| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1290 | | 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) | |
| 84.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1290 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "etched" | | 1 | "weight" | | 2 | "footsteps" | | 3 | "silence" |
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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 | 81 | | matches | (empty) | |
| 72.31% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1290 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1095 | | uniqueNames | 16 | | maxNameDensity | 0.55 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Street" | | discoveredNames | | Quinn | 6 | | High | 1 | | Street | 3 | | Raven | 1 | | Nest | 2 | | Soho | 1 | | Silas | 1 | | Bayham | 1 | | Buck | 1 | | Kentish | 3 | | Town | 3 | | Road | 1 | | South | 2 | | Morris | 1 | | Market | 1 | | Jackdaw | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Buck" | | 4 | "Morris" | | 5 | "Market" | | 6 | "Jackdaw" |
| | places | | 0 | "High" | | 1 | "Soho" | | 2 | "Bayham" | | 3 | "Kentish" | | 4 | "Town" | | 5 | "Road" | | 6 | "South" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like the only solid thing left" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1290 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 28.67 | | std | 25.21 | | cv | 0.879 | | sampleLengths | | 0 | 26 | | 1 | 22 | | 2 | 31 | | 3 | 16 | | 4 | 7 | | 5 | 11 | | 6 | 96 | | 7 | 48 | | 8 | 50 | | 9 | 8 | | 10 | 30 | | 11 | 42 | | 12 | 15 | | 13 | 4 | | 14 | 32 | | 15 | 2 | | 16 | 5 | | 17 | 31 | | 18 | 23 | | 19 | 7 | | 20 | 1 | | 21 | 42 | | 22 | 18 | | 23 | 3 | | 24 | 68 | | 25 | 66 | | 26 | 2 | | 27 | 5 | | 28 | 17 | | 29 | 17 | | 30 | 37 | | 31 | 13 | | 32 | 44 | | 33 | 25 | | 34 | 81 | | 35 | 20 | | 36 | 42 | | 37 | 38 | | 38 | 116 | | 39 | 4 | | 40 | 40 | | 41 | 19 | | 42 | 42 | | 43 | 15 | | 44 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 176 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 96 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 862 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.02088167053364269 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0034802784222737818 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 13.44 | | std | 9.77 | | cv | 0.727 | | sampleLengths | | 0 | 26 | | 1 | 5 | | 2 | 4 | | 3 | 13 | | 4 | 27 | | 5 | 4 | | 6 | 16 | | 7 | 7 | | 8 | 2 | | 9 | 9 | | 10 | 19 | | 11 | 28 | | 12 | 27 | | 13 | 22 | | 14 | 8 | | 15 | 19 | | 16 | 13 | | 17 | 3 | | 18 | 5 | | 19 | 25 | | 20 | 25 | | 21 | 8 | | 22 | 3 | | 23 | 13 | | 24 | 14 | | 25 | 9 | | 26 | 3 | | 27 | 30 | | 28 | 15 | | 29 | 4 | | 30 | 2 | | 31 | 10 | | 32 | 20 | | 33 | 2 | | 34 | 5 | | 35 | 18 | | 36 | 13 | | 37 | 23 | | 38 | 7 | | 39 | 1 | | 40 | 11 | | 41 | 12 | | 42 | 19 | | 43 | 18 | | 44 | 3 | | 45 | 4 | | 46 | 2 | | 47 | 35 | | 48 | 14 | | 49 | 13 |
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| 83.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5520833333333334 | | totalSentences | 96 | | uniqueOpeners | 53 | |
| 86.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 77 | | matches | | 0 | "Then she thumbed the radio" | | 1 | "Somewhere far down the platform," |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 77 | | matches | | 0 | "She hauled herself up the" | | 1 | "She ran through a puddle" | | 2 | "She'd watched him load crates" | | 3 | "Her breath burned." | | 4 | "His didn't show at all." | | 5 | "He cut through the horse" | | 6 | "She counted her steps in" | | 7 | "He'd run a mile and" | | 8 | "He reached into his coat" | | 9 | "He brought out a disc" | | 10 | "Her torch beam caught it" | | 11 | "He tossed the disc" | | 12 | "It arced through the rain" | | 13 | "He stepped backwards through the" | | 14 | "She looked at the doorway" | | 15 | "She crouched and picked up" | | 16 | "It should have been cold" | | 17 | "It sat in her palm" | | 18 | "Her footsteps click-clacked on the" | | 19 | "She counted extra joints, too" |
| | ratio | 0.273 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 77 | | matches | | 0 | "Jackdaw cleared the security shutters" | | 1 | "People don't move like that." | | 2 | "People jump, land, stagger." | | 3 | "This boy flowed over steel" | | 4 | "She hauled herself up the" | | 5 | "She ran through a puddle" | | 6 | "She'd watched him load crates" | | 7 | "Jackdaw vaulted a bike rack" | | 8 | "Quinn took it two-handed, felt" | | 9 | "Rain plastered her cropped hair" | | 10 | "Her breath burned." | | 11 | "His didn't show at all." | | 12 | "He cut through the horse" | | 13 | "She counted her steps in" | | 14 | "Jackdaw swung right onto Kentish" | | 15 | "Bricks painted over, ground floor" | | 16 | "Quinn slowed to a walk" | | 17 | "He'd run a mile and" | | 18 | "He reached into his coat" | | 19 | "He brought out a disc" |
| | ratio | 0.688 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 57.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 5 | | matches | | 0 | "She hauled herself up the shutter, palms screeching on wet metal, and dropped into the alley in time to catch his trainers flashing between two skip bins." | | 1 | "Bricks painted over, ground floor given to a shop that sold phone cases, a tube map from an era of steam still etched into the tiles above the bricked-up entran…" | | 2 | "Three years of a closed coffin with no body in it, of a warrant card she still kept in her glovebox, of case notes about DS Morris that ended mid-sentence becau…" | | 3 | "The gramophone music drifted up the stairs, scratchy and patient, a violin playing something that sounded older than the violin." | | 4 | "Stalls crowded the platform edge, hunched under tarpaulins, selling bottled light, jars of teeth sorted by species, rings of keys labelled in a script that slit…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.05 | | leniency | 0.1 | | rawRatio | 0 | | effectiveRatio | 0 | |