Run: 2026-07-25-01KYCWZZCJB1J6H67YXBAV1KDA

Run Details

Duration:
5.0s
Cost:
$0.002340
Provider:
Google AI Studio

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
Glass rattled against wood. Silas wiped the mahogany counter with a rag that had long surrendered its original color, his left knee locking with a familiar, dull ache. Overhead, the green neon sign of The Raven's Nest buzzed against the Soho damp, casting a sickly emerald smear across the floorboards. The bell above the door chimed. Footsteps crunched over salt-tracked grit. Aurora stood just inside the threshold, shoulders hunched against the London drizzle, her black hair plastered to the sides of her face. Water dripped from the hem of a faded courier jacket onto the worn boards. Bright blue eyes swept the dim room, landing instantly on the man behind the taps. Silas stopped moving the rag. You look like a drowned crow. A sharp, humorless bark escaped Aurora's throat. She unzipped the heavy waterproof bag slung across her chest, dropping it onto the nearest stool with a wet thud. Delivery for the ghost of Prague. Grey-streaked auburn hair caught the amber glow of the wall sconces as Silas leaned his weight onto his good leg. A silver signet ring tapped twice against the rim of a clean tumbler. You missed your usual window by forty minutes, Rory. Traffic on Oxford Street turned to molasses. Aurora peeled off her soaking gloves, tossing them onto the counter. The movement exposed a small crescent-shaped scar on her left wrist, stark white against cold skin. She traced the indentation with a fingernail, gaze dropping to the floor maps glued beneath the bar’s glass top. Cardiff feels like another century. Silas poured two fingers of amber liquid into the tumbler, pushing the glass across the polished wood. People rarely survive Cardiff without scars. Or exes. Her fingers curled around the glass, though she did not drink. The chill from the street clung to her bones, refusing to yield to the stale heat of the pub. Evan found out about the flat in Cathays. I packed two bags and caught the first National Express coach heading east before the sun cleared the Severn Bridge. A heavy silence settled between them, broken only by the low hum of the refrigeration unit behind the bar. Silas studied the sharp angles of her jaw, noting the absence of the hesitant girl who used to stumble over legal briefs in Welsh libraries. Eva gave you sanctuary. Eva gave me a couch and a delivery route for a dumpling house in Chinatown. Pre-law textbooks sit in a dumpster on Tottenham Court Road. Good riddance to torts and contracts. Aurora finally lifted the glass, letting the liquor burn the back of her throat. She set it down hard enough to splash amber droplets across the wood. You traded MI6 for warm beer and old maps. I traded a courtroom for a bicycle and wet socks. We both fell from grace with magnificent style. Silas traced the grain of the mahogany, a shadow passing across his hazel eyes. Grace is overrated. Survival leaves better souvenirs. The bell above the door rattled again, admitting a gust of wind that smelled of exhaust and wet pavement. Neither of them turned to look. Aurora pulled her damp jacket tighter around her ribs, the bright blue of her eyes hardening into something cold and impenetrable. My father still asks if I made partner at the firm in Bristol. Tell him you joined the circus. It requires less lying. A fragile ghost of a smile touched the corners of her mouth before vanishing entirely. She reached for the strap of her delivery bag, heaving the heavy canvas back over her shoulder. The dumplings get cold if I stand here reminiscing with retired spies. Silas reached down, retrieving a dry towel from beneath the counter and tossing it across the space between them. Keep the towel, Rory. The rain is not stopping tonight.
created_at"2026-07-25T15:06:12.765Z" 7/25/2026, 3:06:12 PM
model"google/gemini-3.5-flash-lite-20260721"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency353
moderation_latency(null)
generation_time4958
tokens_prompt1063
tokens_completion952
native_tokens_prompt1032
native_tokens_completion812
native_tokens_completion_images0
native_tokens_reasoning0
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"STOP"
service_tier"default"
usage0.0023396
router(null)
provider_responses
0
endpoint_id"6bd8f433-79e1-416c-b407-1772eb796c9a"
id"5NBkauqENc7h_uMPzfeEqQ4"
is_byokfalse
latency353
model_permaslug"google/gemini-3.5-flash-lite-20260721"
provider_name"Google AI Studio"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1784991972-rgNyNEz2YwuCqdSBEZfg"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784991972-A9yNasenounk2wGiVkIY"
upstream_id"5NBkauqENc7h_uMPzfeEqQ4"
total_cost0.0023396
cache_discount(null)
upstream_inference_cost0
provider_name"Google AI Studio"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount635
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)
29.13% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount635
totalAiIsms9
found
0
word"familiar"
count1
1
word"footsteps"
count1
2
word"weight"
count1
3
word"stark"
count1
4
word"traced"
count2
5
word"chill"
count1
6
word"silence"
count1
7
word"sanctuary"
count1
highlights
0"familiar"
1"footsteps"
2"weight"
3"stark"
4"traced"
5"chill"
6"silence"
7"sanctuary"
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
narrationSentences54
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences54
filterMatches
0"look"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences54
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen25
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords635
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions0
unquotedAttributions0
matches(empty)
94.88% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions37
wordCount635
uniqueNames23
maxNameDensity1.1
worstName"Silas"
maxWindowNameDensity1.5
worstWindowName"Aurora"
discoveredNames
Raven1
Nest1
Soho1
London1
Aurora5
Prague1
Silas7
Rory2
Oxford1
Street1
Cardiff2
Cathays1
National1
Express1
Severn1
Bridge1
Welsh1
Chinatown1
Tottenham1
Court1
Road1
Bristol1
You3
persons
0"Raven"
1"Nest"
2"Aurora"
3"Silas"
4"Rory"
5"You"
places
0"Soho"
1"London"
2"Prague"
3"Oxford"
4"Street"
5"Cardiff"
6"Cathays"
7"Severn"
8"Bridge"
9"Welsh"
10"Chinatown"
11"Tottenham"
12"Court"
13"Road"
14"Bristol"
globalScore0.949
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences44
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount635
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences54
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs31
mean20.48
std14.78
cv0.721
sampleLengths
050
111
251
35
46
527
66
733
89
97
1046
115
1217
138
1430
1528
1644
174
1825
196
2027
2127
2214
237
2446
2513
2610
2732
2812
2919
3010
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences54
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs93
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences54
ratio0
matches(empty)
82.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount639
adjectiveStacks3
stackExamples
0"over salt-tracked grit."
1"small crescent-shaped scar"
2"stark white against cold skin."
adverbCount15
adverbRatio0.023474178403755867
lyAdverbCount6
lyAdverbRatio0.009389671361502348
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences54
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences54
mean11.76
std6.36
cv0.541
sampleLengths
04
124
222
36
45
522
614
715
85
96
107
1120
126
1320
1413
159
167
1711
1816
1919
205
2117
226
232
2411
2519
268
2720
2819
2925
304
3115
3210
336
3414
3513
369
3710
388
3914
403
414
4219
436
4421
4513
466
474
4815
4917
93.83% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.5740740740740741
totalSentences54
uniqueOpeners31
62.89% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences53
matches
0"Bright blue eyes swept the"
ratio0.019
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount13
totalSentences53
matches
0"You look like a drowned"
1"She unzipped the heavy waterproof"
2"You missed your usual window"
3"She traced the indentation with"
4"Her fingers curled around the"
5"I packed two bags and"
6"She set it down hard"
7"You traded MI6 for warm"
8"I traded a courtroom for"
9"We both fell from grace"
10"My father still asks if"
11"It requires less lying."
12"She reached for the strap"
ratio0.245
16.60% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount47
totalSentences53
matches
0"Glass rattled against wood."
1"Silas wiped the mahogany counter"
2"The bell above the door"
3"Footsteps crunched over salt-tracked grit."
4"Aurora stood just inside the"
5"Water dripped from the hem"
6"Silas stopped moving the rag."
7"You look like a drowned"
8"A sharp, humorless bark escaped"
9"She unzipped the heavy waterproof"
10"Delivery for the ghost of"
11"A silver signet ring tapped"
12"You missed your usual window"
13"Traffic on Oxford Street turned"
14"Aurora peeled off her soaking"
15"The movement exposed a small"
16"She traced the indentation with"
17"Cardiff feels like another century."
18"Silas poured two fingers of"
19"People rarely survive Cardiff without"
ratio0.887
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences53
matches(empty)
ratio0
71.43% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences30
technicalSentenceCount3
matches
0"Silas wiped the mahogany counter with a rag that had long surrendered its original color, his left knee locking with a familiar, dull ache."
1"Silas studied the sharp angles of her jaw, noting the absence of the hesitant girl who used to stumble over legal briefs in Welsh libraries."
2"The bell above the door rattled again, admitting a gust of wind that smelled of exhaust and wet pavement."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
88.3589%