| 46.15% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 4 | | adverbTags | | 0 | "Quinn advised gently [gently]" | | 1 | "Quinn replied curtly [curtly]" | | 2 | "she voiced assuredly [assuredly]" | | 3 | "she added analytically [analytically]" |
| | dialogueSentences | 46 | | tagDensity | 0.565 | | leniency | 1 | | rawRatio | 0.154 | | effectiveRatio | 0.154 | |
| 73.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 961 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "reluctantly" | | count | 1 |
| | 4 | |
| | highlights | | 0 | "softly" | | 1 | "sharply" | | 2 | "gently" | | 3 | "reluctantly" | | 4 | "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) | |
| 6.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 961 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "whisper" | | 1 | "echoing" | | 2 | "furrowed" | | 3 | "intricate" | | 4 | "tracing" | | 5 | "otherworldly" | | 6 | "glinting" | | 7 | "standard" | | 8 | "scanned" | | 9 | "measured" | | 10 | "raced" | | 11 | "calculated" | | 12 | "eyebrow" | | 13 | "aligned" | | 14 | "clandestine" | | 15 | "enigmatic" |
| |
| 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 | 59 | | matches | (empty) | |
| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 79 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 953 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 543 | | uniqueNames | 11 | | maxNameDensity | 3.5 | | worstName | "Quinn" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 3 | | Quinn | 19 | | Tube | 1 | | Camden | 1 | | Detective | 2 | | Keith | 1 | | Daley | 11 | | Kowalski | 1 | | Eva | 11 | | Solutions | 1 | | Artiste | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Detective" | | 3 | "Keith" | | 4 | "Daley" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Solutions" | | 8 | "Artiste" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 95.07% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.049 | | wordCount | 953 | | matches | | 0 | "not,\" Quinn replied curtly, \"but I see enough in the field to notice patterns" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 79 | | matches | (empty) | |
| 63.23% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 22.69 | | std | 8.42 | | cv | 0.371 | | sampleLengths | | 0 | 49 | | 1 | 16 | | 2 | 27 | | 3 | 17 | | 4 | 23 | | 5 | 27 | | 6 | 19 | | 7 | 29 | | 8 | 19 | | 9 | 33 | | 10 | 19 | | 11 | 22 | | 12 | 27 | | 13 | 15 | | 14 | 18 | | 15 | 8 | | 16 | 14 | | 17 | 29 | | 18 | 21 | | 19 | 20 | | 20 | 32 | | 21 | 16 | | 22 | 24 | | 23 | 20 | | 24 | 21 | | 25 | 24 | | 26 | 29 | | 27 | 34 | | 28 | 12 | | 29 | 15 | | 30 | 45 | | 31 | 21 | | 32 | 29 | | 33 | 9 | | 34 | 25 | | 35 | 21 | | 36 | 24 | | 37 | 9 | | 38 | 16 | | 39 | 32 | | 40 | 21 | | 41 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 118 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 79 | | ratio | 0.051 | | matches | | 0 | "Quinn peered closer, spotting faint, intricate markings on the ground—a summoning circle, half-scuffed by dragging feet and the fall of heavy objects." | | 1 | "She moved closer to the edge, eyeing faint, almost ethereal shadows where walls crossed—a residue." | | 2 | "\"Persevere for unearthing groundbreaking reality,\" the pursuit leading her through an unforgiving environment; Harlow mettle transcended the ordinary line-duty outsider." | | 3 | "Solutions lay in crossing barriers to reveal elusive truths—Artiste fragments upheld by specific perspectives scouting peculiar energies drawing someone’s grand design." |
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| 98.42% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 548 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.029197080291970802 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.021897810218978103 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 79 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 79 | | mean | 12.06 | | std | 5.91 | | cv | 0.49 | | sampleLengths | | 0 | 19 | | 1 | 12 | | 2 | 18 | | 3 | 16 | | 4 | 12 | | 5 | 9 | | 6 | 6 | | 7 | 17 | | 8 | 6 | | 9 | 17 | | 10 | 19 | | 11 | 3 | | 12 | 5 | | 13 | 9 | | 14 | 10 | | 15 | 22 | | 16 | 7 | | 17 | 15 | | 18 | 4 | | 19 | 33 | | 20 | 11 | | 21 | 8 | | 22 | 4 | | 23 | 18 | | 24 | 13 | | 25 | 14 | | 26 | 15 | | 27 | 13 | | 28 | 5 | | 29 | 4 | | 30 | 4 | | 31 | 11 | | 32 | 3 | | 33 | 21 | | 34 | 8 | | 35 | 13 | | 36 | 8 | | 37 | 20 | | 38 | 10 | | 39 | 22 | | 40 | 16 | | 41 | 3 | | 42 | 21 | | 43 | 9 | | 44 | 11 | | 45 | 13 | | 46 | 8 | | 47 | 14 | | 48 | 10 | | 49 | 7 |
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| 97.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5949367088607594 | | totalSentences | 79 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 53 | | matches | | 0 | "He pulled his wool coat" | | 1 | "Her brow furrowed." | | 2 | "It glowed faintly in the" | | 3 | "she muttered, tracing the sigils" | | 4 | "she replied, her tone evening" | | 5 | "She moved closer to the" | | 6 | "she voiced assuredly" | | 7 | "she added analytically" |
| | ratio | 0.151 | |
| 16.60% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 53 | | matches | | 0 | "Detective Harlow Quinn ducked into" | | 1 | "Grit crunched under her boots" | | 2 | "Daley stood by a circle" | | 3 | "He pulled his wool coat" | | 4 | "Quinn's keen eyes swept over" | | 5 | "Quinn crouched beside the body," | | 6 | "Her brow furrowed." | | 7 | "Daley answered, exhaling sharply" | | 8 | "Quinn peered closer, spotting faint," | | 9 | "It glowed faintly in the" | | 10 | "she muttered, tracing the sigils" | | 11 | "Daley said, running a hand" | | 12 | "Quinn stood and regarded the" | | 13 | "Daley shook his head." | | 14 | "she replied, her tone evening" | | 15 | "Daley jested, trying to lighten" | | 16 | "Quinn advised gently" | | 17 | "Daley's chuckle was soft." | | 18 | "Eva Kowalski stepped into the" | | 19 | "A worn leather satchel hung" |
| | ratio | 0.887 | |
| 94.34% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 53 | | matches | | 0 | "Before Quinn could respond, a" |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 1 | | matches | | 0 | "Quinn crouched beside the body, her fingers brushing the brass casing of a pocket watch in the victim’s hand." |
| |
| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 2 | | matches | | 0 | "called Detective Keith Daley, his voice echoing softly in the vast, vaulted space" | | 1 | "Daley jested, trying to lighten the mood" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 10 | | fancyTags | | 0 | "Daley confirmed (confirm)" | | 1 | "she muttered (mutter)" | | 2 | "Quinn advised gently (advise)" | | 3 | "Quinn noted (note)" | | 4 | "she murmured (murmur)" | | 5 | "Eva confirmed (confirm)" | | 6 | "Daley suggested (suggest)" | | 7 | "she voiced assuredly (voice)" | | 8 | "she added analytically (add)" | | 9 | "Quinn stated (state)" |
| | dialogueSentences | 46 | | tagDensity | 0.348 | | leniency | 0.696 | | rawRatio | 0.625 | | effectiveRatio | 0.435 | |