| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said slowly [slowly]" |
| | dialogueSentences | 27 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.083 | | effectiveRatio | 0.074 | |
| 84.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1329 | | totalAiIsmAdverbs | 4 | | found | | 0 | | adverb | "barely above a whisper" | | count | 1 |
| | 1 | | | 2 | | | 3 | |
| | highlights | | 0 | "barely above a whisper" | | 1 | "slowly" | | 2 | "precisely" | | 3 | "very" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 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 | 1329 | | totalAiIsms | 27 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "stark" | | 1 | "reminder" | | 2 | "echoing" | | 3 | "sinister" | | 4 | "etched" | | 5 | "raced" | | 6 | "navigate" | | 7 | "measured" | | 8 | "testament" | | 9 | "jaw clenched" | | 10 | "resolve" | | 11 | "weight" | | 12 | "flickered" | | 13 | "tracing" | | 14 | "whisper" | | 15 | "delved" | | 16 | "furrowed" | | 17 | "chill" | | 18 | "determined" | | 19 | "comforting" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
| | 2 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "jaw clenched" | | 2 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 67 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1315 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1012 | | uniqueNames | 12 | | maxNameDensity | 2.17 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 22 | | Quinn | 2 | | Tube | 1 | | Thompson | 6 | | Veil | 4 | | Market | 3 | | Compass | 1 | | British | 1 | | Museum | 1 | | Kowalski | 1 | | Eva | 11 | | London | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Thompson" | | 3 | "Market" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | | 0 | "Compass" | | 1 | "British" | | 2 | "London" |
| | globalScore | 0.413 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 1 | | matches | | 0 | "discarded and seemingly broken" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1315 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 79.98% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 32.88 | | std | 14.13 | | cv | 0.43 | | sampleLengths | | 0 | 66 | | 1 | 39 | | 2 | 21 | | 3 | 49 | | 4 | 37 | | 5 | 11 | | 6 | 37 | | 7 | 43 | | 8 | 44 | | 9 | 11 | | 10 | 20 | | 11 | 41 | | 12 | 39 | | 13 | 41 | | 14 | 40 | | 15 | 24 | | 16 | 19 | | 17 | 21 | | 18 | 42 | | 19 | 21 | | 20 | 33 | | 21 | 1 | | 22 | 39 | | 23 | 43 | | 24 | 21 | | 25 | 38 | | 26 | 41 | | 27 | 32 | | 28 | 29 | | 29 | 19 | | 30 | 9 | | 31 | 38 | | 32 | 27 | | 33 | 19 | | 34 | 32 | | 35 | 31 | | 36 | 51 | | 37 | 28 | | 38 | 53 | | 39 | 65 |
| |
| 94.79% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 67 | | matches | | 0 | "was buried" | | 1 | "was pulled" |
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| 73.42% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 158 | | matches | | 0 | "were doing" | | 1 | "were falling" | | 2 | "was brewing" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 8 | | totalSentences | 82 | | ratio | 0.098 | | matches | | 0 | "The body was strangely unmarked, save for a peculiar symbol branded into the flesh of their forearm—a circle intersected by a cross, with strange runes inscribed within." | | 1 | "It was precise, deliberate—the work of someone who knew exactly what they were doing." | | 2 | "The Veil Market—a place that existed in whispers and shadows, a haven for those who dabbled in the occult and the arcane." | | 3 | "She recognized it immediately—a Veil Compass, a tool used by those who sought to navigate the unseen paths between worlds." | | 4 | "Inside, a collection of bone tokens—the entry requirement for the Veil Market—gleamed in the dim light." | | 5 | "The tokens were not just currency; they were keys, each one imbued with the power to open portals to other realms." | | 6 | "The symbol on the victim's arm, the discarded compass, the hidden tokens—they all pointed to something far greater than they had imagined." | | 7 | "She had a lead to follow—a trail that would take her into the heart of the Veil Market, where the line between ally and enemy was as thin as the veil itself." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1023 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.02737047898338221 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.01466275659824047 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 16.02 | | std | 7.96 | | cv | 0.497 | | sampleLengths | | 0 | 21 | | 1 | 20 | | 2 | 25 | | 3 | 17 | | 4 | 22 | | 5 | 11 | | 6 | 10 | | 7 | 6 | | 8 | 16 | | 9 | 27 | | 10 | 23 | | 11 | 14 | | 12 | 11 | | 13 | 7 | | 14 | 30 | | 15 | 3 | | 16 | 22 | | 17 | 18 | | 18 | 15 | | 19 | 20 | | 20 | 9 | | 21 | 11 | | 22 | 4 | | 23 | 16 | | 24 | 17 | | 25 | 24 | | 26 | 25 | | 27 | 4 | | 28 | 10 | | 29 | 19 | | 30 | 22 | | 31 | 16 | | 32 | 3 | | 33 | 21 | | 34 | 14 | | 35 | 10 | | 36 | 13 | | 37 | 6 | | 38 | 15 | | 39 | 6 | | 40 | 20 | | 41 | 22 | | 42 | 16 | | 43 | 5 | | 44 | 12 | | 45 | 21 | | 46 | 23 | | 47 | 16 | | 48 | 19 | | 49 | 24 |
| |
| 61.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3902439024390244 | | totalSentences | 82 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 64 | | matches | (empty) | | ratio | 0 | |
| 95.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 64 | | matches | | 0 | "Her colleague, DS Thompson, was" | | 1 | "He looked up as her" | | 2 | "he greeted, his voice echoing" | | 3 | "She crouched beside the body," | | 4 | "It was precise, deliberate—the work" | | 5 | "She stood, her gaze traveling" | | 6 | "She recognized it immediately—a Veil" | | 7 | "She reached for the compass," | | 8 | "She approached the wall, running" | | 9 | "Her fingers found a hidden" | | 10 | "she called out, her voice" | | 11 | "She explained her theory, her" | | 12 | "She needed answers, and there" | | 13 | "Her curly red hair was" | | 14 | "She reached for the compass," | | 15 | "she murmured, her voice barely" | | 16 | "She stood, her resolve as" | | 17 | "she said, her voice echoing" | | 18 | "She had a lead to" | | 19 | "She was no stranger to" |
| | ratio | 0.313 | |
| 38.13% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 64 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "The air was thick with" | | 2 | "The walls of the abandoned" | | 3 | "Her colleague, DS Thompson, was" | | 4 | "He looked up as her" | | 5 | "he greeted, his voice echoing" | | 6 | "Harlow's eyes swept over the" | | 7 | "The victim lay sprawled on" | | 8 | "The body was strangely unmarked," | | 9 | "She crouched beside the body," | | 10 | "It was precise, deliberate—the work" | | 11 | "Harlow asked, her voice low" | | 12 | "Thompson flipped a page in" | | 13 | "Harlow's mind raced." | | 14 | "The Veil Market—a place that" | | 15 | "She stood, her gaze traveling" | | 16 | "A brass compass lay a" | | 17 | "She recognized it immediately—a Veil" | | 18 | "Harlow asked, gesturing toward the" | | 19 | "Thompson shook his head." |
| | ratio | 0.844 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 64 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 4 | | matches | | 0 | "As she picked it up, the needle settled, pointing unerringly toward a section of wall that appeared no different from any other in the station." | | 1 | "She explained her theory, her words painting a picture of a crime that was more than just a simple murder." | | 2 | "The pieces of the puzzle were falling into place, revealing a picture that was far more dangerous than she had anticipated." | | 3 | "She stood, her resolve as unyielding as the sharp gaze that swept over the ancient texts and artifacts that surrounded her." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 5 | | matches | | 0 | "he greeted, his voice echoing faintly off the worn tiles" | | 1 | "Harlow asked, her voice low and even" | | 2 | "she called out, her voice steady despite the adrenaline coursing through her veins" | | 3 | "she murmured, her voice barely above a whisper" | | 4 | "she said, her voice echoing with the weight of her responsibility" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "she called out (call out)" | | 1 | "she murmured (murmur)" | | 2 | "Eva confirmed (confirm)" |
| | dialogueSentences | 27 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.333 | | effectiveRatio | 0.222 | |