| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 80 | | tagDensity | 0.113 | | leniency | 0.225 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.46% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1967 | | 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) | |
| 74.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1967 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "warmth" | | 1 | "etched" | | 2 | "trembled" | | 3 | "weight" | | 4 | "electric" | | 5 | "chill" | | 6 | "structure" | | 7 | "could feel" | | 8 | "pulse" | | 9 | "traced" |
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| 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 | 170 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 170 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 241 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1966 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.31% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1412 | | uniqueNames | 10 | | maxNameDensity | 2.05 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 29 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Sergeant | 1 | | Nisha | 1 | | Bell | 22 | | Rook | 7 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Sergeant" | | 4 | "Nisha" | | 5 | "Bell" | | 6 | "Rook" | | 7 | "Morris" |
| | places | (empty) | | globalScore | 0.473 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | 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 | 1966 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 241 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 118 | | mean | 16.66 | | std | 17.38 | | cv | 1.043 | | sampleLengths | | 0 | 47 | | 1 | 11 | | 2 | 74 | | 3 | 21 | | 4 | 1 | | 5 | 46 | | 6 | 8 | | 7 | 23 | | 8 | 4 | | 9 | 2 | | 10 | 4 | | 11 | 59 | | 12 | 59 | | 13 | 11 | | 14 | 40 | | 15 | 36 | | 16 | 3 | | 17 | 1 | | 18 | 1 | | 19 | 39 | | 20 | 26 | | 21 | 4 | | 22 | 12 | | 23 | 13 | | 24 | 13 | | 25 | 4 | | 26 | 1 | | 27 | 8 | | 28 | 66 | | 29 | 26 | | 30 | 10 | | 31 | 15 | | 32 | 2 | | 33 | 3 | | 34 | 41 | | 35 | 8 | | 36 | 37 | | 37 | 15 | | 38 | 9 | | 39 | 1 | | 40 | 48 | | 41 | 2 | | 42 | 4 | | 43 | 3 | | 44 | 3 | | 45 | 14 | | 46 | 4 | | 47 | 47 | | 48 | 12 | | 49 | 43 |
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| 88.75% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 170 | | matches | | 0 | "been placed" | | 1 | "were furred" | | 2 | "been mended" | | 3 | "been dragged" | | 4 | "been etched" | | 5 | "been pasted" | | 6 | "been pasted" | | 7 | "been brought" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 237 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 241 | | ratio | 0.008 | | matches | | 0 | "Fine marks had been etched around the face—tight, hooked lines that might have been decoration or writing." | | 1 | "The station air held the smell of wet stone, electric dust, and something faintly sweet—like bruised fruit left too long in a closed room." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1418 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.02609308885754584 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0028208744710860366 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 241 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 241 | | mean | 8.16 | | std | 6.17 | | cv | 0.756 | | sampleLengths | | 0 | 9 | | 1 | 23 | | 2 | 15 | | 3 | 11 | | 4 | 3 | | 5 | 7 | | 6 | 16 | | 7 | 10 | | 8 | 26 | | 9 | 4 | | 10 | 8 | | 11 | 10 | | 12 | 11 | | 13 | 1 | | 14 | 14 | | 15 | 16 | | 16 | 10 | | 17 | 6 | | 18 | 6 | | 19 | 2 | | 20 | 23 | | 21 | 4 | | 22 | 2 | | 23 | 4 | | 24 | 29 | | 25 | 4 | | 26 | 15 | | 27 | 6 | | 28 | 5 | | 29 | 19 | | 30 | 13 | | 31 | 7 | | 32 | 9 | | 33 | 11 | | 34 | 4 | | 35 | 7 | | 36 | 3 | | 37 | 21 | | 38 | 16 | | 39 | 5 | | 40 | 31 | | 41 | 3 | | 42 | 1 | | 43 | 1 | | 44 | 7 | | 45 | 32 | | 46 | 6 | | 47 | 6 | | 48 | 14 | | 49 | 4 |
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| 43.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2987551867219917 | | totalSentences | 241 | | uniqueOpeners | 72 | |
| 21.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 154 | | matches | | 0 | "Only a rust-colored rectangle remained" |
| | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 154 | | matches | | 0 | "It had been cut from" | | 1 | "Her boots rang on the" | | 2 | "They made a weak amber" | | 3 | "She had chalk dust on" | | 4 | "She checked it out of" | | 5 | "His right hand rested on" | | 6 | "His left arm lay at" | | 7 | "He’d been useful enough to" | | 8 | "Its rails were furred with" | | 9 | "She crouched beside Rook." | | 10 | "His suit was expensive, though" | | 11 | "His right cuff was damp," | | 12 | "She checked his neck, then" | | 13 | "She eased his right hand" | | 14 | "Its casing had a green" | | 15 | "She turned the compass in" | | 16 | "She went back to the" | | 17 | "It settled northeast again, though" | | 18 | "She carried it three paces" | | 19 | "It pulled toward the tiled" |
| | ratio | 0.26 | |
| 37.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 130 | | totalSentences | 154 | | matches | | 0 | "The bone token was cold" | | 1 | "It had been cut from" | | 2 | "The man guarding the station" | | 3 | "Quinn descended alone." | | 4 | "Her boots rang on the" | | 5 | "Someone had hung strings of" | | 6 | "They made a weak amber" | | 7 | "A shutter banged somewhere deeper" | | 8 | "The Veil Market had moved" | | 9 | "Detective Sergeant Nisha Bell waited" | | 10 | "She had chalk dust on" | | 11 | "A third stood over a" | | 12 | "Quinn handed the token to" | | 13 | "Bell gave her a look" | | 14 | "Quinn pulled on gloves." | | 15 | "The worn leather watch on" | | 16 | "She checked it out of" | | 17 | "The hands showed one twenty-eight." | | 18 | "The body lay on the" | | 19 | "A man in his fifties," |
| | ratio | 0.844 | |
| 32.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 154 | | matches | | 0 | "By the time the police" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 3 | | matches | | 0 | "They made a weak amber light of the old ticket hall and the stalls that had been set up there: narrow tables, locked trunks, canvas awnings." | | 1 | "Rook had sold information to anyone who could pay, including, she suspected, the clique her unit had been watching for months." | | 2 | "Fine marks had been etched around the face—tight, hooked lines that might have been decoration or writing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 80 | | tagDensity | 0.075 | | leniency | 0.15 | | rawRatio | 0 | | effectiveRatio | 0 | |