| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 44 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said softly [softly]" | | 1 | "Harlow said slowly [slowly]" |
| | dialogueSentences | 102 | | tagDensity | 0.431 | | leniency | 0.863 | | rawRatio | 0.045 | | effectiveRatio | 0.039 | |
| 84.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1945 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slightly" | | 1 | "precisely" | | 2 | "perfectly" | | 3 | "softly" | | 4 | "slowly" |
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| 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) | |
| 51.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1945 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "echoed" | | 1 | "trembled" | | 2 | "crystal" | | 3 | "gleaming" | | 4 | "traced" | | 5 | "silk" | | 6 | "flicker" | | 7 | "resonance" | | 8 | "pulse" | | 9 | "eyebrow" | | 10 | "vibrated" | | 11 | "measured" | | 12 | "quickened" | | 13 | "tracing" | | 14 | "pulsed" | | 15 | "footsteps" | | 16 | "structure" | | 17 | "fluttered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 150 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 150 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 191 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 9 | | markdownWords | 25 | | totalWords | 1932 | | ratio | 0.013 | | matches | | 0 | "Thomas" | | 1 | "my" | | 2 | "invitation" | | 3 | "Aurora’s not a name. It’s a condition." | | 4 | "perfectly" | | 5 | "Shade artisans take comfort from your asymmetry. They colonize the gaps." | | 6 | "terrified" | | 7 | "hired" | | 8 | "solvent" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 29 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 120 | | wordCount | 1309 | | uniqueNames | 32 | | maxNameDensity | 1.91 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Quinn | 1 | | Veil | 4 | | Compass | 22 | | Implement | 1 | | Exchange | 1 | | New | 1 | | Age | 1 | | Lyle | 1 | | Hargate | 18 | | Harlow | 25 | | Morris | 4 | | Licensing | 3 | | Office | 3 | | Ketamine | 1 | | Primrose | 1 | | Hill | 1 | | Canary | 1 | | Wharf | 1 | | Combustion | 1 | | Bay | 1 | | Anti-Resonance | 1 | | Act | 1 | | Shade | 2 | | Realm | 1 | | Female | 1 | | Kowalski | 1 | | Eva | 13 | | Navigator | 1 | | Consequences | 1 | | You | 4 | | Zamboni | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Compass" | | 2 | "Exchange" | | 3 | "Lyle" | | 4 | "Hargate" | | 5 | "Harlow" | | 6 | "Morris" | | 7 | "Office" | | 8 | "Kowalski" | | 9 | "Eva" | | 10 | "Navigator" | | 11 | "You" |
| | places | | 0 | "Licensing" | | 1 | "Primrose" | | 2 | "Hill" | | 3 | "Canary" | | 4 | "Combustion" | | 5 | "Bay" |
| | globalScore | 0.545 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 1 | | matches | | 0 | "as if remarking on the weather, and watched him smirk" |
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| 96.48% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.035 | | wordCount | 1932 | | matches | | 0 | "not pale but ink-black, like pages left in a burning fireplace" | | 1 | "Not the man’s injuries, but his path" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 191 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 95 | | mean | 20.34 | | std | 16.26 | | cv | 0.8 | | sampleLengths | | 0 | 82 | | 1 | 47 | | 2 | 62 | | 3 | 27 | | 4 | 30 | | 5 | 69 | | 6 | 34 | | 7 | 31 | | 8 | 55 | | 9 | 3 | | 10 | 48 | | 11 | 22 | | 12 | 54 | | 13 | 18 | | 14 | 21 | | 15 | 6 | | 16 | 21 | | 17 | 18 | | 18 | 6 | | 19 | 45 | | 20 | 43 | | 21 | 2 | | 22 | 40 | | 23 | 13 | | 24 | 50 | | 25 | 20 | | 26 | 32 | | 27 | 19 | | 28 | 7 | | 29 | 1 | | 30 | 16 | | 31 | 16 | | 32 | 10 | | 33 | 15 | | 34 | 18 | | 35 | 5 | | 36 | 7 | | 37 | 52 | | 38 | 9 | | 39 | 35 | | 40 | 6 | | 41 | 11 | | 42 | 4 | | 43 | 25 | | 44 | 6 | | 45 | 38 | | 46 | 13 | | 47 | 30 | | 48 | 5 | | 49 | 21 |
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| 98.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 150 | | matches | | 0 | "was seared" | | 1 | "been refreshed" | | 2 | "was crafted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 221 | | matches | | 0 | "was reading" | | 1 | "was shouting" | | 2 | "was already stepping" |
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| 23.19% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 191 | | ratio | 0.042 | | matches | | 0 | "The Implement Exchange’s new_forces had already taped off the scene with gauzy plastic—jammed between a toppled New Age crystal stand and a frayed-market awning." | | 1 | "She unclipped her own Compass from a duty belt—bought a stolen version under the alias *Thomas*." | | 2 | "She’d learned not to watch markets shift themselves—seen this one once cling to Ketamine stains in Primrose Hill, once flicker entirely into a ditch near Canary Wharf." | | 3 | "The Licensing Office insisted it was a merculite distillation—impossible under the 1986 Anti-Resonance Act." | | 4 | "The needle snapped to each dark sigil—on the wall, in the sizing of rat droppings, in the curl of the corpse’s little finger." | | 5 | "Measured the corpse’s ridges of vertebrae—ones parallel to the pillar." | | 6 | "Can I?” She unclipped Harlow’s Compass with a glove. “The supraslemn ethics code—the Navigator’s protocol—that must be breached by external bursars.”" | | 7 | "This is—who?”" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1138 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.030755711775043937 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.012302284710017574 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 191 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 191 | | mean | 10.12 | | std | 7.15 | | cv | 0.707 | | sampleLengths | | 0 | 9 | | 1 | 26 | | 2 | 23 | | 3 | 24 | | 4 | 10 | | 5 | 18 | | 6 | 19 | | 7 | 17 | | 8 | 4 | | 9 | 10 | | 10 | 5 | | 11 | 7 | | 12 | 12 | | 13 | 7 | | 14 | 6 | | 15 | 14 | | 16 | 4 | | 17 | 3 | | 18 | 17 | | 19 | 7 | | 20 | 6 | | 21 | 9 | | 22 | 8 | | 23 | 11 | | 24 | 14 | | 25 | 16 | | 26 | 11 | | 27 | 9 | | 28 | 18 | | 29 | 7 | | 30 | 2 | | 31 | 18 | | 32 | 11 | | 33 | 19 | | 34 | 3 | | 35 | 27 | | 36 | 6 | | 37 | 3 | | 38 | 10 | | 39 | 18 | | 40 | 14 | | 41 | 6 | | 42 | 10 | | 43 | 7 | | 44 | 5 | | 45 | 3 | | 46 | 14 | | 47 | 24 | | 48 | 13 | | 49 | 14 |
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| 59.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3717277486910995 | | totalSentences | 191 | | uniqueOpeners | 71 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 132 | | matches | | 0 | "Only the Veil Compass on" | | 1 | "Instead, she read the man’s" | | 2 | "Only handy when the police" | | 3 | "Then found the crack at" |
| | ratio | 0.03 | |
| 89.70% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 132 | | matches | | 0 | "He gestured to the body" | | 1 | "He wore a tailored suit," | | 2 | "Her partner, DS Morris, had" | | 3 | "She didn’t mention it." | | 4 | "He’d hated attention." | | 5 | "His badge glinted" | | 6 | "She unclipped her own Compass" | | 7 | "Its needle wobbled at first," | | 8 | "She slid her finger along" | | 9 | "He yanked a corner of" | | 10 | "She’d learned not to watch" | | 11 | "She knew that tar-black viscosity," | | 12 | "He was reading over her" | | 13 | "His tie snagged on an" | | 14 | "She reached for her sulfate" | | 15 | "she murmured, She turned" | | 16 | "He gestured to the corpse’s" | | 17 | "He stepped back." | | 18 | "She ignored him." | | 19 | "She remembered DS Morris’s last" |
| | ratio | 0.326 | |
| 58.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 132 | | matches | | 0 | "The Tube station stank of" | | 1 | "Harlow Quinn’s boots echoed in" | | 2 | "The Veil Compass in its" | | 3 | "The Implement Exchange’s new_forces had" | | 4 | "Inspector Lyle Hargate greeted, his" | | 5 | "He gestured to the body" | | 6 | "The corpse’s throat was seared," | | 7 | "Harlow stepped around a pile" | | 8 | "The victim wasn’t moving." | | 9 | "He wore a tailored suit," | | 10 | "Hargate’s jaw twitched" | | 11 | "Her partner, DS Morris, had" | | 12 | "She didn’t mention it." | | 13 | "He’d hated attention." | | 14 | "His badge glinted" | | 15 | "The corpse’s lipstick traced a" | | 16 | "Harlow tilted her head, thinking" | | 17 | "The Compass beneath his chin" | | 18 | "She unclipped her own Compass" | | 19 | "Its needle wobbled at first," |
| | ratio | 0.803 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 132 | | matches | | 0 | "If this were a pickpocketing" | | 1 | "Now, the pages held solid." | | 2 | "To throw back to" | | 3 | "If this gets out, the" |
| | ratio | 0.03 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "The Veil Compass in its glass case at the station’s entrance trembled slightly, a guard’s fingers brushing its brass casing as he passed." | | 1 | "The point of contact surfaced a spiderweb of dark sigils, delicate as lace but knotting into a vertical line that mirrored her Compass’s travel." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 40 | | uselessAdditionCount | 1 | | matches | | 0 | "Inspector Lyle Hargate greeted, his voice already preening" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 5 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "she murmured (murmur)" | | 2 | "Hargate said (hargate say)" | | 3 | "she corrected (correct)" | | 4 | "Harlow suggested (suggest)" |
| | dialogueSentences | 102 | | tagDensity | 0.108 | | leniency | 0.216 | | rawRatio | 0.455 | | effectiveRatio | 0.098 | |