| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1245 | | 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) | |
| 95.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1245 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 115 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1254 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1183 | | uniqueNames | 15 | | maxNameDensity | 0.85 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Charing | 1 | | Cross | 1 | | Raven | 1 | | Nest | 2 | | Tomás | 1 | | Herrera | 5 | | Camden | 3 | | Delancey | 1 | | Street | 3 | | Inverness | 1 | | Hackney | 1 | | Tube | 1 | | Met | 1 | | Buck | 1 | | Quinn | 10 |
| | persons | | 0 | "Nest" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Camden" | | 4 | "Street" | | 5 | "Met" | | 6 | "Buck" | | 7 | "Quinn" |
| | places | | 0 | "Charing" | | 1 | "Raven" | | 2 | "Delancey" | | 3 | "Inverness" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1254 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 121 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 26.13 | | std | 19.83 | | cv | 0.759 | | sampleLengths | | 0 | 10 | | 1 | 57 | | 2 | 26 | | 3 | 43 | | 4 | 21 | | 5 | 34 | | 6 | 2 | | 7 | 68 | | 8 | 15 | | 9 | 6 | | 10 | 40 | | 11 | 16 | | 12 | 11 | | 13 | 26 | | 14 | 10 | | 15 | 59 | | 16 | 20 | | 17 | 58 | | 18 | 21 | | 19 | 3 | | 20 | 69 | | 21 | 26 | | 22 | 36 | | 23 | 12 | | 24 | 11 | | 25 | 6 | | 26 | 18 | | 27 | 74 | | 28 | 16 | | 29 | 4 | | 30 | 39 | | 31 | 15 | | 32 | 49 | | 33 | 24 | | 34 | 10 | | 35 | 49 | | 36 | 15 | | 37 | 25 | | 38 | 2 | | 39 | 4 | | 40 | 52 | | 41 | 36 | | 42 | 5 | | 43 | 6 | | 44 | 22 | | 45 | 32 | | 46 | 45 | | 47 | 6 |
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| 93.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 115 | | matches | | 0 | "were gone" | | 1 | "being given" | | 2 | "being ignored" | | 3 | "being noticed" | | 4 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 188 | | matches | | 0 | "wasn’t going" | | 1 | "was rolling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 121 | | ratio | 0.074 | | matches | | 0 | "Quinn had picked him up outside the Raven’s Nest at half past ten — Tomás Herrera, black jacket, hood drawn against the weather, walking with the kind of purpose that told her he wasn’t going home." | | 1 | "Eighteen years on the job and the body remembered before the mind caught up — heel-toe rhythm, breath measured through the nose, the shortened stride you took on wet cobbles because a bad turn would put you on your back." | | 2 | "She marked the scar along his left forearm when his sleeve rode up — the knife scar from the file, the one that matched the statement she’d pulled off a Hackney assault charge four years back." | | 3 | "Then at her watch — 23:07, the leather strap soft and heavy from the rain." | | 4 | "Somewhere far below, water dripped into water, and the sound had a room behind it — a big room." | | 5 | "She knew the map — Camden had a dozen of them, sealed in the fifties, bricked up and forgotten, and the Met had a file of complaints about the one under Buck Street going back years." | | 6 | "Not sealed — stopped." | | 7 | "No — that was the thing." | | 8 | "Short, older, a bone token on a leather cord around her neck — the same token Quinn had seen on every stallholder in the place, hanging against coats and shirts like a dress code." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1177 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.0237892948173322 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002548853016142736 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 10.36 | | std | 9.33 | | cv | 0.9 | | sampleLengths | | 0 | 10 | | 1 | 36 | | 2 | 21 | | 3 | 5 | | 4 | 18 | | 5 | 3 | | 6 | 3 | | 7 | 40 | | 8 | 21 | | 9 | 4 | | 10 | 12 | | 11 | 17 | | 12 | 1 | | 13 | 2 | | 14 | 9 | | 15 | 3 | | 16 | 3 | | 17 | 36 | | 18 | 3 | | 19 | 14 | | 20 | 15 | | 21 | 6 | | 22 | 18 | | 23 | 8 | | 24 | 5 | | 25 | 9 | | 26 | 4 | | 27 | 12 | | 28 | 8 | | 29 | 2 | | 30 | 1 | | 31 | 5 | | 32 | 6 | | 33 | 15 | | 34 | 10 | | 35 | 5 | | 36 | 14 | | 37 | 17 | | 38 | 4 | | 39 | 19 | | 40 | 3 | | 41 | 6 | | 42 | 11 | | 43 | 4 | | 44 | 36 | | 45 | 1 | | 46 | 1 | | 47 | 16 | | 48 | 12 | | 49 | 9 |
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| 64.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4214876033057851 | | totalSentences | 121 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 99 | | matches | | 0 | "Then he ran." | | 1 | "Then she saw it." | | 2 | "Then at the yard behind" | | 3 | "Then at her watch —" | | 4 | "Somewhere far below, water dripped" | | 5 | "Somewhere a woman sang in" | | 6 | "Then at her face." | | 7 | "Then at the platform behind" |
| | ratio | 0.081 | |
| 98.79% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 99 | | matches | | 0 | "She’d given him a block," | | 1 | "He glanced back at the" | | 2 | "She ran too." | | 3 | "He cut left into Inverness" | | 4 | "she shouted, mostly for the" | | 5 | "He had a runner’s build" | | 6 | "She marked the scar along" | | 7 | "Her lungs burned." | | 8 | "Her coat weighed ten pounds" | | 9 | "He turned again at the" | | 10 | "She reached the gap and" | | 11 | "She pulled her torch and" | | 12 | "She looked at the hatch." | | 13 | "She got on her knees" | | 14 | "Her torch fell down a" | | 15 | "She felt it on her" | | 16 | "She knew the map —" | | 17 | "She went down." | | 18 | "Her torch swept past a" | | 19 | "She came out onto a" |
| | ratio | 0.303 | |
| 76.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 99 | | matches | | 0 | "The rain started at Charing" | | 1 | "Quinn had picked him up" | | 2 | "She’d given him a block," | | 3 | "He glanced back at the" | | 4 | "She ran too." | | 5 | "He cut left into Inverness" | | 6 | "Quinn hurdled the wreckage." | | 7 | "A crate lid spun into" | | 8 | "she shouted, mostly for the" | | 9 | "He had a runner’s build" | | 10 | "She marked the scar along" | | 11 | "Her lungs burned." | | 12 | "Her coat weighed ten pounds" | | 13 | "He turned again at the" | | 14 | "She reached the gap and" | | 15 | "She pulled her torch and" | | 16 | "Water drummed on the metal." | | 17 | "A rat went about its" | | 18 | "A service hatch, knee-high, rusted" | | 19 | "The beam picked out fibres" |
| | ratio | 0.768 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn had picked him up outside the Raven’s Nest at half past ten — Tomás Herrera, black jacket, hood drawn against the weather, walking with the kind of purpos…" | | 1 | "She’d given him a block, then two, keeping the green neon of the Nest’s sign burning in the puddles between them." | | 2 | "She saw a woman in a waxed coat selling stoppered bottles that glowed under their own light." | | 3 | "She saw a man with wings folded neat against his back, silver feathers, negotiating over a crate of something that moved." | | 4 | "Rain fell somewhere behind her, far above, on the streets of Camden, and here she stood at the edge of a floor that shouldn’t exist, in a city she’d sworn eight…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.25 | | effectiveRatio | 0.2 | |