| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.211 | | leniency | 0.421 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1307 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 1307 | | totalAiIsms | 28 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | |
| | highlights | | 0 | "echo" | | 1 | "mosaic" | | 2 | "gloom" | | 3 | "measured" | | 4 | "traced" | | 5 | "perfect" | | 6 | "etched" | | 7 | "glint" | | 8 | "jaw clenched" | | 9 | "magnetic" | | 10 | "quivered" | | 11 | "flicked" | | 12 | "echoing" | | 13 | "standard" | | 14 | "echoed" | | 15 | "throbbed" | | 16 | "aligned" | | 17 | "fractured" | | 18 | "resonated" | | 19 | "resolve" | | 20 | "determined" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 102 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1294 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 971 | | uniqueNames | 10 | | maxNameDensity | 2.88 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 28 | | Eva | 19 | | Kowalski | 1 | | Tube | 1 | | Camden | 1 | | Dust | 2 | | Morris | 1 | | Detective | 2 | | Veil | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Camden" | | 5 | "Dust" | | 6 | "Morris" |
| | places | | | globalScore | 0.058 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1294 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 29.41 | | std | 16.1 | | cv | 0.547 | | sampleLengths | | 0 | 47 | | 1 | 56 | | 2 | 42 | | 3 | 7 | | 4 | 54 | | 5 | 40 | | 6 | 34 | | 7 | 55 | | 8 | 7 | | 9 | 44 | | 10 | 28 | | 11 | 17 | | 12 | 22 | | 13 | 27 | | 14 | 7 | | 15 | 23 | | 16 | 22 | | 17 | 28 | | 18 | 44 | | 19 | 16 | | 20 | 42 | | 21 | 16 | | 22 | 42 | | 23 | 17 | | 24 | 7 | | 25 | 8 | | 26 | 31 | | 27 | 23 | | 28 | 26 | | 29 | 6 | | 30 | 49 | | 31 | 36 | | 32 | 15 | | 33 | 27 | | 34 | 11 | | 35 | 48 | | 36 | 64 | | 37 | 34 | | 38 | 21 | | 39 | 23 | | 40 | 17 | | 41 | 9 | | 42 | 55 | | 43 | 47 |
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| 94.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 102 | | matches | | 0 | "were fixed" | | 1 | "were etched" | | 2 | "were smeared" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 185 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 2 | | flaggedSentences | 9 | | totalSentences | 132 | | ratio | 0.068 | | matches | | 0 | "She flipped on her torch—its beam cut through the gloom like a scalpel." | | 1 | "Eva’s curly red hair flared under the torchlight; she tucked a tendril behind her left ear, the gesture betraying a nervous energy despite her calm focus." | | 2 | "No patrol car, no CCTV; this abandoned Tube station beneath Camden shouldn’t exist on any map." | | 3 | "Quinn wiped at one with a gloved thumb—it crumbled to gray ash." | | 4 | "Her watch—leather band worn thin—glinted 03:21." | | 5 | "And in the center, etched in chalk, lay another circle—larger, incomplete." | | 6 | "Behind him, a swirling haze coalesced—a rift, pulsing." | | 7 | "Just as her training kicked in—draw weapon, secure perimeter—but she stood, breath caught between disbelief and resolve." | | 8 | "And so, with everything she had learned—physical evidence, occult insight, every hard-won ounce of instinct—Detective Harlow Quinn took her stance, a lone figure in a hidden market beneath the city, determined to bend the rules of nature and reason until the truth would bleed through the Veil." |
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| 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 | 4 | | lyAdverbRatio | 0.004056795131845842 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 9.8 | | std | 6.72 | | cv | 0.686 | | sampleLengths | | 0 | 15 | | 1 | 10 | | 2 | 9 | | 3 | 13 | | 4 | 11 | | 5 | 19 | | 6 | 26 | | 7 | 6 | | 8 | 14 | | 9 | 16 | | 10 | 6 | | 11 | 6 | | 12 | 1 | | 13 | 13 | | 14 | 5 | | 15 | 26 | | 16 | 10 | | 17 | 8 | | 18 | 12 | | 19 | 5 | | 20 | 12 | | 21 | 3 | | 22 | 13 | | 23 | 14 | | 24 | 7 | | 25 | 9 | | 26 | 16 | | 27 | 7 | | 28 | 11 | | 29 | 12 | | 30 | 4 | | 31 | 3 | | 32 | 10 | | 33 | 16 | | 34 | 7 | | 35 | 4 | | 36 | 7 | | 37 | 20 | | 38 | 4 | | 39 | 4 | | 40 | 8 | | 41 | 4 | | 42 | 5 | | 43 | 8 | | 44 | 14 | | 45 | 14 | | 46 | 6 | | 47 | 7 | | 48 | 7 | | 49 | 2 |
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| 56.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.36363636363636365 | | totalSentences | 132 | | uniqueOpeners | 48 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 96 | | matches | | 0 | "She flipped on her torch—its" | | 1 | "She moved forward, each step" | | 2 | "She produced a loupe and" | | 3 | "She unfolded a small brush" | | 4 | "It felt almost warm against" | | 5 | "She trained her torch on" | | 6 | "She pocketed the compass in" | | 7 | "Her watch—leather band worn thin—glinted" | | 8 | "She shook off the doubt" | | 9 | "She rose, boots echoing." | | 10 | "She clicked open her satchel," | | 11 | "She turned back to Eva." | | 12 | "Her torch highlighted web-coated tracks" | | 13 | "She slipped a slim pry" | | 14 | "It popped loose, revealing a" | | 15 | "She stood, torch trained on" | | 16 | "She paused at the end" | | 17 | "His hand hovered over the" | | 18 | "She raised it, the beam" | | 19 | "He was her partner’s last" |
| | ratio | 0.229 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 96 | | matches | | 0 | "Detective Harlow Quinn’s boot scuffed" | | 1 | "The platform lay deserted, its" | | 2 | "She flipped on her torch—its" | | 3 | "The researcher’s green eyes, magnified" | | 4 | "Eva’s curly red hair flared" | | 5 | "Quinn’s hand hovered near her" | | 6 | "She moved forward, each step" | | 7 | "Quinn said, voice low" | | 8 | "Eva shifted, revealing a cluster" | | 9 | "She produced a loupe and" | | 10 | "Quinn crouched, shining her torch" | | 11 | "The victim lay supine, arms" | | 12 | "The burns were eerily symmetrical." | | 13 | "Quinn wiped at one with" | | 14 | "Eva tapped the chalk circle," | | 15 | "Dust motes drifted through the" | | 16 | "Quinn swept the beam over" | | 17 | "She unfolded a small brush" | | 18 | "Nothing surfaced but a ghost" | | 19 | "The victim’s sneaker prints led" |
| | ratio | 0.875 | |
| 52.08% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 96 | | matches | | 0 | "Just as her training kicked" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 1 | | matches | | 0 | "Her torch highlighted web-coated tracks that webbed toward a grated door set in the wall." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.079 | | leniency | 0.158 | | rawRatio | 0 | | effectiveRatio | 0 | |