| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 3 | | adverbTags | | 0 | "Laila started then [then]" | | 1 | "Laila said suddenly [suddenly]" | | 2 | "Laila said quietly [quietly]" |
| | dialogueSentences | 70 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.214 | | effectiveRatio | 0.086 | |
| 78.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1384 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "barely above a whisper" | | count | 1 |
| | 4 | | | 5 | |
| | highlights | | 0 | "perfectly" | | 1 | "slowly" | | 2 | "quickly" | | 3 | "barely above a whisper" | | 4 | "suddenly" | | 5 | "softly" |
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| 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) | |
| 60.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1384 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "familiar" | | 1 | "eyebrow" | | 2 | "traced" | | 3 | "scanning" | | 4 | "measured" | | 5 | "silence" | | 6 | "whisper" | | 7 | "weight" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 73 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1374 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 845 | | uniqueNames | 13 | | maxNameDensity | 2.13 | | worstName | "Laila" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Laila" | | discoveredNames | | Aurora | 17 | | Raven | 1 | | Nest | 1 | | Silas | 8 | | Prague | 1 | | Edison | 1 | | Burberry | 1 | | Laila | 18 | | Richard | 2 | | Silence | 1 | | Johnny | 1 | | Cash | 1 | | Evan | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Edison" | | 4 | "Laila" | | 5 | "Richard" | | 6 | "Silence" | | 7 | "Johnny" | | 8 | "Cash" | | 9 | "Evan" |
| | places | | 0 | "Raven" | | 1 | "Prague" | | 2 | "Burberry" |
| | globalScore | 0.435 | | windowScore | 0.167 | |
| 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 | 1374 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 82 | | mean | 16.76 | | std | 13.3 | | cv | 0.794 | | sampleLengths | | 0 | 50 | | 1 | 24 | | 2 | 36 | | 3 | 55 | | 4 | 30 | | 5 | 6 | | 6 | 1 | | 7 | 32 | | 8 | 3 | | 9 | 12 | | 10 | 27 | | 11 | 33 | | 12 | 18 | | 13 | 8 | | 14 | 20 | | 15 | 16 | | 16 | 21 | | 17 | 11 | | 18 | 36 | | 19 | 5 | | 20 | 5 | | 21 | 14 | | 22 | 15 | | 23 | 37 | | 24 | 4 | | 25 | 7 | | 26 | 5 | | 27 | 6 | | 28 | 22 | | 29 | 3 | | 30 | 2 | | 31 | 28 | | 32 | 22 | | 33 | 6 | | 34 | 7 | | 35 | 44 | | 36 | 14 | | 37 | 14 | | 38 | 3 | | 39 | 4 | | 40 | 25 | | 41 | 27 | | 42 | 18 | | 43 | 14 | | 44 | 6 | | 45 | 27 | | 46 | 9 | | 47 | 27 | | 48 | 54 | | 49 | 1 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 73 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 154 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 129 | | ratio | 0.054 | | matches | | 0 | "Silas poured her usual—gin and tonic, heavy on the gin—without asking." | | 1 | "Those grey eyes—she'd know them anywhere, even after seven years." | | 2 | "Aurora remembered Richard from university—all cologne and confidence, the type who wore boat shoes unironically and called women \"birds.\" Laila had met him their final year, right when everything started falling apart." | | 3 | "The drink appeared before her like magic—olive speared just so, glass frosted, gin measured to perfection." | | 4 | "She twisted her wedding ring—platinum, naturally, with a stone large enough to have its own postcode." | | 5 | "That was Silas's gift—knowing when to push and when to let the silence speak for itself." | | 6 | "Upstairs, her flat waited—small and cramped and nothing like the life she'd planned." |
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| 91.19% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 853 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.044548651817116064 | | lyAdverbCount | 22 | | lyAdverbRatio | 0.02579132473622509 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 10.65 | | std | 7.37 | | cv | 0.692 | | sampleLengths | | 0 | 20 | | 1 | 15 | | 2 | 15 | | 3 | 24 | | 4 | 11 | | 5 | 25 | | 6 | 13 | | 7 | 16 | | 8 | 26 | | 9 | 4 | | 10 | 17 | | 11 | 9 | | 12 | 6 | | 13 | 1 | | 14 | 6 | | 15 | 10 | | 16 | 16 | | 17 | 3 | | 18 | 2 | | 19 | 10 | | 20 | 27 | | 21 | 6 | | 22 | 27 | | 23 | 17 | | 24 | 1 | | 25 | 8 | | 26 | 7 | | 27 | 13 | | 28 | 14 | | 29 | 2 | | 30 | 17 | | 31 | 4 | | 32 | 11 | | 33 | 4 | | 34 | 32 | | 35 | 5 | | 36 | 5 | | 37 | 6 | | 38 | 8 | | 39 | 15 | | 40 | 13 | | 41 | 24 | | 42 | 4 | | 43 | 7 | | 44 | 5 | | 45 | 6 | | 46 | 10 | | 47 | 12 | | 48 | 3 | | 49 | 2 |
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| 66.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4186046511627907 | | totalSentences | 129 | | uniqueOpeners | 54 | |
| 48.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 69 | | matches | | | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 69 | | matches | | 0 | "She nodded to Silas behind" | | 1 | "She slumped onto a barstool," | | 2 | "She'd been running deliveries for" | | 3 | "She paused at the threshold," | | 4 | "He limped toward the bookshelf," | | 5 | "She addressed the empty bar," | | 6 | "He worked in silence, professional" | | 7 | "Her delivery jacket dripped steadily" | | 8 | "She tried again." | | 9 | "She twisted her wedding ring—platinum," | | 10 | "She stood, smoothing her trench" | | 11 | "He nodded, understanding more than" |
| | ratio | 0.174 | |
| 25.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 69 | | matches | | 0 | "The brass bell above the" | | 1 | "The familiar smell of wood" | | 2 | "She nodded to Silas behind" | | 3 | "She slumped onto a barstool," | | 4 | "Silas poured her usual—gin and" | | 5 | "The bar sat mostly empty" | | 6 | "Aurora traced the rim of" | | 7 | "The crescent scar on her" | | 8 | "She'd been running deliveries for" | | 9 | "The door chimed again." | | 10 | "A woman entered, designer heels" | | 11 | "She paused at the threshold," | | 12 | "Aurora's hand froze on her" | | 13 | "The woman's gaze snapped to" | | 14 | "Those grey eyes—she'd know them" | | 15 | "Silas, polishing a glass with" | | 16 | "He limped toward the bookshelf," | | 17 | "Laila approached slowly, each step" | | 18 | "Laila paused, taking in Aurora's" | | 19 | "The words came out sharper" |
| | ratio | 0.87 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 69 | | matches | | 0 | "Even with the expertly applied" | | 1 | "Before the world showed them" |
| | ratio | 0.029 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "Up close, Aurora catalogued the changes: the sharp cheekbones that spoke of expensive gym memberships, the manicured nails, the subtle botox that kept her foreh…" | | 1 | "The green neon from the sign outside cast strange shadows across Laila's face, making her look like a stranger wearing her best friend's skin." | | 2 | "She stood, smoothing her trench coat, every movement rehearsed for an audience that wasn't there." |
| |
| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "She slumped, water dripping from her sleeves onto the worn mahogany" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "She addressed (address)" |
| | dialogueSentences | 70 | | tagDensity | 0.043 | | leniency | 0.086 | | rawRatio | 0.333 | | effectiveRatio | 0.029 | |