| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1359 | | 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) | |
| 52.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1359 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "measured" | | 1 | "jaw clenched" | | 2 | "absolutely" | | 3 | "etched" | | 4 | "footsteps" | | 5 | "echoing" | | 6 | "chill" | | 7 | "rhythmic" | | 8 | "scanned" | | 9 | "fleeting" | | 10 | "predictable" | | 11 | "familiar" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "jaw clenched" | | 1 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1359 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1359 | | uniqueNames | 20 | | maxNameDensity | 1.03 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 3 | | Harlow | 14 | | Quinn | 1 | | Metropolitan | 2 | | Police | 2 | | London | 2 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Town | 1 | | Underground | 1 | | Morris | 1 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Herrera | 1 | | Seville | 1 | | Tomás | 3 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Raven" | | 4 | "Morris" | | 5 | "Market" | | 6 | "Herrera" | | 7 | "Tomás" | | 8 | "Saint" | | 9 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "Metropolitan" | | 2 | "London" | | 3 | "Soho" | | 4 | "Town" | | 5 | "Underground" | | 6 | "Veil" | | 7 | "Seville" |
| | globalScore | 0.985 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like enchanted amulets, the metals" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1359 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 24.25% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 13 | | mean | 104.54 | | std | 24.55 | | cv | 0.235 | | sampleLengths | | 0 | 80 | | 1 | 131 | | 2 | 105 | | 3 | 85 | | 4 | 87 | | 5 | 132 | | 6 | 117 | | 7 | 60 | | 8 | 123 | | 9 | 153 | | 10 | 93 | | 11 | 97 | | 12 | 96 |
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| 86.99% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 96 | | matches | | 0 | "was plastered" | | 1 | "were etched" | | 2 | "were shuttered" | | 3 | "been transformed" | | 4 | "was supposed" |
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| 43.51% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 213 | | matches | | 0 | "was panicking" | | 1 | "was fumbling" | | 2 | "was selling" | | 3 | "was talking" | | 4 | "was dealing" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 96 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1367 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.019751280175566936 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.011704462326261888 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 14.16 | | std | 6.55 | | cv | 0.462 | | sampleLengths | | 0 | 17 | | 1 | 17 | | 2 | 25 | | 3 | 21 | | 4 | 10 | | 5 | 19 | | 6 | 10 | | 7 | 4 | | 8 | 21 | | 9 | 9 | | 10 | 28 | | 11 | 30 | | 12 | 10 | | 13 | 7 | | 14 | 7 | | 15 | 11 | | 16 | 7 | | 17 | 4 | | 18 | 16 | | 19 | 13 | | 20 | 9 | | 21 | 21 | | 22 | 19 | | 23 | 15 | | 24 | 10 | | 25 | 14 | | 26 | 5 | | 27 | 22 | | 28 | 13 | | 29 | 12 | | 30 | 21 | | 31 | 14 | | 32 | 9 | | 33 | 18 | | 34 | 9 | | 35 | 17 | | 36 | 21 | | 37 | 2 | | 38 | 19 | | 39 | 11 | | 40 | 18 | | 41 | 19 | | 42 | 16 | | 43 | 9 | | 44 | 13 | | 45 | 10 | | 46 | 16 | | 47 | 9 | | 48 | 11 | | 49 | 17 |
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| 39.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3020833333333333 | | totalSentences | 96 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 52.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 93 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She checked the worn leather" | | 2 | "She kept her brown eyes" | | 3 | "He was a mid-level fixer" | | 4 | "They had started the pursuit" | | 5 | "Her breath plumed in the" | | 6 | "He was fast, but he" | | 7 | "He slipped on a patch" | | 8 | "She did not draw her" | | 9 | "He scrambled up and burst" | | 10 | "He veered toward a side" | | 11 | "She caught the heavy steel" | | 12 | "She heard his footsteps echoing" | | 13 | "She moved with silent, deliberate" | | 14 | "It was a damp, unnatural" | | 15 | "It felt exactly like the" | | 16 | "She had seen the impossible," | | 17 | "He heard her boots slapping" | | 18 | "He threw a heavy brass" | | 19 | "He snatched at it, missed," |
| | ratio | 0.419 | |
| 24.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 93 | | matches | | 0 | "Rain lashed against the pavement," | | 1 | "Detective Harlow Quinn pumped her" | | 2 | "Harlow wiped a sheet of" | | 3 | "Her closely cropped salt-and-pepper hair" | | 4 | "She checked the worn leather" | | 5 | "The streets were mostly empty," | | 6 | "She kept her brown eyes" | | 7 | "He was a mid-level fixer" | | 8 | "They had started the pursuit" | | 9 | "The suspect took a sharp" | | 10 | "Harlow followed, her sharp jaw" | | 11 | "Her breath plumed in the" | | 12 | "The alley smelled of rotting" | | 13 | "He was fast, but he" | | 14 | "Panic made people sloppy." | | 15 | "He slipped on a patch" | | 16 | "Harlow closed the distance, her" | | 17 | "She did not draw her" | | 18 | "The rules of engagement were" | | 19 | "He scrambled up and burst" |
| | ratio | 0.871 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 93 | | matches | | 0 | "To Harlow's shock, the heavy" | | 1 | "Even from this distance, she" | | 2 | "If she called it in," |
| | ratio | 0.032 | |
| 72.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 7 | | matches | | 0 | "The streets were mostly empty, save for the occasional late-night reveler who pressed themselves against the brickwork to avoid the collision." | | 1 | "The rules of engagement were etched into her brain, a rigid code that kept her grounded when the world went sideways." | | 2 | "She heard his footsteps echoing down a disused corridor, moving away from the active platforms and deeper into the forgotten infrastructure." | | 3 | "It was a damp, unnatural chill that seeped through her waterproof jacket and settled into her bones." | | 4 | "The runner was fumbling with something in his pocket, his hands shaking violently." | | 5 | "It was unnaturally warm to the touch, pulsing with a faint, rhythmic heat that made the hairs on her arms stand up." | | 6 | "The former paramedic born in Seville who had lost his medical license for treating things that did not officially exist." |
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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 | |