| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.23% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1326 | | 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) | |
| 92.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1326 | | totalAiIsms | 2 | | 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 | 93 | | matches | (empty) | |
| 81.41% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 68 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1342 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1329 | | uniqueNames | 21 | | maxNameDensity | 0.68 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 2 | | Nest | 2 | | Tomás | 1 | | Herrera | 9 | | Quinn | 8 | | Dean | 1 | | Street | 5 | | Old | 1 | | Compton | 1 | | Soho | 1 | | Wardour | 1 | | Saint | 1 | | Christopher | 1 | | Oxford | 2 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Northern | 1 | | Underground | 1 | | Sevillian | 1 | | Morris | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Quinn" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Underground" | | 8 | "Morris" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Old" | | 3 | "Compton" | | 4 | "Soho" | | 5 | "Wardour" | | 6 | "Oxford" | | 7 | "Tottenham" | | 8 | "Court" | | 9 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | 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 | 1342 | | matches | (empty) | |
| 61.40% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 95 | | matches | | 0 | "watching that door" | | 1 | "lose that she" | | 2 | "told that voice" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 51.62 | | std | 32.04 | | cv | 0.621 | | sampleLengths | | 0 | 27 | | 1 | 94 | | 2 | 13 | | 3 | 64 | | 4 | 78 | | 5 | 87 | | 6 | 123 | | 7 | 31 | | 8 | 5 | | 9 | 64 | | 10 | 50 | | 11 | 78 | | 12 | 55 | | 13 | 54 | | 14 | 89 | | 15 | 9 | | 16 | 96 | | 17 | 51 | | 18 | 34 | | 19 | 51 | | 20 | 5 | | 21 | 44 | | 22 | 80 | | 23 | 40 | | 24 | 10 | | 25 | 10 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 93 | | matches | | |
| 48.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 220 | | matches | | 0 | "was pissing" | | 1 | "was plunging" | | 2 | "wasn't checking" | | 3 | "was checking" | | 4 | "was pacing" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 1 | | flaggedSentences | 11 | | totalSentences | 95 | | ratio | 0.116 | | matches | | 0 | "Herrera had his collar up, a canvas trauma bag slung across his body, and the moment he stepped under the sign his head turned — a small, precise sweep of the street, the kind she did herself — and found her face in the café window." | | 1 | "He was twenty-nine, a former paramedic, and he ran like someone who'd spent a decade sprinting to cardiac arrests — economical, no wasted motion, head level." | | 2 | "A black cab laid on its horn and Herrera skipped around its bumper without breaking stride, and here was the thing Quinn filed away even as her lungs started to burn — he swerved wide around a drunk man slumped in a doorway, one hand grazing the man's shoulder to steady him as he passed." | | 3 | "Rain struck the canvas and ran off it wrong — beading, hissing, sliding away like it didn't want to touch the thing — and through the gap of the half-open zipper she caught a green glow, soft and wet, the exact color of the Raven's Nest sign." | | 4 | "He shot across Oxford Street between two buses — she nearly died following him, a taxi's wiper blades an arm's length from her hip — and then he was plunging down the steps into Tottenham Court Road station, tapping through the barriers with a contactless card in one fluid motion." | | 5 | "Instead of going down to the platforms, Herrera shouldered open a grey fire door marked STAFF ONLY — NO ENTRY." | | 6 | "He was pacing her — herding her, a cold voice suggested — and she told that voice to shut up and ran." | | 7 | "She'd given dispatch her location at Oxford Street; that bought her a search starting in the wrong place, hours from now, if anyone thought to look underground at all." | | 8 | "An abandoned station spread before her, arches of glazed white tile furred with grime, and on the walls the ghost of the station's name remained — the outline where each enamel letter had been pried away, so that the tile remembered the word even if no one else did." | | 9 | "He looked back at her — rain-soaked, torch shaking slightly in her hand, chest heaving — and there was no triumph in his face." | | 10 | "The token sat on the iron, white as a finger, and when she picked it up it was warm — warm as something freshly alive, warm in a way metal and bone in a cold tunnel had no right to be — and the wrongness of it traveled up her arm and settled behind her sternum, in the same place the unanswerable questions about Morris had lived for three years." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1320 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.02878787878787879 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004545454545454545 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 14.13 | | std | 14.13 | | cv | 1 | | sampleLengths | | 0 | 27 | | 1 | 33 | | 2 | 46 | | 3 | 6 | | 4 | 4 | | 5 | 5 | | 6 | 11 | | 7 | 2 | | 8 | 3 | | 9 | 46 | | 10 | 2 | | 11 | 13 | | 12 | 13 | | 13 | 26 | | 14 | 16 | | 15 | 23 | | 16 | 5 | | 17 | 55 | | 18 | 1 | | 19 | 10 | | 20 | 16 | | 21 | 25 | | 22 | 42 | | 23 | 9 | | 24 | 47 | | 25 | 14 | | 26 | 2 | | 27 | 1 | | 28 | 6 | | 29 | 8 | | 30 | 5 | | 31 | 50 | | 32 | 14 | | 33 | 20 | | 34 | 3 | | 35 | 27 | | 36 | 2 | | 37 | 5 | | 38 | 18 | | 39 | 20 | | 40 | 21 | | 41 | 4 | | 42 | 8 | | 43 | 26 | | 44 | 12 | | 45 | 6 | | 46 | 11 | | 47 | 4 | | 48 | 11 | | 49 | 7 |
| |
| 70.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.46808510638297873 | | totalSentences | 94 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 84 | | matches | | 0 | "Even running for his life," | | 1 | "Somewhere in the back of" | | 2 | "Instead of going down to" | | 3 | "Twice he glanced back." | | 4 | "Somewhere behind the brick there" | | 5 | "Then he set the bone" |
| | ratio | 0.071 | |
| 53.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 84 | | matches | | 0 | "His expression didn't change." | | 1 | "He simply turned and went." | | 2 | "He went left onto Old" | | 3 | "She'd remember the time the" | | 4 | "He was twenty-nine, a former" | | 5 | "She was forty-one and it" | | 6 | "They tore up Wardour Street." | | 7 | "He ducked down a service" | | 8 | "His sleeve had ridden up" | | 9 | "She slammed the door shut" | | 10 | "He shot across Oxford Street" | | 11 | "She slapped her warrant card" | | 12 | "It wasn't locked." | | 13 | "She noted that too, even" | | 14 | "He knew this dark." | | 15 | "She followed, gravel crunching underfoot," | | 16 | "Her breath came in blades" | | 17 | "Her knees kept their complaints" | | 18 | "He was checking whether she'd" | | 19 | "It never shrank, either." |
| | ratio | 0.417 | |
| 61.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 84 | | matches | | 0 | "The green neon of the" | | 1 | "Harlow Quinn had been watching" | | 2 | "Herrera had his collar up," | | 3 | "His expression didn't change." | | 4 | "He simply turned and went." | | 5 | "Quinn was through the café" | | 6 | "Herrera didn't stop." | | 7 | "He went left onto Old" | | 8 | "She'd remember the time the" | | 9 | "He was twenty-nine, a former" | | 10 | "She was forty-one and it" | | 11 | "They tore up Wardour Street." | | 12 | "A black cab laid on" | | 13 | "That was a man with" | | 14 | "He ducked down a service" | | 15 | "His sleeve had ridden up" | | 16 | "Rain struck the canvas and" | | 17 | "The case that had no" | | 18 | "She slammed the door shut" | | 19 | "He shot across Oxford Street" |
| | ratio | 0.798 | |
| 59.52% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 84 | | matches | | 0 | "To her left, a Northern" |
| | ratio | 0.012 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 4 | | matches | | 0 | "She'd remember the time the way she remembered all the times that mattered." | | 1 | "He was twenty-nine, a former paramedic, and he ran like someone who'd spent a decade sprinting to cardiac arrests — economical, no wasted motion, head level." | | 2 | "She'd given dispatch her location at Oxford Street; that bought her a search starting in the wrong place, hours from now, if anyone thought to look underground …" | | 3 | "Music played on instruments that made her back teeth ache." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |