| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 86.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1440 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "softly" | | 1 | "lazily" | | 2 | "slightly" | | 3 | "really" |
| |
| 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) | |
| 51.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1440 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "silence" | | 1 | "scanning" | | 2 | "velvet" | | 3 | "shimmered" | | 4 | "pristine" | | 5 | "perfect" | | 6 | "etched" | | 7 | "trembled" | | 8 | "weight" | | 9 | "could feel" | | 10 | "pulse" |
| |
| 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 | 171 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 171 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 171 | | 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 | 1440 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 14 | | matches | | 0 | "Exsanguination of the soul, Eva corrected softly." | | 1 | "Explain, Quinn said." | | 2 | "The blood is wrong, Eva said, stepping closer to the body but keeping her distance from Quinn." | | 3 | "No, Eva said." | | 4 | "Not this time, Eva said." | | 5 | "If he was pulled through a rift, Quinn said, walking to the edge of the track bed." | | 6 | "Because something went wrong, Eva said." | | 7 | "The schedule, Quinn said." | | 8 | "It moves at dawn, Eva confirmed." | | 9 | "Brine from the Between, Eva corrected." | | 10 | "You said he was emptied, Quinn said." | | 11 | "Bag him, Quinn called out to the uniforms waiting near the entrance." | | 12 | "To the morgue, Quinn said." | | 13 | "Then we have until dawn to figure out who sent him, Quinn said." |
| |
| 28.47% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 73 | | wordCount | 1440 | | uniqueNames | 13 | | maxNameDensity | 2.43 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 35 | | Veil | 3 | | Market | 2 | | Detective | 4 | | Kowalski | 2 | | Eva | 15 | | Oxford | 1 | | Morris | 5 | | London | 1 | | Compass | 2 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Market" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Morris" | | 7 | "Compass" |
| | places | | | globalScore | 0.285 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | glossingSentenceCount | 1 | | matches | | 0 | "sigils that seemed to shift if she looked at them too long" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.694 | | wordCount | 1440 | | matches | | 0 | "Not with rain, but with a viscous, clear fluid" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 171 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 32 | | std | 21.12 | | cv | 0.66 | | sampleLengths | | 0 | 70 | | 1 | 65 | | 2 | 43 | | 3 | 8 | | 4 | 56 | | 5 | 47 | | 6 | 28 | | 7 | 47 | | 8 | 15 | | 9 | 26 | | 10 | 59 | | 11 | 3 | | 12 | 36 | | 13 | 67 | | 14 | 5 | | 15 | 6 | | 16 | 59 | | 17 | 22 | | 18 | 41 | | 19 | 26 | | 20 | 42 | | 21 | 17 | | 22 | 84 | | 23 | 45 | | 24 | 27 | | 25 | 19 | | 26 | 8 | | 27 | 18 | | 28 | 46 | | 29 | 40 | | 30 | 35 | | 31 | 4 | | 32 | 6 | | 33 | 41 | | 34 | 7 | | 35 | 12 | | 36 | 40 | | 37 | 35 | | 38 | 8 | | 39 | 8 | | 40 | 35 | | 41 | 22 | | 42 | 58 | | 43 | 52 | | 44 | 2 |
| |
| 70.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 17 | | totalSentences | 171 | | matches | | 0 | "been frozen" | | 1 | "was frizzed" | | 2 | "was emptied" | | 3 | "being moved" | | 4 | "was placed" | | 5 | "was etched" | | 6 | "was emptied" | | 7 | "was gone" | | 8 | "was pulled" | | 9 | "was interrupted" | | 10 | "was caught" | | 11 | "been killed" | | 12 | "been deposited" | | 13 | "was terrified" | | 14 | "was emptied" | | 15 | "was extracted" | | 16 | "been emptied" |
| |
| 14.08% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 251 | | matches | | 0 | "was pointing" | | 1 | "wasn't coming" | | 2 | "was coming" | | 3 | "was standing" | | 4 | "was briefing" | | 5 | "was rejecting" | | 6 | "wasn't lying" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 171 | | ratio | 0.006 | | matches | | 0 | "Detective, if the market moves at dawn—" |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1442 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.020804438280166437 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.0062413314840499305 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 171 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 171 | | mean | 8.42 | | std | 5.98 | | cv | 0.71 | | sampleLengths | | 0 | 24 | | 1 | 21 | | 2 | 10 | | 3 | 1 | | 4 | 14 | | 5 | 14 | | 6 | 11 | | 7 | 23 | | 8 | 7 | | 9 | 10 | | 10 | 9 | | 11 | 17 | | 12 | 3 | | 13 | 2 | | 14 | 12 | | 15 | 8 | | 16 | 3 | | 17 | 4 | | 18 | 19 | | 19 | 30 | | 20 | 5 | | 21 | 23 | | 22 | 13 | | 23 | 6 | | 24 | 19 | | 25 | 5 | | 26 | 4 | | 27 | 4 | | 28 | 19 | | 29 | 24 | | 30 | 1 | | 31 | 14 | | 32 | 7 | | 33 | 11 | | 34 | 5 | | 35 | 3 | | 36 | 6 | | 37 | 8 | | 38 | 15 | | 39 | 8 | | 40 | 22 | | 41 | 3 | | 42 | 17 | | 43 | 3 | | 44 | 10 | | 45 | 4 | | 46 | 1 | | 47 | 1 | | 48 | 10 | | 49 | 1 |
| |
| 43.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.31952662721893493 | | totalSentences | 169 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 150 | | matches | | 0 | "Just a stillness that looked" | | 1 | "Just a gap in the" | | 2 | "Then why is the body" | | 3 | "Maybe by the raid." | | 4 | "Maybe by the market moving." | | 5 | "Then we have until dawn" |
| | ratio | 0.04 | |
| 92.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 150 | | matches | | 0 | "She checked the worn leather" | | 1 | "She paused at the edge" | | 2 | "It was on the body" | | 3 | "She knew the voice." | | 4 | "She towered over Eva by" | | 5 | "It's not a heart attack." | | 6 | "She pulled a penlight from" | | 7 | "Her green eyes were wide" | | 8 | "He didn't die here, Detective." | | 9 | "He was emptied." | | 10 | "She walked a slow circle" | | 11 | "They spoke in riddles when" | | 12 | "I checked the lividity." | | 13 | "He wasn't dragged, Quinn murmured." | | 14 | "He was placed." | | 15 | "It was a small brass" | | 16 | "She held it flat in" | | 17 | "It never did." | | 18 | "It spun lazily, then jerked," | | 19 | "It was coming from him." |
| | ratio | 0.32 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 150 | | matches | | 0 | "The air beneath Camden tasted" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "She checked the worn leather" | | 3 | "The witching hour had passed," | | 4 | "She paused at the edge" | | 5 | "The Veil Market had been" | | 6 | "Stalls draped in velvet and" | | 7 | "It was on the body" | | 8 | "Quinn crouched, the knees of" | | 9 | "The victim was a male," | | 10 | "Uniforms are calling it a" | | 11 | "Quinn didn't turn." | | 12 | "She knew the voice." | | 13 | "Eva Kowalski stood behind her," | | 14 | "The younger woman's curly red" | | 15 | "Quinn stood, smoothing her jacket." | | 16 | "She towered over Eva by" | | 17 | "Heart attacks don't happen in" | | 18 | "Eva tucked a stray curl" | | 19 | "It's not a heart attack." |
| | ratio | 0.707 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 10 | | totalSentences | 150 | | matches | | 0 | "If he had been outside" | | 1 | "If he had died here," | | 2 | "If it was pointing down" | | 3 | "If he was emptied here," | | 4 | "If he was pulled through" | | 5 | "Because something went wrong, Eva" | | 6 | "If he was caught in" | | 7 | "To the station?" | | 8 | "To the morgue, Quinn said." | | 9 | "If this was a vacuum" |
| | ratio | 0.067 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 3 | | matches | | 0 | "It was a small brass instrument, the casing covered in a patina of verdigris that stained her thumb whenever she handled it." | | 1 | "She started walking toward the exit, her boots clicking on the tiles." | | 2 | "She had eighteen years of practice in finding the truth, and three years of hunger for the kind of truth that bled." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |