| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1415 | | 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) | |
| 64.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1415 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "clandestine" | | 1 | "glinting" | | 2 | "flicker" | | 3 | "efficient" | | 4 | "streaming" | | 5 | "unravel" | | 6 | "reminder" | | 7 | "gloom" | | 8 | "weight" | | 9 | "grave" |
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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 | 63 | | matches | (empty) | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 3 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 67 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1410 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1278 | | uniqueNames | 16 | | maxNameDensity | 0.86 | | worstName | "Tomás" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Soho | 2 | | Harlow | 7 | | Quinn | 2 | | Tomás | 11 | | Herrera | 1 | | Saint | 3 | | Christopher | 3 | | Seville | 1 | | London | 1 | | Morris | 2 | | Camden | 3 | | Tube | 1 | | Veil | 3 | | Market | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" | | 9 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Seville" | | 2 | "London" | | 3 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 2.94% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 3 | | matches | | 0 | "photographs that seemed to watch the room like silent jurors" | | 1 | "m and had not, apparently, taught him caution" | | 2 | "photographs that seemed to leer at Harlow with judgmental eyes" |
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| 58.16% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.418 | | wordCount | 1410 | | matches | | 0 | "not of her, but of what lay beneath" | | 1 | "not with cold but with the weight of decision" |
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| 67.16% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 67 | | matches | | 0 | "used that endurance" | | 1 | "buried, that this" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 58.75 | | std | 35.98 | | cv | 0.612 | | sampleLengths | | 0 | 91 | | 1 | 114 | | 2 | 11 | | 3 | 90 | | 4 | 79 | | 5 | 78 | | 6 | 38 | | 7 | 3 | | 8 | 62 | | 9 | 6 | | 10 | 111 | | 11 | 73 | | 12 | 28 | | 13 | 17 | | 14 | 88 | | 15 | 20 | | 16 | 68 | | 17 | 114 | | 18 | 69 | | 19 | 19 | | 20 | 81 | | 21 | 68 | | 22 | 5 | | 23 | 77 |
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| 82.99% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 63 | | matches | | 0 | "being told" | | 1 | "been buried" | | 2 | "was gone" | | 3 | "was hidden" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 205 | | matches | | |
| 14.93% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 67 | | ratio | 0.045 | | matches | | 0 | "Harlow had slipped past the front counter, pushed through the mahogany bookshelf—its spines a thin fiction, a secret door—and found Tomás at a low table with a kit she recognized: needles, vials, bandages, a set of tools that had no place in a public bar and every place in a clandestine clinic." | | 1 | "At a narrow alley that smelled of grease, rotting flowers, and old rain, he pulled something from his pocket—small, bone-colored, something that glinted in the dark." | | 2 | "He stood still, rain streaming down his face, and she saw the fear—not of her, but of what lay beneath." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1295 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.021621621621621623 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.0069498069498069494 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 67 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 67 | | mean | 21.04 | | std | 12.6 | | cv | 0.599 | | sampleLengths | | 0 | 32 | | 1 | 44 | | 2 | 15 | | 3 | 6 | | 4 | 28 | | 5 | 28 | | 6 | 52 | | 7 | 11 | | 8 | 28 | | 9 | 29 | | 10 | 22 | | 11 | 7 | | 12 | 4 | | 13 | 8 | | 14 | 24 | | 15 | 27 | | 16 | 20 | | 17 | 14 | | 18 | 51 | | 19 | 13 | | 20 | 14 | | 21 | 24 | | 22 | 3 | | 23 | 31 | | 24 | 26 | | 25 | 5 | | 26 | 6 | | 27 | 24 | | 28 | 28 | | 29 | 14 | | 30 | 24 | | 31 | 21 | | 32 | 5 | | 33 | 25 | | 34 | 43 | | 35 | 26 | | 36 | 2 | | 37 | 17 | | 38 | 26 | | 39 | 14 | | 40 | 23 | | 41 | 25 | | 42 | 20 | | 43 | 9 | | 44 | 7 | | 45 | 52 | | 46 | 6 | | 47 | 32 | | 48 | 18 | | 49 | 30 |
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| 38.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2835820895522388 | | totalSentences | 67 | | uniqueOpeners | 19 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 60 | | matches | | 0 | "Then he moved." | | 1 | "Then he stepped through the" |
| | ratio | 0.033 | |
| 40.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 60 | | matches | | 0 | "She was forty-one, five feet" | | 1 | "She had tracked Tomás Herrera" | | 2 | "He was taller than her" | | 3 | "He did not reach for" | | 4 | "He reached for truth." | | 5 | "His voice was low, accented" | | 6 | "She had read his file:" | | 7 | "Her left wrist felt heavy" | | 8 | "He smiled, but it was" | | 9 | "He did not stop for" | | 10 | "He ran for the door." | | 11 | "She caught him in the" | | 12 | "She pursued with relentless precision," | | 13 | "She called his name once," | | 14 | "She kept pace, her brown" | | 15 | "He was faster than he" | | 16 | "He stood still, rain streaming" | | 17 | "His voice was hoarse now," | | 18 | "She stood at the edge" | | 19 | "He was gone." |
| | ratio | 0.45 | |
| 35.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 60 | | matches | | 0 | "The green neon sign above" | | 1 | "Detective Harlow Quinn stood across" | | 2 | "She was forty-one, five feet" | | 3 | "She had tracked Tomás Herrera" | | 4 | "The former paramedic worked off-the-books" | | 5 | "The Raven’s Nest smelled of" | | 6 | "Harlow had slipped past the" | | 7 | "Tomás was on his feet" | | 8 | "He was taller than her" | | 9 | "A scar ran along his" | | 10 | "He did not reach for" | | 11 | "He reached for truth." | | 12 | "His voice was low, accented" | | 13 | "She had read his file:" | | 14 | "Harlow said, stepping closer" | | 15 | "Her left wrist felt heavy" | | 16 | "He smiled, but it was" | | 17 | "Tomás turned, his left arm" | | 18 | "He did not stop for" | | 19 | "He ran for the door." |
| | ratio | 0.85 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 60 | | matches | | 0 | "If she turned back, Tomás" | | 1 | "If she followed, she entered" |
| | ratio | 0.033 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 16 | | matches | | 0 | "The green neon sign above the Raven’s Nest buzzed like a dying wasp, painting the narrow Soho street in a sickly emerald glow that made the rain look black inst…" | | 1 | "Detective Harlow Quinn stood across the wet cobblestones, her brown eyes narrowed against the downpour, her closely cropped salt-and-pepper hair plastered to he…" | | 2 | "The Raven’s Nest smelled of stale beer and older secrets, its walls covered with old maps and black-and-white photographs that seemed to watch the room like sil…" | | 3 | "Harlow had slipped past the front counter, pushed through the mahogany bookshelf—its spines a thin fiction, a secret door—and found Tomás at a low table with a …" | | 4 | "He was taller than her by an inch, five-foot-ten, with warm brown eyes and short curly dark brown hair that clung to his olive skin in damp rings." | | 5 | "Her left wrist felt heavy with the worn leather watch, the strap softened by years of rain and sweat, and she realized with a shock that she had not checked the…" | | 6 | "He did not stop for the green neon sign or the old maps or the black-and-white photographs that seemed to leer at Harlow with judgmental eyes." | | 7 | "She pursued with relentless precision, her boots crushing water that splashed in silver arcs." | | 8 | "She kept pace, her brown eyes tracking the flicker of olive skin between umbrellas and garbage cans, her military bearing translating into pure, efficient pursu…" | | 9 | "He was faster than he looked, a former paramedic with the endurance of a man who had held life together in chaos, and he used that endurance to vanish around a …" | | 10 | "At a narrow alley that smelled of grease, rotting flowers, and old rain, he pulled something from his pocket—small, bone-colored, something that glinted in the …" | | 11 | "The entrance was a black mouth in the earth, hidden behind rusted fencing and a collapsed stairway that exhaled the smell of wet iron and neglect." | | 12 | "Harlow slowed, her lungs burning, her worn leather watch scraping against her left wrist as she pumped her arms to close the gap." | | 13 | "She stood at the edge of the dark, a detective with a dead partner and a sealed file, a woman who had spent three years chasing shadows that refused to stay dea…" | | 14 | "If she turned back, Tomás disappeared into the dark, and her partner’s death remained a closed file, a ghost, a rumor whispered by bureaucrats who did not want …" | | 15 | "She knew, with the terrible certainty of a woman who had already lost everything and still refused to be buried, that this was where the truth was hidden, and s…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |