| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 831 | | 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) | |
| 63.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 831 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "stomach" | | 2 | "predator" | | 3 | "flickered" | | 4 | "footsteps" | | 5 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 105 | | matches | | |
| 88.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 21 | | totalWords | 831 | | ratio | 0.025 | | matches | | 0 | "predator" | | 1 | "You go in there" | | 2 | "you don't come out the same." | | 3 | "I can help" | | 4 | "But not if you bring the law." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 818 | | uniqueNames | 11 | | maxNameDensity | 0.86 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Nest" | | discoveredNames | | Soho | 1 | | London | 1 | | Nest | 4 | | Raven | 1 | | Morris | 3 | | Thames | 1 | | Sunday | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Quinn | 7 |
| | persons | | 0 | "Raven" | | 1 | "Morris" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Nest" | | 3 | "Thames" |
| | globalScore | 1 | | windowScore | 1 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like at midnight" | | 1 | "quite a knife" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 831 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 109 | | matches | | 0 | "building that file" | | 1 | "knew that sign" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 20.27 | | std | 16.01 | | cv | 0.79 | | sampleLengths | | 0 | 40 | | 1 | 1 | | 2 | 16 | | 3 | 3 | | 4 | 51 | | 5 | 23 | | 6 | 37 | | 7 | 8 | | 8 | 9 | | 9 | 41 | | 10 | 3 | | 11 | 18 | | 12 | 16 | | 13 | 48 | | 14 | 15 | | 15 | 3 | | 16 | 11 | | 17 | 49 | | 18 | 10 | | 19 | 23 | | 20 | 17 | | 21 | 59 | | 22 | 3 | | 23 | 6 | | 24 | 50 | | 25 | 16 | | 26 | 17 | | 27 | 7 | | 28 | 33 | | 29 | 15 | | 30 | 50 | | 31 | 14 | | 32 | 5 | | 33 | 21 | | 34 | 18 | | 35 | 3 | | 36 | 6 | | 37 | 21 | | 38 | 16 | | 39 | 17 | | 40 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 105 | | matches | | |
| 17.35% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 146 | | matches | | 0 | "was heading" | | 1 | "wasn't running" | | 2 | "was running" | | 3 | "was walking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 109 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 293 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.013651877133105802 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0034129692832764505 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 7.62 | | std | 6.65 | | cv | 0.872 | | sampleLengths | | 0 | 10 | | 1 | 30 | | 2 | 1 | | 3 | 5 | | 4 | 1 | | 5 | 10 | | 6 | 3 | | 7 | 5 | | 8 | 33 | | 9 | 4 | | 10 | 9 | | 11 | 4 | | 12 | 1 | | 13 | 15 | | 14 | 1 | | 15 | 2 | | 16 | 4 | | 17 | 20 | | 18 | 4 | | 19 | 3 | | 20 | 6 | | 21 | 8 | | 22 | 6 | | 23 | 3 | | 24 | 14 | | 25 | 5 | | 26 | 3 | | 27 | 19 | | 28 | 3 | | 29 | 5 | | 30 | 1 | | 31 | 4 | | 32 | 8 | | 33 | 4 | | 34 | 4 | | 35 | 8 | | 36 | 10 | | 37 | 4 | | 38 | 10 | | 39 | 3 | | 40 | 2 | | 41 | 19 | | 42 | 7 | | 43 | 5 | | 44 | 2 | | 45 | 1 | | 46 | 3 | | 47 | 6 | | 48 | 5 | | 49 | 12 |
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| 71.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.48623853211009177 | | totalSentences | 109 | | uniqueOpeners | 53 | |
| 76.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 87 | | matches | | 0 | "Too late for that." | | 1 | "Somewhere below, a door slammed." |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 87 | | matches | | 0 | "She'd learned years ago that" | | 1 | "He didn't stop." | | 2 | "She didn't look down." | | 3 | "She knew what London bins" | | 4 | "His shoulder clipped a fish" | | 5 | "He was heading for the" | | 6 | "She vaulted the low railing" | | 7 | "She ignored it." | | 8 | "He didn't even glance back." | | 9 | "She'd been building that file" | | 10 | "She knew that sign." | | 11 | "She'd walked past it a" | | 12 | "He wasn't running toward the" | | 13 | "He was running past it." | | 14 | "She reread the file every" | | 15 | "He pressed the bone token" | | 16 | "He ducked inside." | | 17 | "She had no token." | | 18 | "She had her service weapon," | | 19 | "Her hand found the worn" |
| | ratio | 0.299 | |
| 74.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 87 | | matches | | 0 | "Quinn's boots hit the wet" | | 1 | "The suspect, lean and dressed" | | 2 | "The word came out flat." | | 3 | "She'd learned years ago that" | | 4 | "He didn't stop." | | 5 | "Rain lashed against her face." | | 6 | "The leather watch on her" | | 7 | "She didn't look down." | | 8 | "She knew what London bins" | | 9 | "The suspect cut left." | | 10 | "His shoulder clipped a fish" | | 11 | "Quinn matched his turn." | | 12 | "This one was terrified." | | 13 | "That told her everything she" | | 14 | "He was heading for the" | | 15 | "She vaulted the low railing" | | 16 | "Pain shot up her calf." | | 17 | "She ignored it." | | 18 | "The clipped brown hair plastered" | | 19 | "He didn't even glance back." |
| | ratio | 0.77 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 87 | | matches | | 0 | "Now here she was, three" | | 1 | "*But not if you bring" |
| | ratio | 0.023 | |
| 4.61% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 6 | | matches | | 0 | "The suspect, lean and dressed in a black coat that flapped like something wounded, darted through the alley mouth and vanished into the orange wash of a Soho si…" | | 1 | "The leather watch on her left wrist bit into her skin as she pushed off the wall, leaping over a bin bag that split under her weight, sending something rotten i…" | | 2 | "He pressed the bone token against a specific brick and a section of wall groaned inward, exhaling air that smelled of copper and candle wax and something older." | | 3 | "The market's breath curled around her ankles, cold and wrong, and somewhere inside, a bell rang twice in a pattern that wasn't a pattern, it was a language, and…" | | 4 | "She had her service weapon, twelve rounds, and three years of questions that had eaten her sleep." | | 5 | "Footsteps echoed upward, multiplied, layered, as if a hundred people walked in corridors that shouldn't exist." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |