| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.26% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1338 | | totalAiIsmAdverbs | 1 | | 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) | |
| 6.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1338 | | totalAiIsms | 25 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "flickered" | | 1 | "pulsed" | | 2 | "gloom" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "profound" | | 6 | "silence" | | 7 | "velvet" | | 8 | "whisper" | | 9 | "determined" | | 10 | "familiar" | | 11 | "weight" | | 12 | "structure" | | 13 | "scanned" | | 14 | "dancing" | | 15 | "unravel" | | 16 | "maw" | | 17 | "could feel" |
| |
| 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 | 123 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 123 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1338 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 1290 | | uniqueNames | 10 | | maxNameDensity | 0.62 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 8 | | Herrera | 4 | | Thames | 1 | | Tube | 2 | | Morris | 4 | | Veil | 1 | | Market | 1 | | Met | 1 | | Glock | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 5 | | matches | | 0 | "felt like this" | | 1 | "felt like being underwater" | | 2 | "looked like bottled screams" | | 3 | "looked like captured starlight, their tin" | | 4 | "looked like a human knucklebone" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.747 | | wordCount | 1338 | | matches | | 0 | "Not the sterile corridors of a hospital, but this" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 87.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 51.46 | | std | 23.39 | | cv | 0.455 | | sampleLengths | | 0 | 73 | | 1 | 87 | | 2 | 62 | | 3 | 50 | | 4 | 49 | | 5 | 59 | | 6 | 59 | | 7 | 67 | | 8 | 91 | | 9 | 3 | | 10 | 80 | | 11 | 56 | | 12 | 52 | | 13 | 74 | | 14 | 52 | | 15 | 36 | | 16 | 53 | | 17 | 34 | | 18 | 14 | | 19 | 18 | | 20 | 81 | | 21 | 60 | | 22 | 15 | | 23 | 14 | | 24 | 47 | | 25 | 52 |
| |
| 96.71% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 123 | | matches | | 0 | "was gone" | | 1 | "were lined" | | 2 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 201 | | matches | | 0 | "was telling" | | 1 | "wasn’t running" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 126 | | ratio | 0 | | matches | (empty) | |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1295 | | adjectiveStacks | 3 | | stackExamples | | 0 | "faint, sickly-green luminescence" | | 1 | "same eerie green glow," | | 2 | "small, leather-bound book" |
| | adverbCount | 29 | | adverbRatio | 0.022393822393822392 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0023166023166023165 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 10.62 | | std | 7.25 | | cv | 0.683 | | sampleLengths | | 0 | 29 | | 1 | 20 | | 2 | 10 | | 3 | 14 | | 4 | 24 | | 5 | 2 | | 6 | 7 | | 7 | 12 | | 8 | 6 | | 9 | 16 | | 10 | 12 | | 11 | 3 | | 12 | 5 | | 13 | 12 | | 14 | 3 | | 15 | 11 | | 16 | 14 | | 17 | 18 | | 18 | 3 | | 19 | 1 | | 20 | 16 | | 21 | 4 | | 22 | 11 | | 23 | 13 | | 24 | 6 | | 25 | 10 | | 26 | 17 | | 27 | 3 | | 28 | 2 | | 29 | 17 | | 30 | 7 | | 31 | 16 | | 32 | 8 | | 33 | 7 | | 34 | 21 | | 35 | 3 | | 36 | 15 | | 37 | 5 | | 38 | 4 | | 39 | 12 | | 40 | 6 | | 41 | 7 | | 42 | 7 | | 43 | 14 | | 44 | 7 | | 45 | 7 | | 46 | 3 | | 47 | 13 | | 48 | 10 | | 49 | 13 |
| |
| 38.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2619047619047619 | | totalSentences | 126 | | uniqueOpeners | 33 | |
| 57.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 116 | | matches | | 0 | "Just the frantic, desperate sprint." | | 1 | "Then a crackle." |
| | ratio | 0.017 | |
| 47.59% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 116 | | matches | | 0 | "Her worn leather watch, slick" | | 1 | "She’d had him under surveillance" | | 2 | "Her CI’s information was solid:" | | 3 | "He’d made her the moment" | | 4 | "He was fast, weaving through" | | 5 | "She bulldozed her way through," | | 6 | "Her eyes, brown and sharp" | | 7 | "He turned down a narrow" | | 8 | "She rounded the corner, hand" | | 9 | "He couldn’t have gone anywhere" | | 10 | "It was locked, but a" | | 11 | "She kicked it." | | 12 | "It was a stairwell plunging" | | 13 | "It was the entrance to" | | 14 | "Her partner, Morris, had died" | | 15 | "She’d always known it was" | | 16 | "She pulled her torch from" | | 17 | "Her hand was steady on" | | 18 | "It was a cavernous, vaulted" | | 19 | "She’d heard whispers in the" |
| | ratio | 0.431 | |
| 20.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 102 | | totalSentences | 116 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s shoes slapped" | | 2 | "Her worn leather watch, slick" | | 3 | "She’d had him under surveillance" | | 4 | "Her CI’s information was solid:" | | 5 | "A ghost who patched up" | | 6 | "Tonight, she’d finally caught him" | | 7 | "He’d made her the moment" | | 8 | "He was fast, weaving through" | | 9 | "Quinn was relentless." | | 10 | "She bulldozed her way through," | | 11 | "Her eyes, brown and sharp" | | 12 | "He turned down a narrow" | | 13 | "A dead end." | | 14 | "She rounded the corner, hand" | | 15 | "The alley was empty." | | 16 | "The rain beat a tattoo" | | 17 | "A single, weak bulb flickered" | | 18 | "He couldn’t have gone anywhere" | | 19 | "Quinn approached the door, her" |
| | ratio | 0.879 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 116 | | matches | (empty) | | ratio | 0 | |
| 92.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn’s shoes slapped against the wet concrete, a steady, punishing rhythm that matched the hammering in her chest." | | 1 | "The air grew thick, heavy with a pressure that felt like being underwater." | | 2 | "She could still hear the low murmur of the market, the clink of coin on bone, the sibilant whispers of things that weren’t human." | | 3 | "He was a thread that, if pulled, could unravel the entire tangled mess that had started with Morris’s death and led straight into the heart of this impossible p…" |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice quiet but firm, cutting through the silence" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |