| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said again [again]" |
| | dialogueSentences | 92 | | tagDensity | 0.174 | | leniency | 0.348 | | rawRatio | 0.063 | | effectiveRatio | 0.022 | |
| 86.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1464 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "very" | | 2 | "really" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 55.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1464 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "flickered" | | 1 | "shattered" | | 2 | "lurched" | | 3 | "warmth" | | 4 | "chaotic" | | 5 | "sense of" | | 6 | "silence" | | 7 | "weight" | | 8 | "implication" | | 9 | "unspoken" | | 10 | "tension" |
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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 | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 134 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 204 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1462 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 76 | | wordCount | 878 | | uniqueNames | 22 | | maxNameDensity | 2.85 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Silas | 6 | | Friday | 2 | | Eva | 25 | | Rory | 16 | | Welsh | 1 | | London | 1 | | Blitz | 1 | | British | 1 | | Cardiff | 3 | | Blue | 1 | | Practiced | 1 | | Camden | 2 | | Town | 1 | | Egypt | 1 | | Greece | 1 | | Jordan | 1 | | Burberry | 1 | | Quieter | 1 | | Tears | 1 | | Final | 1 | | Cold | 3 | | Past | 5 |
| | persons | | 0 | "Silas" | | 1 | "Eva" | | 2 | "Rory" | | 3 | "Jordan" |
| | places | | 0 | "Friday" | | 1 | "Welsh" | | 2 | "London" | | 3 | "Blitz" | | 4 | "Cardiff" | | 5 | "Camden" | | 6 | "Town" | | 7 | "Egypt" | | 8 | "Greece" |
| | globalScore | 0.076 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 1462 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 204 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 105 | | mean | 13.92 | | std | 13.6 | | cv | 0.977 | | sampleLengths | | 0 | 12 | | 1 | 46 | | 2 | 1 | | 3 | 21 | | 4 | 9 | | 5 | 25 | | 6 | 25 | | 7 | 8 | | 8 | 3 | | 9 | 3 | | 10 | 36 | | 11 | 3 | | 12 | 18 | | 13 | 52 | | 14 | 29 | | 15 | 3 | | 16 | 21 | | 17 | 2 | | 18 | 3 | | 19 | 30 | | 20 | 5 | | 21 | 4 | | 22 | 14 | | 23 | 3 | | 24 | 2 | | 25 | 19 | | 26 | 8 | | 27 | 3 | | 28 | 3 | | 29 | 15 | | 30 | 23 | | 31 | 20 | | 32 | 22 | | 33 | 6 | | 34 | 15 | | 35 | 18 | | 36 | 5 | | 37 | 3 | | 38 | 8 | | 39 | 5 | | 40 | 23 | | 41 | 3 | | 42 | 30 | | 43 | 9 | | 44 | 13 | | 45 | 30 | | 46 | 3 | | 47 | 4 | | 48 | 10 | | 49 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 134 | | matches | | |
| 77.30% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 163 | | matches | | 0 | "was nursing" | | 1 | "weren't wearing" | | 2 | "was looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 204 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 880 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.03068181818181818 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006818181818181818 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 204 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 204 | | mean | 7.17 | | std | 6.23 | | cv | 0.869 | | sampleLengths | | 0 | 12 | | 1 | 11 | | 2 | 11 | | 3 | 5 | | 4 | 1 | | 5 | 1 | | 6 | 17 | | 7 | 1 | | 8 | 12 | | 9 | 9 | | 10 | 3 | | 11 | 6 | | 12 | 13 | | 13 | 12 | | 14 | 13 | | 15 | 5 | | 16 | 7 | | 17 | 8 | | 18 | 3 | | 19 | 3 | | 20 | 4 | | 21 | 16 | | 22 | 11 | | 23 | 5 | | 24 | 3 | | 25 | 18 | | 26 | 12 | | 27 | 9 | | 28 | 14 | | 29 | 17 | | 30 | 7 | | 31 | 2 | | 32 | 20 | | 33 | 3 | | 34 | 4 | | 35 | 4 | | 36 | 2 | | 37 | 1 | | 38 | 10 | | 39 | 2 | | 40 | 3 | | 41 | 6 | | 42 | 7 | | 43 | 1 | | 44 | 1 | | 45 | 15 | | 46 | 5 | | 47 | 4 | | 48 | 9 | | 49 | 5 |
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| 77.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4852941176470588 | | totalSentences | 204 | | uniqueOpeners | 99 | |
| 30.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 109 | | matches | | 0 | "Then she was gone, swallowed" |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 109 | | matches | | 0 | "She looked down." | | 1 | "She grabbed a cloth, mopped" | | 2 | "He left coins on the" | | 3 | "His limp seemed worse tonight." | | 4 | "He glanced at Eva's end" | | 5 | "He moved away, conversation with" | | 6 | "She used to drink pints" | | 7 | "Her nails were painted." | | 8 | "Her natural brown had been" | | 9 | "They waited until he lurched" | | 10 | "She set the glass down" | | 11 | "He brought it himself, looked" | | 12 | "His silence felt loaded." | | 13 | "Her coat draped over the" | | 14 | "She picked up her bag" | | 15 | "His presence wasn't accidental." | | 16 | "She moved towards the door." | | 17 | "They pushed past, oblivious." | | 18 | "She pulled her coat tighter" | | 19 | "She walked out." |
| | ratio | 0.202 | |
| 60.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 109 | | matches | | 0 | "The woman at the end" | | 1 | "Rory stopped mid-pour, bourbon splashing" | | 2 | "The posture was wrong too." | | 3 | "The businessman in front of" | | 4 | "She looked down." | | 5 | "Amber liquid pooled around the" | | 6 | "She grabbed a cloth, mopped" | | 7 | "He left coins on the" | | 8 | "Silas emerged from the back" | | 9 | "His limp seemed worse tonight." | | 10 | "Rory met his eyes." | | 11 | "Hazel, sharp, the kind that" | | 12 | "He glanced at Eva's end" | | 13 | "Understanding flickered across his face." | | 14 | "He moved away, conversation with" | | 15 | "Rory's feet carried her down" | | 16 | "Eva was nursing something pink" | | 17 | "She used to drink pints" | | 18 | "Eva didn't look up." | | 19 | "Her nails were painted." |
| | ratio | 0.798 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 109 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 5 | | matches | | 0 | "But the laugh, that low rolling sound that cut through the Friday night chatter, that was unmistakable." | | 1 | "The kind that made you look like you weren't wearing any while hiding everything underneath." | | 2 | "Street performers and tourists and locals who'd seen it all." | | 3 | "Rory thought of the Eva who'd spent nights in Cardiff's library, surrounded by books about ancient civilisations, eyes bright with plans for digs in Egypt and G…" | | 4 | "Back to her post, pouring drinks for strangers, wondering if three years was enough time to become someone else or if you just learned to pretend better." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 92 | | tagDensity | 0.043 | | leniency | 0.087 | | rawRatio | 0.25 | | effectiveRatio | 0.022 | |