Run: 2026-07-25-01KYCTS6JPV21TPDN507SDB72Q

Run Details

Duration:
4.4s
Cost:
$0.002080
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
Water hammered the asphalt. Detective Harlow Quinn checked the worn leather watch on her left wrist, her sharp jaw clenched against the drizzle stinging her eyes. Tomás Herrera rounded the corner ahead, his dark curls plastered to his skull, boots splashing through the gutters of Soho. She surged forward, military precision driving her strides past the buzzing green neon sign of The Raven's Nest. The suspect ducked into an abandoned Tube station beneath Camden, vanishing beneath the rusted iron gates. Harlow reached the threshold, her hand dropping to the holster at her hip. The air smelled of ozone, stagnant water, and ancient dust rising from the concrete bowels of the earth. She descended the stone steps into absolute blackness, the roar of the city traffic fading into a hollow, subterranean echo. A rusted ticket barrier blocked the route. She vaulted the waist-high turnstile, boots slamming onto the landing below. Shadows stretched long across the tiled walls, warped by the amber glow of flicker bulbs dangling from frayed cords. The tunnel yawned wide, swallowed by the damp dark. Footsteps clattered ahead, bouncing off the curved ceiling. Harlow drew her service weapon, the steel cold against her palm. She swept the flashlight beam across graffiti-scarred pillars and discarded crates. The tunnel split three ways fifty yards ahead. "Stop right there, Herrera," she shouted, the demand ricocheting down the damp conduit. Tomás stepped out from behind a concrete pillar, his olive skin slick with sweat, the Saint Christopher medallion catching the beam of her light. "You walk down here, Detective, nobody pulls you back up," Tomás said, pressing his back against the brickwork. "You skipped out on three counts of illegal distribution, Tommy. End of the line." Harlow advanced, her boots crunching on broken glass. "You have no idea what circulates in these tunnels," Tomás warned, gesturing to the deeper darkness where the tracks rusted into oblivion. "This isn't your jurisdiction. Leave before you find things science dropped." "I find suspects. Turn around." Harlow raised the Glock, stepping closer. "Wrong door, Quinn," Tomás muttered, turning his back and plunging through a heavy velvet curtain hanging across a side maintenance tunnel. Harlow reached the threshold and pushed the velvet aside. The stale smell of the underground vanished, replaced instantly by the thick, sweet reek of roasting cloves and ozone. Torches flared along rough-hewn stone walls, casting chaotic orange light over a sprawling subterranean bazaar built inside a disused rail siding. Scores of cloaked figures huddled around crates stacked high with glowing glass vials, pelts of unidentifiable beasts, and humming mechanical trinkets. Chatter echoed off the vaulted ceiling, a cacophony of hissed whispers and heavy bartering. At the far end of the market, Tomás disappeared into a dense cluster of stalls selling jars of pulsing, bioluminescent moss. Harlow lowered her weapon slightly, her thumb resting on the safety. The crowd parted just enough to let her pass, hooded merchants fixing her with cold, predatory stares. A massive creature with skin like cracked basalt hefted a cleaver behind a counter of bone fragments, watching her every move. She stood at the precipice of the Veil Market, her pulse hammering against her ribs, weighing the badge in her pocket against the sheer unknown swallowing the suspect ahead.
created_at"2026-07-25T14:27:33.604Z" 7/25/2026, 2:27:33 PM
model"google/gemini-3.5-flash-lite-20260721"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency375
moderation_latency(null)
generation_time4232
tokens_prompt1104
tokens_completion878
native_tokens_prompt1042
native_tokens_completion707
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"
usage0.0020801
router(null)
provider_responses
0
endpoint_id"6bd8f433-79e1-416c-b407-1772eb796c9a"
id"1cdkapGtMfW-jMcP1YDNkQY"
is_byokfalse
latency375
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-1784989653-ZkOdLvuFrhRLuD1OTiuF"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784989653-r0CmNRhJ1vfn5ILPNlf6"
upstream_id"1cdkapGtMfW-jMcP1YDNkQY"
total_cost0.0020801
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
totalTags6
adverbTagCount0
adverbTags(empty)
dialogueSentences7
tagDensity0.857
leniency1
rawRatio0
effectiveRatio0
90.74% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount540
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)
0.00% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount540
totalAiIsms11
found
0
word"jaw clenched"
count1
1
word"echo"
count1
2
word"flicker"
count1
3
word"footsteps"
count1
4
word"velvet"
count2
5
word"chaotic"
count1
6
word"mechanical"
count1
7
word"echoed"
count1
8
word"cacophony"
count1
9
word"pulse"
count1
highlights
0"jaw clenched"
1"echo"
2"flicker"
3"footsteps"
4"velvet"
5"chaotic"
6"mechanical"
7"echoed"
8"cacophony"
9"pulse"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"jaw/fists clenched"
count1
highlights
0"jaw clenched"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences33
matches(empty)
99.57% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences33
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences34
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen29
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords540
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
77.69% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions25
wordCount484
uniqueNames14
maxNameDensity1.45
worstName"Harlow"
maxWindowNameDensity2.5
worstWindowName"Tomás"
discoveredNames
Harlow7
Quinn1
Herrera1
Soho1
Raven1
Nest1
Tube1
Camden1
Tomás6
Saint1
Christopher1
Glock1
Veil1
Market1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Raven"
4"Camden"
5"Tomás"
6"Saint"
7"Christopher"
8"Glock"
places
0"Soho"
globalScore0.777
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences32
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount540
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences34
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs20
mean27
std14.24
cv0.528
sampleLengths
046
118
216
351
47
539
68
730
813
924
1018
1122
1233
1311
1421
1528
1656
1721
1849
1929
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences33
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs88
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences34
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount487
adjectiveStacks0
stackExamples(empty)
adverbCount6
adverbRatio0.012320328542094456
lyAdverbCount2
lyAdverbRatio0.004106776180698152
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences34
echoCount0
echoWords(empty)
86.57% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences34
mean15.88
std5.82
cv0.366
sampleLengths
04
122
220
318
416
513
618
720
87
911
1019
119
128
1311
1411
158
1613
1724
1818
1922
2022
2111
2211
2321
249
2519
2621
2721
2814
2921
3011
3117
3221
3329
82.35% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.5294117647058824
totalSentences34
uniqueOpeners18
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences33
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount6
totalSentences33
matches
0"She surged forward, military precision"
1"She descended the stone steps"
2"She vaulted the waist-high turnstile,"
3"She swept the flashlight beam"
4"she shouted, the demand ricocheting"
5"She stood at the precipice"
ratio0.182
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount32
totalSentences33
matches
0"Water hammered the asphalt."
1"Detective Harlow Quinn checked the"
2"Tomás Herrera rounded the corner"
3"She surged forward, military precision"
4"The suspect ducked into an"
5"Harlow reached the threshold, her"
6"The air smelled of ozone,"
7"She descended the stone steps"
8"A rusted ticket barrier blocked"
9"She vaulted the waist-high turnstile,"
10"Shadows stretched long across the"
11"The tunnel yawned wide, swallowed"
12"Footsteps clattered ahead, bouncing off"
13"Harlow drew her service weapon,"
14"She swept the flashlight beam"
15"The tunnel split three ways"
16"she shouted, the demand ricocheting"
17"Tomás stepped out from behind"
18"Tomás said, pressing his back"
19"Harlow advanced, her boots crunching"
ratio0.97
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences33
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences21
technicalSentenceCount1
matches
0"She stood at the precipice of the Veil Market, her pulse hammering against her ribs, weighing the badge in her pocket against the sheer unknown swallowing the s…"
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount2
matches
0"she shouted, the demand ricocheting down the damp conduit"
1"Harlow advanced, her boots crunching on broken glass"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount3
fancyTags
0"she shouted (shout)"
1"Tomás warned (warn)"
2"Tomás muttered (mutter)"
dialogueSentences7
tagDensity0.571
leniency1
rawRatio0.75
effectiveRatio0.75
77.8972%