| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1630 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
|
| | highlights | | 0 | "perfectly" | | 1 | "deliberately" |
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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) | |
| 53.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1630 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "pulse" | | 1 | "measured" | | 2 | "perfect" | | 3 | "weight" | | 4 | "aftermath" | | 5 | "pristine" | | 6 | "familiar" | | 7 | "etched" | | 8 | "etching" | | 9 | "echoed" | | 10 | "tension" |
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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 | 169 | | matches | (empty) | |
| 92.14% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 3 | | narrationSentences | 169 | | filterMatches | | | hedgeMatches | | 0 | "began to" | | 1 | "tried to" | | 2 | "tended to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 169 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1630 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 7 | | matches | | 0 | "You are late, Eva said without turning." | | 1 | "Traffic on the Northern line does not wait for consultants, Harlow replied." | | 2 | "You are reading it wrong, Harlow said." | | 3 | "You found something, Eva said, stepping into the shadow behind her." | | 4 | "We found a bookkeeping error, Harlow said." | | 5 | "And now you have the route, Eva murmured, peering at the tokens." | | 6 | "As soon as the market moves, Harlow corrected." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1630 | | uniqueNames | 18 | | maxNameDensity | 0.92 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | London | 2 | | Harlow | 15 | | Quinn | 1 | | Camden | 1 | | Veil | 3 | | Market | 2 | | Metropolitan | 1 | | Police | 1 | | Kowalski | 1 | | Eva | 7 | | Northern | 1 | | Detective | 2 | | Compass | 1 | | Shade | 1 | | Morris | 1 | | Vauxhall | 1 | | You | 4 | | Human | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Market" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Compass" | | 7 | "Morris" | | 8 | "You" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 125 | | 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 | 1630 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 169 | | matches | | |
| 74.42% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 70.87 | | std | 29.09 | | cv | 0.41 | | sampleLengths | | 0 | 92 | | 1 | 89 | | 2 | 93 | | 3 | 73 | | 4 | 16 | | 5 | 30 | | 6 | 101 | | 7 | 104 | | 8 | 70 | | 9 | 64 | | 10 | 108 | | 11 | 90 | | 12 | 17 | | 13 | 95 | | 14 | 95 | | 15 | 79 | | 16 | 100 | | 17 | 24 | | 18 | 82 | | 19 | 25 | | 20 | 64 | | 21 | 58 | | 22 | 61 |
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| 80.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 12 | | totalSentences | 169 | | matches | | 0 | "was torn" | | 1 | "were turned" | | 2 | "was untorn" | | 3 | "was etched" | | 4 | "been rubbed" | | 5 | "been forced" | | 6 | "was poured" | | 7 | "was dragged" | | 8 | "was prepared" | | 9 | "been forced" | | 10 | "was meant" | | 11 | "is compromised" | | 12 | "been stripped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 288 | | matches | | 0 | "was already crouching" | | 1 | "was trying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 169 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1641 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.02376599634369287 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.007312614259597806 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 169 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 169 | | mean | 9.64 | | std | 5.43 | | cv | 0.563 | | sampleLengths | | 0 | 24 | | 1 | 17 | | 2 | 9 | | 3 | 11 | | 4 | 18 | | 5 | 13 | | 6 | 16 | | 7 | 9 | | 8 | 18 | | 9 | 5 | | 10 | 25 | | 11 | 16 | | 12 | 8 | | 13 | 4 | | 14 | 11 | | 15 | 18 | | 16 | 14 | | 17 | 14 | | 18 | 6 | | 19 | 18 | | 20 | 8 | | 21 | 19 | | 22 | 8 | | 23 | 19 | | 24 | 19 | | 25 | 7 | | 26 | 9 | | 27 | 12 | | 28 | 15 | | 29 | 3 | | 30 | 4 | | 31 | 15 | | 32 | 4 | | 33 | 19 | | 34 | 5 | | 35 | 8 | | 36 | 5 | | 37 | 5 | | 38 | 12 | | 39 | 3 | | 40 | 12 | | 41 | 5 | | 42 | 4 | | 43 | 4 | | 44 | 15 | | 45 | 16 | | 46 | 8 | | 47 | 6 | | 48 | 9 | | 49 | 7 |
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| 38.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.2781065088757396 | | totalSentences | 169 | | uniqueOpeners | 47 | |
| 62.11% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 161 | | matches | | 0 | "Closely cropped salt-and-pepper hair brushed" | | 1 | "Instead, a perfect circle of" | | 2 | "Just a hollowed-out corpse." |
| | ratio | 0.019 | |
| 65.96% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 62 | | totalSentences | 161 | | matches | | 0 | "Her left wrist rested against" | | 1 | "It smelled of ozone, crushed" | | 2 | "It shifted with the lunar" | | 3 | "She stepped over it." | | 4 | "Her boots met the cracked" | | 5 | "His coat was split down" | | 6 | "Her round glasses had slipped" | | 7 | "She reached up, tucking a" | | 8 | "Her worn leather satchel sat" | | 9 | "You are late, Eva said" | | 10 | "She kept her voice flat," | | 11 | "They were going to contaminate" | | 12 | "Her green eyes were wide," | | 13 | "It is a containment circle." | | 14 | "She moved to the edge" | | 15 | "She crouched, ignoring the protest" | | 16 | "She rubbed a pinch between" | | 17 | "It was too fine, too" | | 18 | "She shifted her gaze to" | | 19 | "His coat was torn, yes," |
| | ratio | 0.385 | |
| 53.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 131 | | totalSentences | 161 | | matches | | 0 | "The service lift groaned its" | | 1 | "Detective Harlow Quinn stood perfectly" | | 2 | "Her left wrist rested against" | | 3 | "The abandoned tube station beneath" | | 4 | "It smelled of ozone, crushed" | | 5 | "Canvas tarps hung from exposed" | | 6 | "This was the Veil Market." | | 7 | "It shifted with the lunar" | | 8 | "Tonight, it had stopped right" | | 9 | "Harlow did not bother ducking" | | 10 | "She stepped over it." | | 11 | "Her boots met the cracked" | | 12 | "A man sat propped against" | | 13 | "His coat was split down" | | 14 | "Eva Kowalski was already crouching" | | 15 | "The red curls escaping her" | | 16 | "Her round glasses had slipped" | | 17 | "She reached up, tucking a" | | 18 | "Her worn leather satchel sat" | | 19 | "You are late, Eva said" |
| | ratio | 0.814 | |
| 31.06% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 161 | | matches | | | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 72 | | technicalSentenceCount | 4 | | matches | | 0 | "Canvas tarps hung from exposed conduit pipes, draping over makeshift stalls that leaned against the curved, tile-clad walls." | | 1 | "The red curls escaping her bun formed a halo around her face, catching the halogen light like struck flint." | | 2 | "He died from an injection, likely a fast-acting sedative paired with a paralytic that stops the heart." | | 3 | "She had spent the intervening years tracking the whispers, the bone tokens, the networks that operated in the blind spots of the city." |
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| 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 | |