Run: 2026-09-29-01M3P2KZ8HEPE3RJX0A4J32FAW

Run Details

Duration:
30.6s
Cost:
$0.031662
Provider:
Claude Platform on AWS

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
Rain hammered Camden Lock in slanting sheets, turning the streetlamps into smeared halos and the pavement into a black mirror. Detective Harlow Quinn kept to the shadow of the shuttered market stalls and watched Tomás Herrera cross the road ahead of her, collar up, head down, one hand pressed against the chest of his jacket as though guarding something there. She had followed him from Soho. Forty minutes on the Northern line, three carriages back, watching his reflection in the window glass. He had left The Raven's Nest by the side door at half past eleven, and no man walked that quickly with a bag that small unless what was in it mattered. She checked the worn leather watch on her left wrist. 12:07. Forty yards of open road between them. Then Herrera glanced back. It was a small movement, a flick of the head, the sort of look a man gives when the hair on his neck has already told him. His eyes found her across the wet dark. Even at that distance she saw the recognition land. He ran. "Police!" Quinn shouted, and was moving before the word finished leaving her mouth. "Herrera, stop!" He didn't. He took the corner into Chalk Farm Road at a full sprint, curls plastered to his forehead, shoes slapping through puddles. Quinn ran the way she had been trained to, long and economical, breath in for three strides, out for three. Eighteen years on the job and a decade of mornings on the parade ground before that had given her lungs that didn't quit. Herrera was twelve years younger, but he ran like a man carrying weight, and the gap closed by a step, then another. He cut left through a gap in the railings, and she followed him onto the towpath. The canal lay flat and oily beside them. Water dripped from the brick arches overhead. Somewhere above, a train rumbled through, and the vibration ran up through the soles of her boots. "You're making this worse!" she called. "Whatever's in that bag, talk to me!" He said something over his shoulder. She caught only a word, *no*, or maybe *go*, tossed back at her in an accent thickened by fear. The towpath narrowed. Herrera vaulted a low chain, skidded on the slick stone, and caught himself with his left hand. Even in the dark she saw the pale line of the scar down his forearm, the old knife wound she'd read about in the file. He was up again in a heartbeat, the Saint Christopher medallion swinging from his neck and flashing silver. Quinn hurdled the chain and felt her knee complain. Ignored it. Ahead, the towpath opened onto a service yard, all wet gravel and chain-link, half hidden behind a hoarding plastered with faded posters for a demolished nightclub. Herrera didn't slow. He hit a section of the hoarding with his shoulder and it swung inward on a hidden hinge. Quinn stopped at the gap, chest heaving, rain running off her cropped grey hair and down the sharp line of her jaw. Beyond the hoarding lay a brick building she'd have sworn wasn't on any map she'd studied. A low arched entrance, the tiles of an old Tube station still clinging to its facade in patches of oxblood red. The roundel above the door had been scraped off long ago. Green lantern-light seeped up from a stairwell within. Herrera was already at the top of the stairs. A figure stood there, broad and motionless, wearing a long coat that seemed too heavy for the weather. Herrera dug in his pocket and held something out. Quinn caught only a glimpse, something small and pale, carved. The big man inspected it, nodded, and stepped aside. Herrera glanced back one last time. The look on his face wasn't triumph. It was warning. Then he went down into the light. Quinn stayed where she was, and the rain went on falling. She knew what she ought to do. Call it in. Get a unit to the address, get a warrant, get backup, get anyone. Four years ago she'd have done exactly that, followed procedure step by step until the case sat clean and unassailable on a prosecutor's desk. But procedure hadn't saved DS Morris. Procedure had been in the room three years ago when Morris walked through a door in a warehouse in Bermondsey and never walked out, and nothing in the report had made sense, and nobody at the Yard had wanted her asking why. She had seen the things in that room. She had spent three years telling herself she hadn't. Her hand went to her phone. The screen showed one bar of signal, flickering. Under the ground there would be none. "Christ," she muttered. The doorman had turned his head toward her. He didn't move, but she felt his attention settle on her like a hand on the back of her neck. His face was in shadow beneath a hood, and he wasn't shaped quite right. The shoulders sat too wide, the head too still. Quinn made herself breathe. Anyone stepping through that door without whatever Herrera had shown was walking into something she had no measure for. No radio. No partner. No one who knew where she was. A hidden place that hid itself well, run by people who did not care about her warrant card. And Herrera was down there, the one thread she had found in three years. The clique, as she'd started calling them in her notebook, the barkeep and the fixers and the disgraced paramedic who patched up wounds that no hospital should have seen. If she let him vanish into that green light, the thread would snap, and she would be back to staring at Morris's photograph and wondering. Something small clicked against the gravel near her boot. She looked down. In the puddle at the gap in the hoarding lay a pale object the length of her thumb, carved with tiny whorls. Herrera must have lost it when he vaulted the chain, or pushed through the hoarding. She crouched and picked it up. It was bone, unmistakably, and warmer than it had any right to be. The whorls seemed to shift under her thumb, like a pulse. A token. She turned it over once. Anyone else would have called it evidence and bagged it. Quinn slid it into her coat pocket, unclipped the catch on her holster, and squared her shoulders the way she had on a hundred cold mornings before a hundred doors. "All right, Morris," she said quietly, to nobody. "Let's see what you found." She crossed the yard with the measured stride of a woman who belonged there. At the top of the stairs the doorman's hood tilted toward her. She held out the bone token on her flat palm, and did not let it shake. For a long moment, the rain was the only sound. Then the big figure stepped aside, and the green light rose up the stairwell to meet her.
created_at"2026-09-29T07:56:09.907Z" 9/29/2026, 7:56:09 AM
model"anthropic/claude-sonnet-5.5-20260928"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency4190
moderation_latency(null)
generation_time30570
tokens_prompt888
tokens_completion1945
native_tokens_prompt1266
native_tokens_completion2913
native_tokens_completion_images(null)
native_tokens_reasoning733
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"
usage0.031662
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1790668569-1AW0R6yyX3xjNQbJ9Y3Y"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790668569-eW73dhcdbv5ieKWZ18Pv"
upstream_id"msg_011CfXNaKiEaeZrDRfTxfg84"
provider_responses
0
endpoint_id"99aaad94-923b-4fc1-b763-271ed5486f7a"
id"msg_011CfXNaKiEaeZrDRfTxfg84"
is_byokfalse
latency740
model_permaslug"anthropic/claude-sonnet-5.5-20260928"
provider_name"Claude Platform on AWS"
status200
total_cost0.031662
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
0.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags4
adverbTagCount1
adverbTags
0"she said quietly [quietly]"
dialogueSentences7
tagDensity0.571
leniency1
rawRatio0.25
effectiveRatio0.25
95.74% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1173
totalAiIsmAdverbs1
found
0
adverb"quickly"
count1
highlights
0"quickly"
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)
82.95% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1173
totalAiIsms4
found
0
word"weight"
count1
1
word"facade"
count1
2
word"pulse"
count1
3
word"measured"
count1
highlights
0"weight"
1"facade"
2"pulse"
3"measured"
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
narrationSentences92
matches(empty)
96.27% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount2
narrationSentences92
filterMatches
0"watch"
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences95
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen42
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans2
markdownWords2
totalWords1173
ratio0.002
matches
0"no"
1"go"
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
totalMentions39
wordCount1150
uniqueNames19
maxNameDensity0.96
worstName"Herrera"
maxWindowNameDensity2
worstWindowName"Herrera"
discoveredNames
Camden1
Lock1
Harlow1
Quinn9
Tomás1
Herrera11
Soho1
Northern1
Raven1
Nest1
Chalk1
Farm1
Road1
Saint1
Christopher1
Tube1
Morris3
Bermondsey1
Yard1
persons
0"Lock"
1"Harlow"
2"Quinn"
3"Tomás"
4"Herrera"
5"Raven"
6"Saint"
7"Christopher"
8"Morris"
places
0"Soho"
1"Chalk"
2"Farm"
3"Road"
4"Bermondsey"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences74
glossingSentenceCount1
matches
0"as though guarding something there"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1173
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences95
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs35
mean33.51
std25.54
cv0.762
sampleLengths
060
153
218
34
444
52
615
788
848
913
1025
1163
1211
1347
1422
1556
1655
1716
187
1911
2095
2117
2221
233
2451
2552
2668
279
2870
292
3045
3113
3242
3310
3417
97.64% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences92
matches
0"been trained"
1"been scraped"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs194
matches
0"was warning"
1"was walking"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences95
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1152
adjectiveStacks0
stackExamples(empty)
adverbCount35
adverbRatio0.030381944444444444
lyAdverbCount8
lyAdverbRatio0.006944444444444444
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences95
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences95
mean12.35
std8.53
cv0.691
sampleLengths
020
140
26
316
431
510
61
77
84
927
108
119
122
1313
142
152
1621
1720
1823
1922
2016
218
227
2317
246
257
266
2719
283
2917
3025
3118
329
332
3426
353
3618
3722
3816
3921
4011
418
429
4318
449
4510
469
476
487
493
68.44% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.43617021276595747
totalSentences94
uniqueOpeners41
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences83
matches
0"Then Herrera glanced back."
1"Somewhere above, a train rumbled"
2"Then he went down into"
3"Then the big figure stepped"
ratio0.048
99.52% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount25
totalSentences83
matches
0"She had followed him from"
1"He had left The Raven's"
2"She checked the worn leather"
3"It was a small movement,"
4"His eyes found her across"
5"He took the corner into"
6"He cut left through a"
7"He said something over his"
8"She caught only a word,"
9"He was up again in"
10"He hit a section of"
11"It was warning."
12"She knew what she ought"
13"She had seen the things"
14"She had spent three years"
15"Her hand went to her"
16"He didn't move, but she"
17"His face was in shadow"
18"She looked down."
19"She crouched and picked it"
ratio0.301
100.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount59
totalSentences83
matches
0"Detective Harlow Quinn kept to"
1"She had followed him from"
2"He had left The Raven's"
3"She checked the worn leather"
4"It was a small movement,"
5"His eyes found her across"
6"Quinn shouted, and was moving"
7"He took the corner into"
8"Quinn ran the way she"
9"Herrera was twelve years younger,"
10"He cut left through a"
11"The canal lay flat and"
12"Water dripped from the brick"
13"He said something over his"
14"She caught only a word,"
15"The towpath narrowed."
16"Herrera vaulted a low chain,"
17"He was up again in"
18"Quinn hurdled the chain and"
19"Herrera didn't slow."
ratio0.711
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount3
totalSentences83
matches
0"Even at that distance she"
1"Even in the dark she"
2"If she let him vanish"
ratio0.036
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences47
technicalSentenceCount2
matches
0"Detective Harlow Quinn kept to the shadow of the shuttered market stalls and watched Tomás Herrera cross the road ahead of her, collar up, head down, one hand p…"
1"A figure stood there, broad and motionless, wearing a long coat that seemed too heavy for the weather."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount2
fancyTags
0"Quinn shouted (shout)"
1"she muttered (mutter)"
dialogueSentences7
tagDensity0.571
leniency1
rawRatio0.5
effectiveRatio0.5
91.3518%