| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1277 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "perfectly" | | 1 | "deliberately" | | 2 | "sweetly" |
| |
| 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) | |
| 84.34% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1277 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "footfall" | | 2 | "pulse" | | 3 | "lilt" |
| |
| 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 | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 88 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 77 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1283 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1211 | | uniqueNames | 18 | | maxNameDensity | 0.58 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Robin | 1 | | Hood | 1 | | Gate | 1 | | Rory | 7 | | Kingston | 1 | | Yu-Fei | 1 | | Golden | 1 | | Empress | 1 | | Glove | 1 | | Hill | 1 | | September | 2 | | London | 2 | | Saturday | 1 | | Silas | 1 | | Sobranie | 1 | | Eva | 6 |
| | persons | | 0 | "Hood" | | 1 | "Gate" | | 2 | "Rory" | | 3 | "Silas" | | 4 | "Eva" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Robin" | | 3 | "Kingston" | | 4 | "Yu-Fei" | | 5 | "Golden" | | 6 | "Glove" | | 7 | "Hill" | | 8 | "September" | | 9 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | 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 | 1283 | | matches | (empty) | |
| 97.94% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 97 | | matches | | 0 | "complained that nobody" | | 1 | "watched that spot" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 37.74 | | std | 26.36 | | cv | 0.699 | | sampleLengths | | 0 | 59 | | 1 | 93 | | 2 | 23 | | 3 | 77 | | 4 | 56 | | 5 | 27 | | 6 | 54 | | 7 | 22 | | 8 | 36 | | 9 | 9 | | 10 | 5 | | 11 | 72 | | 12 | 20 | | 13 | 79 | | 14 | 39 | | 15 | 67 | | 16 | 58 | | 17 | 50 | | 18 | 10 | | 19 | 18 | | 20 | 43 | | 21 | 53 | | 22 | 18 | | 23 | 6 | | 24 | 70 | | 25 | 7 | | 26 | 29 | | 27 | 86 | | 28 | 40 | | 29 | 16 | | 30 | 10 | | 31 | 7 | | 32 | 21 | | 33 | 3 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 88 | | matches | | |
| 45.86% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 173 | | matches | | 0 | "was still freezing" | | 1 | "were reading" | | 2 | "was meeting" | | 3 | "were shaking" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 97 | | ratio | 0.052 | | matches | | 0 | "Properly cold — the cold of a tap run over a wrist, cold that seemed to travel into the bone behind her sternum." | | 1 | "Not faded — vanished, the way a door shuts." | | 2 | "Her boots made no noise in the clipped grass — no crunch, no scuff — so she started stamping, deliberately, childishly, and the sounds came out half a second behind her feet." | | 3 | "Just a crescent, a shallow gouge of a shape, freshly made — the wood pale and weeping at the edges." | | 4 | "She turned her head fast, and there was nothing, only the trees and the white flowers and the violet sky, and she held still and watched that spot for a long count of ten, and on nine, a shadow moved through the wood at the same height as a person’s head, and it moved without the sound of a footfall, and it moved wrong — in a smooth unbroken line, the way a camera slides on a track." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1207 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.03645401822700911 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009113504556752278 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 13.23 | | std | 13.34 | | cv | 1.009 | | sampleLengths | | 0 | 36 | | 1 | 23 | | 2 | 7 | | 3 | 26 | | 4 | 24 | | 5 | 6 | | 6 | 30 | | 7 | 8 | | 8 | 7 | | 9 | 8 | | 10 | 17 | | 11 | 27 | | 12 | 19 | | 13 | 3 | | 14 | 1 | | 15 | 10 | | 16 | 7 | | 17 | 3 | | 18 | 40 | | 19 | 6 | | 20 | 4 | | 21 | 23 | | 22 | 20 | | 23 | 34 | | 24 | 16 | | 25 | 6 | | 26 | 23 | | 27 | 2 | | 28 | 7 | | 29 | 4 | | 30 | 3 | | 31 | 6 | | 32 | 5 | | 33 | 3 | | 34 | 9 | | 35 | 8 | | 36 | 8 | | 37 | 44 | | 38 | 20 | | 39 | 24 | | 40 | 9 | | 41 | 3 | | 42 | 43 | | 43 | 6 | | 44 | 33 | | 45 | 1 | | 46 | 2 | | 47 | 23 | | 48 | 7 | | 49 | 34 |
| |
| 53.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3711340206185567 | | totalSentences | 97 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 75 | | matches | | 0 | "Just the dark pressed flat" | | 1 | "Then it went cold." | | 2 | "Properly cold — the cold" | | 3 | "Even the wind noise in" | | 4 | "Then she stood and looked" | | 5 | "Just a crescent, a shallow" | | 6 | "Then, nearer, sweetly, in a" |
| | ratio | 0.093 | |
| 70.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 75 | | matches | | 0 | "She dropped into wet grass," | | 1 | "It had been warm since" | | 2 | "she said, to nobody in" | | 3 | "Her voice fell flat, swallowed" | | 4 | "She walked west by the" | | 5 | "She checked twice." | | 6 | "They came out of the" | | 7 | "She pressed her thumb against" | | 8 | "It was still freezing." | | 9 | "She stepped between the trees." | | 10 | "She got birdsong instead, a" | | 11 | "She said it steady enough," | | 12 | "She turned to look back" | | 13 | "They were there." | | 14 | "She could see the gap" | | 15 | "she said, and then louder," | | 16 | "Her phone went to voicemail" | | 17 | "She did not remember the" | | 18 | "She walked on." | | 19 | "Her boots made no noise" |
| | ratio | 0.373 | |
| 66.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 75 | | matches | | 0 | "The gate at Richmond Park’s" | | 1 | "She dropped into wet grass," | | 2 | "The pendant hung warm against" | | 3 | "It had been warm since" | | 4 | "That was the reason she" | | 5 | "That, and the man who" | | 6 | "she said, to nobody in" | | 7 | "Her voice fell flat, swallowed" | | 8 | "She walked west by the" | | 9 | "The path bent through shaved" | | 10 | "The Glove Hill car park" | | 11 | "She checked twice." | | 12 | "The wildflowers started before she" | | 13 | "Bluebells, in September." | | 14 | "They came out of the" | | 15 | "The pendant cooled against her" | | 16 | "Rory stopped at the boundary" | | 17 | "The grass on the other" | | 18 | "She pressed her thumb against" | | 19 | "It was still freezing." |
| | ratio | 0.787 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "Warm, then hot, then burning, and she yelped and let go of it and it swung out from her chest on its chain, glowing crimson, throwing red onto the roots and the…" | | 1 | "Then, nearer, sweetly, in a voice that had Eva’s lilt but none of Eva’s hurry, none of her smoke and swearing:" |
| |
| 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 | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.389 | | leniency | 0.778 | | rawRatio | 0 | | effectiveRatio | 0 | |