Run: 2026-09-03-01M1KS10YT4TVGBBEWB9X3Y20C

Run Details

Duration:
21.6s
Cost:
$0.003404
Provider:
Together

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 hammered the neon above The Raven’s Nest and turned it to water. Green light bled across the pavement in Soho, caught in puddles and smeared by tires. The sign stuttered once, then held. Detective Harlow Quinn stood under the awning with her collar up and her left wrist exposed to the air. The worn leather watch on her left wrist had stopped at 02:17 three years ago. She kept it anyway. The back door had opened. A figure slipped out with a satchel slung low, salt-and-pepper hair cropped close to the scalp, jaw sharp under the streetlight. It was not her suspect. It was Tomás Herrera. He moved like a man who knew cameras. One glance up the alley, one glance down, then he cut left toward Greek Street. Quinn stepped off the curb after him. Tomás Herrera did not run. He walked fast, Saint Christopher medallion flashing at his throat with each turn of his head. Olive skin darkened by rain. Short curly dark brown hair plastered to his forehead. The scar running along his left forearm caught light when he pushed the satchel higher. Quinn closed the gap in three strides. “You can’t keep moving patients in the street.” He stopped. Did not turn. “I’m not moving a patient.” “You’re moving something.” He turned then. Warm brown eyes met hers. He lifted his chin. “You have a warrant for me, Detective Quinn? Because the last time you came to me with questions, Morris ended up dead.” The name landed flat between them. Quinn felt her jaw set. She had worn the same uniform for eighteen years. Decorated service. Partner lost three years ago under circumstances she could not explain to a tribunal. “I’m not here for you.” “You’re here for the clique.” Rain ran off her cap. She kept her hands visible. “I’m here for a stolen file. It left the Nest in your satchel ten minutes ago.” He laughed once, short and without humor. “There is no file. There’s a boy with a fever you can’t register in your system.” Quinn stepped in. “You lost your license for unauthorized treatments to supernatural patients. That’s a record.” “Record’s clean.” They stood in the mouth of the alley with a taxi hissing past. A busker packed his guitar under a doorway. Neon reflected in a puddle and made the wet bricks look like oil. Tomás shifted his weight. “You’re going to want to come back inside, Detective. The Nest closes in ten.” She did not move. “You’re going to want to put that satchel down.” He didn’t. He looked past her shoulder toward the main street, then back at her face. “Fine. Come with me. But if you step inside the back room, I can’t guarantee you walk out.” A bookshelf in the Raven’s Nest, hidden back room accessible through it, used for clandestine meetings. She knew the layout from surveillance. She knew the maps on the walls, the black-and-white photographs of faces she could not identify. She didn’t go inside. She followed him out. He turned onto Wardour, then cut through a side street that opened onto Charing Cross Road. The rain intensified. He kept his head down, medallion bouncing. She kept her distance, close enough to see the satchel, far enough that he would not feel cornered. At the Tube entrance he paused. Camden Town station loomed in the distance, its tiles slick, its entrance clogged with commuters who had long since gone home. He looked at her. “You don’t know where this goes.” “I know you’re going down.” He stared at the stairwell. A few figures lingered near the entrance, not waiting for a train. One of them held a small bone token between thumb and forefinger. Quinn saw it glint white against wet skin. The Veil Market moved locations every full moon. It was supposed to be beneath Camden this month. Abandoned Tube station. Entry requirement a bone token. She had read the file three times and filed it under folklore. Tomás took a step toward the stairs. The satchel bounced. “Tomás.” He stopped. Did not turn. “If you go down there, I can’t follow.” “You won’t.” “You won’t let me go?” “I won’t let you die.” He finally turned. Rain streamed down his face. “I’m not dying. I’m buying time. The boy needs a draught that isn’t on your pharmacy list. You want the file, you come.” Quinn looked down the stairwell. The air coming up smelled of damp concrete and iron. No lights burned on the lower platforms. A faint hum rose from below, low and constant, like a generator breathing. She thought of DS Morris. Three years ago. Unexplained circumstances. Supernatural origins she did not yet understand. A partner lost in a flat that smelled of copper and salt. She thought of eighteen years of decorated service. Of military precision. Of a sharp jaw and a watch that no longer told time. “Give me the token.” He shook his head. “You don’t have one.” “I have a badge.” “Badge doesn’t open the Veil.” He pulled the satchel strap tighter. The scar on his left forearm stood white against his skin when he flexed. “You have a choice, Detective. You can call it in, seal the street, and watch the Market move again under a different name. Or you can step down with me and find out why your partner died.” Quinn’s breath fogged. The rain painted her face. She could feel the weight of her service pistol at her hip, the weight of the radio silent in her pocket. She had chased a suspect through rain-soaked city streets at night. The chase had taken her from Soho to Camden, from street level down toward a mouth in the ground that should not be open. The stairs were wet. The figures near the entrance shifted, waiting. One of them lifted the bone token higher, as if testing the light. Tomás Herrera stood on the top step, satchel at his side, medallion dark with water. “You have thirty seconds before they close the gate.” Quinn looked at the stairwell. She looked at the street behind her, safe and lit and full of cameras that would record nothing unusual. She looked at the man in front of her who had lost his license for treating patients the Metropolitan Police could not name. She lifted her left hand. The worn leather watch caught rain. She did not move.
created_at"2026-09-03T13:58:39.329Z" 9/3/2026, 1:58:39 PM
model"meta/muse-glimmer-30b-20260810"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency140
moderation_latency(null)
generation_time21537
tokens_prompt1104
tokens_completion2648
native_tokens_prompt1005
native_tokens_completion2240
native_tokens_completion_images(null)
native_tokens_reasoning1034
native_tokens_cached992
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(null)
usage0.00340423
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788443919-BFPotglvftkqaa4zlIAH"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788443919-kH3GPGlM3uPDAU4QQslL"
upstream_id"oyofZnY-7ArivV-a355453ffc9f042c"
provider_responses
0
endpoint_id"e6c588fa-aef0-4e1b-bb7f-78185dd5b6da"
id"oyofZnY-7ArivV-a355453ffc9f042c"
is_byokfalse
latency117
model_permaslug"meta/muse-glimmer-30b-20260810"
provider_name"Together"
status200
total_cost0.00340423
cache_discount0.00030752
upstream_inference_cost0
provider_name"Together"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

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)
wordCount1073
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)
67.38% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1073
totalAiIsms7
found
0
word"weight"
count3
1
word"clandestine"
count1
2
word"loomed"
count1
3
word"glint"
count1
4
word"could feel"
count1
highlights
0"weight"
1"clandestine"
2"loomed"
3"glint"
4"could feel"
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
narrationSentences97
matches(empty)
98.67% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount0
narrationSentences97
filterMatches
0"watch"
1"look"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences124
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen37
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1073
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
95.05% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions49
wordCount819
uniqueNames25
maxNameDensity1.1
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Raven2
Nest2
Soho2
Harlow1
Quinn9
Tomás5
Herrera3
Greek1
Street1
Saint1
Christopher1
Wardour1
Charing1
Cross1
Road1
Tube2
Town1
Veil1
Market1
Camden3
Morris1
Metropolitan1
Police1
Rain3
One3
persons
0"Raven"
1"Nest"
2"Harlow"
3"Quinn"
4"Tomás"
5"Herrera"
6"Saint"
7"Christopher"
8"Wardour"
9"Market"
10"Morris"
11"Police"
12"Rain"
places
0"Soho"
1"Greek"
2"Street"
3"Charing"
4"Cross"
5"Road"
6"Tube"
7"Town"
8"Camden"
globalScore0.951
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences54
glossingSentenceCount1
matches
0"as if testing the light"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1073
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences124
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs63
mean17.03
std13.58
cv0.797
sampleLengths
034
138
25
330
430
550
67
78
85
95
103
1112
1222
1336
145
155
1610
1716
187
1916
2016
212
2234
234
2414
2513
2616
2718
2838
298
3044
3131
326
335
3437
3537
3610
371
385
398
402
415
425
438
4423
4535
4629
4723
484
494
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences97
matches
0"was supposed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs138
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences124
ratio0
matches(empty)
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount824
adjectiveStacks1
stackExamples
0"white against wet skin."
adverbCount18
adverbRatio0.021844660194174758
lyAdverbCount2
lyAdverbRatio0.0024271844660194173
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences124
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences124
mean8.65
std6.17
cv0.714
sampleLengths
013
115
26
319
415
54
65
721
85
94
108
1115
127
135
1416
155
169
1715
187
198
202
213
225
233
243
255
264
2722
286
295
309
312
3214
335
345
355
365
3716
387
3916
403
4113
422
4313
448
4513
464
4714
484
499
40.32% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats12
diversityRatio0.2903225806451613
totalSentences124
uniqueOpeners36
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences92
matches(empty)
ratio0
67.83% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount35
totalSentences92
matches
0"She kept it anyway."
1"It was not her suspect."
2"It was Tomás Herrera."
3"He moved like a man"
4"He walked fast, Saint Christopher"
5"He turned then."
6"He lifted his chin."
7"She had worn the same"
8"She kept her hands visible."
9"He laughed once, short and"
10"They stood in the mouth"
11"She did not move."
12"He looked past her shoulder"
13"She knew the layout from"
14"She knew the maps on"
15"She didn’t go inside."
16"She followed him out."
17"He turned onto Wardour, then"
18"He kept his head down,"
19"She kept her distance, close"
ratio0.38
52.39% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount75
totalSentences92
matches
0"The sign stuttered once, then"
1"Detective Harlow Quinn stood under"
2"The worn leather watch on"
3"She kept it anyway."
4"The back door had opened."
5"A figure slipped out with"
6"It was not her suspect."
7"It was Tomás Herrera."
8"He moved like a man"
9"Quinn stepped off the curb"
10"Tomás Herrera did not run."
11"He walked fast, Saint Christopher"
12"Olive skin darkened by rain."
13"The scar running along his"
14"Quinn closed the gap in"
15"He turned then."
16"He lifted his chin."
17"The name landed flat between"
18"Quinn felt her jaw set."
19"She had worn the same"
ratio0.815
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences92
matches(empty)
ratio0
53.57% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences32
technicalSentenceCount4
matches
0"He turned onto Wardour, then cut through a side street that opened onto Charing Cross Road."
1"Camden Town station loomed in the distance, its tiles slick, its entrance clogged with commuters who had long since gone home."
2"She looked at the street behind her, safe and lit and full of cameras that would record nothing unusual."
3"She looked at the man in front of her who had lost his license for treating patients the Metropolitan Police could not name."
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
85.6407%