| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1255 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "gently" | | 1 | "very" | | 2 | "softly" |
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| 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) | |
| 80.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1255 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "silence" | | 1 | "pulse" | | 2 | "echo" | | 3 | "weight" |
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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 | 133 | | matches | (empty) | |
| 99.89% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 133 | | filterMatches | | | hedgeMatches | | 0 | "happened to" | | 1 | "happens to" | | 2 | "started to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 16 | | totalWords | 1255 | | ratio | 0.013 | | matches | | 0 | "Moonrise. Isolde's grove. Come alone or don't come at all." | | 1 | "there, that, I saw that" | | 2 | "Rory." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1193 | | uniqueNames | 12 | | maxNameDensity | 0.5 | | worstName | "One" | | maxWindowNameDensity | 3 | | worstWindowName | "One" | | discoveredNames | | Park | 1 | | Heathrow | 1 | | Sheen | 1 | | Gate | 1 | | Hel | 1 | | Silas | 3 | | Cardiff | 1 | | Badly | 1 | | Eva | 1 | | Nine | 3 | | You | 3 | | One | 6 |
| | persons | | 0 | "Silas" | | 1 | "Badly" | | 2 | "Eva" | | 3 | "Nine" | | 4 | "You" |
| | places | | 0 | "Park" | | 1 | "Heathrow" | | 2 | "Hel" | | 3 | "Cardiff" | | 4 | "One" |
| | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like something" |
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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 | 1255 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 139 | | matches | | 0 | "had that shape" | | 1 | "was that I" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 29.19 | | std | 21.58 | | cv | 0.739 | | sampleLengths | | 0 | 6 | | 1 | 65 | | 2 | 7 | | 3 | 76 | | 4 | 31 | | 5 | 46 | | 6 | 52 | | 7 | 7 | | 8 | 61 | | 9 | 10 | | 10 | 56 | | 11 | 1 | | 12 | 26 | | 13 | 50 | | 14 | 1 | | 15 | 52 | | 16 | 12 | | 17 | 67 | | 18 | 4 | | 19 | 46 | | 20 | 20 | | 21 | 45 | | 22 | 7 | | 23 | 38 | | 24 | 1 | | 25 | 28 | | 26 | 19 | | 27 | 16 | | 28 | 28 | | 29 | 32 | | 30 | 5 | | 31 | 56 | | 32 | 45 | | 33 | 26 | | 34 | 24 | | 35 | 32 | | 36 | 7 | | 37 | 25 | | 38 | 12 | | 39 | 3 | | 40 | 70 | | 41 | 22 | | 42 | 18 |
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| 97.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 133 | | matches | | 0 | "been seven" | | 1 | "frightened" | | 2 | "rattled" |
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| 66.67% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 200 | | matches | | 0 | "were still blooming" | | 1 | "weren't watching" | | 2 | "was eating" | | 3 | "were still facing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 139 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 268 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.03731343283582089 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.011194029850746268 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 9.03 | | std | 7.94 | | cv | 0.879 | | sampleLengths | | 0 | 5 | | 1 | 1 | | 2 | 28 | | 3 | 19 | | 4 | 18 | | 5 | 5 | | 6 | 2 | | 7 | 5 | | 8 | 12 | | 9 | 11 | | 10 | 15 | | 11 | 33 | | 12 | 4 | | 13 | 3 | | 14 | 12 | | 15 | 12 | | 16 | 7 | | 17 | 2 | | 18 | 24 | | 19 | 13 | | 20 | 6 | | 21 | 19 | | 22 | 1 | | 23 | 2 | | 24 | 12 | | 25 | 12 | | 26 | 1 | | 27 | 3 | | 28 | 3 | | 29 | 5 | | 30 | 22 | | 31 | 21 | | 32 | 5 | | 33 | 8 | | 34 | 3 | | 35 | 6 | | 36 | 1 | | 37 | 7 | | 38 | 5 | | 39 | 2 | | 40 | 18 | | 41 | 24 | | 42 | 1 | | 43 | 8 | | 44 | 18 | | 45 | 4 | | 46 | 1 | | 47 | 10 | | 48 | 12 | | 49 | 23 |
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| 77.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.5144927536231884 | | totalSentences | 138 | | uniqueOpeners | 71 | |
| 96.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 104 | | matches | | 0 | "Flat, close, as if I'd" | | 1 | "Just warm, the way a" | | 2 | "Then she folded, all four" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 104 | | matches | | 0 | "I counted the stones again." | | 1 | "I said to nobody" | | 2 | "My voice came out wrong." | | 3 | "I'd heard all of it" | | 4 | "I'd come because of the" | | 5 | "They always bloomed here, every" | | 6 | "I turned a slow circle," | | 7 | "They simply happened to be" | | 8 | "I checked my phone." | | 9 | "It had been 1:14 when" | | 10 | "I've been in bad rooms" | | 11 | "I know the shape of" | | 12 | "You learn to read a" | | 13 | "You learn to keep your" | | 14 | "I walked to where I'd" | | 15 | "My own boot prints stopped" | | 16 | "I didn't spin." | | 17 | "You don't spin, because spinning" | | 18 | "I turned, calm as a" | | 19 | "Hers took the light and" |
| | ratio | 0.279 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 104 | | matches | | 0 | "I counted the stones again." | | 1 | "There had been seven when" | | 2 | "I said to nobody" | | 3 | "My voice came out wrong." | | 4 | "Richmond Park in the small" | | 5 | "I'd heard all of it" | | 6 | "Silence has an edge to" | | 7 | "This was more like a" | | 8 | "The pendant sat warm against" | | 9 | "I'd come because of the" | | 10 | "Silas said a lot of" | | 11 | "Here I was." | | 12 | "The wildflowers bothered me first." | | 13 | "They always bloomed here, every" | | 14 | "Foxgloves and campion and cow" | | 15 | "Tonight they were still blooming." | | 16 | "I turned a slow circle," | | 17 | "The flowers turned with me." | | 18 | "They simply happened to be" | | 19 | "The name went out and" |
| | ratio | 0.615 | |
| 96.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 104 | | matches | | 0 | "Now nine stood in the" | | 1 | "Now the voice came from" |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 2 | | matches | | 0 | "Nine stones, all whole, all smooth-topped, standing in grass that had no path pressed into it." | | 1 | "It came from behind the oaks on the north side, then from the west, then from directly above me, which was when I understood the bell wasn't moving." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |