Run: 2026-10-08-01M4D9W8MH7FYE1RNEX1XF08PC

Run Details

Duration:
14.5s
Cost:
$0.001138
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
The rain had been falling since dusk, and by eleven it had soaked through the shoulders of Harlow Quinn's coat and found the gap at her collar. She didn't notice. Her eyes were fixed on the figure ahead, a dark shape cutting between umbrellas on Camden High Street, moving with the loose, economical stride of someone who knew exactly how far he had to go. Tomás Herrera did not look back. That bothered her more than if he had. She had watched him for six weeks. A former paramedic with a clean record that had been scrubbed a little too neatly, a man who had lost his NHS license and kept his hands in the work anyway. Every file she pulled said the same thing: he was kind to the wrong people. Tonight he had walked out of a rented flat in Kentish Town carrying a canvas bag that clinked when he moved, and she had known, the way she knew most things after eighteen years on the job, that he was not going to a hospital. "Tommy," she said under her breath, and then louder, into the radio clipped at her shoulder. "Quinn. Subject heading north on the High Street. Requesting backup at Chalk Farm Road." Static answered. Then a voice she didn't recognise: "Say again, Detective. We've got nothing on the grid." She glanced at the worn leather watch on her left wrist. Eleven-oh-four. The rain ran off its strap and down the back of her hand. She didn't wait for the radio to clear. Herrera turned left without slowing, past the shuttered market stalls and the chained-up bicycles, and Quinn lengthened her stride. Her shoes slapped the wet pavement. Neon bled across the puddles in red and blue, the smell of frying oil and river mud rising from the drains. Ahead, the street narrowed toward the mouth of a Tube station, its old red sign dark and its glass entrance boarded with plywood. The plywood had been pried loose at one corner, and the gap looked like a mouth. He slipped through it. Quinn stopped at the edge of the pavement and breathed, slowly, the way she had been taught in a training hall a long time ago. Her thoughts went where they always went when she was about to do something foolish. Three years ago, DS Morris had gone into a warehouse in Walthamstow after a suspect who had no business being alive, and he had come out of it as something she still had no word for. Nobody had explained it to her. Nobody had explained anything. There had been a report with blank pages and a closed file, and a partner's chair that no one had wanted to fill. She had sworn she would not go into the dark alone again without knowing what the dark was. Her hand went to her coat pocket. Inside was her warrant card, her notebook, and a small, cold object she had taken from a dead man's jacket the week before, a flat disc of yellowed bone with a sigil scratched into its face. She had not yet worked out what it was. The sigil had matched a symbol she'd found etched inside the Nest's back room, a bookshelf that opened like a door, and that was where the trail had begun to feel like something other than a case. She pulled the bone disc out and turned it over in her palm. The sigil seemed to catch the light from the streetlamp and hold it a moment too long. Down the steps, a sound rose up through the gap in the plywood. Not voices, exactly. A low, layered murmur, like a crowd heard through water, and beneath it a clean metallic note, as if someone were tapping a glass with a spoon. It was the night of the full moon. She knew that without looking, the way she knew the rain would not stop. The Veil Market moved on full moons. That was the only thing her informant had said before he stopped answering his phone. She could call it in. She could stand here and wait, and let Herrera vanish into whatever lay underground, and write a report that no one would read beyond the first page. Or she could go down and find out whether anything she had built her career on still made sense. Quinn tucked the bone disc into her palm, closed her fist around it, and stepped through the broken plywood. The air changed at once. It was warmer, thick with incense and damp stone, and the rain's hiss fell away behind her as if a door had shut. The stairwell curved downward into a dim orange glow. Her footsteps echoed. At the bottom, a figure in a grey hood sat on an overturned crate beside a turnstile that had been painted over with white symbols. "Bone," the hooded figure said, without looking up. Quinn opened her fist. The figure's head tilted, and in the shadow of the hood she caught the faint gleam of eyes that did not reflect the light the way human eyes should. "Police," it said softly. "Your friend went through ten minutes ago. He didn't have a token either. He paid in something else." "What did he pay?" The figure smiled. "Blood, Detective. Yours is cheaper." Quinn stepped forward, past the turnstile, and the market opened below her like a wound in the earth.
created_at"2026-10-08T08:25:33.808Z" 10/8/2026, 8:25:33 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency14190
moderation_latency(null)
generation_time14190
tokens_prompt888
tokens_completion1238
native_tokens_prompt1266
native_tokens_completion2022
native_tokens_completion_images(null)
native_tokens_reasoning397
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"
usage0.0011376
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791447933-zu95GRdrn5tl2KDCgOYc"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791447933-qmg7XasejuAqrBqxhaLz"
upstream_id"msg_011CfpT6cPhpobojNLAUrGHe"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpT6cPhpobojNLAUrGHe"
is_byokfalse
latency1003
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0011376
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"it said softly [softly]"
dialogueSentences8
tagDensity0.5
leniency1
rawRatio0.25
effectiveRatio0.25
88.97% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount907
totalAiIsmAdverbs2
found
0
adverb"slowly"
count1
1
adverb"softly"
count1
highlights
0"slowly"
1"softly"
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)
83.46% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount907
totalAiIsms3
found
0
word"etched"
count1
1
word"footsteps"
count1
2
word"echoed"
count1
highlights
0"etched"
1"footsteps"
2"echoed"
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
narrationSentences57
matches(empty)
92.73% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences57
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)
analyzedSentences61
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen45
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords907
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions22
wordCount854
uniqueNames15
maxNameDensity0.7
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Harlow1
Quinn6
Camden1
High1
Street1
Herrera3
Kentish1
Town1
Static1
Tube1
Morris1
Walthamstow1
Nest1
Veil1
Market1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Static"
4"Morris"
5"Market"
places
0"Camden"
1"High"
2"Street"
3"Kentish"
4"Town"
5"Walthamstow"
6"Nest"
globalScore1
windowScore1
94.44% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences45
glossingSentenceCount1
matches
0"looked like a mouth"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount907
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences61
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs23
mean39.43
std31.53
cv0.799
sampleLengths
065
114
298
330
417
533
685
74
8109
918
1089
1130
1243
1344
1451
1519
1665
178
184
1951
204
218
2218
74.48% Passive voice overuse
Target: ≤2% passive sentences
passiveCount5
totalSentences57
matches
0"were fixed"
1"been scrubbed"
2"been pried"
3"been taught"
4"been painted"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs140
matches
0"was not going"
1"were tapping"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences61
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount857
adjectiveStacks0
stackExamples(empty)
adverbCount24
adverbRatio0.028004667444574097
lyAdverbCount6
lyAdverbRatio0.007001166861143524
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences61
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences61
mean14.87
std10.43
cv0.702
sampleLengths
027
13
235
36
48
57
631
715
845
916
1014
112
1215
1311
141
1513
168
1719
186
1921
2023
2116
224
2325
2415
2536
266
274
2823
2918
307
3136
329
3337
3413
3517
3613
373
3827
398
4014
417
4215
435
4427
4519
4619
475
4823
499
81.42% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.5081967213114754
totalSentences61
uniqueOpeners31
60.61% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences55
matches
0"Then a voice she didn't"
ratio0.018
74.55% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount20
totalSentences55
matches
0"She didn't notice."
1"Her eyes were fixed on"
2"She had watched him for"
3"she said under her breath,"
4"She glanced at the worn"
5"She didn't wait for the"
6"Her shoes slapped the wet"
7"He slipped through it."
8"Her thoughts went where they"
9"She had sworn she would"
10"Her hand went to her"
11"She had not yet worked"
12"She pulled the bone disc"
13"It was the night of"
14"She knew that without looking,"
15"She could call it in."
16"She could stand here and"
17"It was warmer, thick with"
18"Her footsteps echoed."
19"it said softly"
ratio0.364
32.73% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount47
totalSentences55
matches
0"The rain had been falling"
1"She didn't notice."
2"Her eyes were fixed on"
3"Tomás Herrera did not look"
4"That bothered her more than"
5"She had watched him for"
6"A former paramedic with a"
7"Every file she pulled said"
8"Tonight he had walked out"
9"she said under her breath,"
10"She glanced at the worn"
11"The rain ran off its"
12"She didn't wait for the"
13"Herrera turned left without slowing,"
14"Her shoes slapped the wet"
15"Neon bled across the puddles"
16"The plywood had been pried"
17"He slipped through it."
18"Quinn stopped at the edge"
19"Her thoughts went where they"
ratio0.855
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences55
matches(empty)
ratio0
0.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences31
technicalSentenceCount7
matches
0"Her eyes were fixed on the figure ahead, a dark shape cutting between umbrellas on Camden High Street, moving with the loose, economical stride of someone who k…"
1"A former paramedic with a clean record that had been scrubbed a little too neatly, a man who had lost his NHS license and kept his hands in the work anyway."
2"Tonight he had walked out of a rented flat in Kentish Town carrying a canvas bag that clinked when he moved, and she had known, the way she knew most things aft…"
3"The sigil had matched a symbol she'd found etched inside the Nest's back room, a bookshelf that opened like a door, and that was where the trail had begun to fe…"
4"A low, layered murmur, like a crowd heard through water, and beneath it a clean metallic note, as if someone were tapping a glass with a spoon."
5"It was warmer, thick with incense and damp stone, and the rain's hiss fell away behind her as if a door had shut."
6"At the bottom, a figure in a grey hood sat on an overturned crate beside a turnstile that had been painted over with white symbols."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount1
matches
0"figure said, without looking up"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount0
fancyTags(empty)
dialogueSentences8
tagDensity0.375
leniency0.75
rawRatio0
effectiveRatio0
79.4466%