Run: 2026-07-25-01KYCXK0NW8ETHHV9DVPXTC0KD

Run Details

Duration:
2.5s
Cost:
$0.001310
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
Rain slicked the asphalt of Camden High Street, turning the sodium lights into bleeding streaks of amber and rust. Detective Harlow Quinn adjusted the collar of her trench coat against the chill, her boots splashing through puddles with military precision. Forty-one years of pavement-pounding left her joints aching, yet adrenaline kept the fatigue at bay. Five feet nine inches of granite focus, she narrowed her brown eyes through the downpour. Ahead, a slim figure in a dark hoodie ducked beneath the corrugated steel awning of an abandoned Underground station. Water sluiced from the rusted iron rafters, hammering a frantic rhythm against the concrete steps leading down into the subterranean dark. Quinn placed a hand on her left wrist, thumb brushing the worn leather of her watch. Eighteen years on the force taught her to smell traps before springing them. The descent smelled of copper, damp earth, and ozone—the distinct signature of the Veil Market. Footsteps echoed off the tiled walls below, rhythmic and mocking. "Stop right there, police!" The runner vanished around a blind corner where the tiles gave way to raw, hacked-out rock. Quinn drew her service weapon, the steel cold against her palm. She descended the final slick steps, flashlight beam cutting through the gloom to illuminate a pile of discarded bone tokens scattered across the turnstile gate. The air grew heavy, thick with the scent of unwashed wool, heavy spices, and something metallic that made the hairs on her arms stand up. She reached the threshold of the forbidden tunnel. Beyond lay a labyrinth of abandoned tracks and cavernous platforms repurposed by Soho's underbelly for trade in things the daylight never touched. Shadows stretched long and distorted under flickering emergency beacons. A rusted train car groaned in the distance, metal grinding against metal. She stood at the precipice of the underworld, boot toe hovering over the platform edge where the concrete dissolved into absolute blackness.
created_at"2026-07-25T15:16:36.675Z" 7/25/2026, 3:16:36 PM
model"google/gemini-3.5-flash-lite-20260721"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency370
moderation_latency(null)
generation_time2490
tokens_prompt1104
tokens_completion509
native_tokens_prompt1042
native_tokens_completion399
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"
usage0.0013101
router(null)
provider_responses
0
endpoint_id"6bd8f433-79e1-416c-b407-1772eb796c9a"
id"VNNkaqaLLtW9jrEP-PulyQo"
is_byokfalse
latency370
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-1784992596-hYuBl5tsu7EvkBCio7qj"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784992596-7F813klswIw47turrLJq"
upstream_id"VNNkaqaLLtW9jrEP-PulyQo"
total_cost0.0013101
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)
wordCount319
totalAiIsmAdverbs0
found(empty)
highlights(empty)
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)
5.96% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount319
totalAiIsms6
found
0
word"chill"
count1
1
word"pounding"
count1
2
word"footsteps"
count1
3
word"echoed"
count1
4
word"rhythmic"
count1
5
word"gloom"
count1
highlights
0"chill"
1"pounding"
2"footsteps"
3"echoed"
4"rhythmic"
5"gloom"
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
narrationSentences19
matches(empty)
67.67% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences19
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences20
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
totalWords318
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions0
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions11
wordCount314
uniqueNames9
maxNameDensity0.96
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Camden1
High1
Street1
Harlow1
Quinn3
Underground1
Veil1
Market1
Soho1
persons
0"Harlow"
1"Quinn"
places
0"Camden"
1"High"
2"Street"
3"Soho"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences19
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount318
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences20
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs9
mean35.33
std32.06
cv0.907
sampleLengths
070
119
265
310
44
516
6100
712
822
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences19
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs50
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences20
ratio0.05
matches
0"The descent smelled of copper, damp earth, and ozone—the distinct signature of the Veil Market."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount318
adjectiveStacks0
stackExamples(empty)
adverbCount2
adverbRatio0.006289308176100629
lyAdverbCount1
lyAdverbRatio0.0031446540880503146
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences20
echoCount0
echoWords(empty)
83.18% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences20
mean15.9
std5.69
cv0.358
sampleLengths
019
121
215
315
419
521
616
713
815
910
104
1116
1211
1325
1425
158
1622
179
1812
1922
100.00% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats0
diversityRatio0.75
totalSentences20
uniqueOpeners15
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences19
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount3
totalSentences19
matches
0"She descended the final slick"
1"She reached the threshold of"
2"She stood at the precipice"
ratio0.158
91.58% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount14
totalSentences19
matches
0"Rain slicked the asphalt of"
1"Detective Harlow Quinn adjusted the"
2"Water sluiced from the rusted"
3"Quinn placed a hand on"
4"The descent smelled of copper,"
5"Footsteps echoed off the tiled"
6"The runner vanished around a"
7"Quinn drew her service weapon,"
8"She descended the final slick"
9"The air grew heavy, thick"
10"She reached the threshold of"
11"Shadows stretched long and distorted"
12"A rusted train car groaned"
13"She stood at the precipice"
ratio0.737
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences19
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences17
technicalSentenceCount0
matches(empty)
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
84.9460%