| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.526 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 74.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1544 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "slightly" | | 1 | "softly" | | 2 | "slowly" | | 3 | "lightly" | | 4 | "suddenly" |
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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) | |
| 31.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1544 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "echoing" | | 1 | "jaw clenched" | | 2 | "footfall" | | 3 | "flickered" | | 4 | "vibrated" | | 5 | "standard" | | 6 | "glinting" | | 7 | "lilt" | | 8 | "echoed" | | 9 | "flicked" | | 10 | "wavered" | | 11 | "weight" | | 12 | "shattered" | | 13 | "footsteps" | | 14 | "unreadable" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 127 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 127 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1544 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.11% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1391 | | uniqueNames | 10 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 20 | | Brewer | 1 | | Street | 1 | | Tail | 1 | | London | 2 | | Seville | 1 | | Footsteps | 1 | | Camden | 1 | | Lights | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tail" | | 3 | "Footsteps" | | 4 | "Lights" |
| | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "London" | | 3 | "Seville" |
| | globalScore | 0.781 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like distant music, garbled throug" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.648 | | wordCount | 1544 | | matches | | 0 | "not at her words but at something over her shoulder" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 136 | | matches | | 0 | "knew that lie" | | 1 | "sound that cut" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 45.41 | | std | 26.88 | | cv | 0.592 | | sampleLengths | | 0 | 36 | | 1 | 71 | | 2 | 56 | | 3 | 86 | | 4 | 58 | | 5 | 86 | | 6 | 74 | | 7 | 44 | | 8 | 25 | | 9 | 34 | | 10 | 47 | | 11 | 37 | | 12 | 20 | | 13 | 14 | | 14 | 39 | | 15 | 51 | | 16 | 12 | | 17 | 28 | | 18 | 94 | | 19 | 83 | | 20 | 83 | | 21 | 79 | | 22 | 72 | | 23 | 22 | | 24 | 13 | | 25 | 23 | | 26 | 21 | | 27 | 7 | | 28 | 13 | | 29 | 47 | | 30 | 55 | | 31 | 4 | | 32 | 28 | | 33 | 82 |
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| 99.74% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 127 | | matches | | 0 | "got swallowed" | | 1 | "been locked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 247 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1395 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.03369175627240143 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.007885304659498209 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 11.35 | | std | 6.65 | | cv | 0.586 | | sampleLengths | | 0 | 15 | | 1 | 21 | | 2 | 15 | | 3 | 11 | | 4 | 15 | | 5 | 2 | | 6 | 28 | | 7 | 15 | | 8 | 7 | | 9 | 10 | | 10 | 9 | | 11 | 15 | | 12 | 5 | | 13 | 21 | | 14 | 3 | | 15 | 3 | | 16 | 5 | | 17 | 25 | | 18 | 8 | | 19 | 8 | | 20 | 8 | | 21 | 14 | | 22 | 18 | | 23 | 8 | | 24 | 12 | | 25 | 6 | | 26 | 17 | | 27 | 19 | | 28 | 4 | | 29 | 6 | | 30 | 7 | | 31 | 1 | | 32 | 14 | | 33 | 13 | | 34 | 5 | | 35 | 9 | | 36 | 17 | | 37 | 30 | | 38 | 10 | | 39 | 8 | | 40 | 14 | | 41 | 12 | | 42 | 6 | | 43 | 12 | | 44 | 7 | | 45 | 18 | | 46 | 12 | | 47 | 22 | | 48 | 14 | | 49 | 16 |
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| 46.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3014705882352941 | | totalSentences | 136 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 121 | | matches | | 0 | "Her worn leather watch bounced" | | 1 | "Her sharp jaw clenched as" | | 2 | "She checked the lit stairwell" | | 3 | "She withdrew her torch and" | | 4 | "She squeezed past, cloth catching" | | 5 | "She found the bone token" | | 6 | "It lay pale against the" | | 7 | "She slipped it into her" | | 8 | "She pulled out her phone," | | 9 | "She descended anyway, boots clanging" | | 10 | "She dropped the last few" | | 11 | "His eyes tracked her without" | | 12 | "He wiped his hands on" | | 13 | "His voice carried the lilt" | | 14 | "He touched the medallion lightly," | | 15 | "She didn't give it over." | | 16 | "His posture remained relaxed but" | | 17 | "She extended the bone between" | | 18 | "He took it without touching" | | 19 | "Her boots moved over uneven" |
| | ratio | 0.298 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 112 | | totalSentences | 121 | | matches | | 0 | "Detective Harlow Quinn burst around" | | 1 | "Her worn leather watch bounced" | | 2 | "The command tore from her" | | 3 | "Tail lights reflected in puddles," | | 4 | "The suspect vaulted over a" | | 5 | "Her sharp jaw clenched as" | | 6 | "The suspect hit the alley's" | | 7 | "Quinn slid to a stop," | | 8 | "Rain drummed on the metal" | | 9 | "She checked the lit stairwell" | | 10 | "A metal sign hanging crooked" | | 11 | "The door still swung slightly." | | 12 | "She withdrew her torch and" | | 13 | "The corridor narrowed." | | 14 | "Pipes groaned overhead." | | 15 | "Quinn reached the bottom landing." | | 16 | "She squeezed past, cloth catching" | | 17 | "The space opened into a" | | 18 | "She found the bone token" | | 19 | "It lay pale against the" |
| | ratio | 0.926 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 4 | | matches | | 0 | "Ahead stood a rusted gate that should have been locked, but someone had bent the bars just enough for a slim body to slip through." | | 1 | "Lanterns flickered from hooks in the ceiling, casting moving shadows over stalls piled with glass vials, bone carvings, stacks of old books bound in something t…" | | 2 | "Stalls crowded closer here, displaying jars filled with ash, silver bells that didn't ring when brushed, folded papers pressed between glass like pressed flower…" | | 3 | "The suspect ahead squeezed through a tight gap that led out into a broader service area with old train tracks running along one side." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 97.37% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "skinned man said (skin say)" |
| | dialogueSentences | 19 | | tagDensity | 0.263 | | leniency | 0.526 | | rawRatio | 0.2 | | effectiveRatio | 0.105 | |