| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.625 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 842 | | 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) | |
| 70.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 842 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulsed" | | 1 | "pulse" | | 2 | "lilt" | | 3 | "echoed" | | 4 | "gleaming" |
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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 | 71 | | matches | (empty) | |
| 62.37% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 829 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 80.20% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 788 | | uniqueNames | 17 | | maxNameDensity | 1.4 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Carnaby | 1 | | Street | 1 | | Morris | 1 | | Soho | 1 | | Oxford | 1 | | Circus | 1 | | Tube | 2 | | Veil | 2 | | Market | 2 | | Herrera | 2 | | English | 1 | | Seville | 1 | | Rain | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Market" | | 4 | "Herrera" | | 5 | "English" | | 6 | "Rain" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | 0 | "Carnaby" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Oxford" | | 4 | "Seville" |
| | globalScore | 0.802 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | 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 | 829 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 68.32% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 46.06 | | std | 17.92 | | cv | 0.389 | | sampleLengths | | 0 | 69 | | 1 | 67 | | 2 | 69 | | 3 | 60 | | 4 | 47 | | 5 | 57 | | 6 | 43 | | 7 | 32 | | 8 | 51 | | 9 | 6 | | 10 | 53 | | 11 | 22 | | 12 | 66 | | 13 | 57 | | 14 | 36 | | 15 | 44 | | 16 | 26 | | 17 | 24 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 137 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 74 | | ratio | 0.122 | | matches | | 0 | "The suspect—a man in a drenched leather jacket—darted around a corner near Carnaby Street, his silhouette flickering under the neon glare of a discounted shuttered shop." | | 1 | "Now, Quinn’s jaw set tight, the scar tissue on her left palm itching beneath her gloves—a souvenir from the case that had buried her partner." | | 2 | "A green neon raven blinked above a doorway—the sign had to be a coincidence." | | 3 | "At Oxford Circus, he surged toward a construction site—a half-collapsed Tube entrance buried under tarps." | | 4 | "Stalls crowded the arches of the abandoned station, their wares shifting—skulls in jars whispered to each other, fabric pulsed like living veins." | | 5 | "Every instinct screamed to turn back, but the scent of copper—real blood or something older—drew her forward." | | 6 | "Farther ahead, a display of live caterpillars—each with tiny fangs—hissed as shadows moved." | | 7 | "A face rose from the mercury—not hers, not his—older, female." | | 8 | "The ground dropped away, and she tumbled out into the night—rain and cold slap back onto her skin." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 804 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.012437810945273632 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0024875621890547263 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 11.2 | | std | 5.54 | | cv | 0.494 | | sampleLengths | | 0 | 16 | | 1 | 26 | | 2 | 13 | | 3 | 2 | | 4 | 12 | | 5 | 21 | | 6 | 25 | | 7 | 6 | | 8 | 12 | | 9 | 1 | | 10 | 2 | | 11 | 14 | | 12 | 14 | | 13 | 19 | | 14 | 8 | | 15 | 14 | | 16 | 12 | | 17 | 16 | | 18 | 15 | | 19 | 4 | | 20 | 13 | | 21 | 11 | | 22 | 9 | | 23 | 14 | | 24 | 2 | | 25 | 11 | | 26 | 16 | | 27 | 12 | | 28 | 22 | | 29 | 7 | | 30 | 9 | | 31 | 11 | | 32 | 6 | | 33 | 17 | | 34 | 6 | | 35 | 20 | | 36 | 6 | | 37 | 11 | | 38 | 12 | | 39 | 11 | | 40 | 9 | | 41 | 8 | | 42 | 6 | | 43 | 7 | | 44 | 11 | | 45 | 13 | | 46 | 22 | | 47 | 5 | | 48 | 8 | | 49 | 9 |
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| 63.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.3918918918918919 | | totalSentences | 74 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 66 | | matches | | 0 | "Somewhere in this trench of" | | 1 | "Somewhere, a chandelier of dried" |
| | ratio | 0.03 | |
| 92.73% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 66 | | matches | | 0 | "She checked her worn leather" | | 1 | "She pounded into the alley," | | 2 | "She knew it was there," | | 3 | "He reappeared outside, sprinting eastward," | | 4 | "She cursed, lungs burning, and" | | 5 | "Her watch ticked 11:34." | | 6 | "she barked, her voice lost" | | 7 | "It gleamed with a sickly" | | 8 | "He shoved it into the" | | 9 | "She paused, her hand hovering" | | 10 | "He waved a switchblade between" | | 11 | "She stepped left, boots squelching" | | 12 | "she said, flat, factual" | | 13 | "She ducked as the houndstooth" | | 14 | "He spat blood, a gurgle" | | 15 | "She yanked the wire taut" | | 16 | "She burst inside, the room" | | 17 | "he said, his English laced" | | 18 | "Her boots lost grip on" | | 19 | "She climbed to her feet," |
| | ratio | 0.318 | |
| 20.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 66 | | matches | | 0 | "Detective Harlow Quinn’s boot splashed" | | 1 | "The suspect—a man in a" | | 2 | "She checked her worn leather" | | 3 | "The rain sheeted down, turning" | | 4 | "The man ahead hadetSocketAddress that" | | 5 | "A jagged crescent along his" | | 6 | "She pounded into the alley," | | 7 | "A green neon raven blinked" | | 8 | "Quinn rammed through the bar’s" | | 9 | "The back room hid behind" | | 10 | "She knew it was there," | | 11 | "He reappeared outside, sprinting eastward," | | 12 | "She cursed, lungs burning, and" | | 13 | "Her watch ticked 11:34." | | 14 | "The Veil Market moved each" | | 15 | "she barked, her voice lost" | | 16 | "The man turned, a key" | | 17 | "It gleamed with a sickly" | | 18 | "He shoved it into the" | | 19 | "Quinn hit the stairs two" |
| | ratio | 0.879 | |
| 75.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 66 | | matches | | 0 | "Now, Quinn’s jaw set tight," |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "Now, Quinn’s jaw set tight, the scar tissue on her left palm itching beneath her gloves—a souvenir from the case that had buried her partner." | | 1 | "She burst inside, the room tilting as if the bones of the Tube were shifting beneath her." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "The speaker wore, his face a blooming bruise of shifting tattoos" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "she barked (bark)" | | 1 | "she demanded (demand)" |
| | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |