| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.275 | | leniency | 0.55 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1172 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 10.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1172 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "echoing" | | 1 | "gloom" | | 2 | "scanned" | | 3 | "shattered" | | 4 | "gleaming" | | 5 | "electric" | | 6 | "etched" | | 7 | "traced" | | 8 | "perfect" | | 9 | "loomed" | | 10 | "dances" | | 11 | "vibrated" | | 12 | "echo" | | 13 | "standard" | | 14 | "measured" | | 15 | "pulsed" | | 16 | "silence" | | 17 | "jaw clenched" |
| |
| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | (empty) | |
| 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 | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1145 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 8.02% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 810 | | uniqueNames | 14 | | maxNameDensity | 2.84 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 23 | | Camden | 1 | | Town | 1 | | Tube | 1 | | Kowalski | 1 | | Veil | 1 | | Market | 1 | | Eva | 18 | | Switchblade | 1 | | Morris | 2 | | Last | 1 | | Silence | 1 | | Blood | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Market" | | 4 | "Eva" | | 5 | "Morris" | | 6 | "Silence" | | 7 | "Blood" |
| | places | | | globalScore | 0.08 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.873 | | wordCount | 1145 | | matches | | 0 | "not by steel, but shadow tendrils uncoiling from the arch" |
| |
| 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 | 45 | | mean | 25.44 | | std | 18.79 | | cv | 0.738 | | sampleLengths | | 0 | 88 | | 1 | 43 | | 2 | 87 | | 3 | 23 | | 4 | 43 | | 5 | 30 | | 6 | 44 | | 7 | 17 | | 8 | 53 | | 9 | 25 | | 10 | 14 | | 11 | 34 | | 12 | 17 | | 13 | 10 | | 14 | 43 | | 15 | 21 | | 16 | 14 | | 17 | 26 | | 18 | 48 | | 19 | 21 | | 20 | 23 | | 21 | 33 | | 22 | 9 | | 23 | 37 | | 24 | 8 | | 25 | 11 | | 26 | 43 | | 27 | 33 | | 28 | 9 | | 29 | 14 | | 30 | 27 | | 31 | 6 | | 32 | 9 | | 33 | 34 | | 34 | 6 | | 35 | 34 | | 36 | 9 | | 37 | 5 | | 38 | 22 | | 39 | 16 | | 40 | 17 | | 41 | 14 | | 42 | 4 | | 43 | 15 | | 44 | 6 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 175 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 13 | | totalSentences | 133 | | ratio | 0.098 | | matches | | 0 | "Rain slicked the concrete stairs leading down, her boots echoing in the stale air thick with mold and something sharper—ozone, like a storm trapped underground." | | 1 | "The Veil Market—Quinn had heard whispers, black market for oddities, but this?" | | 2 | "Six vics, all male, mid-30s, dressed like street dealers—leather jackets, heavy chains." | | 3 | "She lifted the casing—sigils etched deep, needle quivering toward a shadowed alcove." | | 4 | "No footprints scattered the dust there—just a faint shimmer, heat haze in cold air." | | 5 | "She sniffed—sulfur undertone." | | 6 | "She shone her light—empty, but air warped, a ripple like water disturbed." | | 7 | "She recalled DS Morris—three years back, similar scene, wounds that knit themselves shut before autopsy." | | 8 | "Jacket pocket bulged—ledger peeked, pages warped but legible." | | 9 | "No—ink reacting." | | 10 | "Shimmer pulsed once, faint moan escaping—like wind through bone." | | 11 | "She recalled Morris's file—throat wound identical, partner vanished into thin air per witnesses." | | 12 | "Heat surged, vision blurring—flashes: hooded figures circling, chanting low, compass needles aligning like spears." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 827 | | adjectiveStacks | 1 | | stackExamples | | 0 | "lay open nearby, books" |
| | adverbCount | 12 | | adverbRatio | 0.014510278113663845 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0012091898428053204 | |
| 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 | 8.61 | | std | 5.65 | | cv | 0.657 | | sampleLengths | | 0 | 28 | | 1 | 25 | | 2 | 8 | | 3 | 16 | | 4 | 11 | | 5 | 27 | | 6 | 5 | | 7 | 2 | | 8 | 9 | | 9 | 27 | | 10 | 10 | | 11 | 13 | | 12 | 12 | | 13 | 25 | | 14 | 14 | | 15 | 9 | | 16 | 4 | | 17 | 12 | | 18 | 14 | | 19 | 13 | | 20 | 13 | | 21 | 17 | | 22 | 12 | | 23 | 5 | | 24 | 3 | | 25 | 12 | | 26 | 6 | | 27 | 6 | | 28 | 8 | | 29 | 9 | | 30 | 4 | | 31 | 13 | | 32 | 9 | | 33 | 14 | | 34 | 13 | | 35 | 8 | | 36 | 17 | | 37 | 6 | | 38 | 8 | | 39 | 4 | | 40 | 5 | | 41 | 9 | | 42 | 3 | | 43 | 10 | | 44 | 3 | | 45 | 10 | | 46 | 2 | | 47 | 5 | | 48 | 2 | | 49 | 8 |
| |
| 91.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5488721804511278 | | totalSentences | 133 | | uniqueOpeners | 73 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 92 | | matches | | 0 | "She checked her worn leather" | | 1 | "Her jaw tightened as she" | | 2 | "She ducked through the tear," | | 3 | "Her worn leather satchel lay" | | 4 | "She lifted the casing—sigils etched" | | 5 | "She traced a blood spiral" | | 6 | "She stood, military precision in" | | 7 | "She sniffed—sulfur undertone." | | 8 | "She followed the compass needle's" | | 9 | "She shone her light—empty, but" | | 10 | "She recalled DS Morris—three years" | | 11 | "She pocketed it, stood." | | 12 | "She tugged it free, flipped" | | 13 | "Her gut twisted." | | 14 | "She dropped it, wiped her" | | 15 | "She knelt again, measured a" | | 16 | "Her light caught something: on" | | 17 | "She recalled Morris's file—throat wound" | | 18 | "She yanked back, breath sharp." |
| | ratio | 0.207 | |
| 25.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 92 | | matches | | 0 | "Detective Harlow Quinn flashed her" | | 1 | "Rain slicked the concrete stairs" | | 2 | "She checked her worn leather" | | 3 | "The call came in two" | | 4 | "Her jaw tightened as she" | | 5 | "Tunnels branched off the platform," | | 6 | "She ducked through the tear," | | 7 | "Eva Kowalski crouched near a" | | 8 | "Freckles stood out under the" | | 9 | "Her worn leather satchel lay" | | 10 | "The Veil Market—Quinn had heard" | | 11 | "Bodies of men in hoodies" | | 12 | "Eva tucked a curl behind" | | 13 | "Quinn scanned the scene." | | 14 | "Stalls overturned: vials of glowing" | | 15 | "Eva straightened, snapping off her" | | 16 | "Quinn knelt by the compass" | | 17 | "Air hummed, electric." | | 18 | "She lifted the casing—sigils etched" | | 19 | "Blood trails led inward, not" |
| | ratio | 0.87 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 3 | | matches | | 0 | "Rain slicked the concrete stairs leading down, her boots echoing in the stale air thick with mold and something sharper—ozone, like a storm trapped underground." | | 1 | "Bodies of men in hoodies littered the space, throats slashed clean, blood pooling in unnatural spirals that defied gravity, climbing the walls in thin rivulets." | | 2 | "She recalled DS Morris—three years back, similar scene, wounds that knit themselves shut before autopsy." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 40 | | tagDensity | 0.025 | | leniency | 0.05 | | rawRatio | 1 | | effectiveRatio | 0.05 | |