Run: 2026-07-25-01KYCT3G9C35M0Z2BMYYHB03AE

Run Details

Duration:
4.5s
Cost:
$0.002295
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="4416" name="Detective Harlow Quinn" aliases="Quinn, Detective Quinn, Harlow" pronouns="she/her" species="Human" storyRole="Antagonist"> <eyeColor>brown</eyeColor> <hair>closely cropped salt-and-pepper</hair> <height>5'9"</height> <age>41</age> <jawline>sharp jaw</jawline> <watch>worn leather watch on her left wrist</watch> <bearing>military precision</bearing> <description>A Metropolitan Police detective with 18 years of decorated service who suspects the clique is involved in criminal activity.</description> <backstory>She lost her partner, DS Morris, during a case three years ago under unexplained circumstances that had supernatural origins she doesn't yet understand.</backstory> </character> <character id="4417" name="Tomás Herrera" aliases="Tommy, Tomás, Herrera" pronouns="he/him" species="Human" storyRole="Support"> <eyeColor>warm brown</eyeColor> <hair>short curly dark brown</hair> <height>5'10"</height> <age>29</age> <skinTone>olive skin</skinTone> <scar>scar running along his left forearm from a knife attack</scar> <medallion>Saint Christopher medallion around his neck</medallion> <birthplace>Seville, Spain</birthplace> <description>A former paramedic who provides off-the-books medical care for the clique.</description> <backstory>Born in Seville, Spain. He moved to London to work for the NHS but lost his license after administering unauthorized treatments to supernatural patients.</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> <location id="856" name="The Veil Market" aliases="Veil Market, the Market, underground market" realm="Earth"> <currentLocation>abandoned Tube station beneath Camden</currentLocation> <entryRequirement>bone token</entryRequirement> <schedule>moves locations every full moon</schedule> <description>A hidden supernatural black market that sells enchanted goods, banned alchemical substances, and information.</description> </location> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the thriller genre. A detective is pursuing a suspect through rain-soaked city streets at night. The chase takes them from the street level down into an underground market. The detective must decide whether to follow the suspect into unfamiliar and potentially dangerous territory. 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 Soho asphalt gleamed under greasy yellow sodium lights. A worn leather strap dug into Detective Harlow Quinn’s left wrist, the old brass hands of her watch ticking past midnight. Rain slicked her closely cropped salt-and-pepper hair against her temples. Sharp jaw set, she bolted past the buzzing green neon sign of The Raven's Nest, boots slapping rhythmically against the puddles. Ahead, a slim figure in a dark hoodie darted around the corner of Greek Street, slipping into the shadows of the abandoned Camden Tube station stairs. Harlow closed the distance with military precision, her hand hovering over the grip of her service weapon. Water cascaded down the rusted corrugated iron roofing of the old descent, pouring into the stairwell like a broken dam. Cobwebs and soot choked the air. She hit the bottom landing, flashlights beams cutting through the gloom of the defunct ticketing hall. Turnstile gates hung off their hinges. Graffiti smeared the tiled walls in violent strokes of neon pink and acid green. The suspect ducked beneath a rusted security grille marked with hazard stripes. Harlow vaulted the broken barrier, the impact jarring her knees. The smell hit her first—ozone, wet earth, and the unmistakable metallic tang of spilled blood. She descended deeper into the bowels of the earth, past the dead tracks where moss claimed the wooden sleepers. A heavy iron door at the end of the tunnel stood ajar, bleeding faint violet light onto the ballast. Harlow reached the threshold and jammed her shoulder against the iron, shoving it wide. Rows of makeshift stalls stretched into the subterranean dark, lit by flickering lanterns hung from vaulted brick ceilings. Huddled figures in heavy wool coats and cowls lurked over crates piled high with glowing vials, animal pelts, and bone charms. Murmurs and low hisses rippled through the cavernous space as heads snapped toward the intruder. A vendor tossed a fistful of dried roots into a copper brazier, sending up a choking column of blue smoke that smelled of rotting plums. The market swallowed sound. Harlow scanned the sea of watchful faces, her thumb resting on her holster. A towering man with reptilian scales creeping up his neck blocked the primary aisle, clutching a rusted cleaver. The target vanished down a narrow subterranean tunnel marked by crumbling Victorian masonry. Harlow stepped over a threshold of scattered salt lines, her boots crunching on the stone floor. A sharp voice cut through the hum of the subterranean bazaar. You take one more step down here, officer, and you won't walk back up. Tomás Herrera stepped out from behind a stack of wooden crates, rolling up his sleeves to reveal a jagged scar running along his left forearm. A heavy Saint Christopher medallion swung against his chest as he shifted his weight. I want the runner, Tomás. You chase ghosts you cannot cage. Tomás crossed his arms, planting his boots firmly on the damp flagstones. That runner carries answers about DS Morris. Answers cost blood in this market. Harlow narrowed her eyes, studying the dark mouth of the tunnel where the suspect disappeared. I make my own currency. You walk blind into the deep tunnels, and you feed the dark. Tomás pulled a small glass vial from his pocket, turning it over in his palm. I survived three years of this, Tomás. You survived luck. This place doesn't trade in luck. Harlow took a deliberate step past the former paramedic, her boots sinking slightly into the mud of the unpaved corridor. Watch my back then. Tomás let out a dry breath, shaking his head as he pulled a heavy iron pipe from a crate.
created_at"2026-07-25T14:15:42.646Z" 7/25/2026, 2:15:42 PM
model"google/gemini-3.5-flash-lite-20260721"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency269
moderation_latency(null)
generation_time4505
tokens_prompt1104
tokens_completion935
native_tokens_prompt1042
native_tokens_completion793
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"
usage0.0022951
router(null)
provider_responses
0
endpoint_id"6bd8f433-79e1-416c-b407-1772eb796c9a"
id"DsVkaqm-KsCS-8YP-bHWiAY"
is_byokfalse
latency269
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-1784988942-oO2ASTQIgwWYwPkfxulp"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784988942-syn1HZFI8h9H8jsh7sv4"
upstream_id"DsVkaqm-KsCS-8YP-bHWiAY"
total_cost0.0022951
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
91.74% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount605
totalAiIsmAdverbs1
found
0
adverb"slightly"
count1
highlights
0"slightly"
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)
66.94% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount605
totalAiIsms4
found
0
word"gloom"
count1
1
word"scanned"
count1
2
word"weight"
count1
3
word"firmly"
count1
highlights
0"gloom"
1"scanned"
2"weight"
3"firmly"
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
narrationSentences45
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences45
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences45
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen26
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords604
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions0
unquotedAttributions0
matches(empty)
66.67% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions32
wordCount604
uniqueNames17
maxNameDensity1.32
worstName"Harlow"
maxWindowNameDensity3
worstWindowName"Tomás"
discoveredNames
Soho1
Detective1
Harlow8
Quinn1
Raven1
Nest1
Greek1
Street1
Camden1
Tube1
Victorian1
Herrera1
Saint1
Christopher1
Tomás6
Morris1
You4
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Herrera"
4"Saint"
5"Christopher"
6"Tomás"
7"Morris"
8"You"
places
0"Soho"
1"Detective"
2"Greek"
3"Street"
globalScore0.838
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences40
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount604
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences45
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs30
mean20.13
std18.3
cv0.909
sampleLengths
061
126
259
320
412
544
619
714
879
94
1031
1113
1216
1311
1414
1539
165
176
1812
197
206
2115
225
2312
2415
257
269
2720
284
2919
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences45
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs106
matches(empty)
79.37% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences45
ratio0.022
matches
0"The smell hit her first—ozone, wet earth, and the unmistakable metallic tang of spilled blood."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount607
adjectiveStacks1
stackExamples
0"under greasy yellow sodium"
adverbCount7
adverbRatio0.011532125205930808
lyAdverbCount4
lyAdverbRatio0.006589785831960461
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences45
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences45
mean13.42
std6.14
cv0.458
sampleLengths
09
121
210
321
426
517
620
76
816
96
1014
1112
1210
1315
1419
1519
1614
1718
1821
1915
2025
214
2213
2318
2413
2516
2611
2714
2825
2914
305
316
3212
337
346
3515
365
3712
3815
397
403
416
4220
434
4419
77.78% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats0
diversityRatio0.4666666666666667
totalSentences45
uniqueOpeners21
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences45
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount9
totalSentences45
matches
0"She hit the bottom landing,"
1"She descended deeper into the"
2"You take one more step"
3"I want the runner, Tomás."
4"You chase ghosts you cannot"
5"I make my own currency."
6"You walk blind into the"
7"I survived three years of"
8"You survived luck."
ratio0.2
4.44% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount41
totalSentences45
matches
0"The Soho asphalt gleamed under"
1"A worn leather strap dug"
2"Rain slicked her closely cropped"
3"Harlow closed the distance with"
4"Water cascaded down the rusted"
5"Cobwebs and soot choked the"
6"She hit the bottom landing,"
7"Turnstile gates hung off their"
8"Graffiti smeared the tiled walls"
9"The suspect ducked beneath a"
10"Harlow vaulted the broken barrier,"
11"The smell hit her first—ozone,"
12"She descended deeper into the"
13"A heavy iron door at"
14"Harlow reached the threshold and"
15"Rows of makeshift stalls stretched"
16"Murmurs and low hisses rippled"
17"A vendor tossed a fistful"
18"The market swallowed sound."
19"Harlow scanned the sea of"
ratio0.911
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences45
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences32
technicalSentenceCount1
matches
0"A vendor tossed a fistful of dried roots into a copper brazier, sending up a choking column of blue smoke that smelled of rotting plums."
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
86.0311%