| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.36% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1507 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "ever so slightly" | | count | 1 |
|
| | highlights | | 0 | "softly" | | 1 | "ever so slightly" |
| |
| 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) | |
| 40.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1507 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "chilled" | | 1 | "glinting" | | 2 | "silence" | | 3 | "pulse" | | 4 | "gloom" | | 5 | "flicked" | | 6 | "echo" | | 7 | "echoes" | | 8 | "jaw clenched" | | 9 | "familiar" | | 10 | "pulsed" | | 11 | "velvet" | | 12 | "flicker" | | 13 | "potential" | | 14 | "footsteps" | | 15 | "echoed" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 129 | | matches | (empty) | |
| 87.49% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | | 0 | "happened to" | | 1 | "tend to" | | 2 | "seemed to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1493 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1395 | | uniqueNames | 13 | | maxNameDensity | 0.86 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 12 | | Raven | 1 | | Nest | 2 | | Tube | 2 | | Camden | 3 | | Town | 1 | | Veil | 2 | | Market | 3 | | Morris | 2 | | Saint | 1 | | Christopher | 1 | | Flickering | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Market" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Nest" | | 2 | "Camden" | | 3 | "Town" | | 4 | "Veil" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a man who’d walked tightropes" | | 1 | "hum that seemed to weave through the brickwork, tugging at her nerves" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1493 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 135 | | matches | (empty) | |
| 84.81% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 57.42 | | std | 25.66 | | cv | 0.447 | | sampleLengths | | 0 | 69 | | 1 | 72 | | 2 | 83 | | 3 | 29 | | 4 | 73 | | 5 | 112 | | 6 | 83 | | 7 | 68 | | 8 | 84 | | 9 | 82 | | 10 | 23 | | 11 | 40 | | 12 | 58 | | 13 | 35 | | 14 | 24 | | 15 | 52 | | 16 | 73 | | 17 | 72 | | 18 | 53 | | 19 | 106 | | 20 | 15 | | 21 | 36 | | 22 | 40 | | 23 | 29 | | 24 | 49 | | 25 | 33 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 129 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 252 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 2 | | flaggedSentences | 13 | | totalSentences | 135 | | ratio | 0.096 | | matches | | 0 | "He forced the boards aside—more easily than they should have come—and slipped into darkness." | | 1 | "The tiles underfoot were cracked and stained; old adverts for shows and tabloids peeled like dying skin from the walls." | | 2 | "She swept her light across the tracks—no train." | | 3 | "But she’d heard other stories too—whispers of wrongdoers using old tunnels to slip in and out of the city unseen." | | 4 | "Beyond it came voices—low murmurs, a distant clink of glass on metal." | | 5 | "A man with a wool cap—familiar build—stepped forward." | | 6 | "But another voice—older, more cautious—whispered of danger beyond her understanding." | | 7 | "She didn’t know what had happened to him—only that he’d vanished into the unknown and never come back." | | 8 | "Rows of stalls lined a center aisle: one draped in tattered velvet offered jars of glowing ichor; another displayed vials of iridescent powder." | | 9 | "In the lantern light, she saw the flicker of something unnatural in his eyes—something that made the hair prickle at the base of her skull." | | 10 | "She thought of Morris—the empty desk, the unanswered radio call, the smell of ozone on his uniform three nights after he disappeared." | | 11 | "A hand touched her arm—warm skin, calloused." | | 12 | "He looked like a man who’d walked tightropes between two worlds and earned a few scars—she noticed the faint line along his left forearm." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1423 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.02108222066057625 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.008432888264230498 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 11.06 | | std | 6.05 | | cv | 0.547 | | sampleLengths | | 0 | 13 | | 1 | 19 | | 2 | 11 | | 3 | 19 | | 4 | 3 | | 5 | 4 | | 6 | 14 | | 7 | 6 | | 8 | 15 | | 9 | 11 | | 10 | 26 | | 11 | 15 | | 12 | 17 | | 13 | 1 | | 14 | 6 | | 15 | 2 | | 16 | 6 | | 17 | 11 | | 18 | 16 | | 19 | 9 | | 20 | 14 | | 21 | 3 | | 22 | 12 | | 23 | 12 | | 24 | 11 | | 25 | 17 | | 26 | 7 | | 27 | 14 | | 28 | 8 | | 29 | 4 | | 30 | 9 | | 31 | 20 | | 32 | 8 | | 33 | 13 | | 34 | 4 | | 35 | 3 | | 36 | 7 | | 37 | 11 | | 38 | 6 | | 39 | 11 | | 40 | 20 | | 41 | 12 | | 42 | 14 | | 43 | 12 | | 44 | 8 | | 45 | 18 | | 46 | 9 | | 47 | 8 | | 48 | 2 | | 49 | 3 |
| |
| 48.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.34814814814814815 | | totalSentences | 135 | | uniqueOpeners | 47 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 123 | | matches | | 0 | "Only the distant rumble of" | | 1 | "Only the faint hiss of" | | 2 | "Officially, it didn’t exist." |
| | ratio | 0.024 | |
| 67.15% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 123 | | matches | | 0 | "Her raincoat, cinched tight at" | | 1 | "She didn’t slow." | | 2 | "She couldn’t afford to." | | 3 | "She adjusted her grip on" | | 4 | "she barked into the night," | | 5 | "She had eighteen years of" | | 6 | "He forced the boards aside—more" | | 7 | "She wrenched the boards free" | | 8 | "She counted her steps: twenty," | | 9 | "She could taste rust." | | 10 | "She swept her light across" | | 11 | "She squinted at a faded" | | 12 | "She caught sight of a" | | 13 | "She’d heard the rumors." | | 14 | "Her instincts fought her hesitation." | | 15 | "She didn’t know what had" | | 16 | "She raised her voice, low" | | 17 | "She clenched her teeth." | | 18 | "She stepped forward, flashlight beam" | | 19 | "He cocked his head, voice" |
| | ratio | 0.382 | |
| 41.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 103 | | totalSentences | 123 | | matches | | 0 | "Rain pelted the pavement, turning" | | 1 | "Harlow Quinn’s boots slapped against" | | 2 | "Her raincoat, cinched tight at" | | 3 | "Water dripped from the brim" | | 4 | "She didn’t slow." | | 5 | "She couldn’t afford to." | | 6 | "A shadow detached itself from" | | 7 | "Quinn’s heart hammered beneath her" | | 8 | "She adjusted her grip on" | | 9 | "she barked into the night," | | 10 | "She had eighteen years of" | | 11 | "The figure twisted around a" | | 12 | "Quinn vaulted a low wall," | | 13 | "The streets emptied at this" | | 14 | "Taxis idled with their engines" | | 15 | "The suspect hesitated at the" | | 16 | "Quinn closed the gap, breath" | | 17 | "He forced the boards aside—more" | | 18 | "Quinn’s pulse sky-rocketed." | | 19 | "She wrenched the boards free" |
| | ratio | 0.837 | |
| 40.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 123 | | matches | | 0 | "If she called for help," |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 4 | | matches | | 0 | "Water dripped from the brim of her cap, rolling down the bridge of her nose into her eyes, stinging." | | 1 | "Three years ago she lost DS Morris in a case that had supernatural roots." | | 2 | "In the lantern light, she saw the flicker of something unnatural in his eyes—something that made the hair prickle at the base of her skull." | | 3 | "The suspect’s footsteps echoed faintly ahead, accompanied by a low hum that seemed to weave through the brickwork, tugging at her nerves." |
| |
| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "she barked, voice firm, unyielding" |
| |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "she barked (bark)" | | 1 | "He whispered (whisper)" |
| | dialogueSentences | 14 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0.4 | | effectiveRatio | 0.286 | |