| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "She chuckled ominously [ominously]" |
| | dialogueSentences | 21 | | tagDensity | 0.381 | | leniency | 0.762 | | rawRatio | 0.125 | | effectiveRatio | 0.095 | |
| 80.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1052 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "suddenly" | | 1 | "very" | | 2 | "truly" |
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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) | |
| 33.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1052 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "down her spine" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | |
| | highlights | | 0 | "resolve" | | 1 | "quickened" | | 2 | "determined" | | 3 | "pumping" | | 4 | "echoed" | | 5 | "down her spine" | | 6 | "scanning" | | 7 | "sense of" | | 8 | "glint" | | 9 | "depths" | | 10 | "could feel" | | 11 | "desire" | | 12 | "unwavering" | | 13 | "flicked" |
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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 | 62 | | matches | (empty) | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1050 | | ratio | 0 | | matches | (empty) | |
| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | 0 | "But, Harlow told herself, this time she had a chance to do things right, to make up for past mistakes." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 879 | | uniqueNames | 13 | | maxNameDensity | 1.59 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | London | 2 | | Detective | 1 | | Harlow | 14 | | Quinn | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 | | Underground | 1 | | Tomás | 1 | | Herrera | 1 | | Tommy | 4 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Tommy" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | | globalScore | 0.704 | | windowScore | 0.667 | |
| 55.66% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a hammer blow on her head, ch" | | 1 | "felt like more than just bad luck" |
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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 | 1050 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 35 | | std | 22.83 | | cv | 0.652 | | sampleLengths | | 0 | 70 | | 1 | 71 | | 2 | 82 | | 3 | 51 | | 4 | 55 | | 5 | 49 | | 6 | 62 | | 7 | 66 | | 8 | 76 | | 9 | 9 | | 10 | 6 | | 11 | 33 | | 12 | 15 | | 13 | 45 | | 14 | 40 | | 15 | 16 | | 16 | 22 | | 17 | 7 | | 18 | 6 | | 19 | 19 | | 20 | 8 | | 21 | 38 | | 22 | 35 | | 23 | 27 | | 24 | 39 | | 25 | 6 | | 26 | 28 | | 27 | 30 | | 28 | 17 | | 29 | 22 |
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| 99.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 62 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 142 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 3 | | flaggedSentences | 4 | | totalSentences | 75 | | ratio | 0.053 | | matches | | 0 | "The suspect suddenly veered into an opening—a hidden entrance to an underground market, Harlow suspected." | | 1 | "She hesitated; the last time she had gone headfirst into an unknown, it had cost her dearly." | | 2 | "They shared a moment of silent understanding; their eyes locking for a brief second." | | 3 | "\"I have to. At least come with me to the token seller; we need to learn more.\"" |
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| 92.25% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 881 | | adjectiveStacks | 1 | | stackExamples | | 0 | "former paramedic turned rogue" |
| | adverbCount | 37 | | adverbRatio | 0.04199772985244041 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.018161180476730987 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 14 | | std | 6.76 | | cv | 0.483 | | sampleLengths | | 0 | 23 | | 1 | 17 | | 2 | 30 | | 3 | 16 | | 4 | 28 | | 5 | 12 | | 6 | 15 | | 7 | 22 | | 8 | 12 | | 9 | 24 | | 10 | 24 | | 11 | 19 | | 12 | 15 | | 13 | 17 | | 14 | 17 | | 15 | 15 | | 16 | 17 | | 17 | 6 | | 18 | 20 | | 19 | 12 | | 20 | 17 | | 21 | 19 | | 22 | 10 | | 23 | 24 | | 24 | 9 | | 25 | 6 | | 26 | 16 | | 27 | 19 | | 28 | 25 | | 29 | 11 | | 30 | 22 | | 31 | 15 | | 32 | 14 | | 33 | 14 | | 34 | 9 | | 35 | 6 | | 36 | 14 | | 37 | 19 | | 38 | 6 | | 39 | 9 | | 40 | 7 | | 41 | 13 | | 42 | 25 | | 43 | 17 | | 44 | 23 | | 45 | 13 | | 46 | 3 | | 47 | 22 | | 48 | 7 | | 49 | 6 |
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| 68.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.44 | | totalSentences | 75 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 75.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 61 | | matches | | 0 | "She had been chasing leads" | | 1 | "He was a tall, shadowy" | | 2 | "She had a bitter taste" | | 3 | "She entered a rundown district" | | 4 | "Her heart hammered against her" | | 5 | "She hesitated; the last time" | | 6 | "She would not let him" | | 7 | "She instantly knew this was" | | 8 | "She wove her way through" | | 9 | "Her partner's disappearance suddenly felt" | | 10 | "His silver Saint Christopher medallion," | | 11 | "They shared a moment of" | | 12 | "he asked, though he already" | | 13 | "He nodded towards a man" | | 14 | "They weaved through the maze" | | 15 | "He hovered around a stall," | | 16 | "she cursed, realizing what this" | | 17 | "She pointed to the stall," | | 18 | "It was a warning." | | 19 | "He sighed but nodded, and" |
| | ratio | 0.361 | |
| 41.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 61 | | matches | | 0 | "The relentless London rain soaked" | | 1 | "Every drop of rain felt" | | 2 | "She had been chasing leads" | | 3 | "The suspect, a slippery customer" | | 4 | "He was a tall, shadowy" | | 5 | "This game of cat and" | | 6 | "She had a bitter taste" | | 7 | "The chase led her into" | | 8 | "She entered a rundown district" | | 9 | "Her heart hammered against her" | | 10 | "The suspect suddenly veered into" | | 11 | "The faint roar of the" | | 12 | "She hesitated; the last time" | | 13 | "DS Morris had rushed into" | | 14 | "The memory still haunted her" | | 15 | "Instinct told her this suspect" | | 16 | "She would not let him" | | 17 | "The detective dove into the" | | 18 | "The acrid smell of sweat" | | 19 | "She instantly knew this was" |
| | ratio | 0.836 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 61 | | matches | | 0 | "But, Harlow told herself, this" | | 1 | "As if in agreement, a" |
| | ratio | 0.033 | |
| 61.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 5 | | matches | | 0 | "She entered a rundown district where the streetlamps provided little more than a weak glow, casting eerie shadows that danced across the sodden pavement." | | 1 | "The detective dove into the entrance, which opened up into a cavernous space lit by candlelight and gas lamps." | | 2 | "She instantly knew this was the Veil Market, the hidden supernatural black market that surfaces monthly in a different abandoned Underground station across Lond…" | | 3 | "The air buzzed with a foreign energy that pricked Harlow's senses, sending shivers down her spine." | | 4 | "But before she could finish, the suspect was off again, darting towards a dark hall that narrowed before descending into the depths." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 2 | | matches | | 0 | "Tommy asked, confused" | | 1 | "Tommy whispered, a note of fear in his voice" |
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| 54.76% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "She chuckled ominously (chuckle)" | | 1 | "Tommy whispered (whisper)" |
| | dialogueSentences | 21 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.333 | | effectiveRatio | 0.19 | |