| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 3 | | adverbTags | | 0 | "She peeled back [back]" | | 1 | "she said softly [softly]" | | 2 | "she said slowly [slowly]" |
| | dialogueSentences | 20 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0.333 | | effectiveRatio | 0.3 | |
| 69.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 489 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "softly" | | 2 | "slowly" |
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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) | |
| 28.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 489 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "perfect" | | 2 | "echoed" | | 3 | "flicker" | | 4 | "chaotic" | | 5 | "weight" | | 6 | "standard" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "flicker of emotion" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 45 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 45 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 57 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 7 | | totalWords | 480 | | ratio | 0.015 | | matches | | 0 | "pattern" | | 1 | "really" | | 2 | "A brass casing. Verdigris patina." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 46.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 337 | | uniqueNames | 6 | | maxNameDensity | 2.08 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 7 | | Quinn | 1 | | Davies | 6 | | Underground | 2 | | Foley | 3 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Davies" | | 3 | "Foley" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.461 | | windowScore | 1 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 25 | | glossingSentenceCount | 1 | | matches | | 0 | "as if obeying some unseen line" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 480 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 21.82 | | std | 14.34 | | cv | 0.657 | | sampleLengths | | 0 | 29 | | 1 | 22 | | 2 | 50 | | 3 | 23 | | 4 | 46 | | 5 | 11 | | 6 | 13 | | 7 | 25 | | 8 | 7 | | 9 | 37 | | 10 | 26 | | 11 | 50 | | 12 | 10 | | 13 | 37 | | 14 | 11 | | 15 | 26 | | 16 | 4 | | 17 | 20 | | 18 | 14 | | 19 | 4 | | 20 | 5 | | 21 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 62 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 57 | | ratio | 0.07 | | matches | | 0 | "Harlow adjusted her worn leather watch—four hours since the call came in." | | 1 | "More marks—precise, deliberate." | | 2 | "\"Jason Foley wasn't depressed.\" Harlow stalked toward a stack of papers near the body—printouts, scribbled notes in chaotic black ink." | | 3 | "She'd seen photos of this place before—during her lost years searching for Morris." |
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| 97.68% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 211 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.04265402843601896 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.004739336492890996 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 57 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 57 | | mean | 8.42 | | std | 5.56 | | cv | 0.66 | | sampleLengths | | 0 | 14 | | 1 | 15 | | 2 | 17 | | 3 | 5 | | 4 | 20 | | 5 | 10 | | 6 | 12 | | 7 | 2 | | 8 | 6 | | 9 | 10 | | 10 | 5 | | 11 | 8 | | 12 | 3 | | 13 | 13 | | 14 | 2 | | 15 | 3 | | 16 | 15 | | 17 | 10 | | 18 | 11 | | 19 | 2 | | 20 | 11 | | 21 | 15 | | 22 | 3 | | 23 | 7 | | 24 | 2 | | 25 | 5 | | 26 | 8 | | 27 | 29 | | 28 | 7 | | 29 | 6 | | 30 | 13 | | 31 | 20 | | 32 | 8 | | 33 | 11 | | 34 | 11 | | 35 | 4 | | 36 | 6 | | 37 | 2 | | 38 | 12 | | 39 | 10 | | 40 | 13 | | 41 | 11 | | 42 | 16 | | 43 | 10 | | 44 | 2 | | 45 | 2 | | 46 | 3 | | 47 | 6 | | 48 | 6 | | 49 | 3 |
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| 87.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5263157894736842 | | totalSentences | 57 | | uniqueOpeners | 30 | |
| 87.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 38 | | matches | | 0 | "More marks—precise, deliberate." |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 38 | | matches | | 0 | "She leaned closer, gloved fingers" | | 1 | "Her voice echoed off the" | | 2 | "She peeled back Foley's collar" | | 3 | "She tapped the floor" | | 4 | "She stood, the motion sharp," | | 5 | "She held one up to" | | 6 | "She'd seen photos of this" | | 7 | "she said softly" | | 8 | "she said slowly" |
| | ratio | 0.237 | |
| 12.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 38 | | matches | | 0 | "The corpse lay under an" | | 1 | "Detective Harlow Quinn crouched, the" | | 2 | "DS Davies straightened, his clipboard" | | 3 | "The shadows stretched long in" | | 4 | "Harlow adjusted her worn leather" | | 5 | "Every minute made the scene" | | 6 | "Davies nudged a toppled chair" | | 7 | "The legs scraped against tile." | | 8 | "Harlow didn't stand." | | 9 | "The body slumped against the" | | 10 | "The blood pooled in a" | | 11 | "She leaned closer, gloved fingers" | | 12 | "Her voice echoed off the" | | 13 | "She peeled back Foley's collar" | | 14 | "She tapped the floor" | | 15 | "A flicker of doubt crossed" | | 16 | "She stood, the motion sharp," | | 17 | "The station arched around them" | | 18 | "Harlow stalked toward a stack" | | 19 | "She held one up to" |
| | ratio | 0.895 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 38 | | matches | (empty) | | ratio | 0 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 2 | | matches | | 0 | "The corpse lay under an industrial work light that buzzed like an angry wasp." | | 1 | "The blood pooled in a perfect semicircle around him, as if obeying some unseen line." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "DS Davies straightened, his clipboard creaking as he flipped a page" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0 | | effectiveRatio | 0 | |