Run: 2026-10-08-01M4DA9Q4N3Y73BGAH8ZQNKT0E

Run Details

Duration:
12.8s
Cost:
$0.001060
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 been falling since Oxford Street, a thin, mean drizzle that turned to sheets somewhere past Berwick Street. Harlow Quinn kept her eyes on the figure ahead of her, a dark coat moving fast between the shuttered shopfronts. Water ran off the peak of her cap and into her collar. She ignored it. He took the corner onto Old Compton Street without slowing. The green neon above the Raven's Nest bled across the wet pavement, and for a second his silhouette cut through the glow. Quinn saw the crooked medallion swing at his throat as he turned his head to look back. Tomás Herrera. Ex-paramedic, struck off, now a man who patched up things that had no business walking into an NHS ward. "Herrera!" she shouted. "Stop where you are." He didn't stop. He vaulted a low railing, landed badly on one foot, and kept running. The scar on his left forearm flashed pale under a streetlamp as he threw an arm out for balance. Quinn's shoes skidded on the slick tarmac. Her left wrist ached where the worn leather watch rubbed the bone, a dull reminder of the last time she'd chased a man through London at night. Three years. Morris had been ahead of her then, too, and when she'd turned the corner he had simply not been there anymore. No body. No blood. Just a cold stairwell and a smell like burnt matches. She pushed the thought down and kept going. Herrera cut left into an alley behind a kebab shop, and the bins were sitting in a ragged line. He hit one with his hip, sent it rolling, and swore in Spanish. Quinn stepped over the spill of rotten cardboard and felt the pavement tilt as her boot found a patch of oil. "You're making this worse," she called. "Every street you take, I know where you're going." "You know nothing," he panted, not turning. "You think I'm the one hiding things?" "I think you're running." He reached the end of the alley and shouldered through a gap in the hoarding, where a boarded-up doorway had been prised open. Quinn reached it three seconds later. The wood was splintered at the edges, fresh and pale. Beyond it, stairs led down into a darkness that smelled of wet stone and something sweeter underneath, like lilies left too long in a vase. She pulled her torch from her coat pocket, thumbed it on, and swept the beam across the first few steps. Cracked tiles. A faded tube roundel on the wall, its red bar bleached almost white. Below that, someone had painted a small white skull on the plaster, its eyes scratched out. Her radio crackled on her shoulder. Dispatch, thin and distant, asking for her location. She did not answer. Herrera's footsteps echoed somewhere below, growing fainter. Quinn stood at the threshold and listened. Far down, she heard voices, a low hum of conversation, a bass note like an engine idling, and then a bright burst of laughter that didn't sound entirely human. A draft rose up the stairwell and brushed her wet face. It carried the lily smell again, stronger now. She had her warrant card, her cuffs, and her service pistol. None of those would mean a thing down there, and she knew it. Morris would have known it too. Morris would have gone down anyway. Her hand went to her coat pocket, where a small, cold object sat beside the torch. She had taken it from a dead courier's jacket two days earlier and had not yet shown it to anyone. Smooth, pale, carved with a notch on one edge. She had not yet worked out what it was for. Quinn looked down the stairs. The torch beam trembled slightly on the bottom step, though her hand held it steady. "Herrera," she called, her voice flat in the stone throat of the stairwell. "Last chance. Come back up here and talk to me." Only the hum answered her, rising and falling like something breathing in the dark.
created_at"2026-10-08T08:32:54.429Z" 10/8/2026, 8:32:54 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2930
moderation_latency(null)
generation_time12811
tokens_prompt1104
tokens_completion1026
native_tokens_prompt1576
native_tokens_completion1804
native_tokens_completion_images(null)
native_tokens_reasoning508
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"
usage0.0010596
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791448374-FhBIQ10Eaz8D8vDvwoxo"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791448374-Tu8iSRIgeC3UDMg2pgs9"
upstream_id"msg_011CfpTf4xmBDHCNyX3bKUvF"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpTf4xmBDHCNyX3bKUvF"
is_byokfalse
latency762
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010596
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)
dialogueSentences9
tagDensity0.444
leniency0.889
rawRatio0
effectiveRatio0
92.59% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount675
totalAiIsmAdverbs1
found
0
adverb"slightly"
count1
highlights
0"slightly"
80.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found
0"Lily"
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
70.37% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount675
totalAiIsms4
found
0
word"reminder"
count1
1
word"footsteps"
count1
2
word"echoed"
count1
3
word"trembled"
count1
highlights
0"reminder"
1"footsteps"
2"echoed"
3"trembled"
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
narrationSentences54
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences54
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
maxSentenceWordsSeen29
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords675
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
94.62% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions24
wordCount632
uniqueNames12
maxNameDensity1.11
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Street"
discoveredNames
Oxford1
Street3
Berwick1
Quinn7
Old1
Compton1
Raven1
Nest1
Herrera3
London1
Spanish1
Morris3
persons
0"Quinn"
1"Raven"
2"Nest"
3"Herrera"
4"Morris"
places
0"Oxford"
1"Street"
2"Berwick"
3"Old"
4"Compton"
5"London"
6"Spanish"
globalScore0.946
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences42
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount675
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
totalParagraphs20
mean33.75
std22.7
cv0.673
sampleLengths
055
170
27
335
471
58
653
715
814
94
1064
1151
1218
137
1455
1536
1655
1720
1823
1914
92.27% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences54
matches
0"been prised"
1"was splintered"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs99
matches
0"were sitting"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences59
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount634
adjectiveStacks0
stackExamples(empty)
adverbCount19
adverbRatio0.02996845425867508
lyAdverbCount6
lyAdverbRatio0.00946372239747634
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
mean11.44
std7.01
cv0.613
sampleLengths
020
120
212
33
410
522
617
72
819
93
104
113
1213
1319
147
1527
162
1721
182
192
2010
218
2219
2313
2421
256
269
277
287
294
3023
316
3210
3325
3420
352
3613
3716
386
398
404
417
427
4329
4411
458
4611
4713
486
496
75.14% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.4915254237288136
totalSentences59
uniqueOpeners29
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences47
matches
0"Just a cold stairwell and"
1"Only the hum answered her,"
ratio0.043
66.81% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount18
totalSentences47
matches
0"She ignored it."
1"He took the corner onto"
2"He didn't stop."
3"He vaulted a low railing,"
4"Her left wrist ached where"
5"She pushed the thought down"
6"He hit one with his"
7"he panted, not turning"
8"He reached the end of"
9"She pulled her torch from"
10"Her radio crackled on her"
11"She did not answer."
12"It carried the lily smell"
13"She had her warrant card,"
14"Her hand went to her"
15"She had taken it from"
16"She had not yet worked"
17"she called, her voice flat"
ratio0.383
45.11% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount39
totalSentences47
matches
0"The rain had been falling"
1"Harlow Quinn kept her eyes"
2"Water ran off the peak"
3"She ignored it."
4"He took the corner onto"
5"The green neon above the"
6"Quinn saw the crooked medallion"
7"He didn't stop."
8"He vaulted a low railing,"
9"The scar on his left"
10"Quinn's shoes skidded on the"
11"Her left wrist ached where"
12"Morris had been ahead of"
13"She pushed the thought down"
14"Herrera cut left into an"
15"He hit one with his"
16"Quinn stepped over the spill"
17"he panted, not turning"
18"He reached the end of"
19"Quinn reached it three seconds"
ratio0.83
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences47
matches(empty)
ratio0
47.62% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences30
technicalSentenceCount4
matches
0"The rain had been falling since Oxford Street, a thin, mean drizzle that turned to sheets somewhere past Berwick Street."
1"Ex-paramedic, struck off, now a man who patched up things that had no business walking into an NHS ward."
2"Beyond it, stairs led down into a darkness that smelled of wet stone and something sweeter underneath, like lilies left too long in a vase."
3"Far down, she heard voices, a low hum of conversation, a bass note like an engine idling, and then a bright burst of laughter that didn't sound entirely human."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount2
matches
0"he panted, not turning"
1"she called, her voice flat in the stone throat of the stairwell"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount2
fancyTags
0"she shouted (shout)"
1"he panted (pant)"
dialogueSentences9
tagDensity0.444
leniency0.889
rawRatio0.5
effectiveRatio0.444
82.1509%