| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 4 | | adverbTags | | 0 | "she said finally [finally]" | | 1 | "Lily looked away [away]" | | 2 | "Lily's jaw worked slightly [slightly]" | | 3 | "Lily asked abruptly [abruptly]" |
| | dialogueSentences | 30 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.267 | | effectiveRatio | 0.267 | |
| 87.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1240 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "really" | | 2 | "slightly" |
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| 60.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 71.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1240 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "familiar" | | 1 | "electric" | | 2 | "silk" | | 3 | "efficient" | | 4 | "anticipating" | | 5 | "remarkable" |
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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 | 90 | | matches | (empty) | |
| 95.24% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 90 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "managed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1217 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 951 | | uniqueNames | 16 | | maxNameDensity | 1.79 | | worstName | "Lily" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Lily" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 12 | | Covent | 1 | | Garden | 1 | | Fitzroy | 1 | | Christ | 1 | | Rory | 6 | | Chen | 1 | | Evan | 1 | | Lily | 17 | | New | 1 | | York | 1 | | Silas | 7 | | London | 1 | | French-sounding | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Rory" | | 4 | "Chen" | | 5 | "Evan" | | 6 | "Lily" | | 7 | "Silas" |
| | places | | 0 | "Covent" | | 1 | "Garden" | | 2 | "Fitzroy" | | 3 | "New" | | 4 | "York" | | 5 | "London" |
| | globalScore | 0.606 | | windowScore | 0.167 | |
| 29.03% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like a martini with the precision" | | 1 | "not quite but honest in a way that didn't leave room for negotiation" | | 2 | "Something like vulnerability, or regret, or" |
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| 35.66% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.643 | | wordCount | 1217 | | matches | | 0 | "Not cruel, not quite, but honest in a way" | | 1 | "not quite, but honest in a way" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 28.3 | | std | 19.45 | | cv | 0.687 | | sampleLengths | | 0 | 66 | | 1 | 40 | | 2 | 41 | | 3 | 43 | | 4 | 29 | | 5 | 2 | | 6 | 81 | | 7 | 2 | | 8 | 59 | | 9 | 47 | | 10 | 25 | | 11 | 23 | | 12 | 29 | | 13 | 30 | | 14 | 55 | | 15 | 11 | | 16 | 24 | | 17 | 1 | | 18 | 28 | | 19 | 23 | | 20 | 31 | | 21 | 4 | | 22 | 26 | | 23 | 12 | | 24 | 1 | | 25 | 44 | | 26 | 30 | | 27 | 3 | | 28 | 36 | | 29 | 32 | | 30 | 13 | | 31 | 38 | | 32 | 2 | | 33 | 60 | | 34 | 39 | | 35 | 6 | | 36 | 39 | | 37 | 2 | | 38 | 47 | | 39 | 26 | | 40 | 5 | | 41 | 38 | | 42 | 24 |
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| 93.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 90 | | matches | | 0 | "being asked" | | 1 | "being said" | | 2 | "were supposed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 171 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 105 | | ratio | 0.114 | | matches | | 0 | "She'd been running deliveries all afternoon—three orders of dim sum to Covent Garden, a large beef chow fun to some office in Fitzroy—and her feet had begun their protest by order number two." | | 1 | "The other patrons scattered themselves like chess pieces across the space—two men at a corner table speaking in clipped tones, a woman alone with a gin and tonic, a couple who seemed to be having separate conversations despite sitting inches apart." | | 2 | "Not the polite kind—the genuine, head-thrown-back kind that belonged to someone who'd forgotten how to control themselves in public." | | 3 | "She wore a tailored grey blazer over what might have been silk, and her shoes—Christ, her shoes cost more than Rory's monthly rent." | | 4 | "Properly inseparable—the kind of friendship where other people became secondary characters in the narrative of the two of them." | | 5 | "They'd texted for a while—sporadic messages that grew longer and more formal until they collapsed entirely." | | 6 | "Silas appeared with another drink—a whisky something—and set it before the blonde woman." | | 7 | "Send a text later—surprised to hear you're in London, should catch up sometime—the kind of message that established friendly intentions while guaranteeing nothing." | | 8 | "Her eyes—they used to be warmer, Rory thought—went through a moment of calibration." | | 9 | "\"Lily.\" Aurora settled onto the adjacent stool, close enough to smell her perfume—something French-sounding and aggressively expensive." | | 10 | "Her eyes had developed these small lines at the corners—not laugh lines." | | 11 | "This was close to the real Lily—the one who'd existed before the optimization." |
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| 90.92% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 971 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.03810504634397528 | | lyAdverbCount | 30 | | lyAdverbRatio | 0.030895983522142123 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 11.59 | | std | 8.8 | | cv | 0.759 | | sampleLengths | | 0 | 21 | | 1 | 12 | | 2 | 33 | | 3 | 18 | | 4 | 12 | | 5 | 10 | | 6 | 41 | | 7 | 24 | | 8 | 9 | | 9 | 7 | | 10 | 3 | | 11 | 10 | | 12 | 19 | | 13 | 2 | | 14 | 21 | | 15 | 14 | | 16 | 23 | | 17 | 23 | | 18 | 2 | | 19 | 4 | | 20 | 19 | | 21 | 11 | | 22 | 25 | | 23 | 9 | | 24 | 16 | | 25 | 6 | | 26 | 16 | | 27 | 3 | | 28 | 22 | | 29 | 13 | | 30 | 8 | | 31 | 2 | | 32 | 3 | | 33 | 4 | | 34 | 22 | | 35 | 13 | | 36 | 9 | | 37 | 8 | | 38 | 8 | | 39 | 3 | | 40 | 21 | | 41 | 23 | | 42 | 5 | | 43 | 6 | | 44 | 2 | | 45 | 13 | | 46 | 9 | | 47 | 1 | | 48 | 3 | | 49 | 15 |
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| 59.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.41904761904761906 | | totalSentences | 105 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 79 | | matches | | 0 | "Properly inseparable—the kind of friendship" | | 1 | "Then Lily had taken a" | | 2 | "Of course Silas knew." | | 3 | "Instead, she heard herself moving." |
| | ratio | 0.051 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 79 | | matches | | 0 | "She'd been running deliveries all" | | 1 | "She wore a tailored grey" | | 2 | "They'd been inseparable once." | | 3 | "She'd been the one who" | | 4 | "They'd texted for a while—sporadic" | | 5 | "She watched the woman drink" | | 6 | "he said, the words clipped" | | 7 | "He seemed to know when" | | 8 | "She should leave." | | 9 | "Her eyes—they used to be" | | 10 | "she said finally" | | 11 | "She paused to drink." | | 12 | "Her eyes had developed these" | | 13 | "She didn't flag Silas for" | | 14 | "She glanced at Rory" | | 15 | "He didn't ask if Lily" |
| | ratio | 0.203 | |
| 48.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 79 | | matches | | 0 | "The green neon sign of" | | 1 | "Rain clung to her jacket," | | 2 | "She'd been running deliveries all" | | 3 | "The bar exhaled its usual" | | 4 | "Silas polished a rocks glass" | | 5 | "The silver signet ring caught" | | 6 | "The other patrons scattered themselves" | | 7 | "Aurora made for her usual" | | 8 | "Silas poured her a sparkling" | | 9 | "Some people got smiles with" | | 10 | "Rory got efficiency." | | 11 | "A laugh cut through the" | | 12 | "The woman sat three stools" | | 13 | "She wore a tailored grey" | | 14 | "They'd been inseparable once." | | 15 | "Lily had been studying economics," | | 16 | "She'd been the one who" | | 17 | "A good job, the kind" | | 18 | "That had been four years" | | 19 | "They'd texted for a while—sporadic" |
| | ratio | 0.823 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 65.64% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 4 | | matches | | 0 | "The other patrons scattered themselves like chess pieces across the space—two men at a corner table speaking in clipped tones, a woman alone with a gin and toni…" | | 1 | "Not the polite kind—the genuine, head-thrown-back kind that belonged to someone who'd forgotten how to control themselves in public." | | 2 | "But it was the laugh that had given her away, that particular cadence that used to fill university halls and late-night kebab shops." | | 3 | "The marks of someone who spent hours squinting at screens, building arguments, anticipating counterarguments." |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, the words clipped" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.233 | | leniency | 0.467 | | rawRatio | 0 | | effectiveRatio | 0 | |