Agent skill

TDD Workflow

by ab604 in ab604/claude-code-r-skills

Test-driven development workflow for R using testthat. An agent skill from ab604/claude-code-r-skills.

MITAuto-check passedTesting & QA

Install TDD Workflow

skills CLI
$ npx skills add ab604/claude-code-r-skills --skill tdd-workflow -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ab604/claude-code-r-skills tdd-workflow --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ab604/claude-code-r-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/tdd-workflow .claude/skills/tdd-workflow && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tdd-workflow
GitHub stars
206
Used in
2 other repos
Token cost
~4.8k tokens
SKILL.md length
647 words
Files
1
Skills in repo
8
Repo updated
First seen
Licence
MIT

At a glance

Test-driven development workflow for R using testthat. An agent skill from ab604/claude-code-r-skills.

  • Works in 12 steps: Tests BEFORE Code → Coverage Requirements → Test Types → …
  • Writing new features
  • SKILL.md covers When to Activate, Getting Started, Core Principles and Test Design Principles, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

TDD Workflow is an agent skill from ab604/claude-code-r-skills. Test-driven development workflow for R using testthat. Use when writing new features, fixing bugs, or refactoring code. Enforces test-first development with 80%+ coverage.

Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Test-driven development. The repository describes itself as: Claude Code configurations for R development. The licence is MIT.

When your agent uses it

  • Writing new features
  • Refactoring code

Example prompts

  • “/tdd-workflow”

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Tests BEFORE Code
  2. Coverage Requirements
  3. Test Types
  4. Define Expected Behavior
  5. Write Failing Tests
  6. Run Tests (They Should Fail)
  7. Implement Minimal Code
  8. Run Tests Again
  9. Refactor
  10. Verify Coverage
  11. Constructor Functions
  12. Local Functions with Cleanup

What it can do on your machine

Read from SKILL.md and the folder at commit 529de4f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are r).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

TDD Workflow loads about 4.8k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 647 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~4.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ab604/claude-code-r-skills at commit 529de4f, republished under its MIT licence (© ab604). 647 words, ~4,827 tokens.

Download SKILL.mdSave it as .claude/skills/tdd-workflow/SKILL.md (or your agent's skills folder).
name
tdd-workflow
description
Test-driven development workflow for R using testthat. Use when writing new features, fixing bugs, or refactoring code. Enforces test-first development with 80%+ coverage.

Test-Driven Development Workflow for R

This skill ensures all R code development follows TDD principles with comprehensive test coverage using testthat.

When to Activate

  • Writing new functions or features
  • Fixing bugs or issues
  • Refactoring existing code
  • Adding new model types
  • Creating data processing pipelines
  • Building Shiny components

Getting Started

Initialize testing infrastructure for your package:

r
# Set up testthat (Edition 3)
usethis::use_testthat(3)

# Create a test file for an existing source file
usethis::use_test("function_name")

# Or create test and source file together
usethis::use_r("function_name")
usethis::use_test("function_name")

Core Principles

1. Tests BEFORE Code

ALWAYS write tests first, then implement code to make tests pass.

2. Coverage Requirements
  • Minimum 80% coverage (unit + integration)
  • 100% coverage for statistical calculations
  • 100% coverage for data validation
  • All edge cases covered
  • Error scenarios tested
3. Test Types

Tests follow a three-level hierarchy: File → Test → Expectation

Unit Tests

Individual functions and utilities:

r
test_that("rescale01 normalizes to [0, 1] range", {
  expect_equal(rescale01(c(0, 5, 10)), c(0, 0.5, 1))
  expect_equal(rescale01(c(-10, 0, 10)), c(0, 0.5, 1))
})

test_that("rescale01 handles edge cases", {
  expect_equal(rescale01(c(5, 5, 5)), c(NaN, NaN, NaN))
  expect_equal(rescale01(numeric(0)), numeric(0))
  expect_equal(rescale01(c(0, NA, 10)), c(0, NA, 1))
})
Integration Tests

Function interactions and workflows:

r
test_that("data pipeline produces expected output", {
  raw_data <- read_fixture("sample_input.csv")

  result <- raw_data |>
    clean_data() |>
    transform_features() |>
    summarize_results()

  expect_s3_class(result, "tbl_df")
  expect_named(result, c("group", "mean", "sd", "n"))
  expect_true(all(result$n > 0))
})
Snapshot Tests

For complex outputs that are hard to specify:

r
test_that("model summary format is stable", {
  model <- fit_model(test_data)
  expect_snapshot(print(summary(model)))
})

test_that("error messages are informative", {
  expect_snapshot(
    validate_input(invalid_data),
    error = TRUE
  )
})

Snapshot workflow:

r
# Review snapshot changes
testthat::snapshot_review("test_name")

# Accept snapshot changes
testthat::snapshot_accept("test_name")

Snapshots are stored in tests/testthat/_snaps/ directory.

BDD Alternative (Optional)

For behavior-driven development, use describe() and it():

r
describe("matrix()", {
  it("can be multiplied by a scalar", {
    m1 <- matrix(1:4, 2, 2)
    m2 <- m1 * 2
    expect_equal(matrix(c(2, 4, 6, 8), 2, 2), m2)
  })

  it("can be transposed", {
    m <- matrix(1:4, 2, 2)
    expect_equal(t(m), matrix(c(1, 3, 2, 4), 2, 2))
  })
})

Key distinction: "describe() verifies you implement the right things, test_that() ensures you do things right."

Test Design Principles

Self-Sufficient Tests

Each test should contain all setup, execution, and teardown code. Tests must be independent and runnable in isolation without relying on ambient state or prior test execution.

r
# GOOD: Self-contained
test_that("function works with specific data", {
  data <- tibble(x = 1:10, y = rnorm(10))  # Setup
  result <- my_function(data)               # Execute
  expect_equal(nrow(result), 10)            # Assert
})

# BAD: Depends on external state
# setup_data <- tibble(...)  # Created outside test
test_that("function works", {
  result <- my_function(setup_data)  # Relies on external data
  expect_equal(nrow(result), 10)
})
Duplication Over Factoring

Repetition is acceptable in tests—duplicate setup code rather than extracting it elsewhere. Clarity outweighs avoiding duplication.

r
# GOOD: Duplicated but clear
test_that("clean_data handles missing values", {
  data <- tibble(x = c(1, NA, 3), y = c(4, 5, 6))
  result <- clean_data(data)
  expect_equal(nrow(result), 2)
})

test_that("clean_data handles invalid values", {
  data <- tibble(x = c(1, -999, 3), y = c(4, 5, 6))
  result <- clean_data(data, invalid = -999)
  expect_equal(nrow(result), 2)
})

# ACCEPTABLE: Each test is self-contained and readable
Plan for Failure

Write tests assuming they'll fail and require debugging. Make logic explicit and obvious. Run tests in fresh R sessions independently.

Use devtools::load_all()

During development, prefer devtools::load_all() over library(). This:

  • Exposes unexported functions for testing
  • Automatically attaches testthat
  • Eliminates unnecessary library() calls in tests
  • Simulates package loading without installation

testthat Edition 3

Edition 3 provides improved snapshot testing, better diffs via waldo, unified condition handling, parallel execution support, and byte-compiled code compatibility for mocking.

Deprecated Patterns → Modern Alternatives
r
# DEPRECATED: context() calls
context("Data validation")  # Remove - filename serves this purpose

# DEPRECATED: expect_equivalent()
expect_equivalent(x, y)
# MODERN:
expect_equal(x, y, ignore_attr = TRUE)

# DEPRECATED: with_mock()
with_mock(external_call = function() "mocked", {
  result <- my_function()
})
# MODERN:
local_mocked_bindings(
  external_call = function() "mocked"
)
result <- my_function()

# DEPRECATED: expect_is()
expect_is(x, "data.frame")
# MODERN:
expect_s3_class(x, "data.frame")
Initialize Edition 3

In DESCRIPTION, ensure:

Config/testthat/edition: 3

Or initialize with:

r
usethis::use_testthat(3)

Essential Expectations Reference

Equality & Identity
r
expect_equal(x, y)              # With numeric tolerance
expect_equal(x, y, tolerance = 0.001)
expect_equal(x, y, ignore_attr = TRUE)
expect_identical(x, y)          # Exact match required
expect_all_equal(x)             # Every element equal (v3.3.0+)
Conditions
r
expect_error(code)
expect_error(code, "pattern")
expect_error(code, class = "validation_error")
expect_warning(code)
expect_no_warning(code)
expect_message(code)
expect_no_message(code)
Collections & Sets
r
expect_setequal(x, y)          # Same elements, any order
expect_contains(set, element)  # Subset relationship (v3.2.0+)
expect_in(element, set)        # Membership check (v3.2.0+)
expect_disjoint(set1, set2)    # No overlap (v3.3.0+)
expect_named(x, c("a", "b"))   # Named vector/list
Type & Structure
r
expect_type(x, "double")
expect_s3_class(x, "data.frame")
expect_s4_class(x, "S4Class")
expect_r6_class(x, "R6Class")
expect_shape(matrix, c(2, 3))  # Matrix/array dimensions (v3.3.0+)
expect_length(x, 10)
Logical
r
expect_true(x)
expect_false(x)
expect_all_true(x)             # Every element TRUE (v3.3.0+)
expect_all_false(x)            # Every element FALSE (v3.3.0+)
Other Useful Expectations
r
expect_null(x)
expect_invisible(result)
expect_output(print(x), "pattern")
expect_snapshot(complex_output)

File Organization

Tests mirror your package structure:

tests/
├── testthat/
│   ├── test-validation.R      # Tests for R/validation.R
│   ├── test-processing.R      # Tests for R/processing.R
│   ├── test-models.R          # Tests for R/models.R
│   ├── test-output.R          # Tests for R/output.R
│   ├── helper-fixtures.R      # Shared functions (sourced before tests)
│   ├── setup-database.R       # Setup code (runs during R CMD check)
│   ├── helper-expectations.R  # Custom expectations
│   └── fixtures/              # Static test data files
│       ├── sample_input.csv
│       └── expected_output.rds
└── testthat.R                 # Test runner
File Types
  • test-*.R - Actual test files (paired with source files)
  • helper-*.R - Shared utility functions, sourced before tests run
  • setup-*.R - Setup code that runs only during R CMD check
  • fixtures/ - Static test data, accessed via test_path("fixtures/file")

Access fixtures:

r
test_path("fixtures", "sample_data.csv")

TDD Workflow Steps

Step 1: Define Expected Behavior

Document what the function should do:

r
# Function: calculate_ci
# Purpose: Calculate bootstrap confidence intervals
# Inputs:
#   - data: numeric vector
#   - conf_level: confidence level (default 0.95)
#   - n_boot: number of bootstrap samples (default 1000)
# Outputs:
#   - Named numeric vector with lower and upper bounds
# Edge cases:
#   - Handle NA values
#   - Error on non-numeric input
#   - Error on empty input
Step 2: Write Failing Tests
r
# tests/testthat/test-calculate_ci.R
library(testthat)

test_that("calculate_ci returns correct structure", {
  set.seed(123)
  result <- calculate_ci(1:100)

  expect_type(result, "double")
  expect_named(result, c("lower", "upper"))
  expect_true(result["lower"] < result["upper"])
})

test_that("calculate_ci respects confidence level", {
  set.seed(123)
  ci_95 <- calculate_ci(1:100, conf_level = 0.95)
  ci_99 <- calculate_ci(1:100, conf_level = 0.99)

  # 99% CI should be wider
  expect_true(ci_99["upper"] - ci_99["lower"] > ci_95["upper"] - ci_95["lower"])
})

test_that("calculate_ci handles NA values", {
  set.seed(123)
  result <- calculate_ci(c(1:100, NA, NA))

  expect_false(any(is.na(result)))
})

test_that("calculate_ci validates inputs", {
  expect_error(calculate_ci("not numeric"), class = "validation_error")
  expect_error(calculate_ci(numeric(0)), class = "validation_error")
  expect_error(calculate_ci(1:10, conf_level = 1.5), class = "validation_error")
})
Step 3: Run Tests (They Should Fail)
r
devtools::test()
# ✖ calculate_ci returns correct structure
# ✖ calculate_ci respects confidence level
# ✖ calculate_ci handles NA values
# ✖ calculate_ci validates inputs
Show full SKILL.md (260 more words)Show less
Step 4: Implement Minimal Code
r
# R/calculate_ci.R

#' Calculate Bootstrap Confidence Interval
#'
#' @param x Numeric vector
#' @param conf_level Confidence level (default 0.95)
#' @param n_boot Number of bootstrap samples (default 1000)
#' @return Named numeric vector with lower and upper bounds
#' @export
calculate_ci <- function(x, conf_level = 0.95, n_boot = 1000) {
  # Validate inputs
  if (!is.numeric(x)) {
    cli::cli_abort("{.arg x} must be numeric", class = "validation_error")
  }
  if (length(x) == 0) {
    cli::cli_abort("{.arg x} cannot be empty", class = "validation_error")
  }
  if (conf_level <= 0 || conf_level >= 1) {
    cli::cli_abort("{.arg conf_level} must be between 0 and 1", class = "validation_error")
  }

  # Remove NA values
  x <- x[!is.na(x)]

  # Bootstrap
  boot_means <- replicate(n_boot, mean(sample(x, replace = TRUE)))

  # Calculate quantiles
  alpha <- 1 - conf_level
  c(
    lower = unname(quantile(boot_means, alpha / 2)),
    upper = unname(quantile(boot_means, 1 - alpha / 2))
  )
}
Step 5: Run Tests Again
r
devtools::test()
# ✔ calculate_ci returns correct structure
# ✔ calculate_ci respects confidence level
# ✔ calculate_ci handles NA values
# ✔ calculate_ci validates inputs
Step 6: Refactor

Improve while keeping tests green:

r
# Extract validation to helper
validate_ci_inputs <- function(x, conf_level) {
  if (!is.numeric(x)) {
    cli::cli_abort("{.arg x} must be numeric", class = "validation_error")
  }
  if (length(x) == 0) {
    cli::cli_abort("{.arg x} cannot be empty", class = "validation_error")
  }
  if (conf_level <= 0 || conf_level >= 1) {
    cli::cli_abort("{.arg conf_level} must be between 0 and 1", class = "validation_error")
  }
}

calculate_ci <- function(x, conf_level = 0.95, n_boot = 1000) {
  validate_ci_inputs(x, conf_level)

  x <- x[!is.na(x)]
  boot_means <- replicate(n_boot, mean(sample(x, replace = TRUE)))

  alpha <- 1 - conf_level
  c(
    lower = unname(quantile(boot_means, alpha / 2)),
    upper = unname(quantile(boot_means, 1 - alpha / 2))
  )
}
Step 7: Verify Coverage
r
covr::package_coverage()
# calculate_ci.R: 100%

Testing Patterns

Testing Data Transformations
r
test_that("clean_data removes invalid rows", {
  input <- tibble(
    id = 1:4,
    value = c(1, NA, 3, -999)
  )

  result <- clean_data(input, invalid_value = -999)

  expect_equal(nrow(result), 2)
  expect_equal(result$id, c(1, 3))
  expect_false(anyNA(result$value))
})
Testing Statistical Functions
r
test_that("weighted_mean matches manual calculation", {
  x <- c(1, 2, 3)
  w <- c(1, 2, 1)

  result <- weighted_mean(x, w)
  expected <- sum(x * w) / sum(w)  # (1 + 4 + 3) / 4 = 2

  expect_equal(result, expected)
})
Testing with Fixtures
r
# helper-fixtures.R
read_fixture <- function(name) {
  path <- testthat::test_path("fixtures", name)
  readr::read_csv(path, show_col_types = FALSE)
}

# test-pipeline.R
test_that("pipeline handles real data", {
  input <- read_fixture("sample_data.csv")
  result <- process_pipeline(input)

  expect_snapshot(result)
})
Mocking External Dependencies
r
test_that("fetch_data handles API errors", {
  # Mock the API call
  local_mocked_bindings(
    httr2_request = function(...) {
      stop("API unavailable")
    }
  )

  expect_error(
    fetch_data("endpoint"),
    "API unavailable"
  )
})
Using withr for Cleanup

Use withr functions to manage temporary state with automatic restoration:

r
test_that("function respects options", {
  # Temporarily set options
  withr::local_options(list(digits = 2))

  result <- format_number(3.14159)
  expect_equal(result, "3.14")
})

test_that("function writes to temp file", {
  # Create temp file that's automatically cleaned up
  tmp <- withr::local_tempfile(lines = c("line 1", "line 2"))

  result <- process_file(tmp)
  expect_equal(result$n_lines, 2)
})

test_that("function uses custom environment variable", {
  # Temporarily set env var
  withr::local_envvar(MY_VAR = "test_value")

  result <- get_config()
  expect_equal(result$my_var, "test_value")
})

Test Data Strategies

Choose the appropriate approach for your testing needs:

1. Constructor Functions

Create data on-demand with helper functions:

r
# helper-data.R
make_sample_data <- function(n = 100) {
  tibble(
    id = 1:n,
    group = sample(c("A", "B"), n, replace = TRUE),
    value = rnorm(n)
  )
}

# test-analysis.R
test_that("analysis handles grouped data", {
  data <- make_sample_data(n = 50)
  result <- analyze_groups(data)
  expect_s3_class(result, "tbl_df")
})
2. Local Functions with Cleanup

Handle side effects using withr:

r
test_that("function reads CSV correctly", {
  # Create temp file with cleanup
  tmp <- withr::local_tempfile(fileext = ".csv")
  write.csv(mtcars, tmp, row.names = FALSE)

  result <- read_and_process(tmp)
  expect_equal(nrow(result), 32)
})
3. Static Fixtures

Store data files in fixtures/ directory:

r
# Store in: tests/testthat/fixtures/sample_data.csv

test_that("function handles real data format", {
  path <- test_path("fixtures", "sample_data.csv")
  data <- read_csv(path)
  result <- process_data(data)
  expect_true(all(result$valid))
})

Common Testing Mistakes to Avoid

WRONG: Testing Implementation Details
r
# Don't test internal state
expect_equal(obj$internal_cache, expected_cache)
CORRECT: Test Behavior
r
# Test observable behavior
expect_equal(get_result(obj), expected_result)
WRONG: Brittle Tests
r
# Breaks on any output change
expect_equal(as.character(result), "Mean: 5.234567890")
CORRECT: Flexible Assertions
r
# Robust to formatting changes
expect_equal(result$mean, 5.23, tolerance = 0.01)
WRONG: Dependent Tests
r
test_that("creates data", { global_data <<- create() })
test_that("uses data", { process(global_data) })  # Depends on previous!
CORRECT: Independent Tests
r
test_that("creates and uses data", {
  data <- create()
  result <- process(data)
  expect_true(is_valid(result))
})
WRONG: Modifying Tests to Pass
r
# When a test fails, don't change the test (unless it's wrong)
test_that("function returns 42", {
  expect_equal(my_function(), 42)  # Test fails
})

# DON'T DO THIS:
test_that("function returns 41", {
  expect_equal(my_function(), 41)  # Changed to pass - WRONG!
})
CORRECT: Fix the Implementation
r
# Fix the code to match expected behavior
test_that("function returns 42", {
  expect_equal(my_function(), 42)  # Test fails
})

# Fix my_function() implementation instead

When Tests Fail

  1. Do NOT modify tests to make them pass (unless the test is wrong)
  2. Fix the implementation to match expected behavior
  3. Add more tests if the failure reveals missing coverage
  4. Update snapshots only if the change is intentional
r
# Review and accept snapshot changes
testthat::snapshot_review("test_name")
testthat::snapshot_accept("test_name")

Coverage Verification

r
# Run coverage report
covr::package_coverage()

# Interactive HTML report
covr::report()

# Check specific thresholds
cov <- covr::package_coverage()
pct <- covr::percent_coverage(cov)
if (pct < 80) {
  stop("Coverage below 80%: ", round(pct, 1), "%")
}

# In testthat.R or as a coverage check
covr::package_coverage(
  type = "all",
  line_coverage = 0.80,
  function_coverage = 0.80
)

Debugging & Development

Running Tests at Different Scales
r
# Micro: Interactive development
devtools::load_all()
expect_equal(my_function(1), 1)  # Direct expectation

# Mezzo: Single file
testthat::test_file("tests/testthat/test-validation.R")
# RStudio: Ctrl/Cmd+Shift+T

# Macro: Full suite
devtools::test()
devtools::check()  # Full package validation
Test Reporters
r
# Find slow tests
devtools::test(reporter = "slow")

# Progress reporter (verbose)
devtools::test(reporter = "progress")

# Test execution order independence
devtools::test(shuffle = TRUE)
Continuous Testing
r
# Watch mode - auto-run on file changes
testthat::auto_test_package()
Parallel Execution (Edition 3)

Edition 3 supports parallel test execution for faster runs on multi-core systems.

Running Tests

r
# All tests
devtools::test()

# All tests (keyboard shortcut)
# RStudio: Ctrl/Cmd+Shift+T

# With coverage
covr::package_coverage()

# Specific file
testthat::test_file("tests/testthat/test-validation.R")

# Watch mode
testthat::auto_test_package()

# Verbose output
devtools::test(reporter = "progress")

# Find slow tests
devtools::test(reporter = "slow")

# Test independence
devtools::test(shuffle = TRUE)

# Full package check
devtools::check()

Success Metrics

  • 80%+ code coverage achieved
  • All tests passing
  • No skipped tests
  • Fast execution (< 30s for unit tests)
  • Tests catch bugs before production
  • Confident refactoring enabled
  • Tests run independently in any order
  • Clear, descriptive test names
  • Each test validates one concept

Remember: Tests are not optional. They are the safety net that enables confident refactoring, rapid development, and production reliability. Write them FIRST.

© ab604, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

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Open the folder on GitHubat commit 529de4f

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in ab604/claude-code-r-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

TDD Workflow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

TDD Workflow compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
TDD Workflow this skillab604/claude-code-r-skills2062 repos~4.8kAutomated safety check: PassMIT
TDDfossasia/eventyay-interpretation1.6k28 repos~1.1kAutomated safety check: PassApache-2.0
TDD WorkflowhellangleZ/burn-in-cceverywhere-ralph11211 repos~2.4kAutomated safety check: PassNone
TDDsanity-io/sanity6.4k20 repos~1kAutomated safety check: PassMIT
Test Driven Developmentfarm-fe/farm5.6k49 repos~2.5kAutomated safety check: PassMIT
Tapd Story PipelineTencentBlueKing/bk-bcs840—~2.6kAutomated safety check: PassCustom licence

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Categories

Questions about TDD Workflow

What does TDD Workflow do?

Test-driven development workflow for R using testthat. An agent skill from ab604/claude-code-r-skills. TDD Workflow is an agent skill from ab604/claude-code-r-skills. Test-driven development workflow for R using testthat.

When should I use TDD Workflow?

TDD Workflow fits situations like: writing new features; refactoring code.

How do I install TDD Workflow in Claude Code?

Run `npx skills add ab604/claude-code-r-skills --skill tdd-workflow -a claude-code`. Or copy the skill folder (.claude/skills/tdd-workflow in ab604/claude-code-r-skills) into .claude/skills/tdd-workflow in your project. Claude Code loads it when a task matches its description.

How do I install TDD Workflow in Codex?

Run `npx skills add ab604/claude-code-r-skills --skill tdd-workflow -a codex`. Or copy the skill folder (.claude/skills/tdd-workflow in ab604/claude-code-r-skills) into .agents/skills/tdd-workflow in your project. Codex loads it when a task matches its description.

Can I use TDD Workflow in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ab604/claude-code-r-skills --skill tdd-workflow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tdd-workflow, .gemini/skills/tdd-workflow, .github/skills/tdd-workflow and .opencode/skills/tdd-workflow in your project.

What does TDD Workflow need to run?

SKILL.md names no scripts, command-line tools or credentials: TDD Workflow is instructions for the agent only.

Does TDD Workflow access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is TDD Workflow safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does TDD Workflow use?

TDD Workflow is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does TDD Workflow use?

About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to TDD Workflow?

Skills that share tags, products or a category with TDD Workflow: TDD (fossasia/eventyay-interpretation, 1.6k stars), TDD Workflow (hellangleZ/burn-in-cceverywhere-ralph, 112 stars), TDD (sanity-io/sanity, 6.4k stars) and Test Driven Development (farm-fe/farm, 5.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains TDD Workflow?

ab604 (a GitHub user) maintains it in ab604/claude-code-r-skills, which has 206 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 17, 2026.

Source: ab604/claude-code-r-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.