| 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 | 1096 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 22.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1096 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "pulse" | | 2 | "dancing" | | 3 | "tracing" | | 4 | "measured" | | 5 | "chilled" | | 6 | "gloom" | | 7 | "chaotic" | | 8 | "warmth" | | 9 | "efficient" | | 10 | "flickered" | | 11 | "silence" |
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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 | 57 | | matches | (empty) | |
| 92.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 57 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1095 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 801 | | uniqueNames | 9 | | maxNameDensity | 0.75 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Julian" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 2 | | Silas | 6 | | London | 1 | | Cardiff | 1 | | Julian | 6 | | Mediterranean | 1 | | Aurora | 3 |
| | persons | | 0 | "Silas" | | 1 | "Julian" | | 2 | "Mediterranean" | | 3 | "Aurora" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 59.09% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a man who had never known a d" | | 1 | "as if considering reaching out" |
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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 | 1095 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 17.95 | | std | 14.6 | | cv | 0.813 | | sampleLengths | | 0 | 65 | | 1 | 53 | | 2 | 37 | | 3 | 37 | | 4 | 13 | | 5 | 50 | | 6 | 1 | | 7 | 18 | | 8 | 34 | | 9 | 1 | | 10 | 32 | | 11 | 7 | | 12 | 16 | | 13 | 8 | | 14 | 6 | | 15 | 26 | | 16 | 7 | | 17 | 42 | | 18 | 8 | | 19 | 27 | | 20 | 18 | | 21 | 11 | | 22 | 3 | | 23 | 8 | | 24 | 14 | | 25 | 21 | | 26 | 6 | | 27 | 9 | | 28 | 22 | | 29 | 6 | | 30 | 50 | | 31 | 13 | | 32 | 10 | | 33 | 19 | | 34 | 17 | | 35 | 7 | | 36 | 2 | | 37 | 3 | | 38 | 15 | | 39 | 31 | | 40 | 12 | | 41 | 8 | | 42 | 24 | | 43 | 13 | | 44 | 8 | | 45 | 11 | | 46 | 4 | | 47 | 5 | | 48 | 31 | | 49 | 18 |
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| 99.11% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 57 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 115 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 90 | | ratio | 0.011 | | matches | | 0 | "The ice clinked—a lonely, sharp sound in the quiet bar." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 807 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, crescent-shaped scar" |
| | adverbCount | 9 | | adverbRatio | 0.011152416356877323 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0037174721189591076 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 12.17 | | std | 6.48 | | cv | 0.532 | | sampleLengths | | 0 | 23 | | 1 | 18 | | 2 | 24 | | 3 | 21 | | 4 | 12 | | 5 | 20 | | 6 | 16 | | 7 | 21 | | 8 | 19 | | 9 | 8 | | 10 | 10 | | 11 | 2 | | 12 | 11 | | 13 | 3 | | 14 | 13 | | 15 | 16 | | 16 | 18 | | 17 | 1 | | 18 | 18 | | 19 | 8 | | 20 | 17 | | 21 | 9 | | 22 | 1 | | 23 | 17 | | 24 | 15 | | 25 | 7 | | 26 | 16 | | 27 | 8 | | 28 | 6 | | 29 | 15 | | 30 | 11 | | 31 | 7 | | 32 | 26 | | 33 | 16 | | 34 | 8 | | 35 | 6 | | 36 | 21 | | 37 | 8 | | 38 | 10 | | 39 | 11 | | 40 | 3 | | 41 | 8 | | 42 | 14 | | 43 | 21 | | 44 | 6 | | 45 | 9 | | 46 | 22 | | 47 | 6 | | 48 | 23 | | 49 | 11 |
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| 37.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.23333333333333334 | | totalSentences | 90 | | uniqueOpeners | 21 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 12.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 56 | | matches | | 0 | "She watched the way the" | | 1 | "He didn't look up, but" | | 2 | "He paused, letting his eyes" | | 3 | "He wore a charcoal overcoat" | | 4 | "His hair, once a chaotic" | | 5 | "He looked like a man" | | 6 | "She turned her head, meeting" | | 7 | "He pulled out the stool" | | 8 | "He didn't sit so much" | | 9 | "She took a slow sip" | | 10 | "He signaled to Silas, a" | | 11 | "He poured a measure of" | | 12 | "He didn't wait for an" | | 13 | "She turned her body toward" | | 14 | "He set the glass down" | | 15 | "He laid a note on" | | 16 | "She felt the heat rise" | | 17 | "She looked away, her gaze" | | 18 | "She gripped the edge of" | | 19 | "He stood up, the movement" |
| | ratio | 0.518 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 56 | | matches | | 0 | "The green neon of the" | | 1 | "Shadows clung to the corners" | | 2 | "Aurora sat at the far" | | 3 | "The condensation from her glass" | | 4 | "She watched the way the" | | 5 | "Silas moved with a measured," | | 6 | "He didn't look up, but" | | 7 | "The heavy door creaked open," | | 8 | "A man stepped inside, shaking" | | 9 | "He paused, letting his eyes" | | 10 | "The glass slipped a fraction" | | 11 | "The man straightened." | | 12 | "He wore a charcoal overcoat" | | 13 | "His hair, once a chaotic" | | 14 | "He looked like a man" | | 15 | "The voice carried the ghost" | | 16 | "Aurora forced her hand away" | | 17 | "She turned her head, meeting" | | 18 | "He pulled out the stool" | | 19 | "He didn't sit so much" |
| | ratio | 0.964 | |
| 89.29% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 56 | | matches | | 0 | "Now, they held the guarded" |
| | ratio | 0.018 | |
| 6.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 8 | | matches | | 0 | "Shadows clung to the corners of the room, dancing between the framed black-and-white photographs and the yellowed edges of maps that lined the walls." | | 1 | "He didn't look up, but his presence anchored the room, a quiet authority that kept the dim space from feeling hollow." | | 2 | "He wore a charcoal overcoat that spoke of bespoke tailoring and silent boardrooms." | | 3 | "He looked like a man who had never known a day of hunger or a night of uncertainty." | | 4 | "Silas glided toward them, his hazel eyes flicking between the two of them with the clinical precision of a man who had spent decades reading people." | | 5 | "The silence that followed stretched, thick and suffocating, filled only by the distant thrum of a bassline from a club down the street." | | 6 | "He lingered for a moment, his hand hovering near the bar as if considering reaching out." | | 7 | "His stride was long and confident, the walk of a man who owned the pavement beneath his feet." |
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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 | |