Run: 2026-09-29-01M3P2A4RKJKF3H1ZB46BWPFSA

Run Details

Duration:
34.7s
Cost:
$0.035182
Provider:
Claude Platform on AWS

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 Camden High Street, turning the gutters into black rivers that swallowed the neon. Quinn ran through them. Thirty metres ahead, Tomás Herrera cut between a kebab shop and a shuttered vape store, his jacket collar up, one hand clamped over something inside his coat. The other hand held the Saint Christopher medallion against his chest so it wouldn't bounce. "Police! Herrera, stop!" He didn't stop. He glanced back, and the streetlight caught his face. Wide eyes, wet curls plastered to his forehead. Then he was gone around the corner. Quinn's boots slapped the pavement. Eighteen years on the job had taught her that men who run carry something worth running with. Men who run *there*, toward the canal and the railway arches, carry something worse. She took the corner hard. An alley opened in front of her, narrow and slick, stacked with wet cardboard and beer kegs. Herrera vaulted a pallet at the far end. His trainers skidded on the landing, and he caught himself with his left hand. The scar on his forearm showed pale against the grime. "You're making this worse!" Her breath came out in clouds. "I only want to talk!" "Nobody wants to talk with a warrant in their pocket, Detective!" "I don't have a warrant!" "Then go home!" He shoved a dumpster over behind him. It crashed across the alley, spilling bin bags and a slop of something rancid. Quinn hit it with her hip, planted a palm on the lid and swung her legs over. Her watch band bit into her wrist. She landed in the mess and kept moving. The alley spat them onto a service road under the railway arches. Water dripped from the brickwork in ropes. A train rumbled overhead and shook loose more rain, and the noise swallowed her next shout. Herrera sprinted past a row of lock-ups with their shutters down and their padlocks rusted. He didn't slow. He didn't look for a hiding place. He ran like a man with a destination. That bothered her more than the running. "Herrera! Whatever's in that coat, it isn't worth it!" "You don't know what's in the coat!" "Then tell me!" "You wouldn't believe me!" He veered left through a gap in a chain-link fence, where somebody had peeled the wire back and twisted it into a doorway. Quinn ducked through after him. A barb caught her sleeve and tore the wool from elbow to cuff. She didn't feel it. Beyond the fence, a slab of old Victorian brick rose out of the weeds. A stairwell gaped in its base, with iron railings gone orange and a tiled sign so grimed she could read only two letters. The steps dropped into darkness. Green-white light flickered somewhere below, like a faulty fluorescent tube. Herrera took the steps three at a time and vanished. Quinn stopped at the top. Rain ran off her chin. Her lungs burned. She dragged in air and held her hand flat against her ribs, and for a moment the only sounds were the downpour and her pulse. Abandoned station. She knew the type. The Underground had dozens of them, sealed off after the war, bricked up and forgotten. Nobody had told her about one under Camden. Nobody had told her about a working light either. Music drifted up the stairwell, thin and strange, strings played on something that wasn't a violin. Voices layered beneath it. Dozens of them. Haggling, laughing, a bark of argument. Somebody had built a party in a tomb. She thumbed her radio. "Control, this is Quinn. Foot pursuit, Camden, near the railway arches off the High Street. I'm at a disused Tube entrance. Request backup." Static answered, thick as gravel. Then a single word, warped and slow, like a tape dragged through mud. "—unable—" She tried again. Nothing. The screen on the handset glowed dead green and then went black. "Brilliant," she muttered, and shoved it back on her belt. Her phone gave her no signal. She turned in a slow circle. The city lay all around her, sodium orange and ordinary, and she stood at the edge of it like a swimmer on a cliff. Three years ago she had stood at the edge of something else. A warehouse in Deptford. Morris had gone in first, because Morris always went in first, grinning, one hand raised to wave her back. *Give me two minutes, Harlow.* She had given him two minutes. She had given him ten. When she finally went in, she found his coat on the concrete floor and nothing else. No blood. No body. The inquiry called it a disappearance, and the file was still open in her bottom drawer. Her jaw tightened until her teeth ached. Below her, a door slammed, and the music swelled and cut off. Quinn checked her sidearm's holster with her thumb. She wasn't going to draw it. She wanted to feel that the weight was there. Procedure said wait. Procedure said secure the perimeter, log the location, come back at dawn with a team and a warrant and a van full of officers in body armour. Procedure hadn't watched a man's coat fall empty to the floor. Footsteps scraped on the stairs. She slid her hand to her holster's clasp and stepped behind the brick pillar. A woman climbed out of the dark, tall, draped in a coat that looked stitched from black feathers. Her eyes were rimmed in gold paint. She paused at the top and sniffed the air like a dog, then looked straight at the pillar. "You can come out, love. You smell of wet wool and gun oil." Quinn stepped into the light. "Police." "Yes, I gathered." The woman tilted her head. Rain slid off the feathers without soaking them. "You've got the walk. You've got the watch. What you haven't got is a token." "A what?" "Bone token. Little thing, carved, about the size of a thumb joint." She held up a hand and showed nothing but empty fingers, then smiled. "No token, no entry. Your friend had one. Cost him dearly, I'd wager." "The man who just ran past you. Tomás Herrera. Where's he going?" "Down." The woman shrugged. "Everyone goes down. That's the point of down." "What is this place?" "A market." She pulled her coat tighter. "You'd call it a crime scene. We call it Thursday." Quinn looked past her at the stairwell. The green-white light throbbed, and something in the throb resembled breathing. The air rising from the stairs smelled of hot metal, burnt sugar, and old rain. "If I go down there without your token," she said, "what happens?" "Depends who spots you first." The woman stepped aside and swept her arm toward the steps, a doorman's courtesy. "Some would sell you a drink. Some would sell you to someone else. Most won't notice you at all, which is worse, if you think about it." "And if I stay up here?" "Then you're a wet woman on a cold night with a dead radio and a runner who's already gone." She lifted one gold-rimmed eye. "Your choice, Detective." "I never said I was a detective." "You didn't have to." Quinn stood very still. Rain drummed on her shoulders and ran down the small of her back. Somewhere far below, a bell rang once, low and round, and every voice in the market hushed for the length of the note. Herrera was down there. So was whatever he'd carried in his coat. So, maybe, was the answer to what had taken Morris, waiting in the dark like a stone at the bottom of a well. She unclasped her watch, turned it over, and read the inscription on the back, the one Morris had scratched there himself with a pocketknife on her fortieth birthday. Two words. *Wait less.* She buckled the strap tight again. "Mind the fourth step," the woman said. "It bites." Quinn walked past her and put her boot on the first stair.
created_at"2026-09-29T07:50:47.976Z" 9/29/2026, 7:50:47 AM
model"anthropic/claude-sonnet-5.5-20260928"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency4157
moderation_latency(null)
generation_time34483
tokens_prompt1104
tokens_completion2115
native_tokens_prompt1576
native_tokens_completion3203
native_tokens_completion_images(null)
native_tokens_reasoning570
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"end_turn"
service_tier"default"
usage0.035182
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1790668248-MagFeW6XWJN24KQvyNNp"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790668248-oXRwvGxvbW9JIp5nENNL"
upstream_id"msg_011CfXNAbeX5Kg2FtBVdctdb"
provider_responses
0
endpoint_id"99aaad94-923b-4fc1-b763-271ed5486f7a"
id"msg_011CfXNAbeX5Kg2FtBVdctdb"
is_byokfalse
latency794
model_permaslug"anthropic/claude-sonnet-5.5-20260928"
provider_name"Claude Platform on AWS"
status200
total_cost0.035182
cache_discount(null)
upstream_inference_cost0
provider_name"Claude Platform on AWS"
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
totalTags10
adverbTagCount1
adverbTags
0"The woman stepped aside [aside]"
dialogueSentences36
tagDensity0.278
leniency0.556
rawRatio0.1
effectiveRatio0.056
96.24% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1331
totalAiIsmAdverbs1
found
0
adverb"very"
count1
highlights
0"very"
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)
77.46% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1331
totalAiIsms6
found
0
word"flickered"
count1
1
word"pulse"
count1
2
word"weight"
count1
3
word"footsteps"
count1
4
word"throbbed"
count1
5
word"throb"
count1
highlights
0"flickered"
1"pulse"
2"weight"
3"footsteps"
4"throbbed"
5"throb"
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
narrationSentences110
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences110
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences135
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen28
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans3
markdownWords8
totalWords1330
ratio0.006
matches
0"there"
1"Give me two minutes, Harlow."
2"Wait less."
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions36
wordCount1069
uniqueNames14
maxNameDensity0.94
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Camden2
High1
Street1
Tomás1
Herrera5
Saint1
Christopher1
Victorian1
Underground1
Deptford1
Morris4
Rain4
Quinn10
Procedure3
persons
0"Tomás"
1"Herrera"
2"Saint"
3"Christopher"
4"Underground"
5"Morris"
6"Rain"
7"Quinn"
8"Procedure"
places
0"Camden"
1"High"
2"Street"
3"Deptford"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences73
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1330
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences135
matches
0"feel that the"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs60
mean22.17
std18.71
cv0.844
sampleLengths
019
142
23
327
436
554
615
711
85
93
1053
1168
127
139
147
153
164
1745
1852
1910
205
2133
2238
2329
248
2527
2618
271
2816
2910
3036
3187
327
3312
3453
3511
3619
3743
3813
396
4031
412
4238
4312
4412
454
4617
4733
4812
4946
98.88% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences110
matches
0"was gone"
1"were rimmed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs180
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount1
semicolonCount0
flaggedSentences1
totalSentences135
ratio0.007
matches
0"\"—unable—\""
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount695
adjectiveStacks0
stackExamples(empty)
adverbCount15
adverbRatio0.02158273381294964
lyAdverbCount3
lyAdverbRatio0.004316546762589928
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences135
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences135
mean9.85
std6.63
cv0.673
sampleLengths
015
14
227
315
43
53
69
78
87
95
1017
1114
125
1317
148
1514
1610
1710
185
1911
205
213
227
2314
2417
257
268
2712
287
2916
3015
313
327
338
347
359
367
373
384
3923
405
4113
424
4314
4423
455
4610
4710
485
495
64.20% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats10
diversityRatio0.42962962962962964
totalSentences135
uniqueOpeners58
98.04% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences102
matches
0"Then he was gone around"
1"Then a single word, warped"
2"Somewhere far below, a bell"
ratio0.029
86.67% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount34
totalSentences102
matches
0"He didn't stop."
1"He glanced back, and the"
2"She took the corner hard."
3"His trainers skidded on the"
4"Her breath came out in"
5"He shoved a dumpster over"
6"It crashed across the alley,"
7"Her watch band bit into"
8"She landed in the mess"
9"He didn't slow."
10"He didn't look for a"
11"He ran like a man"
12"He veered left through a"
13"She didn't feel it."
14"Her lungs burned."
15"She dragged in air and"
16"She knew the type."
17"She thumbed her radio."
18"She tried again."
19"she muttered, and shoved it"
ratio0.333
38.43% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount86
totalSentences102
matches
0"Quinn ran through them."
1"The other hand held the"
2"He didn't stop."
3"He glanced back, and the"
4"Quinn's boots slapped the pavement."
5"Men who run *there*, toward"
6"She took the corner hard."
7"An alley opened in front"
8"Herrera vaulted a pallet at"
9"His trainers skidded on the"
10"The scar on his forearm"
11"Her breath came out in"
12"He shoved a dumpster over"
13"It crashed across the alley,"
14"Quinn hit it with her"
15"Her watch band bit into"
16"She landed in the mess"
17"The alley spat them onto"
18"Water dripped from the brickwork"
19"A train rumbled overhead and"
ratio0.843
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences102
matches(empty)
ratio0
93.02% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences43
technicalSentenceCount3
matches
0"Rain hammered Camden High Street, turning the gutters into black rivers that swallowed the neon."
1"Men who run *there*, toward the canal and the railway arches, carry something worse."
2"Music drifted up the stairwell, thin and strange, strings played on something that wasn't a violin."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags10
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount1
fancyTags
0"she muttered (mutter)"
dialogueSentences36
tagDensity0.083
leniency0.167
rawRatio0.333
effectiveRatio0.056
91.7649%