| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 767 | | 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) | |
| 67.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 767 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "gloom" | | 1 | "etched" | | 2 | "electric" | | 3 | "searing" | | 4 | "navigate" |
| |
| 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 | 1 | | narrationSentences | 52 | | matches | | |
| 60.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 52 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 58 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 109 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 780 | | ratio | 0 | | matches | (empty) | |
| 97.22% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 1 | | matches | | 0 | "Dropping to one knee, she breathed through the electric pain searing up her spine." |
| |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 704 | | uniqueNames | 12 | | maxNameDensity | 0.71 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Morris" | | discoveredNames | | Harlow | 5 | | Quinn | 1 | | Tube | 1 | | Underground | 2 | | Boy | 1 | | Scout | 1 | | Morris | 5 | | Rising | 1 | | Bending | 1 | | Veil | 2 | | Compass | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 71.79% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.282 | | wordCount | 780 | | matches | | 0 | "not like a heat mirage but its opposite" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 58 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 43.33 | | std | 27.74 | | cv | 0.64 | | sampleLengths | | 0 | 64 | | 1 | 49 | | 2 | 44 | | 3 | 23 | | 4 | 51 | | 5 | 12 | | 6 | 33 | | 7 | 5 | | 8 | 33 | | 9 | 50 | | 10 | 30 | | 11 | 34 | | 12 | 35 | | 13 | 56 | | 14 | 47 | | 15 | 22 | | 16 | 139 | | 17 | 53 |
| |
| 85.02% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 52 | | matches | | 0 | "been assigned" | | 1 | "was etched" | | 2 | "is teamed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 114 | | matches | (empty) | |
| 44.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 58 | | ratio | 0.034 | | matches | | 0 | "Up ahead, a shimmer—not like a heat mirage but its opposite." | | 1 | "* - The setting is an abandoned Underground station that used to house an underground supernatural market * - The detective is following up on a tip from a CI about the market's involvement * - She is teamed with an uneasy partner who doesn't believe in the supernatural * - The detective uses a Veil Compass to navigate the supernatural energies and find a portal/rift leading to the market * - When she crosses through, she's met by someone (possibly the antagonist) who has been expecting her The goal would be to end the scene on this cliffhanger note, leaving the reader eager to know what happens next between the detective and whoever is running the Veil Market." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 178 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.016853932584269662 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 58 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 58 | | mean | 13.45 | | std | 15.59 | | cv | 1.159 | | sampleLengths | | 0 | 27 | | 1 | 9 | | 2 | 10 | | 3 | 13 | | 4 | 5 | | 5 | 14 | | 6 | 17 | | 7 | 5 | | 8 | 13 | | 9 | 18 | | 10 | 4 | | 11 | 18 | | 12 | 4 | | 13 | 11 | | 14 | 12 | | 15 | 25 | | 16 | 17 | | 17 | 9 | | 18 | 12 | | 19 | 16 | | 20 | 13 | | 21 | 4 | | 22 | 5 | | 23 | 33 | | 24 | 11 | | 25 | 19 | | 26 | 20 | | 27 | 12 | | 28 | 4 | | 29 | 3 | | 30 | 5 | | 31 | 6 | | 32 | 10 | | 33 | 2 | | 34 | 7 | | 35 | 15 | | 36 | 21 | | 37 | 13 | | 38 | 1 | | 39 | 11 | | 40 | 17 | | 41 | 9 | | 42 | 3 | | 43 | 2 | | 44 | 14 | | 45 | 13 | | 46 | 14 | | 47 | 17 | | 48 | 2 | | 49 | 1 |
| |
| 89.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5862068965517241 | | totalSentences | 58 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 46 | | matches | | 0 | "Her sharp gaze swept over" | | 1 | "It was the phone call." | | 2 | "Her partner, or rather, the" | | 3 | "he panted, bending to rest" | | 4 | "She didn't acknowledge him, instead" | | 5 | "she corrected, her voice finding" | | 6 | "She checked her watch" | | 7 | "she murmured, but not for" | | 8 | "She resisted the urge to" | | 9 | "He wouldn't come." | | 10 | "She stepped through the rift," | | 11 | "It hinges on a detective" | | 12 | "I don't know the full" |
| | ratio | 0.283 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 46 | | matches | | 0 | "Detective Harlow Quinn stood at" | | 1 | "Her sharp gaze swept over" | | 2 | "The stench of decay and" | | 3 | "The flash of forensic cameras" | | 4 | "It was the phone call." | | 5 | "The one from her CI," | | 6 | "Her partner, or rather, the" | | 7 | "The brass thought if they" | | 8 | "he panted, bending to rest" | | 9 | "She didn't acknowledge him, instead" | | 10 | "she corrected, her voice finding" | | 11 | "Morris asked, uncertainty twitching in" | | 12 | "She checked her watch" | | 13 | "Harlow said, easing down the" | | 14 | "The face was etched with" | | 15 | "Morris called down, his voice" | | 16 | "Something oily and ingrained." | | 17 | "The man was scared shitless." | | 18 | "she murmured, but not for" | | 19 | "The comms on her vest" |
| | ratio | 0.696 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 46 | | matches | | | ratio | 0.022 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "* - The setting is an abandoned Underground station that used to house an underground supernatural market * - The detective is following up on a tip from a CI a…" |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 2 | | matches | | 0 | "he panted, bending to rest his hands on his knees" | | 1 | "Morris asked, uncertainty twitching in his tone" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 4 | | fancyTags | | 0 | "he panted (pant)" | | 1 | "she corrected (correct)" | | 2 | "Morris called down (call dow)" | | 3 | "she murmured (murmur)" |
| | dialogueSentences | 14 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.667 | | effectiveRatio | 0.571 | |