| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 82.91% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1463 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "slowly" | | 1 | "deliberately" | | 2 | "sharply" |
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| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1463 | | totalAiIsms | 31 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "fractured" | | 1 | "variables" | | 2 | "calibrated" | | 3 | "vibrated" | | 4 | "warmth" | | 5 | "tension" | | 6 | "silence" | | 7 | "scanned" | | 8 | "weight" | | 9 | "pulsed" | | 10 | "rhythmic" | | 11 | "echoed" | | 12 | "echo" | | 13 | "trembled" | | 14 | "pulse" | | 15 | "variable" | | 16 | "chilled" | | 17 | "crystallized" | | 18 | "flickered" | | 19 | "could feel" | | 20 | "footsteps" | | 21 | "measured" |
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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 | 201 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 201 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 201 | | 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 | 1 | | markdownWords | 6 | | totalWords | 1463 | | ratio | 0.004 | | matches | | 0 | "Don’t trust the quiet. It’s listening." |
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| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 1463 | | uniqueNames | 6 | | maxNameDensity | 0.48 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Richmond | 2 | | Park | 1 | | Rory | 7 | | Fae | 1 | | Grove | 2 | | November | 2 |
| | persons | | | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Fae" | | 3 | "Grove" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 95 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like old iron and wild honey" |
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| 63.29% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.367 | | wordCount | 1463 | | matches | | 0 | "Not all at once, but in a staggered roll, calyxes pivoting on unseen stems to tra" | | 1 | "not walking, but gliding just above the earth" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 201 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 58.52 | | std | 33.19 | | cv | 0.567 | | sampleLengths | | 0 | 111 | | 1 | 98 | | 2 | 28 | | 3 | 71 | | 4 | 112 | | 5 | 8 | | 6 | 110 | | 7 | 62 | | 8 | 85 | | 9 | 73 | | 10 | 31 | | 11 | 64 | | 12 | 52 | | 13 | 4 | | 14 | 43 | | 15 | 62 | | 16 | 91 | | 17 | 46 | | 18 | 16 | | 19 | 85 | | 20 | 85 | | 21 | 28 | | 22 | 3 | | 23 | 62 | | 24 | 33 |
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| 96.53% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 201 | | matches | | 0 | "was chained" | | 1 | "been pruned" | | 2 | "been promised" | | 3 | "was held" | | 4 | "was gone" |
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| 7.46% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 277 | | matches | | 0 | "was already slipping" | | 1 | "weren’t being" | | 2 | "wasn’t just pointing" | | 3 | "was resonating" | | 4 | "weren’t breaking" | | 5 | "were hesitating" | | 6 | "was fracturing" | | 7 | "was being" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 201 | | ratio | 0 | | matches | (empty) | |
| 95.09% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1469 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 67 | | adverbRatio | 0.045609257998638526 | | lyAdverbCount | 22 | | lyAdverbRatio | 0.014976174268209666 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 201 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 201 | | mean | 7.28 | | std | 5.86 | | cv | 0.805 | | sampleLengths | | 0 | 16 | | 1 | 12 | | 2 | 32 | | 3 | 9 | | 4 | 17 | | 5 | 13 | | 6 | 7 | | 7 | 3 | | 8 | 2 | | 9 | 9 | | 10 | 25 | | 11 | 7 | | 12 | 29 | | 13 | 15 | | 14 | 13 | | 15 | 9 | | 16 | 8 | | 17 | 11 | | 18 | 6 | | 19 | 16 | | 20 | 7 | | 21 | 6 | | 22 | 4 | | 23 | 8 | | 24 | 14 | | 25 | 3 | | 26 | 7 | | 27 | 6 | | 28 | 4 | | 29 | 17 | | 30 | 17 | | 31 | 5 | | 32 | 3 | | 33 | 18 | | 34 | 9 | | 35 | 24 | | 36 | 9 | | 37 | 4 | | 38 | 4 | | 39 | 3 | | 40 | 3 | | 41 | 7 | | 42 | 7 | | 43 | 9 | | 44 | 13 | | 45 | 9 | | 46 | 24 | | 47 | 4 | | 48 | 2 | | 49 | 3 |
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| 43.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.325 | | totalSentences | 200 | | uniqueOpeners | 65 | |
| 99.21% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 168 | | matches | | 0 | "Then her boot crossed the" | | 1 | "Only bracken and shadow." | | 2 | "Just the steady rustle of" | | 3 | "Instead, she slid her hand" | | 4 | "Just the quiet certainty that" |
| | ratio | 0.03 | |
| 58.10% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 68 | | totalSentences | 168 | | matches | | 0 | "She’d long since learned to" | | 1 | "She slipped through a gap" | | 2 | "They formed a natural archway," | | 3 | "She rubbed her thumb over" | | 4 | "She was here to map" | | 5 | "Her contact had been vague" | | 6 | "She’d been promised a fracture" | | 7 | "She’d brought a notebook, a" | | 8 | "Her fingers brushed the silver" | | 9 | "Her source claimed an hour" | | 10 | "She’d accept minutes." | | 11 | "She just needed data she" | | 12 | "She stepped fully into the" | | 13 | "It grew dense, sweet, carrying" | | 14 | "They shouldn’t be blooming now." | | 15 | "It was November." | | 16 | "She knelt, pressing two fingers" | | 17 | "It yielded too easily, then" | | 18 | "She wiped her fingers on" | | 19 | "It was held breath." |
| | ratio | 0.405 | |
| 49.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 138 | | totalSentences | 168 | | matches | | 0 | "The iron pedestrian gate of" | | 1 | "She’d long since learned to" | | 2 | "She slipped through a gap" | | 3 | "The city’s hum thinned, then" | | 4 | "They formed a natural archway," | | 5 | "Isolde’s grove, her source had" | | 6 | "The Fae Grove." | | 7 | "Rory paused with her boots" | | 8 | "She rubbed her thumb over" | | 9 | "She was here to map" | | 10 | "Her contact had been vague" | | 11 | "She’d been promised a fracture" | | 12 | "She’d brought a notebook, a" | | 13 | "Her fingers brushed the silver" | | 14 | "The thumbnail-sized crimson gem lay" | | 15 | "The pendant jerked against her" | | 16 | "A low thrum vibrated through" | | 17 | "Warmth spread outward, pooling at" | | 18 | "Rory exhaled slowly, counting to" | | 19 | "Time was already slipping." |
| | ratio | 0.821 | |
| 29.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 168 | | matches | | | ratio | 0.006 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 8 | | matches | | 0 | "The iron pedestrian gate of Richmond Park was chained shut at dusk, which suited Rory fine." | | 1 | "She rubbed her thumb over the crescent-shaped scar on her left wrist, a childhood habit that grounded her when the variables stacked up too fast." | | 2 | "Her contact had been vague about why, but vague was enough when the alternative was another dead end in a case that had already cost her two sleepless weeks." | | 3 | "It yielded too easily, then sprang back with elastic tension, leaving a smear of viscous sap that smelled like old iron and wild honey." | | 4 | "She stepped toward the direction, counting her steps, keeping her posture loose." | | 5 | "The air around her shifted, compressing and expanding in a slow, tidal rhythm that matched nothing in the clearing." | | 6 | "The pendant’s glow flickered, syncing with a distant, rhythmic thud that wasn’t her heartbeat." | | 7 | "A long, slow release of air that carried the scent of turned earth and wet limestone." |
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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 | |