Run: 2026-02-19-01KHTYCF8QZR6PBM2CDZSC0YZT

Run Details

Duration:
8.6s
Cost:
$0.002262

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="4413" name="Silas Blackwood" aliases="Silas, Si, the Spymaster" pronouns="he/him" species="Human" storyRole="Mentor"> <eyeColor>hazel</eyeColor> <hair>grey-streaked auburn</hair> <height>6'1"</height> <age>58</age> <beard>neatly trimmed, matching his grey-streaked auburn hair</beard> <limp>slight limp in his left leg from an old knee injury</limp> <signetRing>silver signet ring, always worn on his right hand</signetRing> <description>Silas is a retired intelligence operative turned bar owner. He carries himself with quiet authority.</description> <backstory>Former MI6 field agent who retired after a botched operation in Prague left him with a permanent knee injury. He opened "The Raven's Nest" bar in Soho as a front for his network of contacts.</backstory> </character> <location id="854" name="The Raven's Nest" aliases="the Nest, Raven, Silas' bar" realm="Earth"> <signColor>distinctive green neon sign above the entrance</signColor> <secretRoom>hidden back room accessible through a bookshelf, used for clandestine meetings</secretRoom> <description>A dimly lit bar in Soho, London. The walls are covered with old maps and black-and-white photographs.</description> </location> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the literary fiction genre. Two old friends who haven't seen each other in years run into each other unexpectedly at a bar. One of them has changed significantly. Through their conversation and the tension between who they were and who they've become, reveal the weight of time and unspoken regret. 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 night pressed against the windows of The Raven's Nest, a thin film of condensation blurring the streetlights outside. Aurora traced the rim of her whiskey glass, her fingers lingering on the smooth edge. The bar hummed with low conversation, punctuated by the occasional clink of ice against glass. When the door opened, she didn't look up. Not until a familiar silhouette caught her peripheral vision. Eva. Her old friend stood differently now. Gone was the nervous energy that used to vibrate through her college-age frame. This Eva moved with calculated precision, her tailored grey blazer speaking of corporate ambition, her hair pulled back in a tight knot that seemed to pull her face taut with something harder than professionalism. "Rory," Eva said. No smile. Just a statement. Aurora lifted her glass. "Buy you a drink?" Eva slid onto the barstool, her movements controlled. She nodded at Silas, who was polishing glasses at the far end of the bar. A silent communication passed between them that Aurora couldn't read. "You're still here, then," Eva said. It wasn't a question. The weight of years hung between them. Unspoken memories. Promises. Betrayals. "London hasn't gotten rid of me yet," Aurora responded, her voice light but her eyes sharp. Eva's fingers drummed once against the bar. Just once. A gesture that said more than words ever could. "I heard about Evan," she said finally.
total_cost0.0022616

Evaluation details

Result Evaluator Details Meta Data
0.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags4
adverbTagCount1
adverbTags
0"she said finally [finally]"
dialogueSentences5
tagDensity0.8
leniency1
rawRatio0.25
effectiveRatio0.25
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount232
totalAiIsmAdverbs0
found(empty)
highlights(empty)
100.00% AI-ism character names
Target: 0 AI-default names (16 tracked, −20% each)
codexExemptions
0"Blackwood"
found(empty)
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
0.00% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount232
totalAiIsms5
found
0
word"traced"
count1
1
word"familiar"
count1
2
word"calculated"
count1
3
word"weight"
count1
4
word"unspoken"
count1
highlights
0"traced"
1"familiar"
2"calculated"
3"weight"
4"unspoken"
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
narrationSentences27
matches(empty)
89.95% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences27
filterMatches(empty)
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences28
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen34
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords231
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
7.82% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions13
wordCount211
uniqueNames5
maxNameDensity2.84
worstName"Eva"
maxWindowNameDensity3
worstWindowName"Eva"
discoveredNames
Raven1
Nest1
Eva6
Silas1
Aurora4
persons
0"Raven"
1"Eva"
2"Silas"
3"Aurora"
places(empty)
globalScore0.078
windowScore0.667
0.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences15
glossingSentenceCount1
matches
0"knot that seemed to pull her face taut with something harder than professionalism"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount231
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences28
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs11
mean21
std15.81
cv0.753
sampleLengths
049
118
253
38
48
533
610
711
816
918
107
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences27
matches(empty)
14.81% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs36
matches
0"was polishing"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences28
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount212
adjectiveStacks0
stackExamples(empty)
adverbCount8
adverbRatio0.03773584905660377
lyAdverbCount2
lyAdverbRatio0.009433962264150943
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences28
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences28
mean8.25
std7.07
cv0.857
sampleLengths
019
115
215
38
49
51
66
713
834
93
102
113
124
134
148
1515
1610
176
184
197
202
211
221
2316
247
252
269
277
100.00% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats0
diversityRatio0.75
totalSentences28
uniqueOpeners21
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences19
matches
0"Just a statement."
ratio0.053
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount4
totalSentences19
matches
0"Her old friend stood differently"
1"She nodded at Silas, who"
2"It wasn't a question."
3"she said finally"
ratio0.211
65.26% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount15
totalSentences19
matches
0"The night pressed against the"
1"Aurora traced the rim of"
2"The bar hummed with low"
3"Her old friend stood differently"
4"This Eva moved with calculated"
5"Aurora lifted her glass."
6"Eva slid onto the barstool,"
7"She nodded at Silas, who"
8"A silent communication passed between"
9"It wasn't a question."
10"The weight of years hung"
11"Aurora responded, her voice light"
12"Eva's fingers drummed once against"
13"A gesture that said more"
14"she said finally"
ratio0.789
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences19
matches(empty)
ratio0
40.82% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences7
technicalSentenceCount1
matches
0"This Eva moved with calculated precision, her tailored grey blazer speaking of corporate ambition, her hair pulled back in a tight knot that seemed to pull her …"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
25.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount1
fancyTags
0"Aurora responded (respond)"
dialogueSentences5
tagDensity0.8
leniency1
rawRatio0.25
effectiveRatio0.25
74.7887%