Run: 2026-10-08-01M4DA9Z3AZYYNE0S44B62QJCP

Run Details

Duration:
17.5s
Cost:
$0.001534
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 Camden High Street, needling through the gap in Harlow Quinn's collar and pooling in the cuff of her watch. The worn leather had gone black with water. She ignored it. Ahead, a grey coat cut between a shuttered kebab van and a row of overflowing bins, moving with the loose stride of a man who knew every crack in the pavement. "Tomás Herrera." Eighteen years in uniform had sanded her voice down to a flat, clipped edge. "Stop walking." He didn't stop. He glanced over his shoulder, and the sodium light caught the pale ridge of the scar on his left forearm where his sleeve had ridden up. Then he ran. Quinn ran after him. Her knees protested. Her shoes skidded on oil-slick paving, and she lost two seconds at the kerb when a black cab swung past, horn braying. By the time she reached the corner, Herrera was twenty yards clear, weaving through a knot of late-night drinkers who scattered under the awnings. The Saint Christopher medallion bounced against his chest on its thin chain, catching the light each time he turned. "You're making this worse for both of us," he called back, without breaking stride. "Then stop running and make it better." "Detective, if I stop, I'm dead by morning. If you keep following, you'll be the one who gets me killed." She pushed harder. The street narrowed. Shopfronts gave way to hoardings, and the hoardings gave way to a boarded-up entrance at the mouth of an old Tube station. Someone had painted a green neon sign above the next building along, glowing faintly through the downpour, but this entrance held nothing but rot. The sign on the plywood had bleached to the colour of weak tea. CLOSED. LONDON UNDERGROUND. Below it, a sheet of corrugated iron had been bent back on one side, leaving a gap just wide enough for a man to slip through sideways. Herrera went through it without hesitating. Quinn reached the hoarding, put her palm flat against the wet metal, and felt the faint vibration of something moving underneath the city. A breath of warm air rose through the gap. It smelled of damp stone, cloves, and something sweeter underneath that she didn't want to name. She drew her warrant card from her inside pocket, then thought better of it and kept her hand on her hip, near the holster. Her pulse hammered in her ears, steady and uncomfortable. She stepped to the gap. A figure stood on the far side, waiting at the top of a stairwell. Hood up, face swallowed by shadow, one gloved hand extended, palm open. Herrera had already reached the foot of the stairs, but he stopped and turned. His expression in the weak light was tired rather than afraid. He lifted a small object on a length of cord, something pale and carved, and pressed it into the figure's hand. The hood dipped in acknowledgement. The gloved hand withdrew. "Bone token," the figure said, in a voice like gravel sliding down a chute. Then the hood tilted towards Quinn. "You don't have one, Detective Quinn." She didn't ask how it knew her name. "I have a warrant." "Paper doesn't open doors down here." The figure stepped aside, leaving the top step clear, and the warm murmur of voices rose from below, layered with the clink of glass and the sharp, metallic hum of something she couldn't place. "Your sergeant's warrant won't get you past the turnstile either." Quinn looked down the stairwell. The walls curved away into light the colour of amber and dust, and the stone steps descended farther than any Tube line she had ever ridden. Somewhere below, Herrera was already gone, swallowed by the crowd. Behind her, the rain drummed on the plywood and the cab's taillights bled red across the wet road. She thought of Morris, and of the last time she had followed a man into a place she didn't understand. Quinn stepped onto the first stair.
created_at"2026-10-08T08:33:02.579Z" 10/8/2026, 8:33:02 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency17431
moderation_latency(null)
generation_time17431
tokens_prompt1104
tokens_completion963
native_tokens_prompt1576
native_tokens_completion2753
native_tokens_completion_images(null)
native_tokens_reasoning1439
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"
usage0.0015341
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791448382-OP9RgurMlQ5FIRXb5Jqq"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791448382-p9iBgCLob2bRa4xwx5m9"
upstream_id"msg_011CfpTfhBrNoubD7YiMPRuq"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpTfhBrNoubD7YiMPRuq"
is_byokfalse
latency1081
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0015341
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"The figure stepped aside [aside]"
dialogueSentences10
tagDensity0.4
leniency0.8
rawRatio0.25
effectiveRatio0.2
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount670
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)
92.54% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount670
totalAiIsms1
found
0
word"pulse"
count1
highlights
0"pulse"
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
narrationSentences46
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences46
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences52
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen40
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords670
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions19
wordCount603
uniqueNames10
maxNameDensity1
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Camden1
High1
Street1
Harlow1
Quinn6
Herrera4
Saint1
Christopher1
Tube2
Morris1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Saint"
4"Christopher"
5"Morris"
places
0"Camden"
1"High"
2"Street"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences33
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount670
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences52
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs19
mean35.26
std24.77
cv0.702
sampleLengths
066
118
232
372
414
57
620
795
86
948
1038
1126
1255
1326
1412
1550
1659
1720
186
97.64% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences46
matches
0"was tired"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs95
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences52
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount606
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.0231023102310231
lyAdverbCount1
lyAdverbRatio0.0016501650165016502
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences52
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences52
mean12.88
std9.13
cv0.709
sampleLengths
024
18
23
331
416
52
63
726
83
94
103
1122
1224
1319
1414
157
1620
173
183
1922
2024
2113
221
232
2427
256
2623
279
2816
2924
309
315
3214
3312
3414
3511
3621
375
384
3914
406
416
428
434
4440
4510
465
4726
4810
4918
82.05% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.5384615384615384
totalSentences52
uniqueOpeners28
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences44
matches
0"Then he ran."
1"Then the hood tilted towards"
2"Somewhere below, Herrera was already"
ratio0.068
83.64% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount15
totalSentences44
matches
0"She ignored it."
1"He didn't stop."
2"He glanced over his shoulder,"
3"Her knees protested."
4"Her shoes skidded on oil-slick"
5"he called back, without breaking"
6"She pushed harder."
7"It smelled of damp stone,"
8"She drew her warrant card"
9"Her pulse hammered in her"
10"She stepped to the gap."
11"His expression in the weak"
12"He lifted a small object"
13"She didn't ask how it"
14"She thought of Morris, and"
ratio0.341
50.91% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount36
totalSentences44
matches
0"The rain came sideways off"
1"The worn leather had gone"
2"She ignored it."
3"He didn't stop."
4"He glanced over his shoulder,"
5"Quinn ran after him."
6"Her knees protested."
7"Her shoes skidded on oil-slick"
8"The Saint Christopher medallion bounced"
9"he called back, without breaking"
10"She pushed harder."
11"The street narrowed."
12"Shopfronts gave way to hoardings,"
13"Someone had painted a green"
14"The sign on the plywood"
15"Herrera went through it without"
16"Quinn reached the hoarding, put"
17"A breath of warm air"
18"It smelled of damp stone,"
19"She drew her warrant card"
ratio0.818
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences44
matches
0"By the time she reached"
ratio0.023
77.92% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences22
technicalSentenceCount2
matches
0"Ahead, a grey coat cut between a shuttered kebab van and a row of overflowing bins, moving with the loose stride of a man who knew every crack in the pavement."
1"By the time she reached the corner, Herrera was twenty yards clear, weaving through a knot of late-night drinkers who scattered under the awnings."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount1
matches
0"he called back, without breaking stride"
0.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount2
fancyTags
0"uniform had (have)"
1"he called back (call back)"
dialogueSentences10
tagDensity0.3
leniency0.6
rawRatio0.667
effectiveRatio0.4
86.1564%