| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 88.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1251 | | totalAiIsmAdverbs | 3 | | 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) | |
| 32.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1251 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "scanning" | | 1 | "gloom" | | 2 | "familiar" | | 3 | "loomed" | | 4 | "silence" | | 5 | "echoed" | | 6 | "chill" | | 7 | "electric" | | 8 | "velvet" | | 9 | "etched" | | 10 | "glinting" | | 11 | "echoes" | | 12 | "weight" | | 13 | "could feel" | | 14 | "grave" |
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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 | 119 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 119 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | 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 | 1251 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 3 | | matches | | 0 | "Detective Quinn, he said." | | 1 | "You are the one who lost Morris, he said." | | 2 | "I have a suspect in custody who is responsible for the deaths in Chelsea, Harlow said." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1251 | | uniqueNames | 20 | | maxNameDensity | 0.56 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 1 | | Quinn | 3 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Harlow | 7 | | Underground | 1 | | Tube | 1 | | London | 1 | | Metropolitan | 1 | | Police | 1 | | Veil | 2 | | Market | 2 | | Morris | 3 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 7 | | Chelsea | 1 | | You | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Harlow" | | 4 | "Underground" | | 5 | "Market" | | 6 | "Morris" | | 7 | "Herrera" | | 8 | "Saint" | | 9 | "Christopher" | | 10 | "Tomás" | | 11 | "Chelsea" | | 12 | "You" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a clean rib, polished to an i" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 4 | | per1kWords | 3.197 | | wordCount | 1251 | | matches | | 0 | "not the rough tones of street vendors or drifters, but something melodic, strange" | | 1 | "not the stench of sewage or old train tracks, but something sharper" | | 2 | "not electric but bioluminescent, glowing from moss-like clusters on the stati" | | 3 | "not escaped, but his warm brown eyes held a sharp awareness" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 43.14 | | std | 27.95 | | cv | 0.648 | | sampleLengths | | 0 | 94 | | 1 | 43 | | 2 | 71 | | 3 | 73 | | 4 | 70 | | 5 | 6 | | 6 | 60 | | 7 | 67 | | 8 | 85 | | 9 | 55 | | 10 | 25 | | 11 | 102 | | 12 | 25 | | 13 | 28 | | 14 | 19 | | 15 | 12 | | 16 | 16 | | 17 | 10 | | 18 | 48 | | 19 | 46 | | 20 | 32 | | 21 | 74 | | 22 | 11 | | 23 | 22 | | 24 | 9 | | 25 | 4 | | 26 | 46 | | 27 | 34 | | 28 | 64 |
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| 90.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 119 | | matches | | 0 | "was plastered" | | 1 | "been unexplained" | | 2 | "been ordered" | | 3 | "was gone" | | 4 | "was forgotten" | | 5 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 212 | | matches | | 0 | "was running" | | 1 | "was running" | | 2 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 119 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1255 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.022310756972111555 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008764940239043825 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 10.51 | | std | 6.11 | | cv | 0.581 | | sampleLengths | | 0 | 17 | | 1 | 7 | | 2 | 27 | | 3 | 19 | | 4 | 24 | | 5 | 6 | | 6 | 22 | | 7 | 10 | | 8 | 5 | | 9 | 25 | | 10 | 6 | | 11 | 27 | | 12 | 13 | | 13 | 13 | | 14 | 7 | | 15 | 2 | | 16 | 15 | | 17 | 17 | | 18 | 10 | | 19 | 9 | | 20 | 5 | | 21 | 17 | | 22 | 10 | | 23 | 7 | | 24 | 15 | | 25 | 16 | | 26 | 6 | | 27 | 3 | | 28 | 15 | | 29 | 17 | | 30 | 14 | | 31 | 11 | | 32 | 5 | | 33 | 14 | | 34 | 5 | | 35 | 13 | | 36 | 16 | | 37 | 14 | | 38 | 10 | | 39 | 9 | | 40 | 5 | | 41 | 26 | | 42 | 18 | | 43 | 17 | | 44 | 9 | | 45 | 19 | | 46 | 9 | | 47 | 18 | | 48 | 3 | | 49 | 4 |
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| 35.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.2605042016806723 | | totalSentences | 119 | | uniqueOpeners | 31 | |
| 28.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 117 | | matches | | 0 | "Just a heavy iron grate" |
| | ratio | 0.009 | |
| 14.87% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 60 | | totalSentences | 117 | | matches | | 0 | "She pulled the collar of" | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "She had not lost the" | | 3 | "She could hear the heavy" | | 4 | "She was running on training." | | 5 | "She had followed without hesitation," | | 6 | "She checked her worn leather" | | 7 | "She moved to the grate." | | 8 | "It was locked, or so" | | 9 | "She pressed her ear against" | | 10 | "She pulled her hand away" | | 11 | "She didn’t hesitate." | | 12 | "Her grip tightened on her" | | 13 | "She found the release latch" | | 14 | "It gave way with a" | | 15 | "She dropped through the opening" | | 16 | "It was not the stench" | | 17 | "She stood at the top" | | 18 | "She descended slowly, her boots" | | 19 | "Her training screamed at her" |
| | ratio | 0.513 | |
| 36.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 99 | | totalSentences | 117 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Harlow Quinn did not slow" | | 2 | "She pulled the collar of" | | 3 | "Her closely cropped salt-and-pepper hair" | | 4 | "She had not lost the" | | 5 | "She could hear the heavy" | | 6 | "The man was fast, but" | | 7 | "She was running on training." | | 8 | "That had been the turning" | | 9 | "The suspect had ducked past" | | 10 | "She had followed without hesitation," | | 11 | "Harlow skidded to a halt," | | 12 | "She checked her worn leather" | | 13 | "The second hand ticked, a" | | 14 | "She moved to the grate." | | 15 | "It was locked, or so" | | 16 | "She pressed her ear against" | | 17 | "She pulled her hand away" | | 18 | "The suspect had gone down" | | 19 | "She didn’t hesitate." |
| | ratio | 0.846 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 117 | | matches | | 0 | "If she fired shots, the" | | 1 | "To put me in a" | | 2 | "If you walk in, Quinn," | | 3 | "To step past the line" | | 4 | "To leave him here was" | | 5 | "Because she had chosen to" |
| | ratio | 0.051 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 2 | | matches | | 0 | "Vendors displayed things that shouldn't exist: jars of glowing fluids, books bound in skin, weapons etched with runes." | | 1 | "She walked past the stalls that offered knowledge and death for a price." |
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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 | |