| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said quietly [quietly]" |
| | dialogueSentences | 39 | | tagDensity | 0.256 | | leniency | 0.513 | | rawRatio | 0.1 | | effectiveRatio | 0.051 | |
| 96.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1344 | | 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) | |
| 73.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1344 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "footsteps" | | 1 | "flicked" | | 2 | "perfect" | | 3 | "pulsed" | | 4 | "etched" | | 5 | "trembled" | | 6 | "shimmered" |
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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 | 113 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 113 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 142 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1344 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 920 | | uniqueNames | 16 | | maxNameDensity | 1.63 | | worstName | "Eva" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Northern | 1 | | Line | 1 | | Camden | 2 | | Ellis | 8 | | Farm | 1 | | Bovril | 1 | | Lyons | 1 | | Tea | 1 | | Morris | 2 | | Kowalski | 1 | | Quinn | 12 | | Eva | 15 | | Tube | 1 | | Market | 1 | | Festival | 1 | | Britain | 1 |
| | persons | | 0 | "Ellis" | | 1 | "Morris" | | 2 | "Kowalski" | | 3 | "Quinn" | | 4 | "Eva" | | 5 | "Market" | | 6 | "Britain" |
| | places | | | globalScore | 0.685 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "something like it once before, in the photog" |
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| 51.19% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.488 | | wordCount | 1344 | | matches | | 0 | "No footprints but" | | 1 | "not outward, but inward, folding like paper" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 142 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 18.67 | | std | 15.15 | | cv | 0.812 | | sampleLengths | | 0 | 12 | | 1 | 36 | | 2 | 32 | | 3 | 7 | | 4 | 47 | | 5 | 34 | | 6 | 4 | | 7 | 15 | | 8 | 26 | | 9 | 3 | | 10 | 4 | | 11 | 5 | | 12 | 4 | | 13 | 40 | | 14 | 5 | | 15 | 11 | | 16 | 8 | | 17 | 55 | | 18 | 6 | | 19 | 17 | | 20 | 20 | | 21 | 4 | | 22 | 12 | | 23 | 9 | | 24 | 22 | | 25 | 13 | | 26 | 45 | | 27 | 40 | | 28 | 11 | | 29 | 29 | | 30 | 6 | | 31 | 5 | | 32 | 6 | | 33 | 34 | | 34 | 32 | | 35 | 9 | | 36 | 2 | | 37 | 5 | | 38 | 14 | | 39 | 58 | | 40 | 16 | | 41 | 22 | | 42 | 9 | | 43 | 36 | | 44 | 17 | | 45 | 31 | | 46 | 5 | | 47 | 20 | | 48 | 35 | | 49 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 113 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 166 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 142 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 923 | | adjectiveStacks | 1 | | stackExamples | | 0 | "sharp against sudden pallor." |
| | adverbCount | 24 | | adverbRatio | 0.02600216684723727 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00866738894907909 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 142 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 142 | | mean | 9.46 | | std | 7.7 | | cv | 0.813 | | sampleLengths | | 0 | 12 | | 1 | 14 | | 2 | 22 | | 3 | 23 | | 4 | 9 | | 5 | 7 | | 6 | 8 | | 7 | 15 | | 8 | 12 | | 9 | 6 | | 10 | 6 | | 11 | 20 | | 12 | 3 | | 13 | 7 | | 14 | 2 | | 15 | 2 | | 16 | 4 | | 17 | 15 | | 18 | 2 | | 19 | 12 | | 20 | 1 | | 21 | 11 | | 22 | 3 | | 23 | 4 | | 24 | 5 | | 25 | 4 | | 26 | 5 | | 27 | 6 | | 28 | 2 | | 29 | 3 | | 30 | 14 | | 31 | 10 | | 32 | 5 | | 33 | 6 | | 34 | 5 | | 35 | 8 | | 36 | 23 | | 37 | 8 | | 38 | 24 | | 39 | 6 | | 40 | 17 | | 41 | 13 | | 42 | 7 | | 43 | 4 | | 44 | 12 | | 45 | 9 | | 46 | 12 | | 47 | 6 | | 48 | 4 | | 49 | 3 |
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| 69.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4397163120567376 | | totalSentences | 141 | | uniqueOpeners | 62 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 94 | | matches | | 0 | "Her boots hit concrete dust" | | 1 | "He was twenty-four and sweated" | | 2 | "His hands clutched at his" | | 3 | "Her worn leather watch on" | | 4 | "She pulled on nitrile gloves." | | 5 | "His knuckles unmarked." | | 6 | "He had clawed at himself," | | 7 | "Her curly red hair had" | | 8 | "She pushed it back and" | | 9 | "She knelt without asking, too" | | 10 | "She leaned closer." | | 11 | "She pulled out a pair" | | 12 | "She had seen something like" | | 13 | "She withdrew a small brass" | | 14 | "Its needle spun lazily, then" | | 15 | "Her unit called them trinkets." | | 16 | "Her knees popped." | | 17 | "She surveyed the platform again" | | 18 | "It should have been empty." | | 19 | "Her hand shook." |
| | ratio | 0.223 | |
| 39.79% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 94 | | matches | | 0 | "The body shouldn't have been" | | 1 | "Quinn ducked under the cordon" | | 2 | "Her boots hit concrete dust" | | 3 | "DC Ellis stood by the" | | 4 | "He was twenty-four and sweated" | | 5 | "The station didn't exist on" | | 6 | "Chalk Farm had sealed it" | | 7 | "The arches overhead still held" | | 8 | "Someone had swept the platform" | | 9 | "That was the first wrong" | | 10 | "The victim lay prone between" | | 11 | "His hands clutched at his" | | 12 | "Ellis cleared his throat." | | 13 | "Her worn leather watch on" | | 14 | "The second hand stuttered exactly" | | 15 | "Ellis flipped a page." | | 16 | "She pulled on nitrile gloves." | | 17 | "The dead man's fingernails were" | | 18 | "His knuckles unmarked." | | 19 | "He had clawed at himself," |
| | ratio | 0.84 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 89.29% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 3 | | matches | | 0 | "Her boots hit concrete dust and something softer, black mould that had grown thick over the old Northern Line bed beneath Camden." | | 1 | "Under the collar, pressed against the throat, a small perfect circle of blistered skin pulsed faintly, as if something hot had kissed him." | | 2 | "Hidden supernatural black market that sells enchanted goods, banned alchemical substances, and information." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 98.72% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Eva muttered (mutter)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 39 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0.333 | | effectiveRatio | 0.103 | |