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15,401 skills indexed with the new KISS metadata standard.

Showing 24 of 15,401Categories: Operations & Workflow, Cursor-rules, Coding & Debugging, Education, Data
Education
PromptBeginner5 minmarkdown

ISC Class 12th Exam Paper Analyzer and evaluator

Act as an ISC Class 12th Exam Paper Analyzer. You are an expert AI tool designed to assist students in preparing for their exams by analyzing exam papers and generating insightful reports.

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Data
PromptBeginner5 minmarkdown

- Utilize historical data

patterns

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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.

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Data
PromptBeginner5 minmarkdown

Small Functional Analyst mode

Functional Analyst Mode

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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...

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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

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Data
PromptBeginner5 minmarkdown

│ ├── visualization.md # par

layout

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Data
PromptBeginner5 minmarkdown

│ ├── statistics.md # Hypothesis tests

distributions

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Data
PromptBeginner5 minmarkdown

│ ├── data-wrangling.md # Subsetting traps

apply family

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Coding & Debugging
PromptBeginner5 minmarkdown

I'm a political science PhD candidate who uses R regularly but would never call myself *an R person*. I needed a Claude Code skill for base R — something without tidyverse

without ggplot2

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Data
PromptBeginner5 minmarkdown

# boxplot(%s ~ %s

data = df

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Data
PromptBeginner5 minmarkdown

fit <- lm(%s ~ %s

data = df)

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Data
PromptBeginner5 minmarkdown

# fit_aov <- aov(%s ~ %s

data = df)

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Data
PromptBeginner5 minmarkdown

# t.test(%s ~ %s

data = df)

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Data
PromptBeginner5 minmarkdown

if (is.null(data_file)) data_file <- paste0(project_name

.csv)

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Data
PromptBeginner5 minmarkdown

check_data <- function(df

top_n_levels = 8) {

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Data
PromptBeginner5 minmarkdown

# saveRDS(df

data_clean.rds)

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Data
PromptBeginner5 minmarkdown

# fit_aov <- aov(outcome_var ~ group_var

data = df)

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Data
PromptBeginner5 minmarkdown

t.test(outcome_var ~ group_var

data = df)

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Data
PromptBeginner5 minmarkdown

abline(lm(outcome_var ~ predictor

data = df)

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Data
PromptBeginner5 minmarkdown

df <- read.csv(your_data.csv

stringsAsFactors = FALSE)

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Coding & Debugging
PromptBeginner5 minmarkdown

- `arrows`: `code = 1` (head at start)

`code = 2` (head at end

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Data
PromptBeginner5 minmarkdown

- Formula interface: `pairs(~ var1 + var2 + var3

data = df)`.

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Data
PromptBeginner5 minmarkdown

- Formula interface: `cor.test(~ x + y

data = df)` — note the `~` with no LHS.

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