| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1276 | | 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) | |
| 80.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1276 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulsed" | | 1 | "echo" | | 2 | "footfall" | | 3 | "pulse" |
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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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1284 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1094 | | uniqueNames | 14 | | maxNameDensity | 0.55 | | worstName | "Eva" | | maxWindowNameDensity | 2 | | worstWindowName | "Eva" | | discoveredNames | | Old | 1 | | Oaks | 1 | | Richmond | 2 | | Park | 2 | | Isolde | 1 | | Petersham | 1 | | Gate | 1 | | Eva | 6 | | Heathrow | 1 | | Peckham | 1 | | November | 1 | | Golden | 1 | | Empress | 1 | | Rory | 3 |
| | persons | | | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Gate" | | 3 | "Peckham" | | 4 | "November" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | 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 | 1284 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 115 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 23.35 | | std | 20.64 | | cv | 0.884 | | sampleLengths | | 0 | 16 | | 1 | 7 | | 2 | 26 | | 3 | 15 | | 4 | 43 | | 5 | 4 | | 6 | 27 | | 7 | 83 | | 8 | 28 | | 9 | 8 | | 10 | 4 | | 11 | 4 | | 12 | 18 | | 13 | 13 | | 14 | 12 | | 15 | 4 | | 16 | 36 | | 17 | 3 | | 18 | 25 | | 19 | 39 | | 20 | 21 | | 21 | 1 | | 22 | 9 | | 23 | 34 | | 24 | 70 | | 25 | 48 | | 26 | 4 | | 27 | 11 | | 28 | 4 | | 29 | 101 | | 30 | 46 | | 31 | 26 | | 32 | 14 | | 33 | 33 | | 34 | 22 | | 35 | 49 | | 36 | 25 | | 37 | 4 | | 38 | 27 | | 39 | 53 | | 40 | 5 | | 41 | 37 | | 42 | 16 | | 43 | 10 | | 44 | 4 | | 45 | 28 | | 46 | 14 | | 47 | 36 | | 48 | 26 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 163 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 7 | | totalSentences | 115 | | ratio | 0.061 | | matches | | 0 | "For three weeks the little crimson stone had hung cold against her sternum on its silver chain — found in her jacket pocket one morning with no note, no box, no memory of buying it — and she'd stopped wondering about it the way you stop hearing your fridge." | | 1 | "\"With the girl who takes it.\" Paper rustled; Eva was walking, keys jingling, the front door of the pub somewhere behind her." | | 2 | "She'd lived under the Heathrow flight path long enough to set her sleep by it — a plane every ninety seconds, a soft rake of engines across the sky." | | 3 | "At the rim of the beam she kept catching a shape — low, patient, upright the way a person is upright." | | 4 | "She cupped it through her jacket and its glow lit her fingers red — one pulse, then another, quicker now, running." | | 5 | "Not faded — stopped, like a tap shut off mid-splash, and in the new quiet she heard breathing that wasn't hers." | | 6 | "Behind her — close behind, at the nape of her neck, where the warm air ended — Eva's voice, and beneath Eva's voice the low, slowed twin of it, both of them gentle, both of them pleased:" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1093 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.025617566331198535 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 11.17 | | std | 8.89 | | cv | 0.796 | | sampleLengths | | 0 | 16 | | 1 | 7 | | 2 | 10 | | 3 | 6 | | 4 | 2 | | 5 | 8 | | 6 | 15 | | 7 | 19 | | 8 | 24 | | 9 | 4 | | 10 | 21 | | 11 | 6 | | 12 | 9 | | 13 | 3 | | 14 | 49 | | 15 | 8 | | 16 | 14 | | 17 | 13 | | 18 | 15 | | 19 | 8 | | 20 | 4 | | 21 | 4 | | 22 | 9 | | 23 | 9 | | 24 | 13 | | 25 | 12 | | 26 | 4 | | 27 | 22 | | 28 | 14 | | 29 | 3 | | 30 | 25 | | 31 | 3 | | 32 | 3 | | 33 | 19 | | 34 | 5 | | 35 | 2 | | 36 | 3 | | 37 | 4 | | 38 | 21 | | 39 | 1 | | 40 | 9 | | 41 | 6 | | 42 | 28 | | 43 | 10 | | 44 | 29 | | 45 | 7 | | 46 | 10 | | 47 | 14 | | 48 | 6 | | 49 | 10 |
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| 81.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.543859649122807 | | totalSentences | 114 | | uniqueOpeners | 62 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 83 | | matches | | 0 | "Then the line went flat." | | 1 | "Just Eva, gone, mid-word." | | 2 | "Somewhere a stream ran that" | | 3 | "Then the flowers turned." |
| | ratio | 0.048 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 83 | | matches | | 0 | "He untied his apron and" | | 1 | "He counted her float into" | | 2 | "She propped her bike against" | | 3 | "She should have turned around" | | 4 | "She'd lived under the Heathrow" | | 5 | "She walked ten minutes and" | | 6 | "She'd swerved around enough of" | | 7 | "Her map app spun in" | | 8 | "Her torch made a small" | | 9 | "she called, and the park" | | 10 | "She rubbed it with her" | | 11 | "She smelled the flowers before" | | 12 | "She cupped it through her" | | 13 | "She stepped between the oaks." | | 14 | "She checked her phone." | | 15 | "She set the Golden Empress" | | 16 | "Her voice came out flat" | | 17 | "Her own breath loud in" | | 18 | "Her phone read 1:36." | | 19 | "She stared at the numbers," |
| | ratio | 0.253 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 83 | | matches | | 0 | "Yu-Fei slid the ticket across" | | 1 | "Rory pinned it under her" | | 2 | "He untied his apron and" | | 3 | "He counted her float into" | | 4 | "The pendant had been warm" | | 5 | "That was new." | | 6 | "Tonight, crossing the bridge, it" | | 7 | "She propped her bike against" | | 8 | "The chain lay coiled on" | | 9 | "The phone rang twice before" | | 10 | "A pause long enough to" | | 11 | "Paper rustled; Eva was walking," | | 12 | "Eva's voice doubled." | | 13 | "Rory stood at the gate" | | 14 | "The park gave the word" | | 15 | "She should have turned around" | | 16 | "The path ran north through" | | 17 | "She'd lived under the Heathrow" | | 18 | "She walked ten minutes and" | | 19 | "The dark had the quality" |
| | ratio | 0.663 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "she called, and the park took the words and gave nothing back" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "she breathed (breathe)" |
| | dialogueSentences | 28 | | tagDensity | 0.107 | | leniency | 0.214 | | rawRatio | 0.333 | | effectiveRatio | 0.071 | |