| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.063 | | leniency | 0.125 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 709 | | 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) | |
| 92.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 709 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 33 | | matches | (empty) | |
| 99.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 33 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 48 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 699 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 75.86% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 607 | | uniqueNames | 11 | | maxNameDensity | 1.48 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Entry | 1 | | Requires | 1 | | Bone | 1 | | Token | 1 | | Morris | 3 | | Saint | 1 | | Christopher | 1 | | Tomás | 4 | | Herrera | 1 | | Harlow | 9 |
| | persons | | 0 | "Camden" | | 1 | "Entry" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tomás" | | 6 | "Herrera" | | 7 | "Harlow" |
| | places | (empty) | | globalScore | 0.759 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | 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 | 699 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 48 | | matches | (empty) | |
| 75.19% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 11 | | mean | 63.55 | | std | 26.26 | | cv | 0.413 | | sampleLengths | | 0 | 77 | | 1 | 79 | | 2 | 68 | | 3 | 20 | | 4 | 66 | | 5 | 39 | | 6 | 24 | | 7 | 118 | | 8 | 67 | | 9 | 70 | | 10 | 71 |
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| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 33 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 113 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 48 | | ratio | 0.146 | | matches | | 0 | "She’d been chasing the hooded suspect for three blocks, her sharp jaw set in military precision—18 years of decorated service had trained her to block out the rain’s slap against her uniform and the distant wail of sirens she’d chosen not to call." | | 1 | "She patted her pockets—no trinkets, no scavenged bones, just her badge, a tattered case file, and the crumpled photo of her late partner, DS Morris, she kept tucked in her uniform’s breast pocket." | | 2 | "His Saint Christopher medallion glinted in the streetlight’s weak glow, and a scar snaked along his left forearm—Tomás Herrera, the former paramedic she’d linked to the clique’s off-the-books care." | | 3 | "Harlow spotted a small yellowed object tucked in his jacket pocket—carved with a raven’s head, a bone token." | | 4 | "The air shifted immediately—reeking of burnt herbs and melted wax, its cold seeping through her uniform’s sleeves." | | 5 | "Harlow’s breath caught—she’d never seen anything like it, but the blue runes on the walls made her tense." | | 6 | "His face was still hidden, but he held up a small silver locket—its chain twisted, its surface etched with Morris’s initials." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 616 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.012987012987012988 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.003246753246753247 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 48 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 48 | | mean | 14.56 | | std | 9.2 | | cv | 0.632 | | sampleLengths | | 0 | 13 | | 1 | 43 | | 2 | 21 | | 3 | 13 | | 4 | 14 | | 5 | 19 | | 6 | 33 | | 7 | 13 | | 8 | 26 | | 9 | 29 | | 10 | 8 | | 11 | 12 | | 12 | 2 | | 13 | 4 | | 14 | 3 | | 15 | 6 | | 16 | 6 | | 17 | 8 | | 18 | 7 | | 19 | 7 | | 20 | 8 | | 21 | 7 | | 22 | 8 | | 23 | 8 | | 24 | 18 | | 25 | 13 | | 26 | 4 | | 27 | 3 | | 28 | 8 | | 29 | 9 | | 30 | 16 | | 31 | 8 | | 32 | 17 | | 33 | 27 | | 34 | 23 | | 35 | 27 | | 36 | 18 | | 37 | 22 | | 38 | 27 | | 39 | 11 | | 40 | 11 | | 41 | 5 | | 42 | 17 | | 43 | 26 | | 44 | 5 | | 45 | 21 | | 46 | 17 | | 47 | 28 |
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| 59.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4166666666666667 | | totalSentences | 48 | | uniqueOpeners | 20 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 33 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 33 | | matches | | 0 | "She’d been chasing the hooded" | | 1 | "She grabbed the gate’s bar," | | 2 | "She patted her pockets—no trinkets," | | 3 | "She spun, her hand drifting" | | 4 | "His Saint Christopher medallion glinted" | | 5 | "She stepped forward, grabbing his" | | 6 | "She shoves past him and" | | 7 | "They matched the faint carvings" | | 8 | "She spotted the hooded suspect" | | 9 | "She moved forward, her boot" | | 10 | "His face was still hidden," |
| | ratio | 0.333 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 30 | | totalSentences | 33 | | matches | | 0 | "Harlow’s leather watch scraped a" | | 1 | "She’d been chasing the hooded" | | 2 | "The suspect darted through a" | | 3 | "Harlow skidded to a halt" | | 4 | "She grabbed the gate’s bar," | | 5 | "A faded sign tacked to" | | 6 | "She patted her pockets—no trinkets," | | 7 | "That’s when she heard the" | | 8 | "She spun, her hand drifting" | | 9 | "His Saint Christopher medallion glinted" | | 10 | "Harlow’s posture stiffened, her military" | | 11 | "Tomás stopped a few feet" | | 12 | "Tomás flinched, his medallion swinging" | | 13 | "Harlow spotted a small yellowed" | | 14 | "She stepped forward, grabbing his" | | 15 | "Harlow yanked the bone token" | | 16 | "She shoves past him and" | | 17 | "The air shifted immediately—reeking of" | | 18 | "A woman sold banned alchemical" | | 19 | "A man in a tattered" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 33 | | matches | (empty) | | ratio | 0 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "Tiled walls crumbled to reveal glowing blue runes beneath, and wooden stalls lined the curved platform, their vendors haggling over frosted enchanted vials and …" | | 1 | "A man in a tattered cloak traded a scroll of information for a jar of glowing beetles, his voice a low hiss that made Harlow’s skin prickle." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |