Run: 2026-10-08-01M4E1GF0X5RYGS0PD1E82FCBB

Run Details

Duration:
14.1s
Cost:
$0.001175
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 had found the gap between Quinn's collar and her neck within a minute of leaving the car, and it kept working its way down her spine as she ran. Greek Street ran slick and black under the streetlamps, the neon of the strip clubs bleeding red and blue into the puddles. Ahead, the man in the grey parka cut left without slowing, shoulder-checking a delivery cyclist who swore at his back. "Metropolitan Police. Stop where you are, Tomás." He didn't stop. He glanced back, just once, and she caught the lamplight on his face: warm brown eyes, wet curls plastered flat against his forehead, a mouth set in the expression of a man doing sums he didn't like. His left sleeve had ridden up as he pulled a hand out of his pocket to steady himself against a shuttered shopfront. The scar along his forearm showed pale and ridged in the orange light. Quinn gained a yard on the corner. Her knee complained on the kerb. Eighteen years of pounding pavement had taught her the difference between a man who was scared and a man who knew the route, and Tomás Herrera knew the route. "You're making this harder than it needs to be," she called. He laughed, a short breath that carried over the traffic. "You're the one chasing me in the rain, Detective." They crossed Shaftesbury Avenue against the lights. A taxi braked hard, horn blaring. Quinn slapped the bonnet as she passed and kept moving. Her watch caught the light on her left wrist, the old leather strap darkened with damp. Three years ago, almost to the day, she had watched a different man run through streets like these, and she had not caught him in time. Her thumb rubbed the worn face of the watch before she could stop it. Tomás cut into the mouth of an alley off Lexington Street. Quinn followed, slowing as the walls closed in. Bins lined the left side. A drainpipe dripped. The alley smelled of wet cardboard and something sweeter underneath, like flowers left too long in a vase. "Where are you taking me?" she said. "Nowhere you'll enjoy." He was breathing harder now. "Go home, Quinn. Go home and forget my name." "I've got your name on a warrant. Tomás Herrera, unlicensed treatment of a patient who died in a flat in Hackney with a hole in his chest that no blade put there. You want to tell me what kind of medicine does that?" He stopped at the far end of the alley, one hand braced on the brick, and turned. Rain ran off his jaw. For a moment he looked at her the way a doctor looks at a chart he already knows is bad. "Not medicine," he said. "Triage. And you're about to triage yourself." He pushed off the wall and ducked through a gap in the iron railings at the end of the alley. Quinn reached the railings and saw what lay beyond. A set of stone steps descended into a stairwell, and at the bottom, a lamp glowed a sickly violet. Someone had nailed a board across the top step. Chalked on it, in careful lettering, was a single word: CLOSED. Below that, in different hands, someone had written a list of prices that did not match any currency she recognised. Her radio crackled on her shoulder. "Quinn, what's your location? Backup's six minutes out." She keyed the handset. "Greek Street, heading south. He's gone underground. Stairwell off Lexington, behind the bins." "Say again? Underground where?" Quinn didn't answer. She stared down the steps. A cold draught rose up from below, carrying the scent of rust, lamp oil, and something metallic she could not place. Somewhere far beneath her, footsteps echoed off tile, unhurried now, as if the man descending had all the time in the world. She weighed it the way she had weighed every bad decision in her career: the cost of acting against the cost of waiting. Backup would arrive in six minutes. Six minutes was long enough for a suspect to vanish into a warren she had never mapped. It was also long enough for her to think about Morris, about the last thing he had said to her before the lights went out in that cellar and he never came back up. She checked the magazine in her holster, then pulled the torch from her belt and flicked it on. The beam shook, then steadied. Her knuckles were white against the railings. "Bloody hell," she muttered to nobody. She stepped through the gap in the railings and set her foot on the first stone step. The wet stone gave beneath her weight. Below, the violet light flickered, and a voice drifted up from the dark, low and amused. "Mind the third step, Detective. It's got opinions."
created_at"2026-10-08T15:18:32.753Z" 10/8/2026, 3:18:32 PM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3048
moderation_latency(null)
generation_time13860
tokens_prompt1104
tokens_completion1316
native_tokens_prompt1576
native_tokens_completion2035
native_tokens_completion_images(null)
native_tokens_reasoning507
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"
usage0.0011751
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791472712-A3NcWxuxagvT8yfkWhaZ"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791472712-wbNsvGBpGMUwkJT0FNko"
upstream_id"msg_011Cfpzb9vuJnxXjknL6wKjy"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011Cfpzb9vuJnxXjknL6wKjy"
is_byokfalse
latency788
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0011751
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
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences14
tagDensity0.286
leniency0.571
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount809
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)
50.56% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount809
totalAiIsms8
found
0
word"down her spine"
count1
1
word"pounding"
count1
2
word"footsteps"
count1
3
word"echoed"
count1
4
word"the last thing"
count1
5
word"flicked"
count1
6
word"weight"
count1
7
word"flickered"
count1
highlights
0"down her spine"
1"pounding"
2"footsteps"
3"echoed"
4"the last thing"
5"flicked"
6"weight"
7"flickered"
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
emotionTells1
narrationSentences52
matches
0"was scared"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences52
filterMatches
0"watch"
hedgeMatches(empty)
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
maxSentenceWordsSeen43
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords809
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions8
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions15
wordCount680
uniqueNames8
maxNameDensity0.88
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Quinn6
Street2
Tomás2
Herrera1
Shaftesbury1
Avenue1
Lexington1
Morris1
persons
0"Quinn"
1"Tomás"
2"Herrera"
3"Morris"
places
0"Street"
1"Shaftesbury"
2"Avenue"
3"Lexington"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences39
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount809
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
mean35.17
std27.17
cv0.772
sampleLengths
073
17
275
342
411
519
679
745
87
917
1043
1142
1211
1388
1414
1517
164
1751
1880
1930
206
2140
228
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences52
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs114
matches
0"was breathing"
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
wordCount681
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.020558002936857563
lyAdverbCount1
lyAdverbRatio0.0014684287812041115
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
mean13.26
std8.97
cv0.676
sampleLengths
031
122
220
37
43
537
622
713
87
96
1029
1111
1210
139
147
156
1610
1716
1826
1914
2011
218
225
233
2418
257
268
279
2843
2917
305
3120
324
337
3420
359
3619
379
3811
3920
406
418
424
4313
444
453
465
4721
4822
4923
91.26% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.5573770491803278
totalSentences61
uniqueOpeners34
68.03% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences49
matches
0"Somewhere far beneath her, footsteps"
ratio0.02
56.73% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount20
totalSentences49
matches
0"He didn't stop."
1"He glanced back, just once,"
2"His left sleeve had ridden"
3"Her knee complained on the"
4"He laughed, a short breath"
5"They crossed Shaftesbury Avenue against"
6"Her watch caught the light"
7"Her thumb rubbed the worn"
8"He was breathing harder now"
9"He stopped at the far"
10"He pushed off the wall"
11"Her radio crackled on her"
12"She keyed the handset."
13"She stared down the steps."
14"She weighed it the way"
15"It was also long enough"
16"She checked the magazine in"
17"Her knuckles were white against"
18"she muttered to nobody"
19"She stepped through the gap"
ratio0.408
51.84% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount40
totalSentences49
matches
0"The rain had found the"
1"Greek Street ran slick and"
2"He didn't stop."
3"He glanced back, just once,"
4"His left sleeve had ridden"
5"The scar along his forearm"
6"Quinn gained a yard on"
7"Her knee complained on the"
8"He laughed, a short breath"
9"They crossed Shaftesbury Avenue against"
10"A taxi braked hard, horn"
11"Quinn slapped the bonnet as"
12"Her watch caught the light"
13"Her thumb rubbed the worn"
14"Tomás cut into the mouth"
15"Quinn followed, slowing as the"
16"Bins lined the left side."
17"A drainpipe dripped."
18"The alley smelled of wet"
19"He was breathing harder now"
ratio0.816
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences49
matches(empty)
ratio0
66.33% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences28
technicalSentenceCount3
matches
0"Ahead, the man in the grey parka cut left without slowing, shoulder-checking a delivery cyclist who swore at his back."
1"Below that, in different hands, someone had written a list of prices that did not match any currency she recognised."
2"Somewhere far beneath her, footsteps echoed off tile, unhurried now, as if the man descending had all the time in the world."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
78.57% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount1
fancyTags
0"she muttered (mutter)"
dialogueSentences14
tagDensity0.286
leniency0.571
rawRatio0.25
effectiveRatio0.143
88.7770%