| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2593 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 57.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2593 | | totalAiIsms | 22 | | found | | | highlights | | 0 | "scanned" | | 1 | "echoed" | | 2 | "weight" | | 3 | "efficient" | | 4 | "traced" | | 5 | "glint" | | 6 | "unspoken" | | 7 | "echoes" | | 8 | "tension" | | 9 | "reminder" | | 10 | "charged" | | 11 | "echoing" | | 12 | "silence" | | 13 | "dance" | | 14 | "flickered" | | 15 | "familiar" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 306 | | matches | | |
| 86.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 8 | | hedgeCount | 4 | | narrationSentences | 306 | | filterMatches | | 0 | "know" | | 1 | "see" | | 2 | "think" | | 3 | "feel" | | 4 | "listen" |
| | hedgeMatches | | 0 | "tried to" | | 1 | "happened to" | | 2 | "tended to" | | 3 | "seemed to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 306 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2593 | | ratio | 0 | | matches | (empty) | |
| 89.29% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 1 | | matches | | 0 | "Months, I guess, judging by the look of you." |
| |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 77 | | wordCount | 2581 | | uniqueNames | 19 | | maxNameDensity | 0.54 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "You" | | discoveredNames | | Aurora | 6 | | Raven | 1 | | Nest | 4 | | Carter | 1 | | Silas | 14 | | Empress | 2 | | Cardiff | 4 | | Prague | 3 | | Spymaster | 3 | | Evan | 7 | | Eva | 5 | | Jennifer | 1 | | London | 2 | | Brendan | 2 | | Si | 2 | | Soho | 1 | | You | 12 | | Old | 3 | | One | 4 |
| | persons | | 0 | "Aurora" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Carter" | | 4 | "Silas" | | 5 | "Empress" | | 6 | "Spymaster" | | 7 | "Evan" | | 8 | "Eva" | | 9 | "Jennifer" | | 10 | "Brendan" | | 11 | "You" |
| | places | | 0 | "Cardiff" | | 1 | "Prague" | | 2 | "London" | | 3 | "Soho" | | 4 | "Old" | | 5 | "One" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 209 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.771 | | wordCount | 2593 | | matches | | 0 | "Not extinguished, but contained" | | 1 | "Not with bullets but with control" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 306 | | matches | | 0 | "know that stride" | | 1 | "has that effect" | | 2 | "earned that the" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 77 | | mean | 33.68 | | std | 17.38 | | cv | 0.516 | | sampleLengths | | 0 | 73 | | 1 | 60 | | 2 | 15 | | 3 | 15 | | 4 | 53 | | 5 | 11 | | 6 | 18 | | 7 | 47 | | 8 | 36 | | 9 | 28 | | 10 | 15 | | 11 | 40 | | 12 | 13 | | 13 | 51 | | 14 | 15 | | 15 | 36 | | 16 | 17 | | 17 | 38 | | 18 | 25 | | 19 | 52 | | 20 | 16 | | 21 | 51 | | 22 | 13 | | 23 | 37 | | 24 | 36 | | 25 | 51 | | 26 | 9 | | 27 | 37 | | 28 | 10 | | 29 | 55 | | 30 | 16 | | 31 | 46 | | 32 | 17 | | 33 | 44 | | 34 | 11 | | 35 | 48 | | 36 | 13 | | 37 | 53 | | 38 | 14 | | 39 | 41 | | 40 | 25 | | 41 | 28 | | 42 | 17 | | 43 | 57 | | 44 | 8 | | 45 | 50 | | 46 | 22 | | 47 | 37 | | 48 | 12 | | 49 | 37 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 306 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 454 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 306 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 652 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.015337423312883436 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0015337423312883436 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 306 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 306 | | mean | 8.47 | | std | 5.06 | | cv | 0.597 | | sampleLengths | | 0 | 17 | | 1 | 18 | | 2 | 17 | | 3 | 21 | | 4 | 19 | | 5 | 15 | | 6 | 10 | | 7 | 16 | | 8 | 7 | | 9 | 8 | | 10 | 2 | | 11 | 5 | | 12 | 8 | | 13 | 11 | | 14 | 21 | | 15 | 21 | | 16 | 1 | | 17 | 5 | | 18 | 5 | | 19 | 9 | | 20 | 9 | | 21 | 4 | | 22 | 19 | | 23 | 11 | | 24 | 13 | | 25 | 13 | | 26 | 23 | | 27 | 4 | | 28 | 8 | | 29 | 10 | | 30 | 6 | | 31 | 9 | | 32 | 6 | | 33 | 2 | | 34 | 4 | | 35 | 5 | | 36 | 2 | | 37 | 3 | | 38 | 8 | | 39 | 4 | | 40 | 12 | | 41 | 13 | | 42 | 10 | | 43 | 6 | | 44 | 4 | | 45 | 3 | | 46 | 2 | | 47 | 13 | | 48 | 5 | | 49 | 8 |
| |
| 66.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4150326797385621 | | totalSentences | 306 | | uniqueOpeners | 127 | |
| 92.27% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 289 | | matches | | 0 | "Always the fixer." | | 1 | "Then a temper that did" | | 2 | "Instead I let the years" | | 3 | "Maybe sharing eases the unspoken" | | 4 | "Only read them differently with" | | 5 | "Always wondered if the full" | | 6 | "Instead the regret built with" | | 7 | "Just the gesture." |
| | ratio | 0.028 | |
| 99.58% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 87 | | totalSentences | 289 | | matches | | 0 | "She scanned the scattered patrons" | | 1 | "He froze with a glass" | | 2 | "His hazel eyes narrowed, then" | | 3 | "I'd know that stride anywhere." | | 4 | "She crossed the floor, boots" | | 5 | "Her straight black hair, cut" | | 6 | "You, on the other hand." | | 7 | "He set the glass down" | | 8 | "She slid onto the stool," | | 9 | "He returned, wiping the counter" | | 10 | "I know the place." | | 11 | "Your mother sent me letters" | | 12 | "She traced a finger along" | | 13 | "He started small." | | 14 | "I packed a bag the" | | 15 | "He nudged hers forward." | | 16 | "I owe her a pint" | | 17 | "She met his gaze head" | | 18 | "Your postcards stopped after Prague." | | 19 | "I figured the Spymaster had" |
| | ratio | 0.301 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 194 | | totalSentences | 289 | | matches | | 0 | "The green neon sign hummed" | | 1 | "Dust motes danced in the" | | 2 | "The air carried notes of" | | 3 | "She scanned the scattered patrons" | | 4 | "Silas moved with that same" | | 5 | "He froze with a glass" | | 6 | "His hazel eyes narrowed, then" | | 7 | "I'd know that stride anywhere." | | 8 | "She crossed the floor, boots" | | 9 | "Her straight black hair, cut" | | 10 | "The small crescent scar on" | | 11 | "The bar looks the same." | | 12 | "You, on the other hand." | | 13 | "He set the glass down" | | 14 | "The silver signet ring on" | | 15 | "Time carves us all." | | 16 | "She slid onto the stool," | | 17 | "A patron at the end" | | 18 | "The girl grew up." | | 19 | "Yu-Fei pays decent for the" |
| | ratio | 0.671 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 13 | | totalSentences | 289 | | matches | | 0 | "Even if the years tried" | | 1 | "Now you stand here with" | | 2 | "Now I see the fire" | | 3 | "Who we were hangs like" | | 4 | "Now the delivery woman and" | | 5 | "To who we could have" | | 6 | "Now I see the power" | | 7 | "Because checking would mean admitting" | | 8 | "To leave without full backup." | | 9 | "Now we sit here, roles" | | 10 | "To have reached out before" | | 11 | "Before he became the reason" | | 12 | "Before I traded lecture halls" |
| | ratio | 0.045 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 97 | | technicalSentenceCount | 5 | | matches | | 0 | "Her straight black hair, cut to her shoulders, framed a face that bore new angles, sharper than the roundness of youth." | | 1 | "Avoiding the old university crowd who might mention my name in the wrong ears." | | 2 | "Golden Empress gets me through neighborhoods I never knew, past buildings that remind me of home." | | 3 | "I was the kid who quizzed you on ethics and tactics." | | 4 | "Aurora tilted her head toward the nearest map, its pins clustered in clusters that spoke of old operations and lost chances." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |