| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1980 | | totalAiIsmAdverbs | 1 | | 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) | |
| 49.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1980 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "flickered" | | 1 | "warmth" | | 2 | "pulse" | | 3 | "weight" | | 4 | "grave" | | 5 | "throbbed" | | 6 | "silence" | | 7 | "measured" | | 8 | "pulsed" | | 9 | "pounding" | | 10 | "echo" | | 11 | "whisper" | | 12 | "stomach" |
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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 | 222 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 222 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 239 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1980 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1886 | | uniqueNames | 8 | | maxNameDensity | 1.11 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Heartstone | 3 | | Hel | 1 | | Cardiff | 1 | | London | 1 | | Aurora | 21 | | One | 3 |
| | persons | | | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Cardiff" | | 3 | "London" |
| | globalScore | 0.943 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 124 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 98.99% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.01 | | wordCount | 1980 | | matches | | 0 | "not bright enough to cast light, but rich enough to stain her fingers red" | | 1 | "not steady like light, but wet" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 239 | | matches | | 0 | "asking that question" | | 1 | "hated that smile" | | 2 | "knew that number" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 128 | | mean | 15.47 | | std | 17.36 | | cv | 1.122 | | sampleLengths | | 0 | 15 | | 1 | 84 | | 2 | 15 | | 3 | 6 | | 4 | 2 | | 5 | 15 | | 6 | 58 | | 7 | 6 | | 8 | 71 | | 9 | 4 | | 10 | 3 | | 11 | 5 | | 12 | 7 | | 13 | 44 | | 14 | 3 | | 15 | 51 | | 16 | 6 | | 17 | 14 | | 18 | 27 | | 19 | 6 | | 20 | 43 | | 21 | 7 | | 22 | 7 | | 23 | 5 | | 24 | 20 | | 25 | 6 | | 26 | 38 | | 27 | 3 | | 28 | 3 | | 29 | 52 | | 30 | 6 | | 31 | 21 | | 32 | 9 | | 33 | 57 | | 34 | 12 | | 35 | 3 | | 36 | 10 | | 37 | 24 | | 38 | 49 | | 39 | 9 | | 40 | 5 | | 41 | 27 | | 42 | 4 | | 43 | 20 | | 44 | 2 | | 45 | 1 | | 46 | 10 | | 47 | 5 | | 48 | 31 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 222 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 306 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 239 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 270 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.025925925925925925 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 239 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 239 | | mean | 8.28 | | std | 7.08 | | cv | 0.855 | | sampleLengths | | 0 | 15 | | 1 | 11 | | 2 | 23 | | 3 | 6 | | 4 | 22 | | 5 | 22 | | 6 | 15 | | 7 | 2 | | 8 | 4 | | 9 | 2 | | 10 | 4 | | 11 | 4 | | 12 | 7 | | 13 | 16 | | 14 | 10 | | 15 | 1 | | 16 | 14 | | 17 | 17 | | 18 | 6 | | 19 | 23 | | 20 | 8 | | 21 | 40 | | 22 | 4 | | 23 | 3 | | 24 | 5 | | 25 | 7 | | 26 | 5 | | 27 | 2 | | 28 | 5 | | 29 | 32 | | 30 | 3 | | 31 | 7 | | 32 | 15 | | 33 | 8 | | 34 | 21 | | 35 | 6 | | 36 | 4 | | 37 | 10 | | 38 | 9 | | 39 | 7 | | 40 | 2 | | 41 | 3 | | 42 | 6 | | 43 | 6 | | 44 | 5 | | 45 | 6 | | 46 | 12 | | 47 | 14 | | 48 | 6 | | 49 | 1 |
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| 47.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.33472803347280333 | | totalSentences | 239 | | uniqueOpeners | 80 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 191 | | matches | | 0 | "Just a soft, sickening slide," | | 1 | "Then she held the beam" | | 2 | "Only foxgloves, their pale throats" | | 3 | "More like someone drawing a" | | 4 | "Too high for a fox." | | 5 | "Too narrow for a person." | | 6 | "Then one of the stones" | | 7 | "Again, the same response." | | 8 | "Only stillness, taut as wire." | | 9 | "Then the grove spoke from" |
| | ratio | 0.052 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 191 | | matches | | 0 | "She slipped her phone from" | | 1 | "Her voice died fast." | | 2 | "It did not travel." | | 3 | "It folded into the grove" | | 4 | "She had sat up in" | | 5 | "She held still." | | 6 | "It brushed her ears with" | | 7 | "She kept the torch low" | | 8 | "She breathed through her nose" | | 9 | "Her shoulders locked." | | 10 | "Their coats looked too pale" | | 11 | "They did not bolt." | | 12 | "They stood at the tree" | | 13 | "She backed one pace from" | | 14 | "She did not know how" | | 15 | "She had stopped asking that" | | 16 | "She looked over her shoulder." | | 17 | "They had moved closer." | | 18 | "Their legs stood too straight." | | 19 | "Their ears angled forward." |
| | ratio | 0.257 | |
| 83.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 144 | | totalSentences | 191 | | matches | | 0 | "Aurora pushed through the last" | | 1 | "Night pressed down over Richmond" | | 2 | "The city had fallen away" | | 3 | "Here, even the air felt" | | 4 | "The oak stones rose from" | | 5 | "Wildflowers crowded their bases in" | | 6 | "She slipped her phone from" | | 7 | "Her voice died fast." | | 8 | "It did not travel." | | 9 | "It folded into the grove" | | 10 | "Aurora glanced down at the" | | 11 | "The crimson gem held a" | | 12 | "That warmth had brought her" | | 13 | "Tonight it had grown hot" | | 14 | "She had sat up in" | | 15 | "Aurora stepped through the ring" | | 16 | "The change hit at once." | | 17 | "She held still." | | 18 | "The torch beam washed over" | | 19 | "Trees ringed it on all" |
| | ratio | 0.754 | |
| 26.18% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 191 | | matches | | 0 | "If this place sat between" |
| | ratio | 0.005 | |
| 81.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 6 | | matches | | 0 | "Wildflowers crowded their bases in pale drifts, white and violet and bruised blue, all of them open as if it were noon." | | 1 | "Just a soft, sickening slide, as if the ground gave a fraction under her weight and settled around her feet in a shape that did not belong to any map she knew." | | 2 | "It led from the standing stones to the mound, then curved away into the trees beyond it, as if something had walked from the boundary to the centre and then cir…" | | 3 | "All of them half-made, as if they had waited just below the surface and now found enough dark to push through." | | 4 | "She kept turning, keeping them all in view and none in view, which was worse." | | 5 | "Its skin twitched over the skull as if something beneath had turned and turned, searching for a seam." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 13.64% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 3 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Aurora whispered (whisper)" | | 2 | "she muttered (mutter)" |
| | dialogueSentences | 22 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0.75 | | effectiveRatio | 0.273 | |