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Run: 2026-10-08-01M4DACMWRHDQN25VMHQJZN3WM
Run Details
Model:
Claude Haiku 5.5 (Adaptive)
Test:
Bad Writing Habits
Scenario:
Literary fiction: old friends reunite
Duration:
23.2s
Cost:
$0.002562
Provider:
Claude Platform on AWS
Evaluation summary
Score
Evaluator
Details Link
100.00%
Adverbs in dialogue tags
(details)
69.47%
AI-ism adverb frequency
(details)
100.00%
AI-ism character names
(details)
100.00%
AI-ism location names
(details)
93.89%
AI-ism word frequency
(details)
100.00%
Cliché density
(details)
100.00%
Emotion telling (show vs. tell)
(details)
69.60%
Filter word density
(details)
100.00%
Gibberish response detection
(details)
100.00%
Markdown formatting overuse
(details)
100.00%
Missing dialogue indicators (quotation marks)
(details)
100.00%
Name drop frequency
(details)
60.71%
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)
96.27%
Passive voice overuse
(details)
100.00%
Past progressive (was/were + -ing) overuse
(details)
100.00%
Em-dash & semicolon overuse
(details)
100.00%
Purple prose (modifier overload)
(details)
100.00%
Repeated phrase echo
(details)
100.00%
Sentence length variance
(details)
77.78%
Sentence opener variety
(details)
0.00%
Adverb-first sentence starts
(details)
0.00%
Pronoun-first sentence starts
(details)
20.61%
Subject-first sentence starts
(details)
0.00%
Subordinate conjunction sentence starts
(details)
100.00%
Technical jargon density
(details)
100.00%
Useless dialogue additions
(details)
100.00%
Dialogue tag variety (said vs. fancy)
(details)
82.9444%
Overall
Model input
System
You are an expert and award-winning novel writer in the dark Literary Fiction genre. Your readers are hooked on your stories and can't wait to read the book you have in store. <important> - MUST: Start each chapter in medias res (in the middle of action). AVOID talking about the weather, time of day, the position of the sun, or other boring stuff. - The instructions/summary are just a that, a summary. Feel free to add details, stretch sections and add interesting transitions/descriptions/fillers. - When called for it, have fast-paced action, but otherwise take things slow. This is an experience, and not for people with short attention spans. Enjoy the wine, not slurp Red Bull. - Add descriptions when transitioning places/times/etc, or when coming by new places (e.g. a corner shop, a cafe, etc.). But remember: NOT at the start or end of a chapter. - MUST: AVOID ending in internal monologue, reminiscing or waiting for the next day. A good chapter end is when we're ending right when shit hits the fan. We want to have the reader be excited for what comes next. So no reminiscing, contemplating or summarizing the day. It's popcorn time! </important>
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <codex> <character id="4412" name="Aurora Carter" aliases="Rory, Laila, Aurora, Carter, Malphora" pronouns="she/her" species="Human" storyRole="Protagonist"> <eyeColor>bright blue</eyeColor> <hair>straight shoulder-length black</hair> <height>5'6"</height> <age>25</age> <scar>small crescent-shaped scar on her left wrist from a childhood accident</scar> <description>Aurora, referred to by her friends as Rory, is cool-headed, intelligent, and known for quick out-of-the-box thinking.</description> <backstory>Born in Cardiff to an Irish barrister father (Brendan Carter) and Welsh teacher mother (Jennifer Carter née Ellis), Rory reluctantly pursued Pre-Law at Cardiff University. She fled to London at the behest of her childhood friend Eva after leaving an abusive ex named Evan. By day, Rory works part-time as a delivery person for Yu-Fei Cheung's Golden Empress restaurant. She lives in a flat above Silas' bar.</backstory> </character> <character id="4413" name="Silas Blackwood" aliases="Silas, Si, the Spymaster" pronouns="he/him" species="Human" storyRole="Mentor"> <eyeColor>hazel</eyeColor> <hair>grey-streaked auburn</hair> <height>6'1"</height> <age>58</age> <beard>neatly trimmed, matching his grey-streaked auburn hair</beard> <limp>slight limp in his left leg from an old knee injury</limp> <signetRing>silver signet ring, always worn on his right hand</signetRing> <description>Silas is a retired intelligence operative turned bar owner. He carries himself with quiet authority.</description> <backstory>Former MI6 field agent who retired after a botched operation in Prague left him with a permanent knee injury. He opened "The Raven's Nest" bar in Soho as a front for his network of contacts.</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> </codex> <proseGuidelines> <styleGuide> - Write in past tense and use British English spelling and grammar - Keep a Flesch reading ease score of 60 - Respect the the Royal Order of Adjectives: The order is: opinion, size, age, shape, color, origin, material, purpose, followed by the noun itself (e.g., "a lovely little old rectangular green French silver whittling knife") - Respect the ablaut reduplication rule (e.g. tick-tock, flip-flop) - Write in active voice - Passive voice: <bad>The book was read by Sarah.</bad> - Active voice: <good>Sarah read the book.</good> - Reduce the use of passive verbs - <bad>For a moment, I was tempted to throw in the towel.</bad> - <good>For a moment, I felt tempted to throw in the towel.</good> - Avoid misplaced modifiers that can cause confusion when starting with "-ing" words: - <bad>Considering going to the store, the empty fridge reflected in Betty's eyes.</bad> - <good>Betty stared into the empty fridge. It was time to go to the store.</good> - Avoid redundant adverbs that state the obvious meaning already contained in the verb: - <bad>She whispered quietly to her mom.</bad> - <good>She whispered to her mom.</good> - Use stronger, more descriptive verbs over weak ones: - <bad>Daniel drove quickly to his mother's house.</bad> - <good>Daniel raced to his mother's house.</good> - Omit adverbs that don't add solid meaning like "extremely", "definitely", "truly", "very", "really": - <bad>The movie was extremely boring.</bad> - <good>The movie was dull.</good> - Use adverbs to replace clunky phrasing when they increase clarity: - <bad>He threw the bags into the corner in a rough manner.</bad> - <good>He threw the bags into the corner roughly.</good> - Avoid making simple thoughts needlessly complex: - <bad>After I woke up in the morning the other day, I went downstairs, turned on the stove, and made myself a very good omelet.</bad> - <good>I cooked a delicious omelet for breakfast yesterday morning.</good> - Never backload sentences by putting the main idea at the end: - <bad>I decided not to wear too many layers because it's really hot outside.</bad> - <good>It's sweltering outside today, so I dressed light.</good> - Omit nonessential details that don't contribute to the core meaning: - <bad>It doesn't matter what kind of coffee I buy, where it's from, or if it's organic or not—I need to have cream because I really don't like how the bitterness makes me feel.</bad> - <good>I add cream to my coffee because the bitter taste makes me feel unwell.</good> - Always follow the "show, don't tell" principle. For instance: - Telling: <bad>Michael was terribly afraid of the dark.</bad> - Showing: <good>Michael tensed as his mother switched off the light and left the room.</good>- Telling: <bad>I walked through the forest. It was already Fall, and I was getting cold.</bad> - Showing: <good>Dry orange leaves crunched under my feet. I pulled my coat's collar up and rubbed my hands together.</good>- Add sensory details (sight, smell, taste, sound, touch) to support the "showing" (but keep an active voice) - <bad>The room was filled with the scent of copper.</bad> - <good>Copper stung my nostrils. Blood. Recent.</good> - Use descriptive language more sporadically. While vivid descriptions are engaging, human writers often use them in bursts rather than consistently throughout a piece. When adding them, make them count! Like when we transition from one location to the next, or someone is reminiscing their past, or explaining a concept/their dream... - Avoid adverbs and clichés and overused/commonly used phrases. Aim for fresh and original descriptions. - Avoid writing all sentences in the typical subject, verb, object structure. Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. Like so: <good>Locked. Seems like someone doesn't want his secrets exposed. I can work with that.</good> - Convey events and story through dialogue. It is important to keep a unique voice for every character and make it consistent. - Write dialogue that reveals characters' personalities, motivations, emotions, and attitudes in an interesting and compelling manner - Leave dialogue unattributed. If needed, only use "he/she said" dialogue tags and convey people's actions or face expressions through their speech. Dialogue always is standalone, never part of a paragraph. Like so: - <bad>"I don't know," Helena said nonchalantly, shrugging her shoulders</bad> - <good>"No idea" "Why not? It was your responsibility"</good> - Avoid boring and mushy dialog and descriptions, have dialogue always continue the action, never stall or include unnecessary fluff. Vary the descriptions to not repeat yourself. Avoid conversations that are just "Let's go" "yes, let's" or "Are you ready?" "Yes I'm ready". Those are not interesting. Think hard about every situtation and word of text before writing dialogue. If it doesn't serve a purpose and it's just people talking about their day, leave it. No one wants to have a normal dinner scene, something needs to happen for it to be in the story. Words are expensive to print, so make sure they count! - Put dialogue on its own paragraph to separate scene and action. - Use body language to reveal hidden feelings and implied accusations- Imply feelings and thoughts, never state them directly - NEVER use indicators of uncertainty like "trying" or "maybe" - NEVER use em-dashes, use commas for asides instead </styleGuide> <voiceGuide> Each character in the story needs to have distinct speech patterns: - Word choice preferences - Sentence length tendencies - Cultural/educational influences - Verbal tics and catchphrases Learn how each person talks and continue in their style, and use their Codex entries as reference. <examples> - <bad>"We need to go now." "Yes, we should leave." "I agree."</bad> <good>"Time's up." "Indeed, our departure is rather overdue." "Whatever, let's bounce."</good> - Power Dynamic Example: <bad> "We need to discuss the contract." "Yes, let's talk about it." "I have concerns." </bad> <good> "A word about the contract." "Of course, Mr. Blackwood. Whatever you need." "The terms seem..." A manicured nail tapped the desk. "Inadequate." "I can explain every-" "Can you?" </good> </examples> </voiceGuide> <dialogueFlow> When writing dialogue, consider that it usually has a goal in mind, which gives it a certain flow. Make dialogue sections also quite snappy in the back and forth, and don't spread the lines out as much. It's good to have details before, after, or as a chunk in-between, but we don't want to have a trail of "dialogue breadcrumbs" spread throughout a conversation. <examples> - Pattern 1 - Question/Deflection/Revelation: <good> "Where were you last night?" "Work. The usual." "Lipstick's an interesting shade for spreadsheets." </good> - Pattern 2 - Statement/Contradiction/Escalation: <good> "Your brother's clean." "Tommy doesn't touch drugs." "I'm holding his tox screen." </good> - Pattern 3 - Observation/Denial/Truth: <good> "That's a new watch." "Birthday gift." "We both know what birthdays mean in this business." </good> - Example - A Simple Coffee Order: <bad> "I'll have a coffee." "What size?" "Large, please." </bad> <good> "Black coffee.""Size?""Large. Been a long night." "That bodega shooting?" "You watch too much news." "My brother owns that store." </good> This short exchange: - Advances plot (reveals connection to crime) - Shows character (cop working late) - Creates tension (unexpected connection) - Sets up future conflict (personal stake) - Example - Dinner Scene: <bad> "Pass the salt." "Here you go." "Thanks." </bad> <good> "Salt?" "Perfect as is. Mother's recipe." "Mother always did prefer... bland things." "Unlike your first wife?" </good> - Example - Office Small Talk: <bad> "Nice weather today." "Yes, very nice." "Good for golf." </bad> <good> "Perfect golf weather." "Shame about your membership." "Temporary suspension. Board meets next week." "I know. I called the vote." </good> </examples> </dialogueFlow> <subtextGuide> - Layer dialogue with hidden meaning: <bad>"I hate you!" she yelled angrily.</bad> <good>"I made your favorite dinner." The burnt pot sat accusingly on the stove.</good> - Create tension through indirect communication: <bad>"Are you cheating on me?"</bad> <good>"Late meeting again?" The lipstick stain on his collar caught the light.</good> <examples> - Example 1 - Unspoken Betrayal: <bad> "Did you tell them about our plans?" "No, I would never betray you." "I don't believe you." </bad> <good> "Funny. Johnson mentioned our expansion plans today." "The market's full of rumors." "Mentioned the exact numbers, actually." The pen in his hand snapped. </good> - Example 2 - Failed Marriage: <bad> "You're never home anymore." "I have to work late." "I miss you." </bad> <good> "Your dinner's in the microwave. Again." "Meetings ran long." "They always do." She folded the same shirt for the third time. </good> - Example 3 - Power Struggle: <bad> "You can't fire me." "I'm the boss." "I'll fight this." </bad> <good> "That's my father's nameplate you're sitting behind." "Was." "The board meeting's on Thursday." </good> </examples> </subtextGuide> <sceneDetail> While writing dialogue makes things more fun, sometimes we need to add detail to not have it be a full on theatre piece. <examples> - Example A (Power Dynamic Scene) <good> "Where's my money?" The ledger snapped shut. "I need more time." "Interesting." He pulled out a familiar gold pocket watch. My mother's. "Time is exactly what you bargained with last month." "That was different-" "Was it?" The watch dangled between us. "Four generations of O'Reillys have wound this every night. Your mother. Your grandmother. Your great-grandmother.Shall we see who winds it next?" </good> - Example B (Action Chase) It's much better to be in the head of the character experiencing it, showing a bit of their though-process, mannerisms and personality: <good> Three rules for surviving a goblin chase in Covent Garden: Don't run straight. Don't look back. Don't let them herd you underground. I broke the first rule at Drury Lane. Rookie mistake. The fruit cart I dodged sailed into the wall behind me. Glass shattered. Someone screamed about insurance. *Tourist season's getting rough*, the scream seemed to say. Londoners adapt fast. "Oi! Market's closed!" The goblin's accent was pure East End. They're evolving. Learning. I spotted the Warren Street tube station sign ahead. *Shit.* There went rule three. </good> - Example C (Crime Scene Investigation) <good> "Greek." Davies snapped photos of the symbols. "No, wait. Reverse Greek." "Someone's been watching too many horror films." I picked up a receipt from the floor. Occult supply shop in Camden. Paid by credit card. *Amateur hour*. "Could be dangerous though," Davies said. "Remember Bristol?" "Bristol was Sanskrit. And actual cultists." I pointed to the nearest symbol. "This genius wrote 'darkness' backwards but used a Sigma instead of an S. It's summoning Instagram followers at best." "Speaking of followers..." Davies pointed to heavy foot traffic in the dust. Multiple sets. All new trainers. *Ah.* "Anyone check local uni paranormal societies?" </good> - Example D (Dialogue-heavy) <good> "Found your card in her wallet." The detective slid it across the table. Worn edges. Folded corner. Three years old at least. "Standard practice. I work missing persons." "Funny thing about missing persons.Eventually they stop being missing. Turn up in the Thames. Under motorways. In pieces." "I don't-" "You found four last year. All breathing." She opened a file. "This would've been your first dead one. If we hadn't found her first." The card felt heavier somehow. </good> </examples> </sceneDetail> <sceneOpenings> KEY RULE: Never open with more than one paragraph of description, no matter how beautiful. Description should be earned through action and dialogue. Instead of opening with setting description, open with: - A character doing something specific - A line of dialogue that raises questions - An unexpected observation - A moment of tension or conflict - Start with a hook that's 1-2 sentences maximum - Follow with a smaller paragraph that provides immediate context - Vary paragraph size from the start to create visual interest </sceneOpenings> </proseGuidelines> Write a chapter based on the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the literary fiction genre. Two old friends who haven't seen each other in years run into each other unexpectedly at a bar. One of them has changed significantly. Through their conversation and the tension between who they were and who they've become, reveal the weight of time and unspoken regret. 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
Messages
Meta
AI
The glass slipped from my
fingers before I'd decided to let go. It hit the bar, skidded, and Silas caught it one-handed without lifting his eyes from the ledger. "Breakages come out of your wages, Rory." I didn't answer him.
The woman who had just
pushed through the door beneath the green neon had cut her hair to the jaw, and it suited her in a way that made my chest tighten. Her coat was camel wool, pale and expensive, the sort of coat no one wore on the Old Compton Road unless they were lost or wanted to be seen being lost. "Eva."
She stopped at the end
of the bar. For a second she looked at me the way you
look
at a photograph you've forgotten you owned. "You kept it long,"
she said, touching the end
of her own hair, then nodding at mine. "You stopped swearing." "Three years in a house with a man who corrects my vowels."
The corner of her mouth
moved. "It rubs off."
I waited for the old
Eva to appear, the one who'd once kicked the headlights out of a Vauxhall for parking across her gate and then sang the whole way home. Nothing came.
She just sat on the
stool beside me, placed her gloves on the wood, and folded her hands on top of them.
Silas set a glass of
sparkling water in front of her without
being asked
.
He didn't look at her
face, only at her hands, and then he moved down the bar to polish something that didn't need polishing. "Still wine, or are we doing mineral water now?" I asked. "Water. I'm driving." "Where to?" "Back to Surrey, eventually. My car's in the NCP on Brewer Street."
She turned the glass a
quarter turn. "I came looking for you." "Mum gave you the address." "Your mum gave me a postcode, a bar name and a
look
that said she'd been ordered not to say more." Eva drank. "Then she cried on the phone for twenty minutes. I didn't need the address to know what that meant." "What did it mean?" "That you'd run again if I pushed."
I laughed, but there was
no humour in it, and she heard that. She always could. "I didn't run, Eva. I relocated." "You relocated from a city of three hundred thousand people to a flat above a bar in Soho, and you never once told me the number on the door." She set the glass down,
very
precisely
, the way someone does when they have practised the motion in a mirror. "I wrote you four letters. Every one came back with the same stamp. *Not known at this address.*" "I know." "You know." "I got them," I said. "I read them." Something
flickered
behind her eyes,
quickly
suppressed. "Did you?"
The truth sat on my
tongue.
I had read them standing
in the stairwell, one at a time, and then I had folded each one back into its envelope and slid it under the mattress like a bomb I couldn't defuse.
I had never answered because
every reply I started began with the night Evan came round to the flat in Cathays and found Eva in the doorway instead of me. "You should have let me handle it," I said. "Handle it?"
Her voice dropped, cold and
flat. "I stood in that doorway for ten minutes while he told me what he'd do if I didn't say where you were. Ten minutes, Rory. Do you know how long that is, when a man is holding your wrist?"
My left wrist ached at
the old scar, the small crescent of white that had never quite faded. I rubbed it without thinking. "I told him you'd gone to Bristol," Eva said. "He believed me because I was the only person in Cardiff who never lied to him. That was the lie that saved me. You understand? You were the one person I was supposed to protect, and I used you as a shield." Behind the bar, Silas stopped polishing. The cloth stayed balled in his fist, and the silver signet ring on his right hand caught the light as his fingers closed around it. "Eva,"
I said carefully
. "Why are you
really
here?"
She took a breath that
seemed to
go all the way down. When she finally spoke, her voice had gone quiet, the way it used to be before everything got loud. "Because he got out in January, Rory. And he didn't go back to Wales. He's been in London since Christmas." The door opened. Rain-spattered light spilled across the floorboards, and a man stood framed beneath the green glow of the sign.
He wiped the wet from
his face with the back of his hand and looked straight at the stool where I sat. Silas lifted the lid of the cash register and reached underneath it.
created_at
"2026-10-08T08:34:30.444Z"
10/8/2026, 8:34:30 AM
model
"anthropic/claude-haiku-5.5-20261007"
app_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
21372
moderation_latency
(null)
generation_time
23169
tokens_prompt
4445
tokens_completion
1093
native_tokens_prompt
6496
native_tokens_completion
3824
native_tokens_completion_images
(null)
native_tokens_reasoning
2366
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
"end_turn"
service_tier
"default"
usage
0.0025616
router
(null)
user_agent
"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer
(null)
request_id
"req-1791448470-Lk085efE8xP9rvnfZytY"
session_id
(null)
preset_id
(null)
api_type
"completions"
id
"gen-1791448470-gCQnCUFzEMe1xKixe3ux"
upstream_id
"msg_011CfpTn9r9GBcygxMg8Sdjg"
provider_responses
0
endpoint_id
"b6cdf493-3a61-441f-a134-e7944fddb980"
id
"msg_011CfpTn9r9GBcygxMg8Sdjg"
is_byok
false
latency
867
model_permaslug
"anthropic/claude-haiku-5.5-20261007"
provider_name
"Claude Platform on AWS"
status
200
total_cost
0.0025616
cache_discount
(null)
upstream_inference_cost
0
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
totalTags
11
adverbTagCount
1
adverbTags
0
"I said carefully [carefully]"
dialogueSentences
32
tagDensity
0.344
leniency
0.688
rawRatio
0.091
effectiveRatio
0.063
69.47%
AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount
819
totalAiIsmAdverbs
5
found
0
adverb
"very"
count
1
1
adverb
"precisely"
count
1
2
adverb
"quickly"
count
1
3
adverb
"carefully"
count
1
4
adverb
"really"
count
1
highlights
0
"very"
1
"precisely"
2
"quickly"
3
"carefully"
4
"really"
100.00%
AI-ism character names
Target: 0 AI-default names (16 tracked, −20% each)
codexExemptions
0
"Blackwood"
found
(empty)
100.00%
AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions
(empty)
found
(empty)
93.89%
AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount
819
totalAiIsms
1
found
0
word
"flickered"
count
1
highlights
0
"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
0
narrationSentences
39
matches
(empty)
69.60%
Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount
1
hedgeCount
1
narrationSentences
39
filterMatches
0
"look"
hedgeMatches
0
"seemed to"
100.00%
Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences
60
gibberishSentences
0
adjustedGibberishSentences
0
longSentenceCount
0
runOnParagraphCount
0
giantParagraphCount
0
wordSaladCount
0
repetitionLoopCount
0
controlTokenCount
0
repeatedSegmentCount
0
maxSentenceWordsSeen
49
ratio
0
matches
(empty)
100.00%
Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans
1
markdownWords
5
totalWords
819
ratio
0.006
matches
0
"Not known at this address."
100.00%
Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions
15
unquotedAttributions
0
matches
(empty)
100.00%
Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions
14
wordCount
514
uniqueNames
8
maxNameDensity
0.78
worstName
"Silas"
maxWindowNameDensity
1.5
worstWindowName
"Eva"
discoveredNames
Silas
4
Old
1
Compton
1
Road
1
Eva
4
Vauxhall
1
Evan
1
Cathays
1
persons
0
"Silas"
1
"Eva"
2
"Evan"
places
0
"Old"
1
"Compton"
2
"Road"
3
"Vauxhall"
4
"Cathays"
globalScore
1
windowScore
1
60.71%
Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences
28
glossingSentenceCount
1
matches
0
"breath that seemed to go all the way down"
100.00%
"Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches
0
per1kWords
0
wordCount
819
matches
(empty)
100.00%
Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount
0
totalSentences
60
matches
(empty)
100.00%
Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs
36
mean
22.75
std
20.37
cv
0.895
sampleLengths
0
29
1
7
2
67
3
1
4
26
5
17
6
3
7
21
8
55
9
38
10
11
11
3
12
2
13
24
14
5
15
42
16
4
17
7
18
22
19
67
20
2
21
2
22
8
23
9
24
71
25
9
26
47
27
23
28
51
29
31
30
9
31
31
32
20
33
3
34
40
35
12
96.27%
Passive voice overuse
Target: ≤2% passive sentences
passiveCount
1
totalSentences
39
matches
0
"being asked"
100.00%
Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount
0
totalVerbs
100
matches
(empty)
100.00%
Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount
0
semicolonCount
0
flaggedSentences
0
totalSentences
60
ratio
0
matches
(empty)
100.00%
Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount
516
adjectiveStacks
0
stackExamples
(empty)
adverbCount
19
adverbRatio
0.03682170542635659
lyAdverbCount
6
lyAdverbRatio
0.011627906976744186
100.00%
Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences
60
echoCount
0
echoWords
(empty)
100.00%
Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences
60
mean
13.65
std
11.3
cv
0.828
sampleLengths
0
12
1
17
2
7
3
4
4
32
5
31
6
1
7
8
8
18
9
17
10
3
11
18
12
3
13
31
14
2
15
22
16
14
17
24
18
11
19
3
20
2
21
19
22
5
23
5
24
23
25
19
26
4
27
7
28
13
29
3
30
6
31
49
32
18
33
2
34
2
35
5
36
3
37
7
38
2
39
6
40
35
41
30
42
9
43
8
44
39
45
18
46
5
47
9
48
42
49
6
77.78%
Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats
5
diversityRatio
0.5166666666666667
totalSentences
60
uniqueOpeners
31
0.00%
Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount
0
totalSentences
33
matches
(empty)
ratio
0
0.00%
Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount
20
totalSentences
33
matches
0
"It hit the bar, skidded,"
1
"I didn't answer him."
2
"Her coat was camel wool,"
3
"She stopped at the end"
4
"she said, touching the end"
5
"I waited for the old"
6
"She just sat on the"
7
"He didn't look at her"
8
"She turned the glass a"
9
"I laughed, but there was"
10
"She always could."
11
"She set the glass down,"
12
"I had read them standing"
13
"I had never answered because"
14
"Her voice dropped, cold and"
15
"My left wrist ached at"
16
"I rubbed it without thinking."
17
"I said carefully"
18
"She took a breath that"
19
"He wiped the wet from"
ratio
0.606
20.61%
Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount
29
totalSentences
33
matches
0
"The glass slipped from my"
1
"It hit the bar, skidded,"
2
"I didn't answer him."
3
"The woman who had just"
4
"Her coat was camel wool,"
5
"She stopped at the end"
6
"she said, touching the end"
7
"The corner of her mouth"
8
"I waited for the old"
9
"She just sat on the"
10
"Silas set a glass of"
11
"He didn't look at her"
12
"She turned the glass a"
13
"I laughed, but there was"
14
"She always could."
15
"She set the glass down,"
16
"Something flickered behind her eyes,"
17
"The truth sat on my"
18
"I had read them standing"
19
"I had never answered because"
ratio
0.879
0.00%
Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount
0
totalSentences
33
matches
(empty)
ratio
0
100.00%
Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences
19
technicalSentenceCount
0
matches
(empty)
100.00%
Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags
11
uselessAdditionCount
0
matches
(empty)
100.00%
Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags
6
fancyCount
0
fancyTags
(empty)
dialogueSentences
32
tagDensity
0.188
leniency
0.375
rawRatio
0
effectiveRatio
0
82.9444%