| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 3 | | adverbTags | | 0 | "Silas looked around [around]" | | 1 | "Marcus knocked back [back]" | | 2 | "Silas moved around [around]" |
| | dialogueSentences | 92 | | tagDensity | 0.293 | | leniency | 0.587 | | rawRatio | 0.111 | | effectiveRatio | 0.065 | |
| 86.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1527 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 60.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1527 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "weight" | | 1 | "throb" | | 2 | "shattered" | | 3 | "standard" | | 4 | "silence" | | 5 | "traced" | | 6 | "crystal" | | 7 | "warmth" | | 8 | "pulse" | | 9 | "footsteps" | | 10 | "unreadable" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 170 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1528 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 967 | | uniqueNames | 11 | | maxNameDensity | 3.52 | | worstName | "Marcus" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Marcus" | | discoveredNames | | Marcus | 34 | | Webb | 2 | | Vauxhall | 1 | | Cross | 1 | | Silas | 22 | | Brno | 1 | | Czech | 1 | | Old | 1 | | Service | 2 | | Started | 1 | | Silence | 1 |
| | persons | | | places | | 0 | "Vauxhall" | | 1 | "Brno" | | 2 | "Czech" | | 3 | "Old" | | 4 | "Service" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "s's career and apparently ended more than tha" |
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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 | 1528 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 170 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 92 | | mean | 16.61 | | std | 16.25 | | cv | 0.978 | | sampleLengths | | 0 | 13 | | 1 | 20 | | 2 | 20 | | 3 | 44 | | 4 | 86 | | 5 | 7 | | 6 | 6 | | 7 | 25 | | 8 | 32 | | 9 | 21 | | 10 | 42 | | 11 | 26 | | 12 | 7 | | 13 | 8 | | 14 | 7 | | 15 | 37 | | 16 | 13 | | 17 | 19 | | 18 | 6 | | 19 | 18 | | 20 | 54 | | 21 | 3 | | 22 | 3 | | 23 | 30 | | 24 | 51 | | 25 | 5 | | 26 | 18 | | 27 | 9 | | 28 | 13 | | 29 | 12 | | 30 | 2 | | 31 | 2 | | 32 | 43 | | 33 | 6 | | 34 | 16 | | 35 | 10 | | 36 | 3 | | 37 | 4 | | 38 | 24 | | 39 | 3 | | 40 | 1 | | 41 | 1 | | 42 | 3 | | 43 | 5 | | 44 | 8 | | 45 | 52 | | 46 | 5 | | 47 | 9 | | 48 | 29 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 105 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 174 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 170 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 970 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.032989690721649485 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007216494845360825 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 170 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 170 | | mean | 8.99 | | std | 7.39 | | cv | 0.822 | | sampleLengths | | 0 | 13 | | 1 | 20 | | 2 | 15 | | 3 | 5 | | 4 | 3 | | 5 | 5 | | 6 | 36 | | 7 | 9 | | 8 | 21 | | 9 | 18 | | 10 | 20 | | 11 | 4 | | 12 | 4 | | 13 | 10 | | 14 | 7 | | 15 | 6 | | 16 | 6 | | 17 | 6 | | 18 | 9 | | 19 | 4 | | 20 | 4 | | 21 | 16 | | 22 | 6 | | 23 | 6 | | 24 | 14 | | 25 | 7 | | 26 | 8 | | 27 | 24 | | 28 | 6 | | 29 | 4 | | 30 | 19 | | 31 | 3 | | 32 | 3 | | 33 | 1 | | 34 | 7 | | 35 | 8 | | 36 | 7 | | 37 | 23 | | 38 | 14 | | 39 | 6 | | 40 | 7 | | 41 | 11 | | 42 | 3 | | 43 | 5 | | 44 | 6 | | 45 | 18 | | 46 | 20 | | 47 | 5 | | 48 | 3 | | 49 | 26 |
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| 71.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.47058823529411764 | | totalSentences | 170 | | uniqueOpeners | 80 | |
| 37.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 90 | | matches | | 0 | "Bright blue eyes, straight black" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 90 | | matches | | 0 | "He looked awful." | | 1 | "His face had hollowed, cheeks" | | 2 | "He needed a shave." | | 3 | "He needed a meal." | | 4 | "He needed to be six" | | 5 | "He'd swallowed whatever was closest" | | 6 | "He hadn't turned the sign" | | 7 | "His hand hesitated over the" | | 8 | "His throat bobbed." | | 9 | "His hand shook." | | 10 | "They just waited." | | 11 | "They sat in his skull" | | 12 | "His fingers tightened around the" | | 13 | "He'd gone in with Marcus" | | 14 | "He could still cut glass" | | 15 | "He saw the hollowed cheeks," | | 16 | "He saw the grey in" | | 17 | "He saw the way Marcus's" | | 18 | "His movements were stiff and" | | 19 | "Her gaze moved from Silas" |
| | ratio | 0.278 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 90 | | matches | | 0 | "The whisky glass hit the" | | 1 | "Silas kept his voice level," | | 2 | "Marcus Webb laughed, that same" | | 3 | "He looked awful." | | 4 | "That was the first thing." | | 5 | "The second thing was that" | | 6 | "Marcus had always carried extra" | | 7 | "His face had hollowed, cheeks" | | 8 | "He needed a shave." | | 9 | "He needed a meal." | | 10 | "He needed to be six" | | 11 | "Silas let go of the" | | 12 | "Silas poured without asking which" | | 13 | "Marcus had never cared about" | | 14 | "He'd swallowed whatever was closest" | | 15 | "Some things hadn't changed." | | 16 | "The bar was empty." | | 17 | "Tuesday nights brought only regulars," | | 18 | "He hadn't turned the sign" | | 19 | "His hand hesitated over the" |
| | ratio | 0.933 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | 0 | "Now his collarbones jutted out" |
| | ratio | 0.011 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 4 | | matches | | 0 | "Marcus Webb laughed, that same braying donkey bray that had irritated half of Vauxhall Cross." | | 1 | "Now his collarbones jutted out like coat hangers beneath a grey jumper that might once have been black." | | 2 | "Marcus's hand found something else, a knife that materialised from nowhere, sliding from his sleeve into his palm with the ease of long practice." | | 3 | "Cold air rushed in, carrying the smell of rain and exhaust and something else, something that shouldn't have been there, something that made Marcus's whole body…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 1 | | matches | | 0 | "Marcus pulled out, the metal legs scraping against the floor" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "Marcus laughed (laugh)" |
| | dialogueSentences | 92 | | tagDensity | 0.076 | | leniency | 0.152 | | rawRatio | 0.143 | | effectiveRatio | 0.022 | |