| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva countered finally [finally]" | | 1 | "She gestured vaguely [vaguely]" |
| | dialogueSentences | 45 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.1 | | effectiveRatio | 0.089 | |
| 92.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1418 | | totalAiIsmAdverbs | 2 | | 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) | |
| 54.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1418 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "tension" | | 1 | "vibrated" | | 2 | "reminder" | | 3 | "chaotic" | | 4 | "familiar" | | 5 | "lilt" | | 6 | "whisper" | | 7 | "dancing" | | 8 | "velvet" | | 9 | "intensity" | | 10 | "weight" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 108 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 108 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1412 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 1108 | | uniqueNames | 16 | | maxNameDensity | 2.26 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 25 | | Quinn | 1 | | Eva | 18 | | Kowalski | 2 | | British | 1 | | Museum | 1 | | Basic | 1 | | Training | 1 | | Veil | 1 | | Compass | 1 | | Baker | 1 | | Street | 1 | | German | 1 | | Underground | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Training" |
| | places | | 0 | "British" | | 1 | "Compass" | | 2 | "Baker" | | 3 | "Street" |
| | globalScore | 0.372 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.708 | | wordCount | 1412 | | matches | | 0 | "not by time, but by something sharp" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 28.82 | | std | 20.49 | | cv | 0.711 | | sampleLengths | | 0 | 72 | | 1 | 85 | | 2 | 7 | | 3 | 41 | | 4 | 65 | | 5 | 21 | | 6 | 57 | | 7 | 41 | | 8 | 69 | | 9 | 28 | | 10 | 15 | | 11 | 12 | | 12 | 66 | | 13 | 40 | | 14 | 28 | | 15 | 45 | | 16 | 22 | | 17 | 28 | | 18 | 2 | | 19 | 38 | | 20 | 9 | | 21 | 29 | | 22 | 6 | | 23 | 36 | | 24 | 9 | | 25 | 23 | | 26 | 45 | | 27 | 19 | | 28 | 7 | | 29 | 26 | | 30 | 6 | | 31 | 3 | | 32 | 3 | | 33 | 45 | | 34 | 21 | | 35 | 7 | | 36 | 23 | | 37 | 10 | | 38 | 47 | | 39 | 26 | | 40 | 31 | | 41 | 22 | | 42 | 9 | | 43 | 21 | | 44 | 47 | | 45 | 29 | | 46 | 9 | | 47 | 54 | | 48 | 8 |
| |
| 79.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 108 | | matches | | 0 | "been sealed" | | 1 | "was involved" | | 2 | "been found" | | 3 | "been eaten" | | 4 | "was flawed" | | 5 | "were crossed" | | 6 | "been pinned" | | 7 | "being ripped" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 198 | | matches | | 0 | "was wearing" | | 1 | "was spinning" | | 2 | "wasn’t just lying" | | 3 | "wasn't spinning" | | 4 | "was hovering" | | 5 | "was still pointing" |
| |
| 13.96% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 133 | | ratio | 0.045 | | matches | | 0 | "The low hum of distant train tracks vibrated through the soles of her boots, a subsonic reminder of the city above—chaotic, bright, and fast." | | 1 | "Harlow didn't turn until she recognized the tone—the delicate, high-pitched lilt that belonged to Eva Kowalski." | | 2 | "The logic was flawed, the evidence was circumstantial, but the instinct—that cold, hard metal needle in her gut that had served her since Basic Training—vibrated with a warning." | | 3 | "It didn't just point north; it whipped in a chaotic, circular frenzy, trembling against the glass casing." | | 4 | "It was hovering, pointing directly downward—into the floor, through the concrete, toward something deep beneath the Underground." | | 5 | "There was a sound—a wet, tearing sound, like canvas being ripped apart." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1117 | | adjectiveStacks | 1 | | stackExamples | | 0 | "delicate, high-pitched lilt" |
| | adverbCount | 27 | | adverbRatio | 0.024171888988361683 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.008952551477170993 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 10.62 | | std | 6.35 | | cv | 0.598 | | sampleLengths | | 0 | 14 | | 1 | 16 | | 2 | 18 | | 3 | 24 | | 4 | 22 | | 5 | 2 | | 6 | 4 | | 7 | 12 | | 8 | 20 | | 9 | 5 | | 10 | 20 | | 11 | 7 | | 12 | 16 | | 13 | 25 | | 14 | 14 | | 15 | 20 | | 16 | 19 | | 17 | 12 | | 18 | 11 | | 19 | 10 | | 20 | 2 | | 21 | 17 | | 22 | 25 | | 23 | 6 | | 24 | 7 | | 25 | 4 | | 26 | 9 | | 27 | 20 | | 28 | 8 | | 29 | 8 | | 30 | 12 | | 31 | 13 | | 32 | 17 | | 33 | 19 | | 34 | 15 | | 35 | 13 | | 36 | 9 | | 37 | 6 | | 38 | 12 | | 39 | 11 | | 40 | 15 | | 41 | 7 | | 42 | 26 | | 43 | 7 | | 44 | 12 | | 45 | 23 | | 46 | 5 | | 47 | 12 | | 48 | 16 | | 49 | 13 |
| |
| 40.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.2631578947368421 | | totalSentences | 133 | | uniqueOpeners | 35 | |
| 67.34% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 99 | | matches | | 0 | "Then, she looked at the" | | 1 | "Just a small, dark bruise," |
| | ratio | 0.02 | |
| 46.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 99 | | matches | | 0 | "It was a copper tang" | | 1 | "She adjusted the strap of" | | 2 | "She stepped over a pile" | | 3 | "She didn’t like this place." | | 4 | "She stepped into the pool" | | 5 | "She looked less like a" | | 6 | "She wore her curly red" | | 7 | "She indicated the body slumped" | | 8 | "His neck was at an" | | 9 | "She remained crouched, her knees" | | 10 | "She tapped the ground with" | | 11 | "She was a detective with" | | 12 | "She dealt with murders, assaults," | | 13 | "She did not deal with" | | 14 | "She dusted off her trousers," | | 15 | "She tied it shut with" | | 16 | "She gestured vaguely at the" | | 17 | "She had suspected the clique" | | 18 | "It was bare concrete, graffiti" | | 19 | "She aimed the beam at" |
| | ratio | 0.434 | |
| 10.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 99 | | matches | | 0 | "The air in the abandoned" | | 1 | "It was a copper tang" | | 2 | "Detective Harlow Quinn pressed the" | | 3 | "The low hum of distant" | | 4 | "She adjusted the strap of" | | 5 | "Harlow’s jaw tightened, a sharp," | | 6 | "She stepped over a pile" | | 7 | "She didn’t like this place." | | 8 | "The station had been sealed" | | 9 | "a voice called out" | | 10 | "Harlow didn't turn until she" | | 11 | "She stepped into the pool" | | 12 | "Eva Kowalski adjusted her round" | | 13 | "She looked less like a" | | 14 | "She wore her curly red" | | 15 | "Eva said, her voice dropping" | | 16 | "She indicated the body slumped" | | 17 | "The victim was a man" | | 18 | "His neck was at an" | | 19 | "Harlow asked, her voice gravelly" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 79.37% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 4 | | matches | | 0 | "Harlow didn't turn until she recognized the tone—the delicate, high-pitched lilt that belonged to Eva Kowalski." | | 1 | "The logic was flawed, the evidence was circumstantial, but the instinct—that cold, hard metal needle in her gut that had served her since Basic Training—vibrate…" | | 2 | "Harlow narrowed her eyes, overriding the intuitive response telling her to call for backup and get this woman out of the way." | | 3 | "Harlow raised her flashlight, squinting against the sudden glare radiating from the darkness ahead." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 7 | | matches | | 0 | "Eva said, her voice dropping to a conspiratorial whisper" | | 1 | "Harlow asked, her voice gravelly" | | 2 | "Harlow said, her tone leaving no room for argument" | | 3 | "Harlow asked, the name hanging heavy in the stale air" | | 4 | "Eva snapped, her voice dropping an octave" | | 5 | "Harlow said, her voice dropping to match the intensity of the room" | | 6 | "Harlow said, her voice low and dangerous," |
| |
| 61.11% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 4 | | fancyTags | | 0 | "Eva murmured (murmur)" | | 1 | "Eva snapped (snap)" | | 2 | "Eva whispered (whisper)" | | 3 | "Harlow ordered (order)" |
| | dialogueSentences | 45 | | tagDensity | 0.244 | | leniency | 0.489 | | rawRatio | 0.364 | | effectiveRatio | 0.178 | |