| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1200 | | 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) | |
| 29.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1200 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | word | "practiced ease" | | count | 1 |
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| | highlights | | 0 | "fractured" | | 1 | "familiar" | | 2 | "spectral" | | 3 | "scanning" | | 4 | "gloom" | | 5 | "reminder" | | 6 | "flicker" | | 7 | "dancing" | | 8 | "warmth" | | 9 | "cacophony" | | 10 | "predator" | | 11 | "calculating" | | 12 | "tinged" | | 13 | "charm" | | 14 | "scanned" | | 15 | "echoing" | | 16 | "practiced ease" |
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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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1193 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1046 | | uniqueNames | 14 | | maxNameDensity | 0.76 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 8 | | Beretta | 1 | | Ford | 1 | | Sergeant | 1 | | Miller | 1 | | Camden | 1 | | Town | 1 | | Veil | 3 | | Market | 6 | | Belfast | 1 | | Morris | 2 |
| | persons | | 0 | "Raven" | | 1 | "Herrera" | | 2 | "Ford" | | 3 | "Sergeant" | | 4 | "Miller" | | 5 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "Town" | | 3 | "Belfast" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | 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 | 1193 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 27.11 | | std | 19.79 | | cv | 0.73 | | sampleLengths | | 0 | 73 | | 1 | 69 | | 2 | 30 | | 3 | 4 | | 4 | 73 | | 5 | 13 | | 6 | 13 | | 7 | 2 | | 8 | 18 | | 9 | 6 | | 10 | 48 | | 11 | 3 | | 12 | 9 | | 13 | 48 | | 14 | 56 | | 15 | 54 | | 16 | 22 | | 17 | 18 | | 18 | 45 | | 19 | 39 | | 20 | 29 | | 21 | 2 | | 22 | 46 | | 23 | 38 | | 24 | 34 | | 25 | 28 | | 26 | 4 | | 27 | 44 | | 28 | 8 | | 29 | 43 | | 30 | 16 | | 31 | 4 | | 32 | 28 | | 33 | 33 | | 34 | 16 | | 35 | 2 | | 36 | 19 | | 37 | 5 | | 38 | 27 | | 39 | 27 | | 40 | 20 | | 41 | 29 | | 42 | 41 | | 43 | 7 |
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| 85.40% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 106 | | matches | | 0 | "were swallowed" | | 1 | "been lifted" | | 2 | "were lined" | | 3 | "were chalked" | | 4 | "was stopped" | | 5 | "was gone" |
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| 56.63% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 186 | | matches | | 0 | "was running" | | 1 | "was coming" | | 2 | "was heading" | | 3 | "was trying" |
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| 94.43% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 118 | | ratio | 0.017 | | matches | | 0 | "A flash of dark fabric, a glimpse of curly brown hair – Herrera." | | 1 | "The initial surveillance report had been vague – ‘facilitator,’ ‘deals in unusual items,’ ‘frequents questionable establishments.’ Useful, but not enough for a warrant." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 372 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.01881720430107527 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.005376344086021506 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 10.11 | | std | 6.76 | | cv | 0.668 | | sampleLengths | | 0 | 15 | | 1 | 20 | | 2 | 4 | | 3 | 5 | | 4 | 13 | | 5 | 16 | | 6 | 2 | | 7 | 22 | | 8 | 23 | | 9 | 3 | | 10 | 19 | | 11 | 9 | | 12 | 2 | | 13 | 1 | | 14 | 18 | | 15 | 4 | | 16 | 21 | | 17 | 8 | | 18 | 18 | | 19 | 26 | | 20 | 10 | | 21 | 3 | | 22 | 13 | | 23 | 2 | | 24 | 18 | | 25 | 6 | | 26 | 18 | | 27 | 9 | | 28 | 21 | | 29 | 3 | | 30 | 4 | | 31 | 4 | | 32 | 1 | | 33 | 8 | | 34 | 12 | | 35 | 12 | | 36 | 16 | | 37 | 3 | | 38 | 18 | | 39 | 11 | | 40 | 8 | | 41 | 13 | | 42 | 3 | | 43 | 16 | | 44 | 5 | | 45 | 2 | | 46 | 14 | | 47 | 16 | | 48 | 1 | | 49 | 3 |
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| 57.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3983050847457627 | | totalSentences | 118 | | uniqueOpeners | 47 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 95 | | matches | | 0 | "Then, he vanished." | | 1 | "Especially cases that reminded me" | | 2 | "Maybe, just maybe, it held" | | 3 | "Then, I saw him." |
| | ratio | 0.042 | |
| 76.84% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 95 | | matches | | 0 | "He hadn’t expected pursuit." | | 1 | "I adjusted my grip on" | | 2 | "I needed concrete." | | 3 | "I needed to see what" | | 4 | "He'd left the bar with" | | 5 | "He cut a sharp left" | | 6 | "I didn't hesitate, angling to" | | 7 | "My comm clicked, Sergeant Miller's" | | 8 | "He was heading towards the" | | 9 | "I skidded to a halt," | | 10 | "It led to a set" | | 11 | "I'd heard whispers about it" | | 12 | "It found you." | | 13 | "My watch, a worn leather" | | 14 | "I glanced around." | | 15 | "I considered my options." | | 16 | "I took a deep breath," | | 17 | "I moved deeper into the" | | 18 | "I turned to see a" | | 19 | "He was tall, impeccably dressed" |
| | ratio | 0.358 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 95 | | matches | | 0 | "The heel of my boot" | | 1 | "Rain lashed sideways, turning the" | | 2 | "He hadn’t expected pursuit." | | 3 | "That was his first mistake." | | 4 | "A flash of dark fabric," | | 5 | "I adjusted my grip on" | | 6 | "That’s how long I’d watched" | | 7 | "The initial surveillance report had" | | 8 | "I needed concrete." | | 9 | "I needed to see what" | | 10 | "He'd left the bar with" | | 11 | "The kind of wrong that" | | 12 | "He cut a sharp left" | | 13 | "I didn't hesitate, angling to" | | 14 | "The alley opened onto a" | | 15 | "Herrera weaved through the crowd" | | 16 | "My comm clicked, Sergeant Miller's" | | 17 | "The rain was coming down" | | 18 | "Herrera was quick, deceptively so" | | 19 | "He was heading towards the" |
| | ratio | 0.8 | |
| 52.63% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 95 | | matches | | | ratio | 0.011 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 6 | | matches | | 0 | "Herrera weaved through the crowd with a frantic energy, knocking shoulders, muttering apologies that were swallowed by the thrum of music spilling from a nearby…" | | 1 | "The rain was coming down in sheets now, plastering my salt-and-pepper hair to my forehead, blurring my vision." | | 2 | "The stairs levelled out onto a rough-hewn tunnel, lit by flickering gas lamps that cast long, dancing shadows." | | 3 | "The air thrummed with a low, persistent hum, a vibrational energy that made my teeth ache." | | 4 | "I ignored him, my gaze sweeping the crowd, searching for a flash of dark fabric." | | 5 | "I pushed my way through the throng, my shoulder colliding with a cloaked figure who hissed a curse." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "I replied, my voice tight" |
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| 87.50% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 16 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0.333 | | effectiveRatio | 0.125 | |