| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1175 | | 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) | |
| 87.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1175 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "structure" | | 1 | "warmth" | | 2 | "footsteps" |
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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 | 67 | | matches | (empty) | |
| 78.89% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 76 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1182 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 1049 | | uniqueNames | 15 | | maxNameDensity | 0.19 | | worstName | "Street" | | maxWindowNameDensity | 0.5 | | worstWindowName | "Street" | | discoveredNames | | Old | 1 | | Compton | 1 | | Street | 2 | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 2 | | Twelve | 1 | | Golden | 1 | | Square | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Underground | 1 | | Frith | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Square" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Golden" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Road" | | 7 | "Frith" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | 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 | 1182 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 76 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 36.94 | | std | 31.69 | | cv | 0.858 | | sampleLengths | | 0 | 64 | | 1 | 57 | | 2 | 6 | | 3 | 5 | | 4 | 34 | | 5 | 29 | | 6 | 11 | | 7 | 7 | | 8 | 116 | | 9 | 14 | | 10 | 18 | | 11 | 14 | | 12 | 10 | | 13 | 38 | | 14 | 11 | | 15 | 9 | | 16 | 12 | | 17 | 105 | | 18 | 11 | | 19 | 125 | | 20 | 25 | | 21 | 41 | | 22 | 52 | | 23 | 63 | | 24 | 45 | | 25 | 76 | | 26 | 42 | | 27 | 12 | | 28 | 16 | | 29 | 33 | | 30 | 47 | | 31 | 34 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 67 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 163 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 76 | | ratio | 0.066 | | matches | | 0 | "Whatever he saw in her face — the cropped salt-and-pepper hair plastered dark with rain, the jaw set like a draught excluder rail — decided it for him." | | 1 | "Or he made a choice — she never worked out which." | | 2 | "He hauled the gate wide, squeezed through — and the thing he'd used slipped from his fingers, bounced once on the wet concrete, and skittered into a puddle." | | 3 | "Somewhere deeper, something sang — one long, clear note that no human throat should have held for so long." | | 4 | "Wait, and the trail went cold and the gatekeeper — because there was always a gatekeeper — decided the pale woman with the radio wasn't welcome." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1051 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.029495718363463368 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009514747859181732 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 76 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 76 | | mean | 15.55 | | std | 10.8 | | cv | 0.695 | | sampleLengths | | 0 | 23 | | 1 | 41 | | 2 | 12 | | 3 | 16 | | 4 | 29 | | 5 | 6 | | 6 | 5 | | 7 | 4 | | 8 | 28 | | 9 | 2 | | 10 | 15 | | 11 | 14 | | 12 | 11 | | 13 | 2 | | 14 | 5 | | 15 | 43 | | 16 | 19 | | 17 | 18 | | 18 | 36 | | 19 | 8 | | 20 | 6 | | 21 | 4 | | 22 | 14 | | 23 | 14 | | 24 | 10 | | 25 | 22 | | 26 | 16 | | 27 | 11 | | 28 | 9 | | 29 | 2 | | 30 | 10 | | 31 | 7 | | 32 | 11 | | 33 | 28 | | 34 | 38 | | 35 | 3 | | 36 | 18 | | 37 | 7 | | 38 | 4 | | 39 | 23 | | 40 | 17 | | 41 | 11 | | 42 | 28 | | 43 | 5 | | 44 | 41 | | 45 | 4 | | 46 | 21 | | 47 | 10 | | 48 | 16 | | 49 | 15 |
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| 59.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.42105263157894735 | | totalSentences | 76 | | uniqueOpeners | 32 | |
| 54.64% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 61 | | matches | | 0 | "Somewhere deeper, something sang —" |
| | ratio | 0.016 | |
| 56.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 61 | | matches | | 0 | "She stepped out of the" | | 1 | "She took off after him," | | 2 | "She'd lose him in four." | | 3 | "He cut left through Golden" | | 4 | "She followed, the plywood edge" | | 5 | "He was twenty meters ahead," | | 6 | "She was forty-one years old" | | 7 | "she shouted, lungs burning" | | 8 | "He cut north on Tottenham" | | 9 | "She kept the yellow mac" | | 10 | "She keyed off the radio" | | 11 | "He made a mistake at" | | 12 | "He left the main road," | | 13 | "She'd walked past it a" | | 14 | "He took them three at" | | 15 | "She took them faster." | | 16 | "He hauled the gate wide," | | 17 | "He didn't stop for it." | | 18 | "She crouched and fished the" | | 19 | "She tried again, closer to" |
| | ratio | 0.41 | |
| 41.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 61 | | matches | | 0 | "Rain came down in ropes," | | 1 | "The green neon above the" | | 2 | "Deliveries came late, always late," | | 3 | "She stepped out of the" | | 4 | "The courier looked up." | | 5 | "Whatever he saw in her" | | 6 | "She took off after him," | | 7 | "She'd lose him in four." | | 8 | "He cut left through Golden" | | 9 | "She followed, the plywood edge" | | 10 | "He was twenty meters ahead," | | 11 | "She was forty-one years old" | | 12 | "she shouted, lungs burning" | | 13 | "The courier glanced back." | | 14 | "Rain streamed off the hood" | | 15 | "He cut north on Tottenham" | | 16 | "She kept the yellow mac" | | 17 | "She keyed off the radio" | | 18 | "He made a mistake at" | | 19 | "He left the main road," |
| | ratio | 0.836 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 2 | | matches | | 0 | "From somewhere down the tunnel came a sound that stopped her hand halfway to her holster." | | 1 | "Hundreds of them, layered over each other, men and women and things that were neither, speaking languages she half-recognized and some she didn't." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "She took off, boots slapping water off the cobbles" | | 1 | "she shouted, lungs burning" |
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| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 14 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.5 | | effectiveRatio | 0.143 | |