| 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 | 1678 | | 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) | |
| 31.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1678 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | word | "carried the weight" | | count | 1 |
| | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | |
| | highlights | | 0 | "weight" | | 1 | "fractured" | | 2 | "familiar" | | 3 | "chill" | | 4 | "measured" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "carried the weight" | | 8 | "pulse" | | 9 | "quickened" | | 10 | "silk" | | 11 | "velvet" | | 12 | "porcelain" | | 13 | "flicked" | | 14 | "etched" | | 15 | "scanning" | | 16 | "racing" |
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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 | 148 | | matches | (empty) | |
| 84.94% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 2 | | narrationSentences | 148 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 148 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1678 | | 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 | 48 | | wordCount | 1678 | | uniqueNames | 18 | | maxNameDensity | 1.01 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "You" | | discoveredNames | | Soho | 1 | | Quinn | 1 | | Greek | 1 | | Street | 1 | | Metropolitan | 2 | | Police | 2 | | London | 1 | | Morris | 3 | | Harlow | 17 | | Tube | 1 | | Veil | 2 | | Market | 1 | | Camden | 2 | | Saint | 1 | | Christopher | 1 | | Detective | 1 | | Herrera | 2 | | You | 8 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Morris" | | 3 | "Harlow" | | 4 | "Market" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Herrera" | | 8 | "You" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "London" | | 4 | "Veil" |
| | globalScore | 0.993 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like ozone and old pennies and a p" | | 1 | "line that seemed to come from the walls themselves" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.596 | | wordCount | 1678 | | matches | | 0 | "not from fear, but from the sudden, visceral recognition" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 148 | | matches | | 0 | "railing that overlooked" | | 1 | "chasing that line" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 83.9 | | std | 52.62 | | cv | 0.627 | | sampleLengths | | 0 | 152 | | 1 | 159 | | 2 | 170 | | 3 | 106 | | 4 | 35 | | 5 | 122 | | 6 | 164 | | 7 | 89 | | 8 | 6 | | 9 | 87 | | 10 | 28 | | 11 | 13 | | 12 | 72 | | 13 | 9 | | 14 | 104 | | 15 | 110 | | 16 | 84 | | 17 | 75 | | 18 | 85 | | 19 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 148 | | matches | | |
| 23.40% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 302 | | matches | | 0 | "was coming" | | 1 | "was carrying" | | 2 | "was going" | | 3 | "was facing" | | 4 | "was arguing" | | 5 | "was not just laundering" | | 6 | "were trading" | | 7 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 148 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1682 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.022592152199762187 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0035671819262782403 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 148 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 148 | | mean | 11.34 | | std | 7.43 | | cv | 0.655 | | sampleLengths | | 0 | 23 | | 1 | 8 | | 2 | 18 | | 3 | 32 | | 4 | 11 | | 5 | 18 | | 6 | 14 | | 7 | 12 | | 8 | 16 | | 9 | 21 | | 10 | 3 | | 11 | 19 | | 12 | 7 | | 13 | 24 | | 14 | 20 | | 15 | 30 | | 16 | 8 | | 17 | 9 | | 18 | 8 | | 19 | 10 | | 20 | 10 | | 21 | 5 | | 22 | 16 | | 23 | 4 | | 24 | 12 | | 25 | 8 | | 26 | 24 | | 27 | 17 | | 28 | 4 | | 29 | 7 | | 30 | 5 | | 31 | 1 | | 32 | 10 | | 33 | 3 | | 34 | 19 | | 35 | 14 | | 36 | 11 | | 37 | 14 | | 38 | 14 | | 39 | 17 | | 40 | 11 | | 41 | 17 | | 42 | 16 | | 43 | 17 | | 44 | 5 | | 45 | 4 | | 46 | 4 | | 47 | 17 | | 48 | 5 | | 49 | 4 |
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| 37.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.30405405405405406 | | totalSentences | 148 | | uniqueOpeners | 45 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 143 | | matches | (empty) | | ratio | 0 | |
| 52.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 60 | | totalSentences | 143 | | matches | | 0 | "She cared about the back" | | 1 | "Her left boot hit a" | | 2 | "She kept her hands loose," | | 3 | "Her closely cropped salt-and-pepper hair" | | 4 | "She was five nine, but" | | 5 | "She adjusted her grip on" | | 6 | "She had not slept in" | | 7 | "She knew better than to" | | 8 | "She called it a door" | | 9 | "She did not climb." | | 10 | "She needed to see where" | | 11 | "She keyed her radio." | | 12 | "She tried the emergency channel." | | 13 | "She should wait." | | 14 | "She should call it in" | | 15 | "She unclipped the radio and" | | 16 | "Her eyes adjusted to the" | | 17 | "She should have turned back." | | 18 | "She knew the rules." | | 19 | "She knew her limits." |
| | ratio | 0.42 | |
| 47.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 118 | | totalSentences | 143 | | matches | | 0 | "Rain fell in sheets, turning" | | 1 | "Harlow Quinn did not care" | | 2 | "She cared about the back" | | 3 | "Her left boot hit a" | | 4 | "The worn leather strap of" | | 5 | "She kept her hands loose," | | 6 | "Her closely cropped salt-and-pepper hair" | | 7 | "She was five nine, but" | | 8 | "She adjusted her grip on" | | 9 | "Protocol demanded backup." | | 10 | "Protocol also demanded she let" | | 11 | "She had not slept in" | | 12 | "The clique paper trail had" | | 13 | "Harlow knew better than to" | | 14 | "She knew better than to" | | 15 | "The official report called it" | | 16 | "She called it a door" | | 17 | "The suspect rounded a corner" | | 18 | "Iron groaned under heavy boots." | | 19 | "Harlow took the alley two" |
| | ratio | 0.825 | |
| 69.93% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 143 | | matches | | 0 | "Because the quiet ones always" | | 1 | "Now I just keep the" |
| | ratio | 0.014 | |
| 66.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 8 | | matches | | 0 | "Rain fell in sheets, turning the Soho streets into a black mirror that reflected the smeared neon of closing bars and taxi meters." | | 1 | "Eighteen years on the Metropolitan Police had taught her how to read a chase like a street map: footfalls, breath patterns, the subtle shift of weight that mean…" | | 2 | "Broken advertising boards hung like tombstones, their advertisements replaced by hand-painted symbols that made her jaw tighten." | | 3 | "Lanterns swung from rusted iron beams, casting long, trembling shadows over a crowd that moved with purposeful indifference." | | 4 | "Glass jars filled with iridescent powders that shifted color when the light hit them." | | 5 | "The man was arguing in a low, urgent voice with a figure draped in a heavy wool coat, their faces obscured by the shadow of a rusted train car that had not run …" | | 6 | "Olive skin, sharp features, warm brown eyes that held no fear, only calculation." | | 7 | "The suspect turned, his eyes scanning the platform, landing on Harlow." |
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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 | |