| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 793 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "sharply" | | 1 | "suddenly" | | 2 | "very" |
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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 | 793 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "pounding" | | 3 | "whisper" | | 4 | "treacherous" | | 5 | "charged" | | 6 | "echoed" | | 7 | "chill" | | 8 | "loomed" | | 9 | "searing" | | 10 | "tinged" | | 11 | "jaw clenched" | | 12 | "roaring" | | 13 | "chaotic" | | 14 | "gloom" | | 15 | "maw" | | 16 | "flickered" | | 17 | "standard" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "jaw clenched" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 53 | | matches | (empty) | |
| 61.99% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 53 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 53 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 795 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 794 | | uniqueNames | 10 | | maxNameDensity | 1.13 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 9 | | Soho | 1 | | Bolt-down | 1 | | Thames | 1 | | Morris | 1 | | Anya | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Anya" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Thames" |
| | globalScore | 0.933 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 795 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 53 | | matches | (empty) | |
| 19.15% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 9 | | mean | 88.33 | | std | 19.17 | | cv | 0.217 | | sampleLengths | | 0 | 98 | | 1 | 77 | | 2 | 90 | | 3 | 92 | | 4 | 93 | | 5 | 81 | | 6 | 45 | | 7 | 99 | | 8 | 120 |
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| 98.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 53 | | matches | | |
| 93.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 125 | | matches | | 0 | "was leading" | | 1 | "was disorienting" |
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| 35.04% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 53 | | ratio | 0.038 | | matches | | 0 | "The air here was thick with a miasma of unfamiliar scents – petrichor mixed with something metallic, something earthy, and a faint, sickly sweet undertone she couldn't place." | | 1 | "The entry, the bone token – it screamed of a world she only dimly grasped, a world that had swallowed her partner." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 797 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint, sickly sweet undertone" |
| | adverbCount | 23 | | adverbRatio | 0.028858218318695106 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.012547051442910916 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 53 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 53 | | mean | 15 | | std | 6.62 | | cv | 0.442 | | sampleLengths | | 0 | 20 | | 1 | 19 | | 2 | 16 | | 3 | 19 | | 4 | 24 | | 5 | 9 | | 6 | 23 | | 7 | 13 | | 8 | 19 | | 9 | 13 | | 10 | 21 | | 11 | 11 | | 12 | 10 | | 13 | 22 | | 14 | 11 | | 15 | 15 | | 16 | 15 | | 17 | 4 | | 18 | 11 | | 19 | 13 | | 20 | 13 | | 21 | 9 | | 22 | 11 | | 23 | 16 | | 24 | 10 | | 25 | 27 | | 26 | 11 | | 27 | 4 | | 28 | 24 | | 29 | 17 | | 30 | 16 | | 31 | 12 | | 32 | 9 | | 33 | 6 | | 34 | 29 | | 35 | 9 | | 36 | 25 | | 37 | 20 | | 38 | 6 | | 39 | 23 | | 40 | 28 | | 41 | 11 | | 42 | 8 | | 43 | 23 | | 44 | 8 | | 45 | 8 | | 46 | 7 | | 47 | 23 | | 48 | 22 | | 49 | 22 |
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| 40.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3584905660377358 | | totalSentences | 53 | | uniqueOpeners | 19 | |
| 62.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 53 | | matches | | 0 | "Then, the figure veered towards" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 53 | | matches | | 0 | "Her salt-and-pepper hair clung to" | | 1 | "Her worn leather watch, a" | | 2 | "They swung around a corner," | | 3 | "They vaulted a low wall," | | 4 | "She landed clumsily, but her" | | 5 | "Her sharp jaw clenched." | | 6 | "She saw her partner, Morris," | | 7 | "They plunged under a dripping" | | 8 | "She pulled up, boots crunching" | | 9 | "Her hand tightened on the" | | 10 | "Her gaze swept the street," |
| | ratio | 0.208 | |
| 35.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 53 | | matches | | 0 | "Rain lashed down, turning the" | | 1 | "Detective Harlow Quinn’s breath hitched," | | 2 | "Her salt-and-pepper hair clung to" | | 3 | "Her worn leather watch, a" | | 4 | "The figure ahead, a hunched" | | 5 | "Quinn pumped her legs, metres" | | 6 | "The suspect was good, showing" | | 7 | "They swung around a corner," | | 8 | "Quinn followed, her military precision" | | 9 | "The initial pursuit began near" | | 10 | "The figure Bolt-down an alley," | | 11 | "This narrow passage reeked of" | | 12 | "The air grew heavy, charged" | | 13 | "The suspect’s shoes skidded on" | | 14 | "The command ripped from Quinn’s" | | 15 | "The figure didn't falter." | | 16 | "They vaulted a low wall," | | 17 | "Quinn scrambled over it, the" | | 18 | "She landed clumsily, but her" | | 19 | "The streets now opened into" |
| | ratio | 0.849 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "The suspect’s shoes skidded on loose gravel, a frantic scramble that echoed off the brickwork." | | 1 | "The suspect seemed to know these back routes, slipping through gaps in fences, under dripping awnings, their movements becoming more desperate, more erratic the…" | | 2 | "The entry, the bone token – it screamed of a world she only dimly grasped, a world that had swallowed her partner." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |