| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.10% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1450 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 48.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1450 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "gloom" | | 1 | "standard" | | 2 | "echoed" | | 3 | "etched" | | 4 | "weight" | | 5 | "traced" | | 6 | "pulse" | | 7 | "pristine" | | 8 | "racing" | | 9 | "jaw clenched" | | 10 | "flickered" | | 11 | "vibrated" | | 12 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 175 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 175 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 213 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1442 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 29.62% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 72 | | wordCount | 1163 | | uniqueNames | 11 | | maxNameDensity | 2.41 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 28 | | Quinn | 1 | | London | 1 | | Kowalski | 1 | | Eva | 24 | | Veil | 2 | | Market | 6 | | Morris | 4 | | Met | 3 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Market" | | 6 | "Morris" | | 7 | "Met" |
| | places | | | globalScore | 0.296 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 1 | | matches | | 0 | "as if searching for something just out of reach" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.693 | | wordCount | 1442 | | matches | | 0 | "Not the kind you got from a dodgy suspect, but the kind" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 213 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 23.64 | | std | 18.73 | | cv | 0.792 | | sampleLengths | | 0 | 79 | | 1 | 8 | | 2 | 63 | | 3 | 3 | | 4 | 11 | | 5 | 63 | | 6 | 15 | | 7 | 22 | | 8 | 10 | | 9 | 12 | | 10 | 37 | | 11 | 13 | | 12 | 6 | | 13 | 8 | | 14 | 2 | | 15 | 9 | | 16 | 70 | | 17 | 53 | | 18 | 20 | | 19 | 6 | | 20 | 22 | | 21 | 20 | | 22 | 9 | | 23 | 17 | | 24 | 55 | | 25 | 34 | | 26 | 11 | | 27 | 40 | | 28 | 16 | | 29 | 40 | | 30 | 11 | | 31 | 29 | | 32 | 8 | | 33 | 7 | | 34 | 46 | | 35 | 6 | | 36 | 26 | | 37 | 35 | | 38 | 17 | | 39 | 44 | | 40 | 8 | | 41 | 38 | | 42 | 37 | | 43 | 6 | | 44 | 8 | | 45 | 8 | | 46 | 44 | | 47 | 7 | | 48 | 33 | | 49 | 22 |
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| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 175 | | matches | | 0 | "been placed" | | 1 | "been closed" | | 2 | "was etched" | | 3 | "were bought" | | 4 | "was unbuttoned" |
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| 66.67% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 200 | | matches | | 0 | "was watching" | | 1 | "were falling" | | 2 | "was draining" | | 3 | "was pointing" |
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| 62.37% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 213 | | ratio | 0.028 | | matches | | 0 | "She knew that tone—dry, weary, but sharp as a scalpel." | | 1 | "Three years ago, DS Morris had walked into a similar scene—abandoned, no signs of struggle, no cause of death." | | 2 | "The face was etched with symbols she didn’t recognise—sigils, Eva would call them." | | 3 | "Toward a stretch of wall that looked no different from the rest—peeling paint, rust, grime." | | 4 | "But his left wrist bore a mark—a faint, red imprint, like a brand." | | 5 | "The torchlight revealed a sliver of space—dark, endless, humming with something that wasn’t sound but vibrated in her teeth." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1169 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.032506415739948676 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003421727972626176 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 213 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 213 | | mean | 6.77 | | std | 4.78 | | cv | 0.706 | | sampleLengths | | 0 | 12 | | 1 | 14 | | 2 | 21 | | 3 | 12 | | 4 | 11 | | 5 | 9 | | 6 | 6 | | 7 | 2 | | 8 | 3 | | 9 | 10 | | 10 | 13 | | 11 | 17 | | 12 | 20 | | 13 | 3 | | 14 | 2 | | 15 | 9 | | 16 | 4 | | 17 | 5 | | 18 | 4 | | 19 | 2 | | 20 | 10 | | 21 | 18 | | 22 | 13 | | 23 | 7 | | 24 | 4 | | 25 | 2 | | 26 | 6 | | 27 | 3 | | 28 | 8 | | 29 | 14 | | 30 | 9 | | 31 | 1 | | 32 | 12 | | 33 | 3 | | 34 | 1 | | 35 | 13 | | 36 | 9 | | 37 | 8 | | 38 | 3 | | 39 | 13 | | 40 | 5 | | 41 | 1 | | 42 | 4 | | 43 | 4 | | 44 | 2 | | 45 | 9 | | 46 | 3 | | 47 | 4 | | 48 | 2 | | 49 | 3 |
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| 39.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.22535211267605634 | | totalSentences | 213 | | uniqueOpeners | 48 | |
| 88.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 150 | | matches | | 0 | "Just a single body, sprawled" | | 1 | "Too light, like it was" | | 2 | "Just the sigils, spiralling inward" | | 3 | "Just a wall." |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 150 | | matches | | 0 | "She crouched, picked it up," | | 1 | "She knew that tone—dry, weary," | | 2 | "She tucked a curl of" | | 3 | "Her gaze swept the platform." | | 4 | "His eyes were open, staring" | | 5 | "She knelt beside him." | | 6 | "She stood, her torch beam" | | 7 | "She didn’t need to." | | 8 | "They both did." | | 9 | "She moved further down the" | | 10 | "It twitched, restless, as if" | | 11 | "She’d heard the whispers, the" | | 12 | "She held the compass out." | | 13 | "Her breath fogged in front" | | 14 | "She pressed her palm against" | | 15 | "She stepped back, her military" | | 16 | "She turned to Eva." | | 17 | "She peeled it back." | | 18 | "His shirt was pristine." | | 19 | "She’d seen enough of them" |
| | ratio | 0.24 | |
| 46.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 124 | | totalSentences | 150 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The beam of her torch" | | 3 | "A bone token lay near" | | 4 | "She crouched, picked it up," | | 5 | "A voice echoed from the" | | 6 | "Harlow didn’t flinch." | | 7 | "She knew that tone—dry, weary," | | 8 | "Eva Kowalski stepped into the" | | 9 | "A worn leather satchel hung" | | 10 | "She tucked a curl of" | | 11 | "Harlow ignored the jab." | | 12 | "Her gaze swept the platform." | | 13 | "The scene was wrong." | | 14 | "A man in a dark" | | 15 | "His eyes were open, staring" | | 16 | "She knelt beside him." | | 17 | "Eva shifted, her boots scuffing" | | 18 | "Harlow’s fingers hovered over the" | | 19 | "Harlow’s jaw tightened." |
| | ratio | 0.827 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 150 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 2 | | matches | | 0 | "It twitched, restless, as if searching for something just out of reach." | | 1 | "A gust of wind rushed out from the gap, carrying the scent of ozone and something older, something that made Harlow’s skin prickle." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva said, her voice quieter now" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.067 | | leniency | 0.133 | | rawRatio | 0 | | effectiveRatio | 0 | |