| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.94% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 4863 | | totalAiIsmAdverbs | 2 | | 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) | |
| 78.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 4863 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "gloom" | | 1 | "dancing" | | 2 | "scanned" | | 3 | "porcelain" | | 4 | "etched" | | 5 | "perfect" | | 6 | "eyebrow" | | 7 | "unreadable" | | 8 | "structure" | | 9 | "ensure" | | 10 | "output" | | 11 | "pulse" | | 12 | "echoed" | | 13 | "footsteps" | | 14 | "warmth" |
| |
| 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 | 950 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 13 | | hedgeCount | 2 | | narrationSentences | 950 | | filterMatches | | 0 | "watch" | | 1 | "know" | | 2 | "look" | | 3 | "think" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 950 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0.001 | | matches | | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 43 | | markdownWords | 80 | | totalWords | 4902 | | ratio | 0.016 | | matches | | 0 | "Analyze the Request:" | | 1 | "Role:" | | 2 | "Task:" | | 3 | "Genre:" | | 4 | "Setting:" | | 5 | "Characters:" | | 6 | "Word Count:" | | 7 | "Style:" | | 8 | "Constraint:" | | 9 | "Constraint:" | | 10 | "Current Draft Status:" | | 11 | "Plan the Scene:" | | 12 | "POV:" | | 13 | "Setting:" | | 14 | "Characters:" | | 15 | "Conflict:" | | 16 | "Key Elements:" | | 17 | "Instruction:" | | 18 | "Ending:" | | 19 | "Formatting:" | | 20 | "Drafting - Step-by-Step:" | | 21 | "Opening:" | | 22 | "Action:" | | 23 | "Dialogue:" | | 24 | "Investigation:" | | 25 | "Clue:" | | 26 | "Ending:" | | 27 | "Correction:" | | 28 | "Wait, word count check:" | | 29 | "Tone:" | | 30 | "Constraints:" | | 31 | "Let's write." | | 32 | "Wait, constraint check:" | | 33 | "at least" | | 34 | "Wait, looking at the previous output:" | | 35 | "Let's start fresh." | | 36 | "Scene Beat:" | | 37 | "Details:" | | 38 | "Let's go." | | 39 | "Wait, one more check on constraints:" | | 40 | "Wait, word count:" | | 41 | "Plan for 1000 words:" | | 42 | "Let's write." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 366 | | wordCount | 4826 | | uniqueNames | 41 | | maxNameDensity | 3.98 | | worstName | "Harlow" | | maxWindowNameDensity | 11 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 192 | | Quinn | 6 | | Sergeant | 1 | | Wade | 42 | | Veil | 17 | | Market | 16 | | Compass | 6 | | London | 1 | | Process | 1 | | Expert | 1 | | Continue | 1 | | Mystery | 1 | | Crime | 2 | | Detective | 5 | | Colleague | 1 | | Eva | 29 | | Kowalski | 3 | | Past | 1 | | Draft | 1 | | Tube | 4 | | Camden | 3 | | Do | 2 | | Finds | 1 | | Tense | 1 | | Write | 2 | | Since | 1 | | Salt-and-pepper | 1 | | Red | 2 | | Location | 1 | | Abandoned | 1 | | Item | 1 | | Okay | 1 | | Arrival | 1 | | Dialogue | 2 | | Inspection | 1 | | British | 1 | | Museum | 1 | | Cold | 3 | | White | 3 | | Cut | 3 | | Copper | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Wade" | | 4 | "Detective" | | 5 | "Colleague" | | 6 | "Eva" | | 7 | "Kowalski" | | 8 | "Camden" | | 9 | "Dialogue" | | 10 | "Cut" |
| | places | | 0 | "Veil" | | 1 | "Compass" | | 2 | "London" | | 3 | "Draft" | | 4 | "Abandoned" | | 5 | "British" | | 6 | "Copper" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 285 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 4902 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 950 | | matches | | 0 | "sells that kind" | | 1 | "reached that count" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 544 | | mean | 9.01 | | std | 14.55 | | cv | 1.615 | | sampleLengths | | 0 | 70 | | 1 | 35 | | 2 | 16 | | 3 | 45 | | 4 | 5 | | 5 | 33 | | 6 | 5 | | 7 | 43 | | 8 | 3 | | 9 | 16 | | 10 | 16 | | 11 | 31 | | 12 | 9 | | 13 | 8 | | 14 | 8 | | 15 | 46 | | 16 | 7 | | 17 | 8 | | 18 | 29 | | 19 | 12 | | 20 | 9 | | 21 | 7 | | 22 | 44 | | 23 | 3 | | 24 | 5 | | 25 | 26 | | 26 | 4 | | 27 | 37 | | 28 | 5 | | 29 | 10 | | 30 | 3 | | 31 | 10 | | 32 | 16 | | 33 | 3 | | 34 | 5 | | 35 | 30 | | 36 | 5 | | 37 | 12 | | 38 | 17 | | 39 | 7 | | 40 | 7 | | 41 | 15 | | 42 | 4 | | 43 | 15 | | 44 | 5 | | 45 | 16 | | 46 | 4 | | 47 | 16 | | 48 | 9 | | 49 | 1 |
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| 99.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 16 | | totalSentences | 950 | | matches | | 0 | "is supposed" | | 1 | "were curled" | | 2 | "was gone" | | 3 | "were forced" | | 4 | "was planted" | | 5 | "is generated" | | 6 | "is found" | | 7 | "got stuck" | | 8 | "is met" | | 9 | "was staged/faked" | | 10 | "was caught" | | 11 | "were curled" | | 12 | "were forced" | | 13 | "is tuned" | | 14 | "was rolled" | | 15 | "were rusted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 924 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 950 | | ratio | 0.003 | | matches | | 0 | "* **Style:** Past tense, active voice, show don't tell, no adverbs, no clichies, dialogue-driven, varied sentence structure, specific character details from codex (Harlow: salt-and-pepper hair, leather watch, sharp jaw; Eva: red curly hair, glasses, satchel)." | | 1 | "* **Characters:** Harlow Quinn (detective), Eva Kowalski (colleague/consultant - fits codex and offers different interpretation)." | | 2 | "**Drafting - Step-by-Step:**" |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 4802 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 80 | | adverbRatio | 0.01665972511453561 | | lyAdverbCount | 20 | | lyAdverbRatio | 0.004164931278633903 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 950 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 950 | | mean | 5.16 | | std | 3.1 | | cv | 0.602 | | sampleLengths | | 0 | 10 | | 1 | 16 | | 2 | 13 | | 3 | 14 | | 4 | 10 | | 5 | 1 | | 6 | 6 | | 7 | 12 | | 8 | 16 | | 9 | 7 | | 10 | 5 | | 11 | 11 | | 12 | 6 | | 13 | 7 | | 14 | 6 | | 15 | 12 | | 16 | 14 | | 17 | 5 | | 18 | 5 | | 19 | 12 | | 20 | 6 | | 21 | 10 | | 22 | 2 | | 23 | 3 | | 24 | 2 | | 25 | 3 | | 26 | 13 | | 27 | 8 | | 28 | 2 | | 29 | 2 | | 30 | 13 | | 31 | 3 | | 32 | 9 | | 33 | 7 | | 34 | 16 | | 35 | 7 | | 36 | 7 | | 37 | 8 | | 38 | 4 | | 39 | 5 | | 40 | 4 | | 41 | 5 | | 42 | 2 | | 43 | 6 | | 44 | 3 | | 45 | 5 | | 46 | 2 | | 47 | 11 | | 48 | 11 | | 49 | 8 |
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| 41.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 79 | | diversityRatio | 0.153028692879915 | | totalSentences | 941 | | uniqueOpeners | 144 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 820 | | matches | | 0 | "Just a bruising like a" | | 1 | "Just the bruising." | | 2 | "Maybe he was reaching for" | | 3 | "Just the cold steel." | | 4 | "Then how did he die?" | | 5 | "Maybe he just stood there." | | 6 | "Then what is it?" | | 7 | "Then the clue is in" | | 8 | "Just a bruising like a" | | 9 | "Maybe he reached for the" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 170 | | totalSentences | 820 | | matches | | 0 | "She checked the worn leather" | | 1 | "She didn't look at the" | | 2 | "Her eyes scanned the walls" | | 3 | "He pointed to the body." | | 4 | "Her knees popped." | | 5 | "She kept her back straight," | | 6 | "She examined the wrist of" | | 7 | "He didn't bleed." | | 8 | "She wiped her nose on" | | 9 | "She focused on the man's" | | 10 | "His palm faced the ground." | | 11 | "She walked to the head" | | 12 | "I checked the manifest." | | 13 | "She stepped over a fallen" | | 14 | "It skittered across the tile." | | 15 | "She didn't want to guess." | | 16 | "She wanted data." | | 17 | "She reached into the satchel" | | 18 | "He came looking for information." | | 19 | "She lifted another book." |
| | ratio | 0.207 | |
| 36.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 695 | | totalSentences | 820 | | matches | | 0 | "The air in the abandoned" | | 1 | "Flashlights cut cones through the" | | 2 | "Harlow stopped her patrol, eyes" | | 3 | "She checked the worn leather" | | 4 | "The time didn't match the" | | 5 | "Detective Sergeant Wade stood near" | | 6 | "Water dripped from the ceiling" | | 7 | "Wade tapped his pen against" | | 8 | "The body is a thief." | | 9 | "Someone found a spot to" | | 10 | "Harlow stepped around the yellow" | | 11 | "She didn't look at the" | | 12 | "Her eyes scanned the walls" | | 13 | "The tiles were green-tinted porcelain," | | 14 | "The grout was black with" | | 15 | "Thieves don't leave tools behind." | | 16 | "He pointed to the body." | | 17 | "A man in a tattered" | | 18 | "Her knees popped." | | 19 | "She kept her back straight," |
| | ratio | 0.848 | |
| 30.49% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 820 | | matches | | 0 | "If the knife struck the" | | 1 | "If it was a choke," | | 2 | "Because the Veil Market moves" | | 3 | "To hide who wrote it." | | 4 | "(But must be 1000+ words)." |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |