| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.378 | | leniency | 0.756 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1279 | | totalAiIsmAdverbs | 1 | | 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) | |
| 76.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1279 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "silk" | | 1 | "etched" | | 2 | "pulse" | | 3 | "echoed" | | 4 | "silence" | | 5 | "grave" |
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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 | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | 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 | 1288 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 800 | | uniqueNames | 10 | | maxNameDensity | 1 | | worstName | "Okafor" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Eva" | | discoveredNames | | Kentish | 1 | | Town | 1 | | Okafor | 8 | | Quinn | 5 | | Thirty | 1 | | Morris | 1 | | Eva | 5 | | Kowalski | 1 | | Silence | 1 | | Stall | 1 |
| | persons | | 0 | "Okafor" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Stall" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | 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 | 1288 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 29.27 | | std | 24.8 | | cv | 0.847 | | sampleLengths | | 0 | 63 | | 1 | 5 | | 2 | 18 | | 3 | 60 | | 4 | 32 | | 5 | 1 | | 6 | 11 | | 7 | 39 | | 8 | 1 | | 9 | 5 | | 10 | 29 | | 11 | 3 | | 12 | 66 | | 13 | 55 | | 14 | 15 | | 15 | 25 | | 16 | 20 | | 17 | 4 | | 18 | 66 | | 19 | 9 | | 20 | 88 | | 21 | 63 | | 22 | 52 | | 23 | 9 | | 24 | 34 | | 25 | 46 | | 26 | 25 | | 27 | 5 | | 28 | 24 | | 29 | 7 | | 30 | 9 | | 31 | 69 | | 32 | 8 | | 33 | 50 | | 34 | 47 | | 35 | 11 | | 36 | 10 | | 37 | 54 | | 38 | 18 | | 39 | 12 | | 40 | 7 | | 41 | 87 | | 42 | 12 | | 43 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 72 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 125 | | matches | | |
| 28.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 100 | | ratio | 0.04 | | matches | | 0 | "South Kentish Town station had been dead since 1924, and the air down here had kept the corpse — dust and rust and a century of stopped clocks." | | 1 | "The pool spread wide around the man's ribs — and beneath his spine, dead centre, ran a pencil-width seam of dry floor." | | 2 | "A token — bone or ivory, yellowed, a hole drilled clean through it, edges worn silk-smooth by years of handling." | | 3 | "Her own watch — worn leather strap, eighteen years on the same wrist — ticked against her pulse, and for a second she was in another evidence room, another bagged compass on another steel tray, and Morris was laughing about souvenirs." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 802 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.014962593516209476 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0012468827930174563 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 12.88 | | std | 10.14 | | cv | 0.787 | | sampleLengths | | 0 | 28 | | 1 | 8 | | 2 | 27 | | 3 | 5 | | 4 | 18 | | 5 | 27 | | 6 | 2 | | 7 | 3 | | 8 | 8 | | 9 | 20 | | 10 | 9 | | 11 | 23 | | 12 | 1 | | 13 | 11 | | 14 | 20 | | 15 | 14 | | 16 | 5 | | 17 | 1 | | 18 | 5 | | 19 | 3 | | 20 | 23 | | 21 | 3 | | 22 | 3 | | 23 | 35 | | 24 | 2 | | 25 | 6 | | 26 | 7 | | 27 | 8 | | 28 | 8 | | 29 | 12 | | 30 | 18 | | 31 | 6 | | 32 | 6 | | 33 | 1 | | 34 | 12 | | 35 | 7 | | 36 | 8 | | 37 | 3 | | 38 | 22 | | 39 | 8 | | 40 | 12 | | 41 | 4 | | 42 | 18 | | 43 | 9 | | 44 | 8 | | 45 | 20 | | 46 | 11 | | 47 | 9 | | 48 | 34 | | 49 | 3 |
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| 88.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.56 | | totalSentences | 100 | | uniqueOpeners | 56 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 49.51% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 61 | | matches | | 0 | "She pulled on nitrile gloves," | | 1 | "He swung his torch toward" | | 2 | "He marked the line with" | | 3 | "They ended where the man's" | | 4 | "She moved along the prints" | | 5 | "She pressed a knuckle to" | | 6 | "She came back." | | 7 | "She circled the body instead" | | 8 | "His right fist was clenched" | | 9 | "She eased the fingers back" | | 10 | "She nodded down the platform," | | 11 | "She lifted it." | | 12 | "It strained flat against the" | | 13 | "Her own watch — worn" | | 14 | "She put the thought down" | | 15 | "She'd pulled a coat over" | | 16 | "She saw the body." | | 17 | "She crossed herself with a" | | 18 | "Her freckles stood out white." | | 19 | "She opened her satchel one-handed," |
| | ratio | 0.426 | |
| 33.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 61 | | matches | | 0 | "South Kentish Town station had" | | 1 | "Quinn's torch cut a cone" | | 2 | "She pulled on nitrile gloves," | | 3 | "A man in his fifties" | | 4 | "Cardigan beneath it." | | 5 | "Blood had pooled beneath him" | | 6 | "Quinn crouched at the rim" | | 7 | "Dust lay over everything, thick" | | 8 | "Okafor's gum stopped." | | 9 | "He swung his torch toward" | | 10 | "He marked the line with" | | 11 | "They ended where the man's" | | 12 | "Nothing came from the stairwell" | | 13 | "Nobody had walked down the" | | 14 | "She moved along the prints" | | 15 | "The lowest courses of brick" | | 16 | "She pressed a knuckle to" | | 17 | "Okafor had drifted to the" | | 18 | "She came back." | | 19 | "The pool spread wide around" |
| | ratio | 0.852 | |
| 81.97% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 61 | | matches | | 0 | "Even stride, no scuff, no" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "She turned to Okafor, who was still gum-chewing, still notebook-ready, still building the same sentence he'd started with." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "Okafor repeated (repeat)" |
| | dialogueSentences | 45 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.2 | | effectiveRatio | 0.044 | |