| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 91 | | tagDensity | 0.242 | | leniency | 0.484 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2107 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "quickly" | | 2 | "sharply" |
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| 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) | |
| 78.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2107 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoed" | | 1 | "etching" | | 2 | "weight" | | 3 | "flicked" | | 4 | "silence" | | 5 | "pulse" | | 6 | "shattered" | | 7 | "flickered" |
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| 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 | 184 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 184 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 254 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2107 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 74 | | wordCount | 1579 | | uniqueNames | 12 | | maxNameDensity | 2.34 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 37 | | Quinn | 3 | | Tube | 1 | | Eva | 20 | | Kowalski | 1 | | Oyster | 1 | | Camden | 2 | | Met | 1 | | Town | 1 | | Disused | 1 | | Access | 1 | | Dust | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Camden" | | 5 | "Town" | | 6 | "Dust" |
| | places | (empty) | | globalScore | 0.328 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite hidden by shadow" |
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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 | 2107 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 254 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 121 | | mean | 17.41 | | std | 16.68 | | cv | 0.958 | | sampleLengths | | 0 | 3 | | 1 | 42 | | 2 | 81 | | 3 | 18 | | 4 | 53 | | 5 | 7 | | 6 | 5 | | 7 | 52 | | 8 | 19 | | 9 | 9 | | 10 | 13 | | 11 | 7 | | 12 | 15 | | 13 | 53 | | 14 | 64 | | 15 | 9 | | 16 | 50 | | 17 | 8 | | 18 | 19 | | 19 | 30 | | 20 | 3 | | 21 | 4 | | 22 | 6 | | 23 | 15 | | 24 | 55 | | 25 | 46 | | 26 | 3 | | 27 | 9 | | 28 | 7 | | 29 | 19 | | 30 | 5 | | 31 | 8 | | 32 | 12 | | 33 | 3 | | 34 | 10 | | 35 | 22 | | 36 | 13 | | 37 | 33 | | 38 | 12 | | 39 | 25 | | 40 | 12 | | 41 | 6 | | 42 | 41 | | 43 | 37 | | 44 | 6 | | 45 | 10 | | 46 | 12 | | 47 | 20 | | 48 | 19 | | 49 | 35 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 184 | | matches | | 0 | "been dragged" | | 1 | "been pushed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 251 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 254 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 632 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.02531645569620253 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007911392405063292 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 254 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 254 | | mean | 8.3 | | std | 6.74 | | cv | 0.813 | | sampleLengths | | 0 | 3 | | 1 | 21 | | 2 | 21 | | 3 | 28 | | 4 | 39 | | 5 | 14 | | 6 | 16 | | 7 | 2 | | 8 | 17 | | 9 | 26 | | 10 | 10 | | 11 | 7 | | 12 | 5 | | 13 | 13 | | 14 | 3 | | 15 | 1 | | 16 | 2 | | 17 | 3 | | 18 | 29 | | 19 | 1 | | 20 | 12 | | 21 | 7 | | 22 | 3 | | 23 | 6 | | 24 | 8 | | 25 | 5 | | 26 | 7 | | 27 | 8 | | 28 | 7 | | 29 | 6 | | 30 | 20 | | 31 | 2 | | 32 | 2 | | 33 | 1 | | 34 | 12 | | 35 | 3 | | 36 | 7 | | 37 | 15 | | 38 | 6 | | 39 | 11 | | 40 | 19 | | 41 | 13 | | 42 | 6 | | 43 | 3 | | 44 | 10 | | 45 | 22 | | 46 | 18 | | 47 | 6 | | 48 | 2 | | 49 | 9 |
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| 55.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3661417322834646 | | totalSentences | 254 | | uniqueOpeners | 93 | |
| 66.23% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 151 | | matches | | 0 | "Then it came again, closer" | | 1 | "Then she saw it, and" | | 2 | "Then the wall shuddered as" |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 151 | | matches | | 0 | "She had her worn leather" | | 1 | "She angled her torch over" | | 2 | "She pinched a few grains" | | 3 | "She crossed to the old" | | 4 | "She touched the top edge." | | 5 | "She looked at Harlow first." | | 6 | "She walked to the barrier" | | 7 | "She knelt and studied the" | | 8 | "Her eyes narrowed." | | 9 | "She snatched a forensic light" | | 10 | "It spun once, twice, then" | | 11 | "She stepped to the wall" | | 12 | "she said, eyes fixed on" | | 13 | "Her jaw set." |
| | ratio | 0.093 | |
| 72.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 117 | | totalSentences | 151 | | matches | | 0 | "Detective Harlow Quinn caught the" | | 1 | "The compass clicked shut beneath" | | 2 | "Harlow crouched over the body" | | 3 | "The dead man lay half" | | 4 | "Quinn did not look up" | | 5 | "Eva Kowalski stood a pace" | | 6 | "She had her worn leather" | | 7 | "Harlow lifted the dead man’s" | | 8 | "She angled her torch over" | | 9 | "Eva edged closer, ignoring the" | | 10 | "Eva blinked, then looked at" | | 11 | "Harlow’s torch moved over the" | | 12 | "Dust, old gum, a torn" | | 13 | "She pinched a few grains" | | 14 | "The station beneath Camden had" | | 15 | "Tiles cracked under old" | | 16 | "A kiosk stood with its" | | 17 | "A smell of damp concrete," | | 18 | "Harlow glanced at the body" | | 19 | "Quinn’s arrival had dragged half" |
| | ratio | 0.775 | |
| 33.11% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 151 | | matches | | 0 | "Whoever laid him there had" |
| | ratio | 0.007 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 4 | | matches | | 0 | "The dead man lay half in shadow beneath a cracked advertising board, one hand curled around a ticket stub, the other open as if he had dropped something preciou…" | | 1 | "The metal split inward in a broad, rough arc, as if something heavy had pushed through from the far side." | | 2 | "Harlow’s torch beam slid over the seam and caught a smear of fresh blood streaked down from above it, as if something had just been pushed through from the othe…" | | 3 | "Something in the dark beyond the wall snarled, and the old station lights flickered once, as if the whole tunnel had flinched." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, eyes fixed on the seam" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 18 | | fancyCount | 3 | | fancyTags | | 0 | "Harlow repeated (repeat)" | | 1 | "Eva hissed (hiss)" | | 2 | "Harlow snapped (snap)" |
| | dialogueSentences | 91 | | tagDensity | 0.198 | | leniency | 0.396 | | rawRatio | 0.167 | | effectiveRatio | 0.066 | |