| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1156 | | 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) | |
| 0.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1156 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "rhythmic" | | 1 | "weight" | | 2 | "flickered" | | 3 | "standard" | | 4 | "glinting" | | 5 | "pulsed" | | 6 | "calculated" | | 7 | "chaotic" | | 8 | "etched" | | 9 | "unreadable" | | 10 | "scanned" | | 11 | "cacophony" | | 12 | "comforting" | | 13 | "mechanical" | | 14 | "dancing" | | 15 | "gloom" | | 16 | "echoing" | | 17 | "vibrated" |
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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 | 68 | | matches | (empty) | |
| 79.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 68 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 77 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1151 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 9 | | wordCount | 1048 | | uniqueNames | 9 | | maxNameDensity | 0.1 | | worstName | "Soho" | | maxWindowNameDensity | 0 | | worstWindowName | (null) | | discoveredNames | | Soho | 1 | | London | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 1 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Morris | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Herrera" | | 3 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 1151 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 77 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 34.88 | | std | 24.73 | | cv | 0.709 | | sampleLengths | | 0 | 61 | | 1 | 20 | | 2 | 59 | | 3 | 12 | | 4 | 9 | | 5 | 66 | | 6 | 5 | | 7 | 28 | | 8 | 13 | | 9 | 65 | | 10 | 15 | | 11 | 56 | | 12 | 9 | | 13 | 3 | | 14 | 73 | | 15 | 13 | | 16 | 4 | | 17 | 53 | | 18 | 2 | | 19 | 24 | | 20 | 62 | | 21 | 75 | | 22 | 39 | | 23 | 8 | | 24 | 28 | | 25 | 66 | | 26 | 14 | | 27 | 50 | | 28 | 6 | | 29 | 48 | | 30 | 43 | | 31 | 67 | | 32 | 55 |
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| 94.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 68 | | matches | | |
| 37.40% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 164 | | matches | | 0 | "were simply moving" | | 1 | "weren't fighting" | | 2 | "was like trying" | | 3 | "was turning" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 77 | | ratio | 0.052 | | matches | | 0 | "The suspect—a wiry kid with frantic energy—didn't break stride." | | 1 | "My wrist watch ticked—a steady, comforting mechanical heartbeat in a place that defied all reason." | | 2 | "They weren't fighting; they were simply moving, a tide of deliberate, chaotic intent." | | 3 | "The exit—the way I had entered—began to collapse in a flurry of shifting stone and twisting iron." |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1062 | | adjectiveStacks | 2 | | stackExamples | | 0 | "damp, misery-heavy tracks." | | 1 | "steady, comforting mechanical heartbeat" |
| | adverbCount | 23 | | adverbRatio | 0.021657250470809793 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.009416195856873822 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 77 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 77 | | mean | 14.95 | | std | 8.24 | | cv | 0.551 | | sampleLengths | | 0 | 17 | | 1 | 21 | | 2 | 23 | | 3 | 20 | | 4 | 18 | | 5 | 19 | | 6 | 9 | | 7 | 13 | | 8 | 12 | | 9 | 9 | | 10 | 4 | | 11 | 4 | | 12 | 24 | | 13 | 34 | | 14 | 5 | | 15 | 28 | | 16 | 13 | | 17 | 11 | | 18 | 17 | | 19 | 12 | | 20 | 25 | | 21 | 3 | | 22 | 12 | | 23 | 12 | | 24 | 19 | | 25 | 6 | | 26 | 19 | | 27 | 9 | | 28 | 3 | | 29 | 20 | | 30 | 13 | | 31 | 29 | | 32 | 11 | | 33 | 13 | | 34 | 4 | | 35 | 11 | | 36 | 18 | | 37 | 24 | | 38 | 2 | | 39 | 24 | | 40 | 4 | | 41 | 16 | | 42 | 2 | | 43 | 2 | | 44 | 38 | | 45 | 15 | | 46 | 32 | | 47 | 28 | | 48 | 18 | | 49 | 5 |
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| 49.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.37662337662337664 | | totalSentences | 77 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 66 | | matches | | 0 | "Just the weight of the" | | 1 | "Then, the ground beneath me" |
| | ratio | 0.03 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 66 | | matches | | 0 | "My boots hammered the slick," | | 1 | "My voice was a gravelly" | | 2 | "I rounded the corner, my" | | 3 | "He pivoted sharply toward a" | | 4 | "His fingers clawed at the" | | 5 | "I didn't turn around." | | 6 | "I knew the voice." | | 7 | "He looked at me with" | | 8 | "I pushed past the chain-link" | | 9 | "My flashlight cut a violent" | | 10 | "He was fast." | | 11 | "I heard the muffled *skitter-thump*" | | 12 | "My descent was calculated, military" | | 13 | "I reached the bottom level," | | 14 | "It was an abandoned Tube" | | 15 | "He held a bone token" | | 16 | "He didn't look at me," | | 17 | "I scanned the crowd." | | 18 | "My wrist watch ticked—a steady," | | 19 | "I had spent a thousand" |
| | ratio | 0.5 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 66 | | matches | | 0 | "The sharp smack of salt-and-pepper" | | 1 | "My boots hammered the slick," | | 2 | "My voice was a gravelly" | | 3 | "I rounded the corner, my" | | 4 | "The neon buzz of The" | | 5 | "The suspect—a wiry kid with" | | 6 | "He pivoted sharply toward a" | | 7 | "His fingers clawed at the" | | 8 | "I didn't turn around." | | 9 | "I knew the voice." | | 10 | "Tomás Herrera stood beneath the" | | 11 | "He looked at me with" | | 12 | "I pushed past the chain-link" | | 13 | "Steam hissed from antique pipes" | | 14 | "My flashlight cut a violent" | | 15 | "He was fast." | | 16 | "I heard the muffled *skitter-thump*" | | 17 | "My descent was calculated, military" | | 18 | "I reached the bottom level," | | 19 | "This wasn't the municipal tunnel" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 46.70% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 7 | | matches | | 0 | "He looked at me with those steady, warm brown eyes, one hand resting casually on the scar that marred his left forearm, a permanent souvenir of a life he wasn't…" | | 1 | "The architecture shifted, the sharp concrete giving way to curved, limestone walls that felt older than the empire itself." | | 2 | "He didn't look at me, but his presence was a blockade, a wall of implied violence that stopped me in my damp, misery-heavy tracks." | | 3 | "My wrist watch ticked—a steady, comforting mechanical heartbeat in a place that defied all reason." | | 4 | "He looked expectant, his lips curved into a thin, mocking smile that didn't reach his eyes." | | 5 | "I pushed back, my elbow catching a hooded merchant in the ribs, but the force of the crowd was like trying to stem a rising river." | | 6 | "I drew my service weapon, the steel cold and grounding in my palm, but the safety switch felt sluggish, as if the air itself was turning to syrup." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 1 | | effectiveRatio | 0.182 | |