| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.475 | | leniency | 0.95 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 78.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1374 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | | | 3 | |
| | highlights | | 0 | "slowly" | | 1 | "deliberately" | | 2 | "carefully" | | 3 | "very" |
| |
| 60.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 81.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1374 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "traced" | | 2 | "perfect" | | 3 | "churn" | | 4 | "aligned" |
| |
| 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 | 65 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 74 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1388 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 735 | | uniqueNames | 14 | | maxNameDensity | 1.22 | | worstName | "Carver" | | maxWindowNameDensity | 3 | | worstWindowName | "Carver" | | discoveredNames | | Harlow | 1 | | Quinn | 8 | | Prewitt | 1 | | Northern | 1 | | Veil | 1 | | Market | 1 | | Adeyemi | 5 | | June | 1 | | Marcus | 1 | | Carver | 9 | | Dr | 1 | | Osei | 2 | | Five | 1 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Marcus" | | 4 | "Carver" | | 5 | "Dr" | | 6 | "Osei" | | 7 | "Kowalski" |
| | places | (empty) | | globalScore | 0.888 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1388 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 37.51 | | std | 32.23 | | cv | 0.859 | | sampleLengths | | 0 | 60 | | 1 | 15 | | 2 | 23 | | 3 | 93 | | 4 | 16 | | 5 | 49 | | 6 | 3 | | 7 | 38 | | 8 | 13 | | 9 | 2 | | 10 | 49 | | 11 | 77 | | 12 | 42 | | 13 | 22 | | 14 | 63 | | 15 | 12 | | 16 | 5 | | 17 | 122 | | 18 | 15 | | 19 | 7 | | 20 | 19 | | 21 | 96 | | 22 | 8 | | 23 | 78 | | 24 | 7 | | 25 | 56 | | 26 | 29 | | 27 | 106 | | 28 | 16 | | 29 | 79 | | 30 | 36 | | 31 | 6 | | 32 | 5 | | 33 | 50 | | 34 | 10 | | 35 | 35 | | 36 | 26 |
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| 89.07% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 65 | | matches | | 0 | "been folded" | | 1 | "been walked" | | 2 | "been told" | | 3 | "being told" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 127 | | matches | | |
| 43.19% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 86 | | ratio | 0.035 | | matches | | 0 | "The young man — Prewitt, badge still bright enough to catch the beam of his torch — stumbled anyway." | | 1 | "The Veil Market had left in a hurry — that much was obvious before she'd crossed the yellow tape." | | 2 | "\"—this is a procession. Someone walked him down here. Six people, six tokens, laid down in order as they went, into the tunnel. That's not a drug deal. That's a rite. And he wasn't a buyer.\"" |
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| 99.66% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 619 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.04038772213247173 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.012924071082390954 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 16.14 | | std | 14.96 | | cv | 0.927 | | sampleLengths | | 0 | 26 | | 1 | 1 | | 2 | 33 | | 3 | 11 | | 4 | 4 | | 5 | 19 | | 6 | 4 | | 7 | 19 | | 8 | 27 | | 9 | 19 | | 10 | 2 | | 11 | 11 | | 12 | 15 | | 13 | 16 | | 14 | 12 | | 15 | 37 | | 16 | 3 | | 17 | 23 | | 18 | 15 | | 19 | 3 | | 20 | 10 | | 21 | 2 | | 22 | 12 | | 23 | 19 | | 24 | 18 | | 25 | 28 | | 26 | 18 | | 27 | 3 | | 28 | 3 | | 29 | 25 | | 30 | 19 | | 31 | 18 | | 32 | 5 | | 33 | 18 | | 34 | 1 | | 35 | 3 | | 36 | 7 | | 37 | 56 | | 38 | 10 | | 39 | 2 | | 40 | 3 | | 41 | 2 | | 42 | 25 | | 43 | 41 | | 44 | 56 | | 45 | 6 | | 46 | 9 | | 47 | 7 | | 48 | 4 | | 49 | 15 |
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| 95.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.6046511627906976 | | totalSentences | 86 | | uniqueOpeners | 52 | |
| 59.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 56 | | matches | | 0 | "Somewhere in the dark, at" |
| | ratio | 0.018 | |
| 98.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 56 | | matches | | 0 | "she said to the constable" | | 1 | "She ducked under the tape" | | 2 | "She had learned in eighteen" | | 3 | "His face held an expression" | | 4 | "He was a broad man" | | 5 | "She crouched beside Dr" | | 6 | "He gestured at the scattered" | | 7 | "She turned her torch slowly" | | 8 | "She traced the beam along" | | 9 | "He crouched again, slower this" | | 10 | "She crouched at the dead" | | 11 | "She looked at the arranged" | | 12 | "She shone her torch on" | | 13 | "She pulled on a glove" | | 14 | "She opened it under her" | | 15 | "Her eyes moved down it" | | 16 | "She folded the paper and" |
| | ratio | 0.304 | |
| 67.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 56 | | matches | | 0 | "The entrance to the abandoned" | | 1 | "The same as the last" | | 2 | "she said to the constable" | | 3 | "The young man — Prewitt," | | 4 | "Quinn didn't look back." | | 5 | "The platform opened up below" | | 6 | "The Veil Market had left" | | 7 | "A trestle table collapsed under" | | 8 | "Something had frightened a market" | | 9 | "Sergeant Adeyemi met her at" | | 10 | "Adeyemi paused in the specific" | | 11 | "Quinn's jaw tightened." | | 12 | "She ducked under the tape" | | 13 | "She had learned in eighteen" | | 14 | "The dead man lay on" | | 15 | "His face held an expression" | | 16 | "He was a broad man" | | 17 | "She crouched beside Dr" | | 18 | "Osei said, without preamble" | | 19 | "Quinn looked up." |
| | ratio | 0.786 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 5 | | matches | | 0 | "The same as the last time, though the last time the stairwell had been dry and the walls had not been sweating with a condensation that smelled faintly of rosem…" | | 1 | "Something had frightened a market full of people who trafficked in things that frightened other people." | | 2 | "The dead man lay on his back near the old ticket booth, arms arranged at his sides with a neatness that made the hairs rise on her forearms." | | 3 | "Male, mid-forties, heavy coat too warm for June, boots that had cost money and been walked in hard." | | 4 | "His face held an expression she associated with people who had been told something astonishing a moment before they stopped being told anything at all." |
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| 98.68% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 1 | | matches | | 0 | "Sergeant Adeyemi met, his notebook already open" |
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| 75.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "She traced (trace)" | | 1 | "Quinn continued (continue)" | | 2 | "Carver demanded (demand)" |
| | dialogueSentences | 40 | | tagDensity | 0.225 | | leniency | 0.45 | | rawRatio | 0.333 | | effectiveRatio | 0.15 | |