| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1696 | | 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) | |
| 55.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1696 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "chill" | | 1 | "churned" | | 2 | "blown wide" | | 3 | "silk" | | 4 | "scanned" | | 5 | "dance" | | 6 | "measured" | | 7 | "magnetic" | | 8 | "etched" | | 9 | "tracing" | | 10 | "stark" | | 11 | "trembled" | | 12 | "pulsed" |
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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 | 75 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1687 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1184 | | uniqueNames | 16 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 2 | | Harlow | 1 | | Quinn | 17 | | London | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Victorian | 2 | | Sergeant | 1 | | Miller | 11 | | Tube | 1 | | Rotherhithe | 1 | | Morris | 1 | | Met | 1 | | Oxford | 1 | | Eva | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Victorian" | | 3 | "Sergeant" | | 4 | "Miller" | | 5 | "Morris" | | 6 | "Eva" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Oxford" |
| | globalScore | 0.782 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | 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 | 1687 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 27.66 | | std | 18.56 | | cv | 0.671 | | sampleLengths | | 0 | 64 | | 1 | 52 | | 2 | 7 | | 3 | 32 | | 4 | 16 | | 5 | 48 | | 6 | 53 | | 7 | 16 | | 8 | 40 | | 9 | 47 | | 10 | 8 | | 11 | 12 | | 12 | 19 | | 13 | 43 | | 14 | 8 | | 15 | 11 | | 16 | 42 | | 17 | 10 | | 18 | 73 | | 19 | 9 | | 20 | 32 | | 21 | 13 | | 22 | 55 | | 23 | 33 | | 24 | 62 | | 25 | 4 | | 26 | 8 | | 27 | 43 | | 28 | 26 | | 29 | 27 | | 30 | 14 | | 31 | 2 | | 32 | 20 | | 33 | 33 | | 34 | 35 | | 35 | 10 | | 36 | 12 | | 37 | 21 | | 38 | 16 | | 39 | 57 | | 40 | 7 | | 41 | 24 | | 42 | 23 | | 43 | 29 | | 44 | 10 | | 45 | 68 | | 46 | 44 | | 47 | 12 | | 48 | 23 | | 49 | 30 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 187 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 3 | | flaggedSentences | 9 | | totalSentences | 102 | | ratio | 0.088 | | matches | | 0 | "Grey dust coated the metal treads like untouched felt, broken by four sets of boot prints—two heavy patrol soles and two ribbed forensics overshoes." | | 1 | "Hundreds of overlapping prints churned the pale dust along a sixty-yard stretch of concourse—heavy combat treads, narrow Oxford brogues, delicate pointed heels, hobnailed boots." | | 2 | "No tunnel mud stained his cuffs; no limestone dust greyed his knees." | | 3 | "No stamped degrees or maker's marks ringed the bezel; angular protective sigils coiled across the tarnished face where cardinal letters belonged." | | 4 | "The metalwork defied factory tooling—seamless, devoid of solder joints or lathe striations." | | 5 | "Brogues, combat boots, pointed heels—hundreds of soles pressed shoulder-to-shoulder into a six-foot corridor terminating against red brickwork." | | 6 | "Beside the left rut, wedged beneath the chipped Victorian skirting, gleamed a splintered crescent of notched pale bone—a twin to the token from the dead man's overcoat." | | 7 | "Toe boxes pointed into the masonry; crisp heel crescents bit deep behind them, angled in full stride." | | 8 | "She checked the moon-phase subdial on her worn watch—hours past the full moon—before rising to study the chalked lintel above the bricks and the silent archivist standing rigid beside the ticket booth." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1220 | | adjectiveStacks | 1 | | stackExamples | | 0 | "smooth, coin-sized token" |
| | adverbCount | 6 | | adverbRatio | 0.004918032786885246 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001639344262295082 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 16.54 | | std | 8.5 | | cv | 0.514 | | sampleLengths | | 0 | 12 | | 1 | 23 | | 2 | 10 | | 3 | 1 | | 4 | 18 | | 5 | 11 | | 6 | 9 | | 7 | 24 | | 8 | 8 | | 9 | 7 | | 10 | 21 | | 11 | 11 | | 12 | 16 | | 13 | 17 | | 14 | 10 | | 15 | 21 | | 16 | 4 | | 17 | 7 | | 18 | 14 | | 19 | 28 | | 20 | 16 | | 21 | 40 | | 22 | 8 | | 23 | 15 | | 24 | 24 | | 25 | 8 | | 26 | 12 | | 27 | 19 | | 28 | 22 | | 29 | 21 | | 30 | 8 | | 31 | 11 | | 32 | 42 | | 33 | 10 | | 34 | 11 | | 35 | 22 | | 36 | 11 | | 37 | 12 | | 38 | 17 | | 39 | 9 | | 40 | 17 | | 41 | 15 | | 42 | 13 | | 43 | 15 | | 44 | 16 | | 45 | 24 | | 46 | 33 | | 47 | 11 | | 48 | 22 | | 49 | 14 |
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| 99.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.6078431372549019 | | totalSentences | 102 | | uniqueOpeners | 62 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Barely twenty-six and standing five-foot-four," |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 74 | | matches | | 0 | "She checked the worn leather" | | 1 | "She angled her torch across" | | 2 | "He wore a charcoal cashmere" | | 3 | "She laid her torch flat" | | 4 | "She checked the moon-phase subdial" |
| | ratio | 0.068 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 74 | | matches | | 0 | "Rust flaked from the cast-iron" | | 1 | "She checked the worn leather" | | 2 | "Morning sun warmed the pavements" | | 3 | "Quinn halted on the bottom" | | 4 | "She angled her torch across" | | 5 | "Nothing beyond those four trails" | | 6 | "Detective Sergeant Miller ducked beneath" | | 7 | "Sweat darkened his collar under" | | 8 | "Quinn stepped onto the abandoned" | | 9 | "Halogen tripods bleached the soot-streaked" | | 10 | "The air tasted wrong." | | 11 | "Miller gestured toward the centre" | | 12 | "Quinn’s brown eyes tracked across" | | 13 | "Hundreds of overlapping prints churned" | | 14 | "Miller flipped open his notepad," | | 15 | "Quinn walked to the coping" | | 16 | "Miller frowned, leaning past the" | | 17 | "Quinn crouched beside the white" | | 18 | "A man in his late" | | 19 | "He wore a charcoal cashmere" |
| | ratio | 0.649 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |