | 25 | 5 | | 26 | 5 | | 27 | 3 | | 28 | 4 | | 29 | 3 | | 30 | 6 | | 31 | 6 | | 32 | 3 | | 33 | 40 | | 34 | 49 | | 35 | 13 | | 36 | 7 | | 37 | 4 | | 38 | 4 | | 39 | 34 | | 40 | 39 | | 41 | 19 | | 42 | 8 | | 43 | 1 | | 44 | 7 | | 45 | 28 | | 46 | 34 | | 47 | 4 | | 48 | 6 | | 49 | 8 |
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| 88.82% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 128 | | matches | | 0 | "was built" | | 1 | "been worn" | | 2 | "was gone" | | 3 | "were tiled" | | 4 | "been walled" | | 5 | "been broken" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 198 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 146 | | ratio | 0.014 | | matches | | 0 | "Her knee caught the table edge; the glass of water tipped and spilled across maps." | | 1 | "The closely cropped hair had grown since Morris died; it used to be nearly white at the temples, now it had grey threading through black." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1173 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.01619778346121057 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0034100596760443308 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 146 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 146 | | mean | 9.07 | | std | 6.12 | | cv | 0.674 | | sampleLengths | | 0 | 18 | | 1 | 21 | | 2 | 16 | | 3 | 11 | | 4 | 4 | | 5 | 9 | | 6 | 7 | | 7 | 18 | | 8 | 10 | | 9 | 2 | | 10 | 14 | | 11 | 6 | | 12 | 13 | | 13 | 5 | | 14 | 11 | | 15 | 20 | | 16 | 6 | | 17 | 7 | | 18 | 15 | | 19 | 9 | | 20 | 3 | | 21 | 11 | | 22 | 6 | | 23 | 10 | | 24 | 6 | | 25 | 9 | | 26 | 6 | | 27 | 2 | | 28 | 7 | | 29 | 10 | | 30 | 13 | | 31 | 22 | | 32 | 12 | | 33 | 7 | | 34 | 12 | | 35 | 4 | | 36 | 4 | | 37 | 11 | | 38 | 1 | | 39 | 1 | | 40 | 1 | | 41 | 4 | | 42 | 25 | | 43 | 12 | | 44 | 10 | | 45 | 10 | | 46 | 10 | | 47 | 3 | | 48 | 7 | | 49 | 17 |
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| 49.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3356164383561644 | | totalSentences | 146 | | uniqueOpeners | 49 | |
| 56.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 118 | | matches | | 0 | "Only a metal door set" | | 1 | "Somewhere beyond it, Herrera's voice" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 118 | | matches | | 0 | "They were a wall." | | 1 | "He came out with a" | | 2 | "He noticed her at the" | | 3 | "His warm brown eyes narrowed," | | 4 | "Her knee caught the table" | | 5 | "She was three steps behind" | | 6 | "His boots splashed through a" | | 7 | "Her right calf pulled, a" | | 8 | "She came out into Charing" | | 9 | "His curly hair was dark" | | 10 | "She did not shout." | | 11 | "She stepped into the flow" | | 12 | "Her jaw tightened, sharp as" | | 13 | "He pulled a set of" | | 14 | "Her unmarked Vauxhall sat two" | | 15 | "She got in, started the" | | 16 | "She watched Herrera pay through" | | 17 | "He had the medical bag." | | 18 | "He looked back once." | | 19 | "She ducked beneath the dashboard." |
| | ratio | 0.229 | |
| 19.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 118 | | matches | | 0 | "Rain ran down the green" | | 1 | "Detective Harlow Quinn sat with" | | 2 | "The volumes looked solid, encyclopaedias," | | 3 | "They were a wall." | | 4 | "The wall had moved twice" | | 5 | "Tomás Herrera had gone through" | | 6 | "He came out with a" | | 7 | "Quinn's left thumb brushed the" | | 8 | "He noticed her at the" | | 9 | "His warm brown eyes narrowed," | | 10 | "The old barman polished a" | | 11 | "The photographs on the wall," | | 12 | "Herrera turned for the side" | | 13 | "Quinn moved before the chair" | | 14 | "Her knee caught the table" | | 15 | "Herrera had already hit the" | | 16 | "The hinges shrieked." | | 17 | "She was three steps behind" | | 18 | "The alley was narrow and" | | 19 | "A green bin stood against" |
| | ratio | 0.881 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 1 | | matches | | 0 | "Herrera kept going, cutting across a forecourt where a black cab stood with its meter blinking, empty." |
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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 | 19 | | tagDensity | 0.053 | | leniency | 0.105 | | rawRatio | 0 | | effectiveRatio | 0 | |