| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1135 | | 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) | |
| 69.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1135 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "footfall" | | 1 | "echoing" | | 2 | "shimmered" | | 3 | "silk" | | 4 | "pulse" | | 5 | "shattered" | | 6 | "footsteps" |
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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 | 1 | | narrationSentences | 112 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 112 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1131 | | ratio | 0 | | matches | (empty) | |
| 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 | 26 | | wordCount | 1053 | | uniqueNames | 14 | | maxNameDensity | 0.66 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Quinn | 7 | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Morris | 5 | | Underground | 1 | | Glock | 1 | | Saint | 1 | | Christopher | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Underground" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Market" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Raven" | | 4 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like bottled thunder" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.884 | | wordCount | 1131 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 24.06 | | std | 27.28 | | cv | 1.134 | | sampleLengths | | 0 | 110 | | 1 | 43 | | 2 | 84 | | 3 | 12 | | 4 | 4 | | 5 | 19 | | 6 | 3 | | 7 | 10 | | 8 | 2 | | 9 | 11 | | 10 | 13 | | 11 | 48 | | 12 | 40 | | 13 | 51 | | 14 | 66 | | 15 | 65 | | 16 | 6 | | 17 | 97 | | 18 | 35 | | 19 | 21 | | 20 | 1 | | 21 | 6 | | 22 | 3 | | 23 | 3 | | 24 | 2 | | 25 | 18 | | 26 | 2 | | 27 | 19 | | 28 | 17 | | 29 | 55 | | 30 | 54 | | 31 | 4 | | 32 | 4 | | 33 | 3 | | 34 | 5 | | 35 | 32 | | 36 | 43 | | 37 | 3 | | 38 | 2 | | 39 | 4 | | 40 | 9 | | 41 | 24 | | 42 | 5 | | 43 | 6 | | 44 | 4 | | 45 | 58 | | 46 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 193 | | matches | (empty) | |
| 52.15% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 126 | | ratio | 0.032 | | matches | | 0 | "The hooded figure ahead—wiry, olive-skinned, a flash of silver chain at his throat—cut left between two shuttered boutiques with a motion cramped by panic." | | 1 | "Graffiti covered the tiles in languages that didn't register at first—symbols that squirmed at the edge of her vision, curves that straightened when she looked away." | | 2 | "A vendor thrust a tray of eyeballs toward her; she batted it aside." | | 3 | "\"—unconventional materials. Requesting immediate backup.\"" |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1000 | | adjectiveStacks | 1 | | stackExamples | | 0 | "against calcite-slick walls" |
| | adverbCount | 15 | | adverbRatio | 0.015 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 8.98 | | std | 7.13 | | cv | 0.794 | | sampleLengths | | 0 | 22 | | 1 | 21 | | 2 | 24 | | 3 | 43 | | 4 | 4 | | 5 | 20 | | 6 | 19 | | 7 | 2 | | 8 | 9 | | 9 | 8 | | 10 | 17 | | 11 | 21 | | 12 | 6 | | 13 | 8 | | 14 | 5 | | 15 | 8 | | 16 | 12 | | 17 | 4 | | 18 | 19 | | 19 | 3 | | 20 | 10 | | 21 | 2 | | 22 | 11 | | 23 | 4 | | 24 | 9 | | 25 | 11 | | 26 | 19 | | 27 | 18 | | 28 | 4 | | 29 | 4 | | 30 | 12 | | 31 | 8 | | 32 | 5 | | 33 | 4 | | 34 | 3 | | 35 | 15 | | 36 | 10 | | 37 | 9 | | 38 | 17 | | 39 | 4 | | 40 | 18 | | 41 | 3 | | 42 | 10 | | 43 | 24 | | 44 | 7 | | 45 | 4 | | 46 | 9 | | 47 | 12 | | 48 | 26 | | 49 | 5 |
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| 58.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3888888888888889 | | totalSentences | 126 | | uniqueOpeners | 49 | |
| 31.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 107 | | matches | | 0 | "Instead, light bled from stalls" |
| | ratio | 0.009 | |
| 62.99% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 107 | | matches | | 0 | "She'd picked him out two" | | 1 | "Her sharp jaw tightened." | | 2 | "He hurdled a kicked-over dustbin" | | 3 | "Her cropped salt-and-pepper hair shed" | | 4 | "She wiped it away with" | | 5 | "Her left hand dropped to" | | 6 | "Her superiors had called it" | | 7 | "She had seen the room." | | 8 | "She thumbed her radio." | | 9 | "She killed the channel." | | 10 | "He dropped onto the other" | | 11 | "She hoisted herself, the tactical" | | 12 | "Her landing sent pain lancing" | | 13 | "She ran on it anyway." | | 14 | "She reached the entrance." | | 15 | "Her jaw tightened." | | 16 | "She'd found him three stories" | | 17 | "She descended, each footfall echoing" | | 18 | "She kept the beam steady." | | 19 | "She had expected debris, darkness," |
| | ratio | 0.393 | |
| 34.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 107 | | matches | | 0 | "Harlow Quinn swerved past a" | | 1 | "The hooded figure ahead—wiry, olive-skinned," | | 2 | "She'd picked him out two" | | 3 | "Her sharp jaw tightened." | | 4 | "He hurdled a kicked-over dustbin" | | 5 | "Her cropped salt-and-pepper hair shed" | | 6 | "She wiped it away with" | | 7 | "Her left hand dropped to" | | 8 | "The coroner had sealed the" | | 9 | "Her superiors had called it" | | 10 | "She had seen the room." | | 11 | "Walls didn't crack like that" | | 12 | "The suspect veered into an" | | 13 | "She thumbed her radio." | | 14 | "Static hissed back." | | 15 | "She killed the channel." | | 16 | "The alley ended in a" | | 17 | "The suspect scaled it in" | | 18 | "He dropped onto the other" | | 19 | "Quinn gripped the fence." |
| | ratio | 0.85 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 107 | | matches | (empty) | | ratio | 0 | |
| 30.08% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 6 | | matches | | 0 | "Rainneedled Camden High Street in silver threads, turning the pavement into a dark mirror that reflected the sodium glare of dead streetlamps." | | 1 | "The suspect kicked through a rotted plywood barrier and vanished down a stairwell that exhaled damp air." | | 2 | "Morris had died breathing air that carried this same sweetness." | | 3 | "She descended, each footfall echoing against calcite-slick walls that wept mineral tears." | | 4 | "Figures moved between them, some in tailored suits, some in rags that left too much skin uncovered, browsing jars that glowed with violet phosphorescence and ra…" | | 5 | "She looked back at the bazaar, at the mercury-eyed woman stirring on the tile, at the goods that defied physics and the traders who peddled them." |
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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 | |