Coding & Debugging
PromptBeginner5 minmarkdown
Xcode MCP tools are accessed via `mcporter` CLI
which bridges MCP servers to standard command-line tools. This skill defines when to use Xcode MCP and when to prefer standard tools.
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Coding & Debugging
PromptBeginner5 minmarkdown
xcode-mcp (for pi agent)
---
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Coding & Debugging
PromptBeginner5 minmarkdown
description: Guidelines for efficient Xcode MCP tool usage via mcporter CLI. This skill should be used to understand when to use Xcode MCP tools vs standard tools. Xcode MCP consumes many tokens - use only for build
test
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Data
PromptBeginner5 minmarkdown
- Provide a detailed report with strategies on how to excel in exams
including study tips and areas to focus on.
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Creative
PromptBeginner5 minmarkdown
- Generate infographics
including graphs and pie charts
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Coding & Debugging
PromptBeginner5 minmarkdown
code generation for online assessments
SOLVE THE QUESTION IN CPP, USING NAMESPACE STD, IN A SIMPLE BUT HIGHLY EFFICIENT WAY, AND PROVIDE IT WITH THIS RESTYLING:
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Creative
PromptBeginner5 minmarkdown
Improve
What's the single smartest and most radically innovative and accretive and useful and compelling addition you could make to the project at this point?
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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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Creative
PromptBeginner5 minmarkdown
Start every task by restating requirements
constraints
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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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