| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 904 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 904 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "fleeting" | | 1 | "rhythmic" | | 2 | "navigating" | | 3 | "familiar" | | 4 | "glint" | | 5 | "treacherous" | | 6 | "flicker" | | 7 | "resolve" | | 8 | "echoed" | | 9 | "weight" | | 10 | "silence" | | 11 | "pulsed" | | 12 | "beacon" | | 13 | "gloom" | | 14 | "cacophony" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "A flicker of doubt" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 86 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 900 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 896 | | uniqueNames | 12 | | maxNameDensity | 0.56 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 1 | | Tomás | 1 | | Herrera | 5 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Veil | 2 | | Market | 1 | | Silence | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a derelict building" |
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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 | 900 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 68.60% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 15 | | mean | 60 | | std | 23.41 | | cv | 0.39 | | sampleLengths | | 0 | 62 | | 1 | 18 | | 2 | 96 | | 3 | 90 | | 4 | 55 | | 5 | 58 | | 6 | 64 | | 7 | 37 | | 8 | 64 | | 9 | 9 | | 10 | 89 | | 11 | 70 | | 12 | 69 | | 13 | 55 | | 14 | 64 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 140 | | matches | (empty) | |
| 76.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 86 | | ratio | 0.023 | | matches | | 0 | "The air grew colder, thick with a strange, cloying scent she couldn’t place – like ozone and old incense." | | 1 | "The uniform, the shield, the law – all of it meant nothing here." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 900 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.024444444444444446 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.01 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 10.47 | | std | 5.98 | | cv | 0.572 | | sampleLengths | | 0 | 10 | | 1 | 19 | | 2 | 13 | | 3 | 20 | | 4 | 18 | | 5 | 3 | | 6 | 16 | | 7 | 15 | | 8 | 19 | | 9 | 18 | | 10 | 14 | | 11 | 11 | | 12 | 14 | | 13 | 23 | | 14 | 12 | | 15 | 5 | | 16 | 17 | | 17 | 19 | | 18 | 10 | | 19 | 6 | | 20 | 19 | | 21 | 12 | | 22 | 3 | | 23 | 5 | | 24 | 16 | | 25 | 12 | | 26 | 12 | | 27 | 15 | | 28 | 3 | | 29 | 5 | | 30 | 15 | | 31 | 10 | | 32 | 4 | | 33 | 11 | | 34 | 6 | | 35 | 13 | | 36 | 11 | | 37 | 16 | | 38 | 4 | | 39 | 3 | | 40 | 3 | | 41 | 13 | | 42 | 19 | | 43 | 15 | | 44 | 17 | | 45 | 9 | | 46 | 1 | | 47 | 7 | | 48 | 9 | | 49 | 23 |
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| 58.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3953488372093023 | | totalSentences | 86 | | uniqueOpeners | 34 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 82 | | matches | | 0 | "All that mattered was Herrera." | | 1 | "Only the faint thrum of" |
| | ratio | 0.024 | |
| 83.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 82 | | matches | | 0 | "Her voice, a ragged bellow," | | 1 | "He didn't slow." | | 2 | "Her worn leather watch, a" | | 3 | "She pushed past overflowing bins," | | 4 | "She kicked it aside, her" | | 5 | "His olive skin, usually warm," | | 6 | "His strides were long, powerful." | | 7 | "He moved with a practised" | | 8 | "She ignored him." | | 9 | "He veered left, into a" | | 10 | "She slid, a gasp catching" | | 11 | "Her palm smarted." | | 12 | "Her brown eyes narrowed." | | 13 | "She pushed past the threshold," | | 14 | "Her mind clicked." | | 15 | "Her hand went to the" | | 16 | "Her voice echoed, swallowed by" | | 17 | "She moved cautiously, her boots" | | 18 | "He was a medic." | | 19 | "He lost his partner under" |
| | ratio | 0.341 | |
| 27.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 82 | | matches | | 0 | "Rain lashed against the grimy" | | 1 | "Detective Harlow Quinn’s breath plumed" | | 2 | "Water streamed down her face," | | 3 | "The silhouette of Tomás Herrera," | | 4 | "Her voice, a ragged bellow," | | 5 | "He didn't slow." | | 6 | "The rhythmic slap of his" | | 7 | "Her worn leather watch, a" | | 8 | "She pushed past overflowing bins," | | 9 | "A discarded umbrella, its ribs" | | 10 | "She kicked it aside, her" | | 11 | "The alley opened onto a" | | 12 | "Herrera, a dark blur, wove" | | 13 | "His olive skin, usually warm," | | 14 | "His strides were long, powerful." | | 15 | "He moved with a practised" | | 16 | "A familiar glint caught her" | | 17 | "Harlow accelerated, her military precision" | | 18 | "The slick pavement offered little" | | 19 | "A taxi, its yellow paint" |
| | ratio | 0.866 | |
| 60.98% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 82 | | matches | | | ratio | 0.012 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 5 | | matches | | 0 | "Water streamed down her face, mingling with the sweat that pricked her eyebrows." | | 1 | "She slid, a gasp catching in her throat, catching herself on a cold, damp wall." | | 2 | "Water dripped from unseen cracks, splashing into puddles that reflected the barest slivers of light from above." | | 3 | "The scent intensified, becoming a dizzying mix of roasted spices, something floral and sweet, and an acrid tang that made her nose twitch." | | 4 | "Stepping in meant a direct violation of protocol, a dive into an unknown that could swallow her whole." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |