| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1192 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1192 | | totalAiIsms | 25 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | word | "down her spine" | | count | 1 |
| | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | |
| | highlights | | 0 | "glistening" | | 1 | "treacherous" | | 2 | "navigated" | | 3 | "stark" | | 4 | "fleeting" | | 5 | "amidst" | | 6 | "pounding" | | 7 | "quickened" | | 8 | "intricate" | | 9 | "flickered" | | 10 | "electric" | | 11 | "down her spine" | | 12 | "testament" | | 13 | "silence" | | 14 | "otherworldly" | | 15 | "flicker" | | 16 | "etched" | | 17 | "whisper" | | 18 | "shattered" | | 19 | "tension" | | 20 | "familiar" | | 21 | "eyebrow" | | 22 | "unravel" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 52 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 52 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 69 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 61 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1188 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 74.81% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 798 | | uniqueNames | 10 | | maxNameDensity | 1.5 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 12 | | Quinn | 1 | | Metropolitan | 1 | | Police | 1 | | Raven | 2 | | Nest | 2 | | Saint | 1 | | Christopher | 1 | | London | 1 | | Underground | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | | globalScore | 0.748 | | windowScore | 1 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "something like recognition or perhaps fear d" |
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| 31.65% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.684 | | wordCount | 1188 | | matches | | 0 | "not from the exertion but from the thrill of the chase" | | 1 | "not fond of surprises but had learned to roll with them" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 69 | | matches | (empty) | |
| 86.63% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 44 | | std | 19.94 | | cv | 0.453 | | sampleLengths | | 0 | 55 | | 1 | 69 | | 2 | 77 | | 3 | 73 | | 4 | 65 | | 5 | 31 | | 6 | 35 | | 7 | 39 | | 8 | 45 | | 9 | 30 | | 10 | 36 | | 11 | 84 | | 12 | 16 | | 13 | 72 | | 14 | 11 | | 15 | 31 | | 16 | 43 | | 17 | 47 | | 18 | 52 | | 19 | 44 | | 20 | 37 | | 21 | 18 | | 22 | 30 | | 23 | 34 | | 24 | 20 | | 25 | 25 | | 26 | 69 |
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| 91.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 52 | | matches | | 0 | "were adorned" | | 1 | "was broken" |
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| 33.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 120 | | matches | | 0 | "was pursuing" | | 1 | "was going" | | 2 | "was already running" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 69 | | ratio | 0.014 | | matches | | 0 | "She knew this place from the grapevine—a hidden bar, a haven for the city's underground." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 803 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 15 | | adverbRatio | 0.0186799501867995 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007471980074719801 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 69 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 69 | | mean | 17.22 | | std | 10.8 | | cv | 0.627 | | sampleLengths | | 0 | 14 | | 1 | 17 | | 2 | 24 | | 3 | 18 | | 4 | 17 | | 5 | 15 | | 6 | 19 | | 7 | 20 | | 8 | 31 | | 9 | 26 | | 10 | 11 | | 11 | 24 | | 12 | 14 | | 13 | 3 | | 14 | 15 | | 15 | 6 | | 16 | 21 | | 17 | 20 | | 18 | 24 | | 19 | 22 | | 20 | 9 | | 21 | 18 | | 22 | 17 | | 23 | 14 | | 24 | 25 | | 25 | 19 | | 26 | 11 | | 27 | 15 | | 28 | 14 | | 29 | 16 | | 30 | 11 | | 31 | 16 | | 32 | 9 | | 33 | 23 | | 34 | 61 | | 35 | 4 | | 36 | 12 | | 37 | 19 | | 38 | 53 | | 39 | 3 | | 40 | 8 | | 41 | 8 | | 42 | 23 | | 43 | 4 | | 44 | 20 | | 45 | 18 | | 46 | 1 | | 47 | 20 | | 48 | 19 | | 49 | 8 |
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| 71.01% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.43478260869565216 | | totalSentences | 69 | | uniqueOpeners | 30 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 94.51% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 51 | | matches | | 0 | "Her sharp jawline, emphasized by" | | 1 | "She quickened her pace, her" | | 2 | "She knew this place from" | | 3 | "She produced her warrant card," | | 4 | "He moved with the fluidity" | | 5 | "She had grown accustomed to" | | 6 | "It came with the territory" | | 7 | "She pulled up a stool," | | 8 | "He poured her a generous" | | 9 | "She regarded him closely." | | 10 | "She considered the proposition." | | 11 | "She pressed a few bills" | | 12 | "He leaned in, whispering," | | 13 | "She fished it out of" | | 14 | "She pressed it to her" | | 15 | "She sighed, eyes flicking to" |
| | ratio | 0.314 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 51 | | matches | | 0 | "The rain pounded the pavement," | | 1 | "Detective Harlow Quinn navigated the" | | 2 | "Her sharp jawline, emphasized by" | | 3 | "The suspect, a man in" | | 4 | "Harlow's eyes narrowed, her heart" | | 5 | "She quickened her pace, her" | | 6 | "The alley was a dim" | | 7 | "Harlow's hand rested on the" | | 8 | "The man in the trench" | | 9 | "The man she was pursuing" | | 10 | "A neon green sign above" | | 11 | "The Raven's Nest." | | 12 | "She knew this place from" | | 13 | "A place to start asking" | | 14 | "Harlow stepped inside, her eyes" | | 15 | "The walls were adorned with" | | 16 | "The silence was broken only" | | 17 | "A man behind the bar," | | 18 | "She produced her warrant card," | | 19 | "The bartender's eyebrows rose, his" |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 48.87% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 5 | | matches | | 0 | "Detective Harlow Quinn navigated the slick sidewalks with a purpose, her gaze slicing through the downpour, searching." | | 1 | "Her sharp jawline, emphasized by the stark lighting of a nearby streetlamp, showcased the determination that had become her trademark in the Metropolitan Police…" | | 2 | "On the surface, she was going against the code, making deals that hovered in the grays between right and wrong." | | 3 | "Harlow swallowed the burning liquid in her glass, feeling it slide down her throat, warming her from the inside out." | | 4 | "She sighed, eyes flicking to the bartender, who was now diligently polishing glasses, his attentive gaze everywhere but on her." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow explained, her voice low and steady" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Harlow explained (explain)" |
| | dialogueSentences | 22 | | tagDensity | 0.045 | | leniency | 0.091 | | rawRatio | 1 | | effectiveRatio | 0.091 | |