Run: 2026-10-09-01M4G06FAR2T1YQNEG7MXWP6V3

Run Details

Duration:
13.5s
Cost:
$0.001141
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
The rain came sideways off the Regent's Canal, needling the back of Quinn's neck where her collar had given up. Ahead, Tomás Herrera vaulted a row of bollards on Camden High Street and didn't look back. The denim jacket he wore was soaked black, and it flapped behind him like a torn sail. "Herrera! Stop, or I'll put you on the pavement!" He laughed. The sound carried over the traffic hiss and the thump of a bass line from a pub doorway. "You won't. You've got a rule about shooting people in the back, Detective. I've read your file." Quinn's shoes slapped the wet paving stones. Her knee complained on every stride, a reminder of the stairwell in Hackney last spring. She pulled her radio from her belt. "Control, Quinn. Pursuing north on Camden High, one male, mid-thirties, dark jacket. Requesting units at Chalk Farm Road." The radio crackled and died. Static, then nothing. She glanced at the worn leather watch on her left wrist. Twelve minutes past midnight. The hands had stopped working on her radio and the watch kept ticking, which was the sort of small wrongness she had stopped trying to explain to herself three years ago. Herrera swerved left into a lane between a shuttered kebab shop and a boarded-up record store. Quinn followed, her breath sawing in her chest. The alley narrowed to the width of her shoulders. Wet cardboard, a rat's tail vanishing into a drain, the sour reek of spilled lager. "You lost someone too, didn't you?" Herrera called over his shoulder. "Morris. Your partner." Quinn kept running. The name landed like a fist somewhere under her ribs. "Don't say his name." "I'm only saying what everyone at the Nest already knows. You've been sniffing around us for months. You think we put him in the river. We didn't. But you won't listen to that, will you?" "I'll listen when you're in cuffs." He burst out at the far end of the alley onto a patch of cracked tarmac behind the station, where the railings bent outward like broken ribs. At the centre of the railings, a set of stone steps descended into the ground. Above them, the old Underground roundel had been stripped of its paint, leaving only a grey ghost of a circle. Someone had nailed a bone-white token to the frame of the entrance, and it caught the sodium light like an eye. Herrera stopped at the top of the steps. He pulled something from his pocket and held it up. It was a small carved piece of bone, threaded on a black cord. He turned it between his fingers, and his Saint Christopher medallion swung against his chest. "You don't have one of these, Detective," he said. "Not a real one. Not the kind that opens the door." Quinn's hand went to the inside pocket of her coat. Her fingers closed around a plastic evidence bag, creased and damp. Inside lay a single bone token, recovered from beneath DS Morris's body in a flooded car park three years ago. The lab had returned it as "unidentified animal material." She had never once believed that. She drew it out. Rain beaded on the bag's surface. "Where did you get that?" Herrera asked, and for the first time his voice lost its easy tilt. He moved two steps closer to the stairs, his eyes fixed on the bag. "Where do you think?" Quinn said. "Your people left it in my partner's hand." "Then you know what it costs to go down there." "I know what it cost me to come this far." He studied her face in the orange light, the way a doctor might read a chart before delivering bad news. His scarred forearm flexed. "Below, the Market moves every full moon. Tonight it's underneath us. Sellers who don't ask questions. Buyers who don't leave with their own names. You walk in with a warrant and a badge, you walk out with nothing. Or you don't walk out." Quinn looked down the steps. Cold air breathed up from the dark, carrying the scent of wet stone, burnt sugar, and something metallic beneath it, like a coin held too long in the mouth. Far below, faint lights wavered, violet and amber, and somewhere a bell rang once. She could call it in again. She could wait at the top of the steps for backup that would never come through the static. Or she could follow a man she didn't trust into the place that had swallowed Morris whole. Quinn tightened her grip on the bag, tucked her wet hair behind one ear, and set her foot on the first step.
created_at"2026-10-09T09:34:05.664Z" 10/9/2026, 9:34:05 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3009
moderation_latency(null)
generation_time13518
tokens_prompt1104
tokens_completion1279
native_tokens_prompt1576
native_tokens_completion1966
native_tokens_completion_images(null)
native_tokens_reasoning477
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"
usage0.0011406
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791538445-bOf4fyWRVyUsUasjvXXV"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791538445-w2OHop4X9b4Q4ACwKiJ2"
upstream_id"msg_011CfrS8cJwJv3oGANDUQKzg"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrS8cJwJv3oGANDUQKzg"
is_byokfalse
latency834
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0011406
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
totalTags5
adverbTagCount0
adverbTags(empty)
dialogueSentences17
tagDensity0.294
leniency0.588
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount779
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)
87.16% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount779
totalAiIsms2
found
0
word"reminder"
count1
1
word"wavered"
count1
highlights
0"reminder"
1"wavered"
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
narrationSentences47
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences47
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences59
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen43
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords779
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions6
unquotedAttributions0
matches(empty)
81.03% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions25
wordCount580
uniqueNames13
maxNameDensity1.38
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Quinn"
discoveredNames
Regent1
Canal1
Quinn8
Tomás1
Herrera5
Camden1
High1
Street1
Hackney1
Underground1
Saint1
Christopher1
Morris2
persons
0"Regent"
1"Canal"
2"Quinn"
3"Tomás"
4"Herrera"
5"Saint"
6"Christopher"
7"Morris"
places
0"Camden"
1"High"
2"Street"
3"Hackney"
globalScore0.81
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences36
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount779
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences59
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs23
mean33.87
std20.92
cv0.618
sampleLengths
053
19
237
347
454
548
614
717
835
96
1083
1146
1220
1356
1410
1532
1614
1710
1810
1967
2048
2141
2222
97.80% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences47
matches
0"been stripped"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs87
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences59
ratio0
matches(empty)
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount582
adjectiveStacks1
stackExamples
0"Far below, faint lights"
adverbCount13
adverbRatio0.022336769759450172
lyAdverbCount1
lyAdverbRatio0.001718213058419244
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences59
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences59
mean13.2
std8.25
cv0.625
sampleLengths
020
116
217
39
42
518
617
77
815
97
1018
115
123
1311
144
1531
1616
178
189
1915
2011
213
223
2310
244
2535
266
2727
2815
2920
3021
318
3210
3313
3415
359
3611
3710
3811
3920
4015
414
426
4318
4414
456
468
4710
4810
4920
81.36% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.5084745762711864
totalSentences59
uniqueOpeners30
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences44
matches(empty)
ratio0
92.73% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount14
totalSentences44
matches
0"Her knee complained on every"
1"She pulled her radio from"
2"She glanced at the worn"
3"He burst out at the"
4"He pulled something from his"
5"It was a small carved"
6"He turned it between his"
7"Her fingers closed around a"
8"She drew it out."
9"He moved two steps closer"
10"He studied her face in"
11"His scarred forearm flexed."
12"She could call it in"
13"She could wait at the"
ratio0.318
62.27% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount35
totalSentences44
matches
0"The rain came sideways off"
1"The denim jacket he wore"
2"The sound carried over the"
3"Quinn's shoes slapped the wet"
4"Her knee complained on every"
5"She pulled her radio from"
6"The radio crackled and died."
7"Static, then nothing."
8"She glanced at the worn"
9"The hands had stopped working"
10"Herrera swerved left into a"
11"Quinn followed, her breath sawing"
12"The alley narrowed to the"
13"Herrera called over his shoulder"
14"Quinn kept running."
15"The name landed like a"
16"He burst out at the"
17"Someone had nailed a bone-white"
18"Herrera stopped at the top"
19"He pulled something from his"
ratio0.795
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences44
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences27
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags5
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount0
fancyTags(empty)
dialogueSentences17
tagDensity0.235
leniency0.471
rawRatio0
effectiveRatio0
89.8784%