| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.72% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1526 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 77.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1526 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "measured" | | 1 | "weight" | | 2 | "silence" |
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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 | 161 | | matches | (empty) | |
| 89.62% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 1 | | narrationSentences | 161 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 161 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1526 | | 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 | 70 | | wordCount | 1526 | | uniqueNames | 21 | | maxNameDensity | 0.85 | | worstName | "You" | | maxWindowNameDensity | 2 | | worstWindowName | "You" | | discoveredNames | | Raven | 1 | | Nest | 6 | | Soho | 1 | | Rory | 8 | | Cardiff | 5 | | Blackwood | 1 | | Prague | 4 | | Golden | 2 | | Empress | 2 | | Silas | 9 | | Laila | 2 | | Gibbon | 1 | | Charles | 1 | | Bridge | 1 | | Evan | 3 | | London | 2 | | Pre-Law | 1 | | Paddington | 1 | | Eva | 3 | | You | 13 | | Tell | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Blackwood" | | 4 | "Empress" | | 5 | "Silas" | | 6 | "Laila" | | 7 | "Evan" | | 8 | "Eva" | | 9 | "You" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "Prague" | | 3 | "Charles" | | 4 | "Bridge" | | 5 | "London" | | 6 | "Paddington" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1526 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 161 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 40.16 | | std | 23.38 | | cv | 0.582 | | sampleLengths | | 0 | 119 | | 1 | 28 | | 2 | 5 | | 3 | 55 | | 4 | 16 | | 5 | 27 | | 6 | 50 | | 7 | 31 | | 8 | 12 | | 9 | 53 | | 10 | 27 | | 11 | 25 | | 12 | 52 | | 13 | 23 | | 14 | 54 | | 15 | 29 | | 16 | 14 | | 17 | 48 | | 18 | 19 | | 19 | 70 | | 20 | 28 | | 21 | 19 | | 22 | 25 | | 23 | 65 | | 24 | 37 | | 25 | 64 | | 26 | 51 | | 27 | 55 | | 28 | 23 | | 29 | 56 | | 30 | 20 | | 31 | 102 | | 32 | 22 | | 33 | 49 | | 34 | 40 | | 35 | 50 | | 36 | 28 | | 37 | 35 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 161 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 282 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 161 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1535 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.02996742671009772 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0013029315960912053 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 161 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 161 | | mean | 9.48 | | std | 6.59 | | cv | 0.695 | | sampleLengths | | 0 | 18 | | 1 | 21 | | 2 | 7 | | 3 | 31 | | 4 | 19 | | 5 | 23 | | 6 | 4 | | 7 | 19 | | 8 | 5 | | 9 | 5 | | 10 | 5 | | 11 | 21 | | 12 | 29 | | 13 | 3 | | 14 | 13 | | 15 | 7 | | 16 | 13 | | 17 | 7 | | 18 | 14 | | 19 | 7 | | 20 | 4 | | 21 | 25 | | 22 | 14 | | 23 | 17 | | 24 | 4 | | 25 | 4 | | 26 | 4 | | 27 | 9 | | 28 | 13 | | 29 | 5 | | 30 | 26 | | 31 | 7 | | 32 | 3 | | 33 | 9 | | 34 | 5 | | 35 | 3 | | 36 | 7 | | 37 | 13 | | 38 | 5 | | 39 | 4 | | 40 | 7 | | 41 | 6 | | 42 | 13 | | 43 | 14 | | 44 | 8 | | 45 | 9 | | 46 | 6 | | 47 | 8 | | 48 | 11 | | 49 | 20 |
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| 54.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.35403726708074534 | | totalSentences | 161 | | uniqueOpeners | 57 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 151 | | matches | (empty) | | ratio | 0 | |
| 69.01% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 57 | | totalSentences | 151 | | matches | | 0 | "His left leg held a" | | 1 | "Her boots struck each plank" | | 2 | "She stopped at the rail." | | 3 | "You pour future first now." | | 4 | "His hazel eyes measured the" | | 5 | "You still prefer it over" | | 6 | "I prefer the man who" | | 7 | "I walked past that neon" | | 8 | "You still dust the maps" | | 9 | "He took a bottle from" | | 10 | "You look like the city" | | 11 | "I hung the scarves." | | 12 | "His left hand rested on" | | 13 | "You never wrote after the" | | 14 | "I kept a stool warm." | | 15 | "It got cold." | | 16 | "I stopped answering when they" | | 17 | "I hear it drip when" | | 18 | "I retired the day Prague" | | 19 | "I waited for you to" |
| | ratio | 0.377 | |
| 36.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 128 | | totalSentences | 151 | | matches | | 0 | "The green neon of The" | | 1 | "Dust motes drifted through the" | | 2 | "Whiskey and aged wood filled" | | 3 | "Silas Blackwood wiped a glass" | | 4 | "His left leg held a" | | 5 | "Rory crossed the floorboards." | | 6 | "Her boots struck each plank" | | 7 | "She stopped at the rail." | | 8 | "You pour future first now." | | 9 | "Silas set the glass down." | | 10 | "The motion carried none of" | | 11 | "His hazel eyes measured the" | | 12 | "The same brand." | | 13 | "You still prefer it over" | | 14 | "I prefer the man who" | | 15 | "I walked past that neon" | | 16 | "You still dust the maps" | | 17 | "He took a bottle from" | | 18 | "The liquid caught fire under" | | 19 | "Rory lifted her glass." |
| | ratio | 0.848 | |
| 66.23% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 151 | | matches | | 0 | "Now you carry takeaway cartons" | | 1 | "Either way the Nest keeps" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 3 | | matches | | 0 | "Dust motes drifted through the low amber light that clung to the old maps and framed black-and-white photographs along the walls." | | 1 | "His left leg held a deliberate stillness that spoke of that Prague knee, the one he never named in letters that stopped arriving." | | 2 | "The girl who recites statutes by heart packs orders of crispy dumplings and listens for the next knock that might be Silas or might be someone who remembers Eva…" |
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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 | |