| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.348 | | leniency | 0.696 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 82.62% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1151 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | | | 3 | |
| | highlights | | 0 | "slightly" | | 1 | "deliberately" | | 2 | "quickly" | | 3 | "nervously" |
| |
| 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) | |
| 34.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1151 | | totalAiIsms | 15 | | found | | 0 | | word | "practiced ease" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | |
| | highlights | | 0 | "practiced ease" | | 1 | "fractured" | | 2 | "familiar" | | 3 | "leveraging" | | 4 | "weight" | | 5 | "echoing" | | 6 | "otherworldly" | | 7 | "crystal" | | 8 | "scanned" | | 9 | "glinting" | | 10 | "velvet" | | 11 | "measured" | | 12 | "resolve" | | 13 | "echoed" | | 14 | "gleaming" |
| |
| 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 | 1 | | narrationSentences | 99 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1146 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 995 | | uniqueNames | 15 | | maxNameDensity | 0.8 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 2 | | Soho | 1 | | Herrera | 8 | | Raven | 1 | | Nest | 1 | | Alistair | 2 | | Finch | 3 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Man | 1 | | Tiber | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Alistair" | | 4 | "Finch" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Man" | | 8 | "Tiber" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | 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 | 1146 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 26.05 | | std | 20.52 | | cv | 0.788 | | sampleLengths | | 0 | 69 | | 1 | 54 | | 2 | 63 | | 3 | 63 | | 4 | 2 | | 5 | 32 | | 6 | 33 | | 7 | 1 | | 8 | 49 | | 9 | 44 | | 10 | 22 | | 11 | 52 | | 12 | 1 | | 13 | 53 | | 14 | 10 | | 15 | 57 | | 16 | 45 | | 17 | 45 | | 18 | 42 | | 19 | 26 | | 20 | 9 | | 21 | 31 | | 22 | 5 | | 23 | 7 | | 24 | 19 | | 25 | 8 | | 26 | 8 | | 27 | 18 | | 28 | 2 | | 29 | 12 | | 30 | 3 | | 31 | 43 | | 32 | 8 | | 33 | 13 | | 34 | 4 | | 35 | 37 | | 36 | 35 | | 37 | 11 | | 38 | 24 | | 39 | 3 | | 40 | 7 | | 41 | 6 | | 42 | 50 | | 43 | 20 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 169 | | matches | | |
| 93.17% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 115 | | ratio | 0.017 | | matches | | 0 | "Finch’s disappearance, the whispers about the clique's involvement, the strange energy readings she’d been picking up—everything led back to these people, to this network of shadows." | | 1 | "A small, tarnished bone, almost lost amongst the damp concrete, lay near the opening of the stairwell – almost deliberately placed." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 296 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 5 | | adverbRatio | 0.016891891891891893 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.006756756756756757 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 9.97 | | std | 5.97 | | cv | 0.599 | | sampleLengths | | 0 | 15 | | 1 | 17 | | 2 | 21 | | 3 | 12 | | 4 | 4 | | 5 | 13 | | 6 | 2 | | 7 | 4 | | 8 | 20 | | 9 | 15 | | 10 | 12 | | 11 | 15 | | 12 | 12 | | 13 | 24 | | 14 | 17 | | 15 | 11 | | 16 | 15 | | 17 | 17 | | 18 | 3 | | 19 | 2 | | 20 | 12 | | 21 | 3 | | 22 | 8 | | 23 | 9 | | 24 | 4 | | 25 | 13 | | 26 | 10 | | 27 | 2 | | 28 | 4 | | 29 | 1 | | 30 | 8 | | 31 | 12 | | 32 | 7 | | 33 | 22 | | 34 | 2 | | 35 | 6 | | 36 | 6 | | 37 | 26 | | 38 | 4 | | 39 | 10 | | 40 | 2 | | 41 | 10 | | 42 | 21 | | 43 | 6 | | 44 | 11 | | 45 | 3 | | 46 | 11 | | 47 | 1 | | 48 | 9 | | 49 | 6 |
| |
| 60.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.41739130434782606 | | totalSentences | 115 | | uniqueOpeners | 48 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 94 | | matches | | 0 | "Too quiet, even for a" | | 1 | "Then she saw it." | | 2 | "Just the relentless drumming of" | | 3 | "More than just a missing" |
| | ratio | 0.043 | |
| 75.32% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 94 | | matches | | 0 | "She hadn’t anticipated Herrera being" | | 1 | "He’d bolted from The Raven's" | | 2 | "He’d shoved past a couple" | | 3 | "she barked, her voice lost" | | 4 | "Her worn leather watch, a" | | 5 | "She pushed harder, legs burning" | | 6 | "He darted into a narrow" | | 7 | "She followed, drawing her service" | | 8 | "He was gone." | | 9 | "Her gaze swept across the" | | 10 | "Her instincts, honed by years" | | 11 | "She knelt, running a gloved" | | 12 | "She strained, leveraging her weight" | | 13 | "It groaned, then shifted inward" | | 14 | "she called out, her voice" | | 15 | "She understood what it was," | | 16 | "She’d read the reports, the" | | 17 | "She hesitated for a fraction" | | 18 | "Her jaw tightened." | | 19 | "She'd learned long ago that" |
| | ratio | 0.362 | |
| 23.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 94 | | matches | | 0 | "Rain lashed against Detective Harlow" | | 1 | "The slick pavement reflected the" | | 2 | "She hadn’t anticipated Herrera being" | | 3 | "This one clearly did." | | 4 | "He’d bolted from The Raven's" | | 5 | "That’s all it took." | | 6 | "The green neon sign buzzed" | | 7 | "He’d shoved past a couple" | | 8 | "she barked, her voice lost" | | 9 | "Her worn leather watch, a" | | 10 | "Each tick was a beat" | | 11 | "She pushed harder, legs burning" | | 12 | "He darted into a narrow" | | 13 | "The smell of stale beer" | | 14 | "She followed, drawing her service" | | 15 | "The alley opened into a" | | 16 | "He was gone." | | 17 | "Her gaze swept across the" | | 18 | "The square felt…wrong." | | 19 | "Her instincts, honed by years" |
| | ratio | 0.872 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 3 | | matches | | 0 | "she called out, her voice echoing in the darkness" | | 1 | "Herrera replied, his voice tight with anxiety" | | 2 | "she said, her voice cutting through the market’s hum" |
| |
| 19.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she barked (bark)" | | 1 | "she called out (call out)" | | 2 | "Tiber croaked (croak)" |
| | dialogueSentences | 23 | | tagDensity | 0.217 | | leniency | 0.435 | | rawRatio | 0.6 | | effectiveRatio | 0.261 | |