| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1304 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 38.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1304 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "pulsed" | | 1 | "rhythmic" | | 2 | "warmth" | | 3 | "glint" | | 4 | "tracing" | | 5 | "pulse" | | 6 | "quickened" | | 7 | "silence" | | 8 | "flickered" | | 9 | "footsteps" | | 10 | "throb" |
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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 | 113 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 113 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1304 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1303 | | uniqueNames | 12 | | maxNameDensity | 0.46 | | worstName | "Aurora" | | maxWindowNameDensity | 1 | | worstWindowName | "Richmond" | | discoveredNames | | Richmond | 2 | | Park | 2 | | Heartstone | 4 | | Eva | 2 | | Cardiff | 2 | | Aurora | 6 | | London | 2 | | English | 1 | | October | 2 | | Celtic | 1 | | Rory | 1 | | Three | 3 |
| | persons | | 0 | "Heartstone" | | 1 | "Eva" | | 2 | "Aurora" | | 3 | "Rory" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Cardiff" | | 3 | "London" | | 4 | "English" | | 5 | "Three" |
| | globalScore | 1 | | windowScore | 1 | |
| 78.57% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like faces in her torchlight" | | 1 | "something between two of the oak trunks" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.767 | | wordCount | 1304 | | matches | | 0 | "not random but deliberate, paths of colour" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 29.64 | | std | 27.14 | | cv | 0.916 | | sampleLengths | | 0 | 6 | | 1 | 76 | | 2 | 2 | | 3 | 71 | | 4 | 10 | | 5 | 5 | | 6 | 81 | | 7 | 5 | | 8 | 64 | | 9 | 9 | | 10 | 4 | | 11 | 52 | | 12 | 9 | | 13 | 5 | | 14 | 70 | | 15 | 11 | | 16 | 46 | | 17 | 37 | | 18 | 40 | | 19 | 3 | | 20 | 60 | | 21 | 4 | | 22 | 27 | | 23 | 51 | | 24 | 13 | | 25 | 4 | | 26 | 92 | | 27 | 45 | | 28 | 2 | | 29 | 46 | | 30 | 14 | | 31 | 1 | | 32 | 14 | | 33 | 33 | | 34 | 10 | | 35 | 60 | | 36 | 26 | | 37 | 7 | | 38 | 84 | | 39 | 37 | | 40 | 9 | | 41 | 46 | | 42 | 4 | | 43 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 113 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 189 | | matches | | 0 | "was burning" | | 1 | "was breathing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 114 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1304 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.016104294478527608 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0023006134969325155 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 11.44 | | std | 9.59 | | cv | 0.839 | | sampleLengths | | 0 | 6 | | 1 | 19 | | 2 | 15 | | 3 | 6 | | 4 | 36 | | 5 | 2 | | 6 | 7 | | 7 | 24 | | 8 | 40 | | 9 | 10 | | 10 | 5 | | 11 | 8 | | 12 | 24 | | 13 | 13 | | 14 | 9 | | 15 | 27 | | 16 | 1 | | 17 | 4 | | 18 | 17 | | 19 | 2 | | 20 | 21 | | 21 | 24 | | 22 | 4 | | 23 | 5 | | 24 | 4 | | 25 | 18 | | 26 | 24 | | 27 | 10 | | 28 | 2 | | 29 | 7 | | 30 | 5 | | 31 | 3 | | 32 | 8 | | 33 | 21 | | 34 | 38 | | 35 | 11 | | 36 | 5 | | 37 | 3 | | 38 | 27 | | 39 | 11 | | 40 | 4 | | 41 | 1 | | 42 | 7 | | 43 | 14 | | 44 | 11 | | 45 | 12 | | 46 | 28 | | 47 | 3 | | 48 | 10 | | 49 | 7 |
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| 58.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.37719298245614036 | | totalSentences | 114 | | uniqueOpeners | 43 | |
| 68.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 98 | | matches | | 0 | "Then the light caught something" | | 1 | "Too many legs." |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 98 | | matches | | 0 | "It had never done that" | | 1 | "She'd climbed them, which was" | | 2 | "She stepped off the path." | | 3 | "Her phone's torch cut a" | | 4 | "She caught herself tracing the" | | 5 | "She lowered the torch." | | 6 | "Her imagination needed a leash." | | 7 | "They carpeted the ground in" | | 8 | "She almost laughed at the" | | 9 | "She stepped between two of" | | 10 | "She breathed it in and" | | 11 | "She yanked the chain over" | | 12 | "It glowed, a deep arterial" | | 13 | "Her breath plumed in front" | | 14 | "She smacked it against her" | | 15 | "She moved toward the centre" | | 16 | "She didn't like that." | | 17 | "She didn't like any of" | | 18 | "She knelt beside it and" | | 19 | "It came from behind her," |
| | ratio | 0.296 | |
| 82.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 98 | | matches | | 0 | "The pendant burned against her" | | 1 | "Aurora stopped at the edge" | | 2 | "The deep crimson gemstone pulsed" | | 3 | "It had never done that" | | 4 | "The park gates stood locked" | | 5 | "She'd climbed them, which was" | | 6 | "The trees ahead swayed in" | | 7 | "She stepped off the path." | | 8 | "Richmond Park at night was" | | 9 | "The streetlights from the surrounding" | | 10 | "Her phone's torch cut a" | | 11 | "The pendant tugged at her," | | 12 | "The oaks grew older as" | | 13 | "She caught herself tracing the" | | 14 | "She lowered the torch." | | 15 | "Her imagination needed a leash." | | 16 | "The wildflowers came first." | | 17 | "They carpeted the ground in" | | 18 | "Bluebells and foxgloves and cornflowers," | | 19 | "The petals caught her torchlight" |
| | ratio | 0.755 | |
| 51.02% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 98 | | matches | | 0 | "Now the paths gaped empty," |
| | ratio | 0.01 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 6 | | matches | | 0 | "The oaks grew older as she walked, their bark thickening into ridges and furrows that looked like faces in her torchlight." | | 1 | "She caught herself tracing the contours of one trunk with her beam, seeing a mouth, a brow, a hollow where an eye should be." | | 2 | "Bluebells and foxgloves and cornflowers, blooms that belonged to spring and summer, all thriving as though the seasons had forgotten this patch of earth." | | 3 | "Bark covered them in patterns that looked less like natural grain and more like script, spiralling lines and angular marks that reminded her of the Celtic patte…" | | 4 | "She could hear it now, the creak and crack of wood flexing, the deliberate rhythm of steps that didn't match any human gait." | | 5 | "The bark had split to reveal something underneath, something pale and wet and eyeless, with a mouth that opened sideways and contained too many teeth, each one …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |