| 86.79% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 3 | | adverbTags | | 0 | "His gaze swung back [back]" | | 1 | "he said softly [softly]" | | 2 | "Leo gestured vaguely [vaguely]" |
| | dialogueSentences | 53 | | tagDensity | 0.415 | | leniency | 0.83 | | rawRatio | 0.136 | | effectiveRatio | 0.113 | |
| 83.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1210 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "suddenly" | | 1 | "very" | | 2 | "softly" | | 3 | "really" |
| |
| 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) | |
| 38.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1210 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "familiar" | | 1 | "scanning" | | 2 | "weight" | | 3 | "flickered" | | 4 | "echoing" | | 5 | "silence" | | 6 | "crystal" | | 7 | "tension" | | 8 | "unspoken" | | 9 | "comfortable" | | 10 | "charged" | | 11 | "echo" | | 12 | "grave" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
| | 2 | | label | "sent a shiver through" | | count | 1 |
|
| | highlights | | 0 | "blood went cold" | | 1 | "hung in the air" | | 2 | "sent a jolt through" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 93 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 18 | | totalWords | 1210 | | ratio | 0.015 | | matches | | 0 | "I have to go, Leo. Don’t ask. Please." | | 1 | "Wrap it up." | | 2 | "Leo Bennett, Associate, Pendragon & Locke Solicitors." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.25% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 881 | | uniqueNames | 15 | | maxNameDensity | 1.14 | | worstName | "Leo" | | maxWindowNameDensity | 2 | | worstWindowName | "Leo" | | discoveredNames | | Rory | 5 | | Raven | 1 | | Nest | 1 | | Bennett | 2 | | Byron | 1 | | Leo | 10 | | Silas | 6 | | Evan | 2 | | London | 4 | | Eva | 1 | | London-long | 1 | | Associate | 1 | | Pendragon | 1 | | Locke | 1 | | Cardiff | 2 |
| | persons | | 0 | "Rory" | | 1 | "Bennett" | | 2 | "Byron" | | 3 | "Leo" | | 4 | "Silas" | | 5 | "Evan" | | 6 | "Eva" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 0.932 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 34.71% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.653 | | wordCount | 1210 | | matches | | 0 | "Not in years, but in bearing" | | 1 | "not just for her disappearance, but for the path he’d taken" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 122 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 22.83 | | std | 17.73 | | cv | 0.777 | | sampleLengths | | 0 | 51 | | 1 | 38 | | 2 | 76 | | 3 | 27 | | 4 | 10 | | 5 | 43 | | 6 | 12 | | 7 | 27 | | 8 | 32 | | 9 | 13 | | 10 | 56 | | 11 | 15 | | 12 | 4 | | 13 | 39 | | 14 | 19 | | 15 | 1 | | 16 | 3 | | 17 | 2 | | 18 | 34 | | 19 | 47 | | 20 | 4 | | 21 | 14 | | 22 | 1 | | 23 | 61 | | 24 | 16 | | 25 | 34 | | 26 | 2 | | 27 | 29 | | 28 | 41 | | 29 | 28 | | 30 | 49 | | 31 | 22 | | 32 | 7 | | 33 | 30 | | 34 | 3 | | 35 | 16 | | 36 | 13 | | 37 | 10 | | 38 | 2 | | 39 | 50 | | 40 | 33 | | 41 | 3 | | 42 | 29 | | 43 | 22 | | 44 | 17 | | 45 | 3 | | 46 | 2 | | 47 | 14 | | 48 | 27 | | 49 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 93 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 144 | | matches | | 0 | "was scanning" | | 1 | "was picking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 122 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 886 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.025959367945823927 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.013544018058690745 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 9.92 | | std | 7.82 | | cv | 0.788 | | sampleLengths | | 0 | 16 | | 1 | 12 | | 2 | 23 | | 3 | 8 | | 4 | 15 | | 5 | 1 | | 6 | 2 | | 7 | 12 | | 8 | 5 | | 9 | 30 | | 10 | 3 | | 11 | 6 | | 12 | 32 | | 13 | 14 | | 14 | 13 | | 15 | 10 | | 16 | 5 | | 17 | 2 | | 18 | 24 | | 19 | 12 | | 20 | 10 | | 21 | 2 | | 22 | 12 | | 23 | 11 | | 24 | 4 | | 25 | 23 | | 26 | 6 | | 27 | 3 | | 28 | 12 | | 29 | 1 | | 30 | 18 | | 31 | 13 | | 32 | 11 | | 33 | 5 | | 34 | 2 | | 35 | 4 | | 36 | 3 | | 37 | 15 | | 38 | 4 | | 39 | 11 | | 40 | 15 | | 41 | 7 | | 42 | 6 | | 43 | 9 | | 44 | 9 | | 45 | 1 | | 46 | 1 | | 47 | 3 | | 48 | 2 | | 49 | 31 |
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| 60.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4180327868852459 | | totalSentences | 122 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 15.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 86 | | matches | | 0 | "She placed it back on" | | 1 | "He shook it off, a" | | 2 | "He hadn’t seen her yet." | | 3 | "He was scanning the room," | | 4 | "He looked older." | | 5 | "He hadn’t looked up from" | | 6 | "He walked towards the bar," | | 7 | "He said her name like" | | 8 | "She mirrored his formality, leaning" | | 9 | "He shrugged out of his" | | 10 | "He draped it over a" | | 11 | "She gestured to the back" | | 12 | "She’d been packing a single" | | 13 | "*I have to go, Leo." | | 14 | "She’d hung up." | | 15 | "she asked, falling into the" | | 16 | "She turned, aware of Silas’s" | | 17 | "She selected a bottle from" | | 18 | "His fingers brushed the crystal" | | 19 | "He finally took a sip," |
| | ratio | 0.512 | |
| 0.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 86 | | matches | | 0 | "The glass slipped from her" | | 1 | "A reflexive save, honed from" | | 2 | "She placed it back on" | | 3 | "London rain glistened on his" | | 4 | "He shook it off, a" | | 5 | "He hadn’t seen her yet." | | 6 | "He was scanning the room," | | 7 | "He looked older." | | 8 | "The boy who used to" | | 9 | "Silas’s voice was a low" | | 10 | "He hadn’t looked up from" | | 11 | "Rory said, her own voice" | | 12 | "Leo’s eyes found her then." | | 13 | "A series of micro-expressions flickered" | | 14 | "He walked towards the bar," | | 15 | "He said her name like" | | 16 | "She mirrored his formality, leaning" | | 17 | "The small crescent scar on" | | 18 | "He shrugged out of his" | | 19 | "He draped it over a" |
| | ratio | 0.919 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "He was scanning the room, his gaze sliding over the maps on the walls, the empty booths, the green neon glow from the sign reflected in puddles at his feet." | | 1 | "She thought of the first week in London, sleeping on Eva’s floor, jumping at every shadow." |
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| 11.36% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 5 | | matches | | 0 | "Rory said, her own voice surprisingly steady" | | 1 | "His gaze swung back, intense" | | 2 | "He held up, bare" | | 3 | "He gave, the ghost of the old Leo" | | 4 | "she said, her voice flat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 53 | | tagDensity | 0.17 | | leniency | 0.34 | | rawRatio | 0 | | effectiveRatio | 0 | |