| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1007 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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 | 1007 | | totalAiIsms | 29 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "reminder" | | 1 | "jaw clenched" | | 2 | "flickered" | | 3 | "clandestine" | | 4 | "determined" | | 5 | "scanning" | | 6 | "could feel" | | 7 | "fleeting" | | 8 | "pulse" | | 9 | "quickened" | | 10 | "macabre" | | 11 | "racing" | | 12 | "steeled" | | 13 | "depths" | | 14 | "cacophony" | | 15 | "gleaming" | | 16 | "unwavering" | | 17 | "raced" | | 18 | "pounding" | | 19 | "tinged" | | 20 | "dance" | | 21 | "glistening" |
| |
| 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 | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "jaw clenched" | | 1 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 77 | | matches | | 0 | "e with fear" | | 1 | "t with determination" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1003 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 982 | | uniqueNames | 9 | | maxNameDensity | 2.14 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Camden | 1 | | Veil | 3 | | Market | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Market" |
| | places | | | globalScore | 0.431 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "activity that seemed to pulse with life" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1003 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 88.80% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 41.79 | | std | 19.26 | | cv | 0.461 | | sampleLengths | | 0 | 68 | | 1 | 68 | | 2 | 66 | | 3 | 73 | | 4 | 55 | | 5 | 55 | | 6 | 69 | | 7 | 18 | | 8 | 42 | | 9 | 39 | | 10 | 15 | | 11 | 21 | | 12 | 24 | | 13 | 11 | | 14 | 32 | | 15 | 48 | | 16 | 49 | | 17 | 36 | | 18 | 9 | | 19 | 39 | | 20 | 22 | | 21 | 53 | | 22 | 41 | | 23 | 50 |
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| 73.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 77 | | matches | | 0 | "was determined" | | 1 | "was marked" | | 2 | "were traded" | | 3 | "was made" | | 4 | "were lined" | | 5 | "being struck" | | 6 | "being whispered" | | 7 | "was determined" |
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| 76.54% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 162 | | matches | | 0 | "was running" | | 1 | "was unwavering" | | 2 | "was coming" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 78 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 986 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.018255578093306288 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005070993914807302 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 12.86 | | std | 5.61 | | cv | 0.437 | | sampleLengths | | 0 | 16 | | 1 | 13 | | 2 | 22 | | 3 | 17 | | 4 | 25 | | 5 | 23 | | 6 | 10 | | 7 | 10 | | 8 | 20 | | 9 | 8 | | 10 | 18 | | 11 | 14 | | 12 | 3 | | 13 | 3 | | 14 | 23 | | 15 | 15 | | 16 | 23 | | 17 | 12 | | 18 | 7 | | 19 | 5 | | 20 | 12 | | 21 | 11 | | 22 | 20 | | 23 | 14 | | 24 | 4 | | 25 | 1 | | 26 | 1 | | 27 | 15 | | 28 | 20 | | 29 | 20 | | 30 | 16 | | 31 | 19 | | 32 | 14 | | 33 | 18 | | 34 | 11 | | 35 | 8 | | 36 | 23 | | 37 | 19 | | 38 | 10 | | 39 | 10 | | 40 | 15 | | 41 | 15 | | 42 | 3 | | 43 | 3 | | 44 | 10 | | 45 | 14 | | 46 | 7 | | 47 | 4 | | 48 | 11 | | 49 | 12 |
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| 47.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.23076923076923078 | | totalSentences | 78 | | uniqueOpeners | 18 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 76.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 75 | | matches | | 0 | "Her worn leather watch ticked" | | 1 | "She knew the bar well," | | 2 | "He was running, and she" | | 3 | "Her years of service had" | | 4 | "She spotted him, a fleeting" | | 5 | "Her pulse quickened." | | 6 | "She had heard whispers of" | | 7 | "Her decision was made in" | | 8 | "She couldn't let him escape." | | 9 | "It was cold to the" | | 10 | "She slipped it into the" | | 11 | "She steeled herself, her senses" | | 12 | "She caught snippets of conversation," | | 13 | "Her instincts guided her through" | | 14 | "She spotted him near a" | | 15 | "She was close now, close" | | 16 | "She had him." | | 17 | "Her voice cut through the" | | 18 | "She knew what was coming" | | 19 | "He bolted, shoving past the" |
| | ratio | 0.36 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 75 | | matches | | 0 | "Detective Harlow Quinn sprinted through" | | 1 | "Her worn leather watch ticked" | | 2 | "The suspect, a shadowy figure," | | 3 | "Quinn's sharp jaw clenched as" | | 4 | "The neon sign of The" | | 5 | "She knew the bar well," | | 6 | "He was running, and she" | | 7 | "Quinn's heart pounded as she" | | 8 | "The suspect was fast, but" | | 9 | "Her years of service had" | | 10 | "She spotted him, a fleeting" | | 11 | "The Veil Market." | | 12 | "Her pulse quickened." | | 13 | "The entrance to the underground" | | 14 | "Quinn hesitated, her mind racing" | | 15 | "The Veil Market was notorious" | | 16 | "She had heard whispers of" | | 17 | "Her decision was made in" | | 18 | "She couldn't let him escape." | | 19 | "Quinn approached the entrance, her" |
| | ratio | 0.947 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 66.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 6 | | matches | | 0 | "The entrance to the underground market was marked by a bone token, a macabre talisman that granted access to the hidden world below." | | 1 | "Quinn hesitated, her mind racing with the implications of following him into such unfamiliar territory." | | 2 | "She slipped it into the slot, and the door creaked open, revealing a dimly lit staircase that descended into darkness." | | 3 | "The market was a cacophony of sound and colour, a bustling hive of activity that seemed to pulse with life." | | 4 | "Her instincts guided her through the maze of stalls, her eyes flicking from face to face, searching for the one that didn't belong." | | 5 | "She spotted him near a stall selling alchemical substances, his eyes darting nervously as he haggled with the vendor." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 3 | | matches | | 0 | "A vendor called out, his eyes gleaming with the promise of forbidden knowledge" | | 1 | "the vendor said, his voice low and conspiratorial" | | 2 | "she said, her voice low and steady" |
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| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "A vendor called out (call out)" |
| | dialogueSentences | 6 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0.25 | | effectiveRatio | 0.25 | |