Run: 2026-10-09-01M4G0XFD0GTPKMSBND0SF7RWP

Run Details

Duration:
14.9s
Cost:
$0.001205
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 Lock, slapping the shopfronts and turning the tourist-tat stalls into shuttered teeth. Harlow Quinn kept her eyes on the figure ahead, a dark coat cutting through the crowd like a blade through wet paper. Her left wrist ached under the leather strap of her watch. She ignored it. "Tomás Herrera," she called, not loud enough to carry past the nearest kebab van. "Stop running and I'll stop chasing." He did not stop. He glanced back over his shoulder, and the orange glow from a streetlamp caught the scar on his forearm as he flung a hand out to shove a drunk student aside. The student stumbled into a bin. Bottles rolled across the pavement. Quinn cut left past the bin, boots skidding on a slick of cardboard and lager. Her knee protested. Eighteen years of service had taught her that knees kept score. "You're not going to make this easy, Detective," Tomás shouted over the traffic hiss. His accent softened the consonants. "Nobody in your position ever does." "My position is the same as yours. Standing in the rain." She closed the gap to fifteen metres. "Your position is the one with the handcuffs." He veered off the main road, down a side passage between a vintage clothes shop and a closed noodle bar. The passage narrowed, walls sweating condensation, and the sound of the high street dropped away behind them like a door closing. Quinn slowed, pulled her torch from her coat, and kept it low. The beam showed wet brick, a rusted fire escape, and a single chalk mark on the bricks near the ground. Three curved lines, like a broken ribcage. Someone had drawn it recently. The rain hadn't washed it out. At the end of the passage, a rusted iron gate hung open on one hinge. Beyond it, a set of stone steps dropped into the earth, and at the bottom, a sallow yellow light flickered over what had once been the entrance to a Tube station. The old roundel had been painted over, and a fresh sign hung in its place, scrawled by hand: *Bone or nothing*. Tomás stood on the third step from the top, one palm flat against the wall. He was breathing hard, but his face held a calm that Quinn didn't trust. "You know what's down there," she said. "I know what's down there." He didn't turn around. "Which is more than you know, Detective Quinn. That's why you should go home." "You know my name." "I know a lot of names. It's the only currency that doesn't get stolen in that place." He finally looked at her, and his warm brown eyes were steady. "Three years ago, you came down these steps with a man who had a good heart and a bad habit of trusting the wrong people. You didn't come back up with him." The words landed somewhere under her ribs and stayed there. Quinn's jaw tightened until the muscle jumped. She kept her voice flat. "Don't you dare use Morris to get me to turn around." "I'm not using him. I'm telling you where he went." Tomás reached into his coat and drew out a small object on a cord, pale and smoothed by handling. He held it up so she could see. A knuckle bone, carved with a tiny spiral. "This is what gets you through the turnstile. You don't have one. I can see it on you. You're the kind of woman who never walks into a room she hasn't read the exits for." "Then give me one." "No." He tucked the token back beneath his shirt. "Because you'd spend it on the wrong question." Below them, a bell rang once, low and cracked, and the yellow light pulsed. Somewhere in the tunnels, voices rose and fell in a language that wasn't English, and then a laugh that sounded like it had been dragged over gravel. Quinn stepped down onto the first stair. The concrete was cold through the sole of her boot. Tomás watched her, one hand still on the wall, and she realised he was waiting to see whether she would go on or back out into the rain. Her radio was dead. Her backup was twelve minutes away, if they came at all. The watch on her wrist ticked on, indifferent. She stared down into the light, then looked up at him. "Move," she said. "Or I'll take you back up in cuffs and let your friends down there figure out what you're worth."
created_at"2026-10-09T09:46:39.398Z" 10/9/2026, 9:46:39 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2644
moderation_latency(null)
generation_time14914
tokens_prompt1104
tokens_completion1323
native_tokens_prompt1576
native_tokens_completion2094
native_tokens_completion_images(null)
native_tokens_reasoning679
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"
usage0.0012046
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791539199-zAy9JOISNpiwCxXc4KQW"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791539199-1I1TIle1sIh1W64WxLde"
upstream_id"msg_011CfrT6AzEwqo8aDd9eTkjC"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrT6AzEwqo8aDd9eTkjC"
is_byokfalse
latency866
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0012046
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
totalTags8
adverbTagCount0
adverbTags(empty)
dialogueSentences20
tagDensity0.4
leniency0.8
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount758
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)
86.81% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount758
totalAiIsms2
found
0
word"flickered"
count1
1
word"pulsed"
count1
highlights
0"flickered"
1"pulsed"
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)
analyzedSentences58
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen35
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords3
totalWords758
ratio0.004
matches
0"Bone or nothing"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions6
unquotedAttributions0
matches(empty)
95.55% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions14
wordCount551
uniqueNames6
maxNameDensity1.09
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Camden1
Lock1
Quinn6
Tube1
English1
Tomás4
persons
0"Lock"
1"Quinn"
2"Tomás"
places
0"Tube"
globalScore0.956
windowScore1
80.56% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences36
glossingSentenceCount1
matches
0"sounded like it had been dragged over grav"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount758
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences58
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs23
mean32.96
std23.47
cv0.712
sampleLengths
054
120
246
329
425
526
691
767
829
97
1023
114
1261
1322
1411
1580
164
1717
1841
1945
2023
2111
2222
90.33% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences47
matches
0"been painted"
1"been dragged"
50.19% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs89
matches
0"was breathing"
1"was waiting"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences58
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount552
adjectiveStacks0
stackExamples(empty)
adverbCount16
adverbRatio0.028985507246376812
lyAdverbCount2
lyAdverbRatio0.0036231884057971015
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences58
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences58
mean13.07
std8.56
cv0.655
sampleLengths
018
122
211
33
414
56
64
731
86
95
1015
113
1211
1314
145
156
1618
178
1820
1921
2012
2120
227
235
246
2515
2631
2721
2815
2914
307
319
3214
334
3429
3532
3610
377
385
3911
4029
418
428
4335
444
459
468
4714
4827
497
85.63% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats2
diversityRatio0.5344827586206896
totalSentences58
uniqueOpeners31
74.07% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences45
matches
0"Somewhere in the tunnels, voices"
ratio0.022
60.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount18
totalSentences45
matches
0"Her left wrist ached under"
1"She ignored it."
2"she called, not loud enough"
3"He did not stop."
4"He glanced back over his"
5"Her knee protested."
6"His accent softened the consonants."
7"She closed the gap to"
8"He veered off the main"
9"He was breathing hard, but"
10"He didn't turn around"
11"He finally looked at her,"
12"She kept her voice flat."
13"He held it up so"
14"He tucked the token back"
15"Her radio was dead."
16"Her backup was twelve minutes"
17"She stared down into the"
ratio0.4
26.67% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount39
totalSentences45
matches
0"The rain came sideways off"
1"Harlow Quinn kept her eyes"
2"Her left wrist ached under"
3"She ignored it."
4"she called, not loud enough"
5"He did not stop."
6"He glanced back over his"
7"The student stumbled into a"
8"Bottles rolled across the pavement."
9"Quinn cut left past the"
10"Her knee protested."
11"Tomás shouted over the traffic"
12"His accent softened the consonants."
13"She closed the gap to"
14"He veered off the main"
15"The passage narrowed, walls sweating"
16"Quinn slowed, pulled her torch"
17"The beam showed wet brick,"
18"Someone had drawn it recently."
19"The rain hadn't washed it"
ratio0.867
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences45
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences22
technicalSentenceCount0
matches(empty)
62.50% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags8
uselessAdditionCount1
matches
0"she called, not loud enough to carry past the nearest kebab van"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount1
fancyTags
0"Tomás shouted (shout)"
dialogueSentences20
tagDensity0.2
leniency0.4
rawRatio0.25
effectiveRatio0.1
87.0770%