| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1770 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 60.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1770 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "electric" | | 1 | "measured" | | 2 | "efficient" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "pulsed" | | 6 | "calculating" | | 7 | "pulse" | | 8 | "footfall" | | 9 | "flicked" | | 10 | "weight" | | 11 | "stomach" |
| |
| 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 | 131 | | matches | (empty) | |
| 99.24% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 131 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1757 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1722 | | uniqueNames | 21 | | maxNameDensity | 0.87 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 4 | | Town | 1 | | Harlow | 1 | | Quinn | 15 | | Met | 2 | | Herrera | 9 | | Saint | 1 | | Christopher | 1 | | Nest | 2 | | Parkway | 1 | | High | 1 | | Tube | 1 | | Victorian | 1 | | Veil | 1 | | Market | 2 | | English | 1 | | Morris | 4 | | Control | 1 | | Accent | 1 | | Seville | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Nest" | | 6 | "Tube" | | 7 | "Market" | | 8 | "Morris" | | 9 | "Control" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Parkway" | | 3 | "Seville" | | 4 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like city utility access, chalked" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1757 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 136 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 56.68 | | std | 36.69 | | cv | 0.647 | | sampleLengths | | 0 | 78 | | 1 | 83 | | 2 | 64 | | 3 | 2 | | 4 | 13 | | 5 | 90 | | 6 | 66 | | 7 | 74 | | 8 | 102 | | 9 | 87 | | 10 | 69 | | 11 | 61 | | 12 | 58 | | 13 | 8 | | 14 | 6 | | 15 | 136 | | 16 | 87 | | 17 | 16 | | 18 | 79 | | 19 | 45 | | 20 | 3 | | 21 | 97 | | 22 | 72 | | 23 | 123 | | 24 | 55 | | 25 | 17 | | 26 | 24 | | 27 | 52 | | 28 | 16 | | 29 | 3 | | 30 | 71 |
| |
| 97.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 131 | | matches | | 0 | "been reclaimed" | | 1 | "being loaded" | | 2 | "been made" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 281 | | matches | | 0 | "were already disappearing" | | 1 | "were bleeding" | | 2 | "was buying" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 3 | | flaggedSentences | 12 | | totalSentences | 136 | | ratio | 0.088 | | matches | | 0 | "Quinn saw the scar along his left forearm when he shoved a market stall out of his way—the same scar the file photos had documented, the one earned in a knife fight no one had ever properly explained." | | 1 | "Traffic snarled at the junction of Parkway and Camden High; car horns blared as Herrera vaulted the bonnet of a black cab." | | 2 | "Buses idled wheezing in the wet; yellow light poured from their steamed windows and lit the rain in sudden gold." | | 3 | "She could hear him now—sharp, efficient breaths, the soft wet thud of trainers." | | 4 | "Footsteps echoed ahead—his, then hers—until the tunnel bent and the sound split and multiplied against tiled walls that still carried the ghost of Victorian posters." | | 5 | "And something else—a soft golden haze that didn’t match any lighting Quinn had ever catalogued." | | 6 | "Tables of black iron and scavenged glass displayed objects that pulsed weakly as if they still held heartbeats—vials of luminous fluid, knives with edges that drank the light, folded cloth that whispered against the teens of buyers moving among them." | | 7 | "Her brown eyes tracked the path he had taken, calculating angles of approach, places a gun—if she drew it—would only create more witnesses she could never name in court." | | 8 | "Sound arrived differently—closer, wetter, every transaction and footfall magnified." | | 9 | "Her soaked shirt clung to her shoulders; her baton sat ready against her hip." | | 10 | "Surprise there—brief, sharp—then calculation." | | 11 | "He reached a stair descending further still—maintenance cut, or something older—and for a heartbeat she thought she would lose him into the deeper black." |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1746 | | adjectiveStacks | 2 | | stackExamples | | 0 | "harsh blue-white pools" | | 1 | "old electric animal crawl" |
| | adverbCount | 68 | | adverbRatio | 0.038946162657502864 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.0074455899198167235 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 12.92 | | std | 8.5 | | cv | 0.658 | | sampleLengths | | 0 | 22 | | 1 | 29 | | 2 | 17 | | 3 | 10 | | 4 | 9 | | 5 | 16 | | 6 | 20 | | 7 | 38 | | 8 | 20 | | 9 | 10 | | 10 | 24 | | 11 | 10 | | 12 | 2 | | 13 | 6 | | 14 | 5 | | 15 | 2 | | 16 | 4 | | 17 | 16 | | 18 | 22 | | 19 | 20 | | 20 | 28 | | 21 | 14 | | 22 | 2 | | 23 | 15 | | 24 | 8 | | 25 | 11 | | 26 | 13 | | 27 | 2 | | 28 | 1 | | 29 | 3 | | 30 | 1 | | 31 | 21 | | 32 | 9 | | 33 | 22 | | 34 | 14 | | 35 | 4 | | 36 | 12 | | 37 | 2 | | 38 | 26 | | 39 | 8 | | 40 | 10 | | 41 | 2 | | 42 | 1 | | 43 | 32 | | 44 | 9 | | 45 | 19 | | 46 | 18 | | 47 | 13 | | 48 | 10 | | 49 | 9 |
| |
| 69.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4485294117647059 | | totalSentences | 136 | | uniqueOpeners | 61 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 119 | | matches | | 0 | "Somewhere behind them a siren" | | 1 | "Somewhere a generator coughed and" | | 2 | "Instead he stopped, turned, and" | | 3 | "Somewhere deeper, a door she" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 119 | | matches | | 0 | "Her brown eyes never left" | | 1 | "She had been watching the" | | 2 | "Her shout cracked through the" | | 3 | "He didn’t even glance back." | | 4 | "He cut left into an" | | 5 | "She could hear him now—sharp," | | 6 | "She did not slow." | | 7 | "She had bolted from the" | | 8 | "She still did not understand" | | 9 | "She only knew she would" | | 10 | "She took them three at" | | 11 | "She saw him again as" | | 12 | "He pressed it to a" | | 13 | "He stepped through and the" | | 14 | "It made the scar where" | | 15 | "She had read the half-buried" | | 16 | "She had no token." | | 17 | "Her brown eyes tracked the" | | 18 | "She thought of the duffel." | | 19 | "She stepped through." |
| | ratio | 0.244 | |
| 69.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 93 | | totalSentences | 119 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s boots slapped" | | 2 | "Water sheeted off her closely" | | 3 | "Her brown eyes never left" | | 4 | "Tomás Herrera cut through the" | | 5 | "The Saint Christopher medallion at" | | 6 | "Quinn saw the scar along" | | 7 | "She had been watching the" | | 8 | "Whatever the clique was moving" | | 9 | "Her shout cracked through the" | | 10 | "He didn’t even glance back." | | 11 | "Quinn opened her stride." | | 12 | "The leather watch on her" | | 13 | "Traffic snarled at the junction" | | 14 | "Buses idled wheezing in the" | | 15 | "Quinn took the gap between" | | 16 | "He cut left into an" | | 17 | "A cat exploded out from" | | 18 | "Quinn followed hard, lungs burning" | | 19 | "Rain funneled down the brick" |
| | ratio | 0.782 | |
| 42.02% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 119 | | matches | | 0 | "Now there was only the" |
| | ratio | 0.008 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 80 | | technicalSentenceCount | 8 | | matches | | 0 | "Detective Harlow Quinn’s boots slapped hard against the pavement, each stride precise, economical, measured by the same military cadence that had carried her th…" | | 1 | "And something else—a soft golden haze that didn’t match any lighting Quinn had ever catalogued." | | 2 | "Tables of black iron and scavenged glass displayed objects that pulsed weakly as if they still held heartbeats—vials of luminous fluid, knives with edges that d…" | | 3 | "She had read the half-buried intelligence notes, the ones that lived in brown folders stamped with warnings rather than case numbers." | | 4 | "Herrera’s dark curls were already disappearing between two stalls that sold glass jars of red fog." | | 5 | "Forty-one years old, eighteen of them in this job, and she still felt the old electric animal crawl of hunting something that might hunt back." | | 6 | "A tunnel cat the size of a small dog watched her pass with eyes that reflected too much silver." | | 7 | "He broke left toward what used to be the station’s second platform, now strung with more of those impossible lights and crowded with shadows that did not match …" |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "she answered, voice low and even" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |