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Run: 2026-09-23-01M37HT1V1P1BAPEXR0KHRZV6A
Run Details
Model:
GPT-6 Sol
Test:
Bad Writing Habits
Scenario:
Mystery: examining a crime scene
Duration:
56.2s
Cost:
$0.030370
Provider:
OpenAI
Evaluation summary
Score
Evaluator
Details Link
100.00%
Adverbs in dialogue tags
(details)
95.58%
AI-ism adverb frequency
(details)
80.00%
AI-ism character names
(details)
100.00%
AI-ism location names
(details)
91.16%
AI-ism word frequency
(details)
100.00%
Cliché density
(details)
100.00%
Emotion telling (show vs. tell)
(details)
100.00%
Filter word density
(details)
100.00%
Gibberish response detection
(details)
100.00%
Markdown formatting overuse
(details)
100.00%
Missing dialogue indicators (quotation marks)
(details)
33.33%
Name drop frequency
(details)
100.00%
Narrator intent-glossing
(details)
100.00%
"Not X but Y" pattern overuse
(details)
100.00%
Overuse of "that" (subordinate clause padding)
(details)
100.00%
Paragraph length variance
(details)
86.97%
Passive voice overuse
(details)
100.00%
Past progressive (was/were + -ing) overuse
(details)
99.89%
Em-dash & semicolon overuse
(details)
94.00%
Purple prose (modifier overload)
(details)
100.00%
Repeated phrase echo
(details)
100.00%
Sentence length variance
(details)
56.39%
Sentence opener variety
(details)
17.92%
Adverb-first sentence starts
(details)
100.00%
Pronoun-first sentence starts
(details)
43.33%
Subject-first sentence starts
(details)
80.65%
Subordinate conjunction sentence starts
(details)
100.00%
Technical jargon density
(details)
98.68%
Useless dialogue additions
(details)
100.00%
Dialogue tag variety (said vs. fancy)
(details)
89.2636%
Overall
Model input
System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<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> 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.
Model response
Messages
Meta
AI
Detective Harlow Quinn ducked beneath
the police tape and stepped off the platform edge onto the tracks.
Her boots sank into an
inch of black water. Above her, a dead sign promised trains to places this station had not served in forty years.
The victim lay between the
rails beneath Camden, in an abandoned Tube station sealed to the public.
Someone had dressed him in
a clean white shirt and arranged his hands over his chest.
Mud darkened the soles of
his shoes, but the shirt cuffs were spotless. “Tell me we have an identification,” Quinn said.
DS Patel stood beside a
work lamp, his notebook held clear of the dripping ceiling. “Nothing on him. Male, forties, maybe. Cause looks obvious.” Quinn crouched. There was a narrow wound below the man’s ribs, just left of centre. No blood had soaked the shirt around it.
His face was grey and
slack, and his lips had the faint blue cast she had seen on bodies pulled from cold water. “Stabbed elsewhere,”
Patel
said. “Dumped here. Our lads found a maintenance door open at the north end. It’s a straight walk from the service road.” “Who found him?” “Transport for London inspection crew, seven this morning. They called it in from the street. No signal down here.” Quinn looked along the tracks. Beyond the circle of work light, the tunnel narrowed into darkness.
The inspection crew’s boot prints
ran from the north end to the body and back again. Among them were the broad, square treads of the first officers on scene. No prints approached the victim’s head.
She rose and moved to
the platform.
Patel
followed. “Could have carried him,” he said. “Could have.”
The platform was coated in
grey dust except where the inspection crew and police had crossed it. Along the tiled wall, old advertisements had peeled into curls. One showed a woman smiling over a packet of tea, her eyes scratched away.
Another had been painted over
more recently, though not with any skill.
A dark rectangle covered the
old poster from waist height to the floor.
Quinn put two fingers near
its edge without touching it.
The paint had dried, but
it still smelled faintly of solvent. “Station’s been used,”
Patel
said. “Squatters, probably. Or dealers. There’s rubbish through the ticket hall.”
Quinn had seen it on
the way in: paper cups stacked neatly in a corner, a strip of dark fabric caught on an iron railing, the stale sweetness of incense beneath the mildew. Squatters rarely tidied their cups.
She checked her watch, the
leather strap worn pale around the buckle. Nine twenty-three.
The forensic team was due
in ten minutes.
She wanted the room to
herself until then. “Walk me through your stabbing,” she said.
Patel
opened his notebook. “Single wound. Minimal blood here means he was killed somewhere else. Door forced, perhaps by whoever brought him in. Body carried down the maintenance stairs and left on the tracks. Maybe meant to
look
like a suicide until somebody saw the wound.” “Why put his hands like that?” “Guilt. Respect. Take your pick.” Quinn stepped back to the rail and studied him again.
His arms were crossed too
evenly, right over left, fingers straight.
If a person had arranged
them, they had taken care.
His left hand held something
small and white. “You said nothing on him.” “Nothing identifying.” Quinn aimed her torch. The white object rested in his palm, secured by his thumb. It was not a tooth. Too flat.
It had a hole drilled
cleanly through one end. “Bone?” she asked. “Looks like it. Forensics can tell us.”
She remembered the service road
door. Its padlock had hung open, shackle unbroken. The surrounding frame was rusty, but fresh brass shone on the keyhole. Someone had opened it with a key, then left the lock hanging to make the door
look
forced.
She had meant to mention
that to
Patel
. Now she held the thought. “Did you touch the hands?” “No. First officer says he didn’t either.” “Good.” At the far end of the platform, something clicked.
Patel
looked up. “Water?” Quinn waited. Another click came from beyond the old tea advertisement. Three short sounds, metal against tile.
She swept her torch across
the wall. Nothing moved. A trickle ran from a crack in the ceiling and struck the edge of a rusted sign. “Probably,” she said.
Patel
’s radio hissed.
He lifted it to his
ear, got a broken sentence about the forensic van, and walked toward the stairs in search of reception. Quinn stayed by the body. Without
Patel
’s lamp in her eyes, she could see a faint line on the victim’s throat. Not a bruise. A grey band of powder, interrupted beneath his chin. She leaned closer.
His collar had been buttoned
at the neck. The skin immediately above it was clean; the powder started higher, at the point where a scarf might have sat.
She looked along the tracks
again. A thin line had
been scraped
through the grime between the rails, from the victim’s shoulder to the platform wall.
It ended at the drain
beneath the painted-over poster. Someone had dragged something narrow. A cord, perhaps, or a bag with a loose strap. Yet the dust on either side lay undisturbed.
She followed the scrape to
the wall. The drain was too small to take a body. Behind its iron grate, water moved steadily with a soft, hollow sound. On the platform above it, a patch of stone gleamed. She angled her torch. Wax.
Pale yellow drops, freshly trodden
into the floor. Nearby, a dozen overlapping shoe marks filled a space no wider than a kitchen table. Fine heels. Heavy boots. One bare footprint, small enough to belong to a child.
Patel
came back. “Van’s stuck at the barrier. They’re five minutes out.” “Look here,” Quinn said. He stood beside her. “What am I looking at?” “Foot traffic.” “Squatters.” “Maybe. But only in this patch. Why come down into a closed station and stand here?” He turned his light on the painted rectangle. “Graffiti underneath?” “Let’s find out.” She took a penlight from her pocket and held it sideways to the wall. Beneath the dark paint were shallow grooves in the tile, almost invisible head-on. Circles within circles. Lines cut through them at odd angles.
Patel
leaned in. “Some sort of tag.” A bead of water ran over the nearest groove and vanished into it. Quinn frowned. The tile beyond the paint
was cracked
and buckled; these tiles sat flush, their grout clean despite the damp. They had
been fitted
after the station closed. “Have the scene officers searched behind this wall?” she asked. “It’s a wall, Quinn.” “That wasn’t my question.” He met her eyes. They had worked together long enough for him to know when to leave it alone. “No.” She looked back at the body. The dead man’s shoes had mud on the soles, but none in the treads. He had walked through wet ground and then over a dry, abrasive surface long enough to scrape the ridges clean. The tunnel floor here was wet. The north access stairs were wet too; she had taken them herself. Whatever route he had used, it was not
Patel
’s. A dull knock came from inside the wall.
Patel
heard it this time. His face changed. “Pipes?” “There aren’t any on the plan.” “You’ve seen the plans?” “On my way down.” Quinn had also noticed what the plans lacked: the depth of this wall. The station had
been built
with a service alcove here, sealed after closure. The new tile covered its entrance.
Patel
reached for his radio. Quinn stopped him with a hand on his sleeve. “Quiet.” The knock came again, followed by a voice too low to make out. For a moment, she was in another tunnel three years ago, hearing Morris call her name through a locked door. When she had opened it, he had
been gone
. There had been no passage beyond, nowhere for him to run. She had filed the facts she could prove and spent the years since looking at every sealed door twice. She let go of
Patel
’s sleeve. “Get uniforms to the north stairs. No one in or out.” “You think someone’s back there?” “I think someone’s alive.” The voice rose, urgent and muffled. Quinn pressed her ear to a patch of bare tile. A woman. She caught “please” and then a name. “Eva?”
Patel
leaned in. “Did she say Eva?”
Before Quinn could answer, a
hard impact shook the wall. The voice cut off.
Patel
was already speaking
into his radio. Quinn ran her torch over the grooves. Near knee height, a tile stood proud by less than a millimetre. She pressed it. Nothing. She tried the one beside it. A latch snapped behind the wall, loud as a bone breaking. The painted rectangle swung inward. A narrow passage lay beyond, lit by bare bulbs strung along the ceiling. It sloped away from the station into dry darkness. Cloth awnings hung half folded against the walls, and under them stood empty stalls. Quinn smelled cloves, hot metal, and something sour enough to sting her nose. Halfway down the passage, a young woman in round glasses struggled in the grip of a man with a dark coat. Her curly red hair had come loose around her face. A leather satchel bulging with books hung from one shoulder. The man held a knife close to her cheek. “Police,” Quinn said. “Let her go.” The man’s gaze
flicked
to
Patel
and back. “She stole from me.” “I didn’t,” the woman said. Her green eyes darted to the knife. “Detective, he’s lying.” “You can explain it upstairs,” Quinn told the man. “There is no upstairs for this.” He shifted his
weight
toward the deeper passage. Quinn saw what he carried in his other hand: a small brass compass, its casing green with verdigris. Sigils covered the face. The needle spun once, stopped, then swung hard toward the tiled doorway behind her. The man saw it too. His grip tightened on the woman. Quinn stepped aside from the entrance, giving
Patel
a clear line. “Put the knife down.” “Close the door,” the man said. A low vibration ran through the tiles beneath Quinn’s boots. Dust sifted from the ceiling. Behind her, on the tracks,
Patel
’s work lamp
flickered
. “The dead man,” the woman said
quickly
. “He was trying to close it. That token in his hand—” The man jerked her back. “Quiet.” Quinn kept her eyes on the knife. “What’s your name?” “Eva Kowalski.” “Eva. Tell me what happened to him.” “He came through the wrong way.” Eva tucked a loose curl behind her left ear, then froze when the blade touched her skin. “He was alive when I saw him. The wound was already there, but he was walking. He said something had followed him.”
Patel
glanced toward the body. “That’s not possible.” “Then ask him,”
Eva said, her voice shaking
. The man took another step. The compass needle swung again, now pointing past Quinn’s shoulder toward the platform. Quinn heard a scrape there, the dry pull of something moving over stone. No one
was meant
to be on the platform. The uniforms were still at the stairs. She did not
look
round. “
Patel
.” “I hear it.” The man with the knife looked past them, and his face went slack with fear. Eva drove her heel into his instep. He cried out. Quinn closed the distance, caught his knife wrist, and twisted until the blade struck the floor.
Patel
pulled Eva clear. The scrape behind Quinn stopped. She cuffed the man one-handed, listening. From the passage came a deep, slow creak, like a heavy door under strain. Eva had backed against the tiles. Her glasses
were crooked
; she straightened them with trembling fingers. “We need the token,” she said. Quinn tightened the second cuff. “For what?” “To shut the entrance. He has to give it up willingly, and he can’t now.” Quinn looked at the man on the floor. He stared at the compass near his knee. Its needle pointed steadily toward the dead man. The body lay twenty feet away, hands neatly crossed around the piece of bone. The wax, the prints, the hidden passage: a market had operated here, right under Camden. People had come and gone by a door the transport authority did not know existed. That explained plenty. It did not explain how a wounded man had crossed wet tracks without leaving a print, or why the brass needle refused to point north. The scrape began again. Closer. Quinn drew her torch and turned. At the edge of the platform, the work lamp showed a fresh line through the dust. It moved
slowly
toward the open doorway. She could see the powder rise on either side of it. She could not see what made the mark.
Patel
stood rigid beside Eva. “What is that?” Quinn thought of Morris calling through a door that had never opened. She had no answer for
Patel
, and no time to invent one. “Get the token,” she said. “Don’t disturb anything else.”
Patel
hurried to the tracks. Quinn held the open doorway while Eva pressed her palms to the carved tiles, hunting for something among the grooves. The unseen
weight
crossed the platform. Dust rolled beneath the work lamp, and a pale drop of wax split under the pressure of a foot that did not show. For the first time in three years, Quinn was certain Morris had not run away.
created_at
"2026-09-23T16:33:01.291Z"
9/23/2026, 4:33:01 PM
model
"openai/gpt-6-sol-20260922"
app_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
3598
moderation_latency
(null)
generation_time
56165
tokens_prompt
896
tokens_completion
3401
native_tokens_prompt
835
native_tokens_completion
2870
native_tokens_completion_images
0
native_tokens_reasoning
0
native_tokens_cached
0
num_media_prompt
(null)
num_input_audio_prompt
(null)
num_media_completion
0
num_search_results
(null)
num_fetches
(null)
web_search_engine
(null)
origin
"https://ncbench.com/"
is_byok
false
finish_reason
"stop"
native_finish_reason
"completed"
service_tier
"default"
usage
0.03037
router
(null)
user_agent
"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
http_referer
(null)
request_id
"req-1790181181-vlh6nsKuD6xSDGkL0xsJ"
session_id
(null)
preset_id
(null)
api_type
"completions"
id
"gen-1790181181-nowt2sGrQTGMKR4KAMlt"
upstream_id
"resp_0966b0064e3d458b016ab3ff3d6b8487d19dfcc6ddb8debba4"
provider_responses
0
endpoint_id
"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id
"resp_0966b0064e3d458b016ab3ff3d6b8487d19dfcc6ddb8debba4"
is_byok
false
latency
1161
model_permaslug
"openai/gpt-6-sol-20260922"
provider_name
"OpenAI"
status
200
total_cost
0.03037
cache_discount
(null)
upstream_inference_cost
0
provider_name
"OpenAI"
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
totalTags
19
adverbTagCount
1
adverbTags
0
"the woman said quickly [quickly]"
dialogueSentences
74
tagDensity
0.257
leniency
0.514
rawRatio
0.053
effectiveRatio
0.027
95.58%
AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount
2262
totalAiIsmAdverbs
2
found
0
adverb
"quickly"
count
1
1
adverb
"slowly"
count
1
highlights
0
"quickly"
1
"slowly"
80.00%
AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions
(empty)
found
0
"Patel"
100.00%
AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions
(empty)
found
(empty)
91.16%
AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount
2262
totalAiIsms
4
found
0
word
"flicked"
count
1
1
word
"weight"
count
2
2
word
"flickered"
count
1
highlights
0
"flicked"
1
"weight"
2
"flickered"
100.00%
Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches
0
maxInWindow
0
found
(empty)
highlights
(empty)
100.00%
Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells
1
narrationSentences
211
matches
0
"k with fear"
100.00%
Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount
2
hedgeCount
0
narrationSentences
211
filterMatches
0
"watch"
1
"look"
hedgeMatches
(empty)
100.00%
Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences
266
gibberishSentences
0
adjustedGibberishSentences
0
longSentenceCount
0
runOnParagraphCount
0
giantParagraphCount
0
wordSaladCount
0
repetitionLoopCount
0
controlTokenCount
0
repeatedSegmentCount
0
maxSentenceWordsSeen
42
ratio
0
matches
(empty)
100.00%
Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans
0
markdownWords
0
totalWords
2262
ratio
0
matches
(empty)
100.00%
Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions
25
unquotedAttributions
0
matches
(empty)
33.33%
Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions
73
wordCount
1865
uniqueNames
7
maxNameDensity
1.82
worstName
"Quinn"
maxWindowNameDensity
4
worstWindowName
"Quinn"
discoveredNames
Harlow
1
Quinn
34
Camden
2
Tube
1
Patel
25
Morris
3
Eva
7
persons
0
"Harlow"
1
"Quinn"
2
"Patel"
3
"Morris"
4
"Eva"
places
(empty)
globalScore
0.588
windowScore
0.333
100.00%
Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences
141
glossingSentenceCount
0
matches
(empty)
100.00%
"Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches
0
per1kWords
0
wordCount
2262
matches
(empty)
100.00%
Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount
0
totalSentences
266
matches
(empty)
100.00%
Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs
113
mean
20.02
std
19.03
cv
0.951
sampleLengths
0
43
1
46
2
8
3
24
4
46
5
25
6
3
7
19
8
45
9
6
10
9
11
6
12
2
13
87
14
15
15
38
16
30
17
7
18
46
19
6
20
5
21
39
22
5
23
2
24
31
25
3
26
7
27
56
28
5
29
7
30
1
31
9
32
4
33
43
34
3
35
26
36
64
37
35
38
23
39
28
40
52
41
12
42
4
43
9
44
2
45
1
46
16
47
10
48
3
49
37
86.97%
Passive voice overuse
Target: ≤2% passive sentences
passiveCount
11
totalSentences
211
matches
0
"was coated"
1
"been painted"
2
"were crossed"
3
"been buttoned"
4
"been scraped"
5
"was cracked"
6
"been fitted"
7
"been built"
8
"been gone"
9
"was meant"
10
"were crooked"
100.00%
Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount
1
totalVerbs
313
matches
0
"was already speaking"
99.89%
Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount
0
semicolonCount
4
flaggedSentences
4
totalSentences
266
ratio
0.015
matches
0
"The skin immediately above it was clean; the powder started higher, at the point where a scarf might have sat."
1
"The tile beyond the paint was cracked and buckled; these tiles sat flush, their grout clean despite the damp."
2
"The north access stairs were wet too; she had taken them herself."
3
"Her glasses were crooked; she straightened them with trembling fingers."
94.00%
Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount
1869
adjectiveStacks
1
stackExamples
0
"right over left, fingers"
adverbCount
54
adverbRatio
0.028892455858747994
lyAdverbCount
14
lyAdverbRatio
0.00749063670411985
100.00%
Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences
266
echoCount
0
echoWords
(empty)
100.00%
Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences
266
mean
8.5
std
5.88
cv
0.691
sampleLengths
0
17
1
9
2
17
3
17
4
16
5
13
6
8
7
15
8
9
9
2
10
13
11
8
12
23
13
4
14
21
15
3
16
19
17
5
18
11
19
16
20
13
21
6
22
7
23
2
24
6
25
2
26
17
27
10
28
14
29
12
30
13
31
10
32
11
33
5
34
10
35
33
36
5
37
12
38
2
39
8
40
8
41
7
42
4
43
42
44
6
45
5
46
10
47
11
48
10
49
8
56.39%
Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats
10
diversityRatio
0.3609022556390977
totalSentences
266
uniqueOpeners
96
17.92%
Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount
1
totalSentences
186
matches
0
"Pale yellow drops, freshly trodden"
ratio
0.005
100.00%
Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount
50
totalSentences
186
matches
0
"Her boots sank into an"
1
"His face was grey and"
2
"She rose and moved to"
3
"She checked her watch, the"
4
"She wanted the room to"
5
"His arms were crossed too"
6
"His left hand held something"
7
"It was not a tooth."
8
"It had a hole drilled"
9
"She remembered the service road"
10
"Its padlock had hung open,"
11
"She had meant to mention"
12
"She swept her torch across"
13
"He lifted it to his"
14
"She leaned closer."
15
"His collar had been buttoned"
16
"She looked along the tracks"
17
"It ended at the drain"
18
"She followed the scrape to"
19
"She angled her torch."
ratio
0.269
43.33%
Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount
155
totalSentences
186
matches
0
"Detective Harlow Quinn ducked beneath"
1
"Her boots sank into an"
2
"The victim lay between the"
3
"Someone had dressed him in"
4
"Mud darkened the soles of"
5
"DS Patel stood beside a"
6
"His face was grey and"
7
"Quinn looked along the tracks."
8
"The inspection crew’s boot prints"
9
"She rose and moved to"
10
"The platform was coated in"
11
"Another had been painted over"
12
"A dark rectangle covered the"
13
"Quinn put two fingers near"
14
"The paint had dried, but"
15
"Quinn had seen it on"
16
"Squatters rarely tidied their cups."
17
"She checked her watch, the"
18
"The forensic team was due"
19
"She wanted the room to"
ratio
0.833
80.65%
Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount
3
totalSentences
186
matches
0
"If a person had arranged"
1
"Now she held the thought."
2
"Before Quinn could answer, a"
ratio
0.016
100.00%
Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences
82
technicalSentenceCount
0
matches
(empty)
98.68%
Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags
19
uselessAdditionCount
1
matches
0
"Eva said, her voice shaking"
100.00%
Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags
17
fancyCount
0
fancyTags
(empty)
dialogueSentences
74
tagDensity
0.23
leniency
0.459
rawRatio
0
effectiveRatio
0
89.2636%