Run: 2026-02-19-01KHTY3ATH504S60RSX15CJ1C5

Run Details

Duration:
1m 37s
Cost:
$0.001215
Provider:
SambaNova

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="4414" name="Eva Kowalski" aliases="Eva, Evie" pronouns="she/her" species="Human" storyRole="Best Friend"> <eyeColor>green</eyeColor> <hair>curly red</hair> <height>5'4"</height> <age>26</age> <glasses>round glasses</glasses> <satchel>worn leather satchel full of books</satchel> <freckles>freckled complexion</freckles> <nervousHabit>tucking hair behind her left ear</nervousHabit> <description>Aurora's childhood friend and an occult researcher.</description> <backstory>Moved to London two years before Aurora and works as a research assistant at the British Museum's restricted archives. She holds a master's degree in Ancient History from Oxford.</backstory> </character> <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> <item id="2004" name="Veil Compass" aliases="the Compass, shadow compass" type="Tool" origin="The Veil Market"> <casingMaterial>small brass compass</casingMaterial> <faceMarkings>face etched with protective sigils</faceMarkings> <needle>points toward the nearest supernatural rift or portal</needle> <patina>casing has a patina of verdigris</patina> <creator>crafted by a Shade artisan</creator> <description>A compass attuned to supernatural energy.</description> </item> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the mystery genre. A detective arrives at a crime scene in an unusual location. Something is wrong — the evidence doesn't add up. A colleague is present, offering a different interpretation. Through careful observation and deduction, the detective begins to see what others have missed. 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
damp air of the abandoned Camden Town tube station clung to every surface, thick with the ghosts of old ozone and something metallic. Detective Harlow Quinn's boots echoed sharply against the wet tile as she descended the last flight lit by harsh temporary halogens that cast long, dancing shadows. The light glinted off an array of objects strewn across the floor, a chaos of strange artifacts and toppled velvet-d. No body remained, only a dark, rust-colored stain that spread like a grotesque blossom. Her colleague hands shoved deep into the pockets of his overcoat. "Quinn. Late to the party, as usual." "I, a tone that offered no warmth or apology. She scanned the room, her sharp jaw tightening as she took in the details. "What have we got?" "B was down. No one saw a thing. Well, no seller. This was his stall." Miller gestured to the overturned of these weirdo stalls." Harlow’s eyes narrowed. An accident. They always wanted it to be an accident. Something over the scene again, lingering on the strange geometry of a fallen silvericcated herbs. Her worn leather watch felt tightShe ignored him and moved closer to the stall. She spilled from the table were a collection of the arcane and the odd: jars of iridescent powder, bundles was practical, academic. Out of place. She nudged the velvet aside with a gloved finger. The satchel had spilledObserved fluctuation in the aural resonance of the ley-line confluence near Highgate. Correlation to This wasn't the note-taking of a novelty merchant. This was research. Her eyes lifted from the card, scanning the perimeter of the stall once more. The halo of debris from the fall was inconsistent. A set of heavy iron scales had toppled toward the body had acted at once. "The flash of light?" "That's the consensus." She pointed to the heaviest items. "Look at the fall pattern. The force that knocked these over came from *that* direction." She indicated a dark archway leading deeper into the tunnels. "But the lighter material was blown back toward the wall. A secondary force, fromMiller shifted his weight, his skepticism a physical presence. "So someone pushed him, and his weird science project blew up in his face. That still on the floor tiles just beyond the edge of the stall. The patina of grime and dust was undisturbed except for one area. Two small, clean arcs in the shape of a parentheses, as if someone had stood there for a long time, rocking back and forth on the balls of their feet. A nervous habit. She looked from the marks to the position of the stall. It was a perfect vantage point to observe the victim customer." "Customers don't stand in the shadows behind a stall. They browse. They haggle." Her merchant. The two conflicting force vectors. The hidden observer. Her eyes caught a final, almost invisible detail. A tiny speck of brass, gleaming near the scuff marks. She bent down, her knees bigger than a fingernail clipping, with a minute segment of an etched line. It looked familiar. She straightened up, her blood running cold. She’d seen work like this before. Three years ago, on a case that had cost her her partner. The evidence locker still held the few strange artifacts recovered from that scene, items no one could explain, marked with similar, intricate sigils. This was no accident. This was a hit. A professional, precise elimination that used the victim's own work—or something planted on him—as the weapon. The flash of light wasn't an electrical fault; it was the murder weapon itself. The 'unknown energy signature' was the calling card. She looked at Miller, his face still arranged in comfortable doubt. He saw a random tragedy in a den of freaks. She saw a pattern emerging from the darkness, a pattern she had been searching for ever since Morris had died staring at something he couldn't possibly have seen. "This was no accident," she said, her voice leaving no room for argument. She held up the tiny brass fragment. "Someone turned this man's research into a bomb and made sure they had a front-row seat to watch it work."
created_at"2026-02-19T12:30:49.688Z" 2/19/2026, 12:30:49 PM
model"deepseek/deepseek-chat-v3.1"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3211
moderation_latency(null)
generation_time96973
tokens_prompt1166
tokens_completion1038
native_tokens_prompt1033
native_tokens_completion1414
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.00121545
router(null)
provider_responses
0
endpoint_id"1dda4bb9-c626-4816-bb54-99665323fef0"
id"149a3b32-dd7c-408a-bf84-432dc5fce617"
is_byokfalse
latency3155
model_permaslug"deepseek/deepseek-chat-v3.1"
provider_name"SambaNova"
status200
api_type"completions"
id"gen-1771504249-gpiL852nDHKo0BiYWiv3"
upstream_id"149a3b32-dd7c-408a-bf84-432dc5fce617"
total_cost0.00121545
cache_discount(null)
upstream_inference_cost0
provider_name"SambaNova"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences12
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
92.86% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount700
totalAiIsmAdverbs1
found
0
adverb"sharply"
count1
highlights
0"sharply"
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)
0.00% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount700
totalAiIsms15
found
0
word"echoed"
count1
1
word"dancing"
count1
2
word"velvet"
count2
3
word"warmth"
count1
4
word"scanned"
count1
5
word"resonance"
count1
6
word"scanning"
count1
7
word"weight"
count1
8
word"perfect"
count1
9
word"gleaming"
count1
10
word"etched"
count1
11
word"familiar"
count1
12
word"intricate"
count1
13
word"comfortable"
count1
highlights
0"echoed"
1"dancing"
2"velvet"
3"warmth"
4"scanned"
5"resonance"
6"scanning"
7"weight"
8"perfect"
9"gleaming"
10"etched"
11"familiar"
12"intricate"
13"comfortable"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes narrowed"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences39
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences39
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences44
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen49
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords1
totalWords693
ratio0.001
matches
0"that"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions8
wordCount551
uniqueNames7
maxNameDensity0.36
worstName"Harlow"
maxWindowNameDensity1
worstWindowName"Harlow"
discoveredNames
Camden1
Town1
Harlow2
Quinn1
Highgate1
Miller1
Morris1
persons
0"Harlow"
1"Quinn"
2"Miller"
3"Morris"
places
0"Camden"
1"Town"
2"Highgate"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences28
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount693
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences44
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs15
mean46.2
std37.3
cv0.807
sampleLengths
083
118
227
324
4104
538
64
73
8147
923
1039
1148
1246
1349
1440
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences39
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs81
matches(empty)
77.92% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount1
flaggedSentences1
totalSentences44
ratio0.023
matches
0"This was no accident. This was a hit. A professional, precise elimination that used the victim's own work—or something planted on him—as the weapon. The flash of light wasn't an electrical fault; it was the murder weapon itself. The 'unknown energy signature' was the calling card."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount395
adjectiveStacks0
stackExamples(empty)
adverbCount10
adverbRatio0.02531645569620253
lyAdverbCount2
lyAdverbRatio0.005063291139240506
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences44
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences44
mean15.75
std13.08
cv0.83
sampleLengths
023
126
220
314
411
57
627
73
85
93
1013
113
122
138
1415
1514
1622
173
189
1915
2010
213
2214
239
2415
254
263
276
2825
2936
3013
3112
3230
333
3411
3511
369
372
3812
3939
4048
4146
4249
4340
88.64% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.5454545454545454
totalSentences44
uniqueOpeners24
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences35
matches(empty)
ratio0
60.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount14
totalSentences35
matches
0"Her colleague hands shoved deep"
1"They always wanted it to"
2"Her worn leather watch felt"
3"She spilled from the table"
4"She nudged the velvet aside"
5"Her eyes lifted from the"
6"She pointed to the heaviest"
7"She indicated a dark archway"
8"She looked from the marks"
9"It was a perfect vantage"
10"Her eyes caught a final,"
11"She straightened up, her blood"
12"She looked at Miller, his"
13"she said, her voice leaving"
ratio0.4
45.71% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount29
totalSentences35
matches
0"Detective Harlow Quinn's boots echoed"
1"The light glinted off an"
2"Her colleague hands shoved deep"
3"This was his"
4"Harlow’s eyes narrowed."
5"They always wanted it to"
6"Something over the scene again,"
7"Her worn leather watch felt"
8"She spilled from the table"
9"She nudged the velvet aside"
10"The satchel had spilledObserved fluctuation"
11"Correlation to This wasn't the"
12"This was research."
13"Her eyes lifted from the"
14"The halo of debris from"
15"A set of heavy iron"
16"She pointed to the heaviest"
17"She indicated a dark archway"
18"someone pushed him, and his"
19"That still on the floor"
ratio0.829
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences35
matches(empty)
ratio0
67.67% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences19
technicalSentenceCount2
matches
0"Two small, clean arcs in the shape of a parentheses, as if someone had stood there for a long time, rocking back and forth on the balls of their feet."
1"She straightened up, her blood running cold. She’d seen work like this before. Three years ago, on a case that had cost her her partner. The evidence locker sti…"
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount1
matches
0"she said, her voice leaving no room for argument"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount0
fancyTags(empty)
dialogueSentences12
tagDensity0.083
leniency0.167
rawRatio0
effectiveRatio0
81.0933%