Run: 2026-07-25-01KYCPQK6ZBP4TTY5FCCWGMKJQ

Run Details

Duration:
48.7s
Cost:
$0.068420
Provider:
Amazon Bedrock

Model input

System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<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> 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.

Model response

AI
The rain came down like it had a grudge. Quinn heard him before she saw him — the wet slap of trainers on pavement, the sound bouncing off the shuttered fronts of Brewer Street. She'd been standing under the green neon of the Raven's Nest for forty minutes, watching the door, drinking nothing, when a man came out through it sideways with his shoulders hunched and his collar up, and her body knew before her brain did. Olive skin. Curly hair flattened by the wet. And the coat — the coat with the deep pockets, the one caught on the CCTV at the Chalk Farm loading bay at 3:14 a.m. on the ninth, carrying a black medical case away from a body that had bled out on the concrete and then, according to two witnesses she couldn't put on a stand, got up and walked. "Herrera," she called. He ran. Which was, in Quinn's experience of eighteen years, the closest thing to a confession the world ever handed out for free. She went after him. Her boots hit the puddles hard and the neon smeared green across the black water and she felt the old familiar thing come alive in her chest — not excitement exactly, something colder and more useful. Her radio was in her jacket pocket. She left it there. She'd been told, twice now, in a room with a closed door, to stop pulling files on the Chalk Farm incident. She'd said yes, sir. She'd meant it about as much as anyone means anything at half four in the afternoon in a room with a closed door. Herrera was fast. Twenty-nine years old, and he ran like a man who'd spent years hauling stretchers up stairwells — economical, no wasted motion, using the crown of the road where the camber shed water. He cut left down Great Windmill Street and she lost a half-second on the corner, her shoulder clipping a bollard, pain flaring bright and clean along her arm. "Police!" she shouted. "Stop running and this is a conversation!" He glanced back. Just once, at the mouth of the alley. She saw his face under a streetlamp — warm brown eyes gone wide, water running off his jaw, and something in his expression that snagged on her. Not guilt. Not the animal panic of a man who'd done murder. Apology. He looked like he was sorry. Then he was gone into the dark between buildings, and Quinn went after him with rain in her teeth. The alley stank of bins and old fat. Fire escapes dripped. She hurdled a crate of bottles, felt her knee complain — forty-one, she thought, forty-one and chasing a paramedic through Soho — and came out onto Shaftesbury Avenue into headlights and horns. Herrera was already across, dodging a black cab that stopped hard enough to rock on its suspension. The driver leaned on the horn. Quinn went through the gap behind him, one hand slapping the wet metal of the bonnet, and kept going. He wasn't running blind. That was the thing she noticed at Cambridge Circus, and it turned the cold thing in her chest colder. He wasn't taking the obvious routes — not toward the crowds on Charing Cross Road, not toward the Tube at Leicester Square where a crowd could swallow him. He turned north. He turned north like a man following a line he'd walked before, and Quinn thought: he's not escaping. He's arriving. She keyed her radio then. Thumbed it, held it near her mouth, and said nothing. The rain hissed against the plastic. Somewhere behind her, three years back, DS Morris was standing in a doorway telling her to call it in, boss, just call it in, and she had, and the response unit had arrived eleven minutes later to find the door open and Morris gone and the inside of the room smelling faintly of copper and lilacs. They'd written it up as a disappearance. She'd read that report so many times the words had stopped being words. She put the radio away. Herrera led her a mile and a half north through the wet, and Quinn's lungs went from burning to something past burning, a flat mechanical ache she could work with. Bloomsbury. The long grey stretch of Euston Road, buses throwing sheets of water off their wheels. Then the streets narrowed and the graffiti got denser and she smelled the canal, and she knew where they were before the sign told her. Camden. He went off Kentish Town Road down a service lane she'd have sworn was a dead end. There was a hoarding at the end of it — plywood, blue, plastered with fly posters for a club night in 2019 — and a gap in the hoarding held shut with a loop of chain that Herrera lifted off its nail without breaking stride. Quinn reached the gap eight seconds behind him and stopped. Beyond it: a stairwell. Old cream tiles, most of them cracked. A curve of wall with the ghost of a roundel on it, the paint faded to the colour of weak tea, the station name half scoured away. Cold air came up out of it, and the cold air carried things. Woodsmoke. Cardamom. Something like a butcher's shop. And under all of it a low sound, not quite music, that she felt in her back teeth. Down the stairs, fifteen feet below her, Tomás Herrera stood on the landing looking up at her. He was soaked through. His chest heaved. He'd pulled something out from under his collar and was holding it in his fist — a chain, a little medallion, his knuckles white around it. "Don't," he said. His voice came up the tiles strangely, too clear. "Come up here," Quinn said. "Hands where I can see them. You're not in the kind of trouble you think you're in, Tomás. Not yet." "You don't know what's down here." He wiped his mouth with the back of his wrist. She could see, even at that distance, the pale line of an old scar running up his left forearm. Knife wound. Well-healed. Not a hospital job. "You come down those stairs, you're not a detective. There's no warrant. There's nobody to call. Do you understand me? You're just — meat that wandered in." "Was Morris meat?" The words came out before she'd decided to say them, and they landed. She watched them land. Herrera's face did something complicated and awful. "I don't know that name," he said. And then, quieter, worse: "I'm sorry. I don't." He turned and went down. Quinn stood in the gap in the hoarding with the rain running off the plywood and down the back of her collar, and did the arithmetic. She had no backup. She had no jurisdiction over whatever was making that sound. She had a warrant card, a set of cuffs, a torch, and a radio she had already decided not to use because the moment she used it, somebody at a desk would take this away from her the way they had taken the last one, gently, with paperwork. She had eighteen years. Decorated. A pension in nine. She had a photograph of DS Morris on the inside of her locker door, and she had never once in three years been able to say the word *dead* about him, because nobody had ever given her the courtesy of a body. Quinn looked at her watch. Old leather strap, gone dark with sweat and rain, the buckle worn through to brass. 11:52. She noted the time the way she'd noted the time at ten thousand scenes, because if this went badly there might be someone one day who needed to know when it had started. Then she took her hand off the hoarding, and stepped through, and went down into the dark after him — one hand on the tiled wall, her boots ringing on the stairs, the cardamom-and-butcher smell rising to meet her, and the not-music getting louder and louder until it drowned out the rain.
created_at"2026-07-25T13:16:46.693Z" 7/25/2026, 1:16:46 PM
model"anthropic/claude-opus-5-20260723"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3164
moderation_latency(null)
generation_time48556
tokens_prompt888
tokens_completion1863
native_tokens_prompt1264
native_tokens_completion2484
native_tokens_completion_images(null)
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"end_turn"
service_tier"default"
usage0.06842
router(null)
provider_responses
0
endpoint_id"76cb4608-f48c-483d-8da8-9957fb44244e"
id"msg_011CdNqe2uUrSrXeKZS14ifB"
is_byokfalse
latency3164
model_permaslug"anthropic/claude-opus-5-20260723"
provider_name"Amazon Bedrock"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1784985406-VfjQiZ39hhRNMr9FlrxK"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784985406-zDYO4mi3eHoMJvMEMckv"
upstream_id"msg_011CdNqe2uUrSrXeKZS14ifB"
total_cost0.06842
cache_discount(null)
upstream_inference_cost0
provider_name"Amazon Bedrock"
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)
dialogueSentences11
tagDensity0.545
leniency1
rawRatio0
effectiveRatio0
96.25% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1335
totalAiIsmAdverbs1
found
0
adverb"gently"
count1
highlights
0"gently"
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)
92.51% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1335
totalAiIsms2
found
0
word"familiar"
count1
1
word"mechanical"
count1
highlights
0"familiar"
1"mechanical"
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
narrationSentences95
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences95
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences99
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen56
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords1
totalWords1348
ratio0.001
matches
0"dead"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions11
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions47
wordCount1270
uniqueNames26
maxNameDensity0.79
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Brewer1
Street2
Raven1
Nest1
Chalk2
Farm2
Quinn10
Great1
Windmill1
Soho1
Shaftesbury1
Avenue1
Cambridge1
Circus1
Charing1
Cross1
Road3
Tube1
Leicester1
Square1
Morris3
Euston1
Kentish1
Town1
Herrera6
Tomás1
persons
0"Raven"
1"Quinn"
2"Morris"
3"Herrera"
4"Tomás"
places
0"Brewer"
1"Street"
2"Chalk"
3"Farm"
4"Windmill"
5"Soho"
6"Shaftesbury"
7"Avenue"
8"Cambridge"
9"Charing"
10"Cross"
11"Road"
12"Tube"
13"Leicester"
14"Euston"
15"Kentish"
16"Town"
globalScore1
windowScore1
59.09% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences55
glossingSentenceCount2
matches
0"looked like he was sorry"
1"not quite music, that she felt in her back teeth"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1348
matches(empty)
99.33% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount2
totalSentences99
matches
0"read that report"
1"making that sound"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs39
mean34.56
std28.25
cv0.817
sampleLengths
09
168
268
33
42
521
699
763
810
938
1012
117
1219
1385
1474
1577
1620
175
1871
191
2062
2110
2251
2325
2450
253
269
2725
2869
293
3024
3115
325
3326
3462
359
3642
3754
3852
97.88% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences95
matches
0"been told"
1"was gone"
35.80% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount5
totalVerbs203
matches
0"wasn't running"
1"wasn't taking"
2"was standing"
3"was holding"
4"was making"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount12
semicolonCount0
flaggedSentences10
totalSentences99
ratio0.101
matches
0"Quinn heard him before she saw him — the wet slap of trainers on pavement, the sound bouncing off the shuttered fronts of Brewer Street."
1"And the coat — the coat with the deep pockets, the one caught on the CCTV at the Chalk Farm loading bay at 3:14 a.m."
2"Her boots hit the puddles hard and the neon smeared green across the black water and she felt the old familiar thing come alive in her chest — not excitement exactly, something colder and more useful."
3"Twenty-nine years old, and he ran like a man who'd spent years hauling stretchers up stairwells — economical, no wasted motion, using the crown of the road where the camber shed water."
4"She saw his face under a streetlamp — warm brown eyes gone wide, water running off his jaw, and something in his expression that snagged on her."
5"She hurdled a crate of bottles, felt her knee complain — forty-one, she thought, forty-one and chasing a paramedic through Soho — and came out onto Shaftesbury Avenue into headlights and horns."
6"He wasn't taking the obvious routes — not toward the crowds on Charing Cross Road, not toward the Tube at Leicester Square where a crowd could swallow him."
7"There was a hoarding at the end of it — plywood, blue, plastered with fly posters for a club night in 2019 — and a gap in the hoarding held shut with a loop of chain that Herrera lifted off its nail without breaking stride."
8"He'd pulled something out from under his collar and was holding it in his fist — a chain, a little medallion, his knuckles white around it."
9"Then she took her hand off the hoarding, and stepped through, and went down into the dark after him — one hand on the tiled wall, her boots ringing on the stairs, the cardamom-and-butcher smell rising to meet her, and the not-music getting louder and louder until it drowned out the rain."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1266
adjectiveStacks0
stackExamples(empty)
adverbCount38
adverbRatio0.030015797788309637
lyAdverbCount7
lyAdverbRatio0.005529225908372828
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences99
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences99
mean13.62
std12.94
cv0.95
sampleLengths
09
125
243
32
46
525
635
73
82
921
104
1136
127
134
1421
154
1623
173
1832
1928
203
217
223
238
2427
252
2610
271
286
2919
308
313
3232
3317
346
3519
364
3719
3828
393
4018
412
425
4310
446
4556
467
4713
485
4930
62.59% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats12
diversityRatio0.4489795918367347
totalSentences98
uniqueOpeners44
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount5
totalSentences77
matches
0"Just once, at the mouth"
1"Then he was gone into"
2"Somewhere behind her, three years"
3"Then the streets narrowed and"
4"Then she took her hand"
ratio0.065
32.99% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount36
totalSentences77
matches
0"She'd been standing under the"
1"She went after him."
2"Her boots hit the puddles"
3"Her radio was in her"
4"She left it there."
5"She'd been told, twice now,"
6"She'd said yes, sir."
7"She'd meant it about as"
8"He cut left down Great"
9"He glanced back."
10"She saw his face under"
11"He looked like he was"
12"She hurdled a crate of"
13"He wasn't running blind."
14"He wasn't taking the obvious"
15"He turned north."
16"He turned north like a"
17"She keyed her radio then."
18"They'd written it up as"
19"She'd read that report so"
ratio0.468
100.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount55
totalSentences77
matches
0"The rain came down like"
1"Quinn heard him before she"
2"She'd been standing under the"
3"She went after him."
4"Her boots hit the puddles"
5"Her radio was in her"
6"She left it there."
7"She'd been told, twice now,"
8"She'd said yes, sir."
9"She'd meant it about as"
10"Herrera was fast."
11"He cut left down Great"
12"He glanced back."
13"She saw his face under"
14"He looked like he was"
15"The alley stank of bins"
16"She hurdled a crate of"
17"Herrera was already across, dodging"
18"The driver leaned on the"
19"Quinn went through the gap"
ratio0.714
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences77
matches(empty)
ratio0
57.82% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences42
technicalSentenceCount5
matches
0"on the ninth, carrying a black medical case away from a body that had bled out on the concrete and then, according to two witnesses she couldn't put on a stand,…"
1"Twenty-nine years old, and he ran like a man who'd spent years hauling stretchers up stairwells — economical, no wasted motion, using the crown of the road wher…"
2"He cut left down Great Windmill Street and she lost a half-second on the corner, her shoulder clipping a bollard, pain flaring bright and clean along her arm."
3"She saw his face under a streetlamp — warm brown eyes gone wide, water running off his jaw, and something in his expression that snagged on her."
4"Then she took her hand off the hoarding, and stepped through, and went down into the dark after him — one hand on the tiled wall, her boots ringing on the stair…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount0
matches(empty)
59.09% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences11
tagDensity0.455
leniency0.909
rawRatio0.2
effectiveRatio0.182
83.1113%