| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva said quietly [quietly]" | | 1 | "Eva said quickly [quickly]" |
| | dialogueSentences | 42 | | tagDensity | 0.619 | | leniency | 1 | | rawRatio | 0.077 | | effectiveRatio | 0.077 | |
| 89.61% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1444 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "carefully" | | 1 | "quickly" | | 2 | "lazily" |
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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) | |
| 96.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1444 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 71 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1454 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.83% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1209 | | uniqueNames | 16 | | maxNameDensity | 1.32 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 3 | | Camden | 3 | | Detective | 1 | | Quinn | 16 | | Town | 1 | | Met | 1 | | Kowalski | 2 | | Davies | 6 | | Miss | 1 | | Veil | 1 | | Market | 1 | | Morris | 2 | | Crafted | 1 | | Shade | 1 | | Eva | 14 | | Pimlico | 1 |
| | persons | | 0 | "Detective" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Davies" | | 4 | "Miss" | | 5 | "Market" | | 6 | "Morris" | | 7 | "Eva" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Pimlico" |
| | globalScore | 0.838 | | windowScore | 1 | |
| 25.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 3 | | matches | | 0 | "smelled like burned hair" | | 1 | "as if reading her mind" | | 2 | "something close to pity. Her brown eyes were har" |
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| 62.45% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.376 | | wordCount | 1454 | | matches | | 0 | "Not open, but not closed either" | | 1 | "not down, but north along the tunnel, toward the dark" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 24.64 | | std | 19.42 | | cv | 0.788 | | sampleLengths | | 0 | 14 | | 1 | 32 | | 2 | 20 | | 3 | 61 | | 4 | 32 | | 5 | 25 | | 6 | 2 | | 7 | 48 | | 8 | 26 | | 9 | 39 | | 10 | 11 | | 11 | 13 | | 12 | 65 | | 13 | 23 | | 14 | 20 | | 15 | 6 | | 16 | 4 | | 17 | 8 | | 18 | 71 | | 19 | 16 | | 20 | 33 | | 21 | 35 | | 22 | 46 | | 23 | 2 | | 24 | 15 | | 25 | 12 | | 26 | 16 | | 27 | 23 | | 28 | 5 | | 29 | 19 | | 30 | 9 | | 31 | 6 | | 32 | 30 | | 33 | 48 | | 34 | 34 | | 35 | 16 | | 36 | 8 | | 37 | 57 | | 38 | 23 | | 39 | 9 | | 40 | 3 | | 41 | 67 | | 42 | 16 | | 43 | 12 | | 44 | 32 | | 45 | 13 | | 46 | 32 | | 47 | 14 | | 48 | 3 | | 49 | 12 |
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| 71.85% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 10 | | totalSentences | 105 | | matches | | 0 | "been sealed" | | 1 | "were gone" | | 2 | "was perched" | | 3 | "been scrubbed" | | 4 | "been overturned" | | 5 | "was outstretched" | | 6 | "was gone" | | 7 | "been tossed" | | 8 | "was etched" | | 9 | "get tossed" | | 10 | "got sucked" | | 11 | "was attuned" |
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| 20.63% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 223 | | matches | | 0 | "was screaming" | | 1 | "was telling" | | 2 | "was drying" | | 3 | "was spinning" | | 4 | "wasn't pointing" | | 5 | "was coming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 115 | | ratio | 0.087 | | matches | | 0 | "Officer down — no, not down." | | 1 | "Her brown eyes tracked the handprint on the doorframe — small, smudged white, chalk or salt." | | 2 | "Davies must have taken one off — off him.\"" | | 3 | "And his arm — maybe a fight?" | | 4 | "And the blood — it's not spatter, it's drip." | | 5 | "And his apron —\"" | | 6 | "And in that second, Quinn saw it — a faint seam of light under the body, no wider than a hair, running along the tracks. A rift. Not open, but not closed either. Caught half-way, as if something had pulled it shut from the other side while the man was still halfway through." | | 7 | "The arm — it didn't get cut off." | | 8 | "That's why the blood drips from above — he's leaking down from the seam." | | 9 | "If his compass was attuned to the rift, and he tried to anchor it —\"" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 832 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.036057692307692304 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010817307692307692 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 12.64 | | std | 13.56 | | cv | 1.072 | | sampleLengths | | 0 | 7 | | 1 | 7 | | 2 | 5 | | 3 | 3 | | 4 | 6 | | 5 | 3 | | 6 | 9 | | 7 | 6 | | 8 | 20 | | 9 | 8 | | 10 | 23 | | 11 | 4 | | 12 | 26 | | 13 | 4 | | 14 | 11 | | 15 | 1 | | 16 | 16 | | 17 | 20 | | 18 | 5 | | 19 | 2 | | 20 | 5 | | 21 | 20 | | 22 | 9 | | 23 | 14 | | 24 | 7 | | 25 | 7 | | 26 | 12 | | 27 | 13 | | 28 | 3 | | 29 | 23 | | 30 | 11 | | 31 | 8 | | 32 | 5 | | 33 | 4 | | 34 | 61 | | 35 | 14 | | 36 | 9 | | 37 | 20 | | 38 | 6 | | 39 | 1 | | 40 | 3 | | 41 | 8 | | 42 | 71 | | 43 | 16 | | 44 | 33 | | 45 | 10 | | 46 | 5 | | 47 | 5 | | 48 | 3 | | 49 | 4 |
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| 64.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4298245614035088 | | totalSentences | 114 | | uniqueOpeners | 49 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 92 | | matches | | 0 | "She knew because she'd worked" | | 1 | "She checked her watch." | | 2 | "Her brown eyes tracked the" | | 3 | "Her freckled face was pale" | | 4 | "It came out sharper than" | | 5 | "Her bearing snapped straight, military" | | 6 | "She produced it. A small," | | 7 | "It blew in." | | 8 | "She glanced up. Above the" | | 9 | "she said, and only then" | | 10 | "He must have made" | | 11 | "She stood, knees popping, and" | | 12 | "She looked at the black" | | 13 | "She looked at the cauterized" | | 14 | "She squatted again and, before" | | 15 | "He was coming through the" | | 16 | "It got left behind on" | | 17 | "It got sucked toward the" | | 18 | "She reached into her pocket," |
| | ratio | 0.207 | |
| 79.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 92 | | matches | | 0 | "The call had come at" | | 1 | "Requesting Detective Quinn." | | 2 | "Officer down — no, not" | | 3 | "Officer present but..." | | 4 | "the constable on the radio" | | 5 | "Harlow Quinn stood at the" | | 6 | "Camden Town station had been" | | 7 | "She knew because she'd worked" | | 8 | "The bricks were gone." | | 9 | "She checked her watch." | | 10 | "The worn leather strap on" | | 11 | "Her brown eyes tracked the" | | 12 | "That feeling was screaming now." | | 13 | "The voice came from below." | | 14 | "Eva Kowalski was perched halfway" | | 15 | "Her freckled face was pale" | | 16 | "The worn leather satchel full" | | 17 | "It came out sharper than" | | 18 | "Her bearing snapped straight, military" | | 19 | "Eva said, tucking a loose" |
| | ratio | 0.761 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 92 | | matches | | 0 | "If someone tossed the stall" | | 1 | "If his compass was attuned" |
| | ratio | 0.022 | |
| 56.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 4 | | matches | | 0 | "Someone had strung work lights on extension cords that vanished into dark alcoves. The old tiles had been scrubbed clean in patches and covered in chalk sigils …" | | 1 | "Quinn stepped carefully down onto the track bed, her boots crunching salt. The abandoned Tube station beneath Camden. A hidden supernatural black market that se…" | | 2 | "Quinn felt the cold deepen. Three years ago, DS Morris had died in a flat in Pimlico that smelled exactly like this before the fire started. Unexplained circums…" | | 3 | "And in that second, Quinn saw it — a faint seam of light under the body, no wider than a hair, running along the tracks. A rift. Not open, but not closed either…" |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva whispered, as if reading her mind" | | 1 | "Quinn said, her voice flat, certain" |
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| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 3 | | fancyTags | | 0 | "Eva muttered (mutter)" | | 1 | "Eva breathed (breathe)" | | 2 | "Eva whispered (whisper)" |
| | dialogueSentences | 42 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.214 | | effectiveRatio | 0.143 | |