Run: 2026-02-19-01KHTYC8VCMN1WXFPRD189NGN1

Run Details

Duration:
6.6s
Cost:
$0.001866

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <codex> <character id="4412" name="Aurora Carter" aliases="Rory, Laila, Aurora, Carter, Malphora" pronouns="she/her" species="Human" storyRole="Protagonist"> <eyeColor>bright blue</eyeColor> <hair>straight shoulder-length black</hair> <height>5'6"</height> <age>25</age> <scar>small crescent-shaped scar on her left wrist from a childhood accident</scar> <description>Aurora, referred to by her friends as Rory, is cool-headed, intelligent, and known for quick out-of-the-box thinking.</description> <backstory>Born in Cardiff to an Irish barrister father (Brendan Carter) and Welsh teacher mother (Jennifer Carter née Ellis), Rory reluctantly pursued Pre-Law at Cardiff University. She fled to London at the behest of her childhood friend Eva after leaving an abusive ex named Evan. By day, Rory works part-time as a delivery person for Yu-Fei Cheung's Golden Empress restaurant. She lives in a flat above Silas' bar.</backstory> </character> <character id="4415" name="Lucien Moreau" aliases="Lucien, Luc, the Frenchman" pronouns="he/him" species="Half-Demon" storyRole="Ally"> <eyeColor>heterochromatic — one amber, one black</eyeColor> <hair>slicked-back platinum blond</hair> <height>5'11"</height> <age>32</age> <cane>ivory-handled cane that conceals a thin blade</cane> <dress>tailored charcoal suits</dress> <description>An impeccably dressed fixer and information broker in London's supernatural underworld. He speaks four languages fluently.</description> <backstory>Born in Marseille to a human mother and a demon father from the realm of Avaros.</backstory> </character> <location id="858" name="Eva's Flat" aliases="Eva's place, the flat on Brick Lane" realm="Earth"> <cat>tabby cat named Ptolemy</cat> <security>three deadbolts on the front door</security> <description>A cramped one-bedroom flat above a curry house on Brick Lane, East London. Every surface is covered with books, scrolls, and research notes.</description> </location> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the romance genre. Two people who parted on complicated terms are forced together again when one shows up unannounced at the other's flat. There's history between them — attraction, hurt, and things left unsaid. Write the scene from the moment the door opens. Use the codex entries above as your world reference for characters and setting. Only include the prose of the scene without any markdown or commentary. </instructions>

Model response

AI
The knock came sharp and unexpected, three precise raps that cut through the quiet of Eva's flat. Aurora's hand froze mid-tea-pour, her bright blue eyes narrowing. Those weren't Eva's knocks. Those weren't anyone's knocks who should be here right now. She set down the mug and approached the door, Ptolemy the tabby cat watching with disinterested green eyes from the cluttered bookshelf. The three deadbolts felt suddenly heavy under her fingers. When she opened the door, Lucien Moreau stood there. Impeccable as always in a charcoal suit, one amber eye and one black eye taking her in with surgical precision. He looked precisely the same as the last time they'd seen each other — which meant exactly nothing had changed, and everything had changed. "Hello, Rory," he said, his French accent sliding around the words like silk. She didn't move. Didn't invite him in. Didn't step back. "You shouldn't be here." His slight smile didn't reach either of his mismatched eyes. "Clearly, that has never stopped me before."
total_cost0.0018655999999999998

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences3
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
40.83% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount169
totalAiIsmAdverbs2
found
0
adverb"suddenly"
count1
1
adverb"precisely"
count1
highlights
0"suddenly"
1"precisely"
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)
70.41% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount169
totalAiIsms1
found
0
word"silk"
count1
highlights
0"silk"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches0
maxInWindow0
found(empty)
highlights(empty)
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences14
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences14
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences16
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen23
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords168
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
85.48% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions6
wordCount155
uniqueNames5
maxNameDensity1.29
worstName"Eva"
maxWindowNameDensity1
worstWindowName"Eva"
discoveredNames
Eva2
Ptolemy1
Lucien1
Moreau1
French1
persons
0"Eva"
1"Lucien"
2"Moreau"
places(empty)
globalScore0.855
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences10
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount168
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences16
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs7
mean24
std16.53
cv0.689
sampleLengths
040
131
253
313
410
54
617
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences14
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs26
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences16
ratio0.063
matches
0"He looked precisely the same as the last time they'd seen each other — which meant exactly nothing had changed, and everything had changed."
95.74% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount156
adjectiveStacks0
stackExamples(empty)
adverbCount7
adverbRatio0.04487179487179487
lyAdverbCount3
lyAdverbRatio0.019230769230769232
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences16
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences16
mean10.5
std6.67
cv0.635
sampleLengths
017
19
24
310
422
59
69
720
824
913
103
114
123
134
1410
157
87.50% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.75
totalSentences16
uniqueOpeners12
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences14
matches(empty)
ratio0
77.14% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount5
totalSentences14
matches
0"She set down the mug"
1"He looked precisely the same"
2"he said, his French accent"
3"She didn't move."
4"His slight smile didn't reach"
ratio0.357
100.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount10
totalSentences14
matches
0"The knock came sharp and"
1"Aurora's hand froze mid-tea-pour, her"
2"Those weren't Eva's knocks."
3"Those weren't anyone's knocks who"
4"She set down the mug"
5"The three deadbolts felt suddenly"
6"He looked precisely the same"
7"he said, his French accent"
8"She didn't move."
9"His slight smile didn't reach"
ratio0.714
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences14
matches(empty)
ratio0
40.82% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences7
technicalSentenceCount1
matches
0"The knock came sharp and unexpected, three precise raps that cut through the quiet of Eva's flat."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount1
matches
0"he said, his French accent sliding around the words like silk"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount0
fancyTags(empty)
dialogueSentences3
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
79.9308%