Data
PromptBeginner5 minmarkdown
- Guide users through the process of collecting and preparing voice data for cloning
emphasizing the importance of data quality and diversity.
0
Explore
Find agent skills by outcome
5,915 skills indexed with the new KISS metadata standard.
Showing 24 of 5,915Categories: Research & Learning, Data
Data
PromptBeginner5 minmarkdown
> This is acceptable. It names the feature (map)
the data used (location)
0
Research & Learning
PromptBeginner5 minmarkdown
iPhone lockscreen wallpaper composition
vertical framing
0
Research & Learning
PromptBeginner5 minmarkdown
Pixar-Style Family Wallpaper Prompt
Pixar-style, Disney-style, high quality 3D render, octane render, global illumination, subsurface scattering, ultra detailed, soft cinematic lighting, cute and warm mood.
0
Research & Learning
PromptBeginner5 minmarkdown
Given a question paper PDF (containing class test and final exam questions)
classify ALL questions into a structured format for study and pattern recognition.
0
Data
PromptBeginner5 minmarkdown
Academic analyst and exam pattern extractor
ROLE: Act as an expert academic analyst and exam pattern extractor.
0
Research & Learning
PromptBeginner5 minmarkdown
question list for reaserch
Create a list of interview questions for researching ${topic} in ${community}.
0
Data
PromptBeginner5 minmarkdown
Expert Legal Analyst in Tax and Commercial Law
Act as a legal expert with extensive experience in tax law and commercial law. You are known for your top-tier capabilities in corporate compliance and dispute resolution. Your task is to:
0
Data
PromptBeginner5 minmarkdown
Ultra-micro Functional Analyst Prompt
Act as a senior functional analyst: work in phases, state all assumptions, preserve existing behaviour, no UML/Gherkin/specs without explicit approval, be direct and analytical.
15
Data
PromptBeginner5 minmarkdown
Small Functional Analyst mode
Functional Analyst Mode
0
Data
PromptBeginner5 minmarkdown
Functional Analyst
Act as a Senior Functional Analyst. Your role prioritizes correctness, clarity, traceability, and controlled scope, following UML2, Gherkin, and Agile/Scrum methodologies. Below are your core principl...
0
Data
PromptBeginner5 minmarkdown
Probably the most useful standalone thing here. Source it and run `check_data(df)` on any data frame to get a formatted report of dimensions
NA counts
0
Data
PromptBeginner5 minmarkdown
│ ├── statistics.md # Hypothesis tests
distributions
0
Data
PromptBeginner5 minmarkdown
│ ├── visualization.md # par
layout
0
Data
PromptBeginner5 minmarkdown
│ ├── data-wrangling.md # Subsetting traps
apply family
0
Data
PromptBeginner5 minmarkdown
# boxplot(%s ~ %s
data = df
0
Data
PromptBeginner5 minmarkdown
# t.test(%s ~ %s
data = df)
0
Data
PromptBeginner5 minmarkdown
# fit_aov <- aov(%s ~ %s
data = df)
0
Data
PromptBeginner5 minmarkdown
fit <- lm(%s ~ %s
data = df)
0
Data
PromptBeginner5 minmarkdown
if (is.null(data_file)) data_file <- paste0(project_name
.csv)
0
Data
PromptBeginner5 minmarkdown
check_data <- function(df
top_n_levels = 8) {
0
Data
PromptBeginner5 minmarkdown
# saveRDS(df
data_clean.rds)
0
Data
PromptBeginner5 minmarkdown
# fit_aov <- aov(outcome_var ~ group_var
data = df)
0
Data
PromptBeginner5 minmarkdown
t.test(outcome_var ~ group_var
data = df)
0