Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and…

MITAuto-check passedTesting & QA

Install TDD Guide

skills CLI
$ npx skills add alirezarezvani/claude-skills --skill tdd-guide -a claude-code

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

GitHub CLI
$ gh skill install alirezarezvani/claude-skills tdd-guide --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/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering-team/skills/tdd-guide .claude/skills/tdd-guide && 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-guide
GitHub stars
28k
Token cost
~3.4k tokens
SKILL.md length
946 words
Files
18 (incl. scripts, references, assets)
Skills in repo
342
Repo updated
First seen
Licence
MIT

At a glance

Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and…

  • Works in 5 steps: Provide source code (TypeScript,… → Specify target framework (Jest, Pytest,… → Run test_generator.py with requirements → …
  • The user asks to write tests
  • SKILL.md covers Workflows, Examples, Key Tools and Input Requirements, plus 7 more sections
  • Runs Python scripts from its folder; calls python, npm and npx

What it does

TDD Guide is an agent skill from alirezarezvani/claude-skills. Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and Mocha. Use when the user asks to write tests, improve test coverage, practice TDD, generate mocks or stubs, or mentions testing frameworks like Jest, pytest, or JUnit.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files, including scripts, reference files and assets (for example `HOW_TO_USE.md`, `README.md` and `assets/expected_output.json`).

It sits in Testing & QA, covering Unit testing, Test-driven development and Test generation. It works with pytest, Jest, JUnit and Vitest. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.

When your agent uses it

  • The user asks to write tests
  • Improve test coverage
  • Mentions testing frameworks like Jest

Example prompts

  • “/tdd-guide”

Requirements

  • Python 3
  • Node.js

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Provide source code (TypeScript, JavaScript, Python, Java)
  2. Specify target framework (Jest, Pytest, JUnit, Vitest)
  3. Run test_generator.py with requirements
  4. Review generated test stubs
  5. Validation: Tests compile and cover happy path, error cases, edge cases

What it can do on your machine

Read from SKILL.md and the folder at commit 19392f7. 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

    Ships 8 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • npm
    • npx
    • mvn

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

  • Network

    No URLs in SKILL.md. Its commands use npm and npx, which can reach the network depending on how they are called.

    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 Guide loads about 3.4k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 93 tokens; SKILL.md has 946 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~93
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.4k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 946 words, ~3,393 tokens.

Download SKILL.mdSave it as .claude/skills/tdd-guide/SKILL.md (or your agent's skills folder). This skill also uses 17 other files; get the full folder from GitHub.
name
tdd-guide
description
Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and Mocha. Use when the user asks to write tests, improve test coverage, practice TDD, generate mocks or stubs, or mentions testing frameworks like Jest, pytest, or JUnit.

TDD Guide

Test-driven development skill for generating tests, analyzing coverage, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, and Vitest.


Workflows

Generate Tests from Code
  1. Provide source code (TypeScript, JavaScript, Python, Java)
  2. Specify target framework (Jest, Pytest, JUnit, Vitest)
  3. Run test_generator.py with requirements
  4. Review generated test stubs
  5. Validation: Tests compile and cover happy path, error cases, edge cases
Analyze Coverage Gaps
  1. Generate coverage report from test runner (npm test -- --coverage)
  2. Run coverage_analyzer.py on LCOV/JSON/XML report
  3. Review prioritized gaps (P0/P1/P2)
  4. Generate missing tests for uncovered paths
  5. Validation: Coverage meets target threshold (typically 80%+)
TDD New Feature
  1. Write failing test first (RED)
  2. Run tdd_workflow.py --phase red to validate
  3. Implement minimal code to pass (GREEN)
  4. Run tdd_workflow.py --phase green to validate
  5. Refactor while keeping tests green (REFACTOR)
  6. Validation: All tests pass after each cycle

Examples

Test Generation — Input → Output (Pytest)

Input source function (math_utils.py):

python
def divide(a: float, b: float) -> float:
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b

Command:

bash
python scripts/test_generator.py --input math_utils.py --framework pytest

Generated test output (test_math_utils.py):

python
import pytest
from math_utils import divide

class TestDivide:
    def test_divide_positive_numbers(self):
        assert divide(10, 2) == 5.0

    def test_divide_negative_numerator(self):
        assert divide(-10, 2) == -5.0

    def test_divide_float_result(self):
        assert divide(1, 3) == pytest.approx(0.333, rel=1e-3)

    def test_divide_by_zero_raises_value_error(self):
        with pytest.raises(ValueError, match="Cannot divide by zero"):
            divide(10, 0)

    def test_divide_zero_numerator(self):
        assert divide(0, 5) == 0.0

Coverage Analysis — Sample P0/P1/P2 Output

Command:

bash
python scripts/coverage_analyzer.py --report lcov.info --threshold 80

Sample output:

Coverage Report — Overall: 63% (threshold: 80%)

P0 — Critical gaps (uncovered error paths):
  auth/login.py:42-58   handle_expired_token()       0% covered
  payments/process.py:91-110  handle_payment_failure()   0% covered

P1 — High-value gaps (core logic branches):
  users/service.py:77   update_profile() — else branch  0% covered
  orders/cart.py:134    apply_discount() — zero-qty guard  0% covered

P2 — Low-risk gaps (utility / helper functions):
  utils/formatting.py:12  format_currency()            0% covered

Recommended: Generate tests for P0 items first to reach 80% threshold.

Key Tools

ToolPurposeUsage
test_generator.pyGenerate test cases from code/requirementspython scripts/test_generator.py --input source.py --framework pytest
coverage_analyzer.pyParse and analyze coverage reportspython scripts/coverage_analyzer.py --report lcov.info --threshold 80
tdd_workflow.pyGuide red-green-refactor cyclespython scripts/tdd_workflow.py --phase red --test test_auth.py
fixture_generator.pyGenerate test data and mockspython scripts/fixture_generator.py --entity User --count 5

Additional scripts: framework_adapter.py (convert between frameworks), metrics_calculator.py (quality metrics), format_detector.py (detect language/framework), output_formatter.py (CLI/desktop/CI output).


Input Requirements

For Test Generation:

  • Source code (file path or pasted content)
  • Target framework (Jest, Pytest, JUnit, Vitest)
  • Coverage scope (unit, integration, edge cases)

For Coverage Analysis:

  • Coverage report file (LCOV, JSON, or XML format)
  • Optional: Source code for context
  • Optional: Target threshold percentage

For TDD Workflow:

  • Feature requirements or user story
  • Current phase (RED, GREEN, REFACTOR)
  • Test code and implementation status

Spec-First Workflow

TDD is most effective when driven by a written spec. The flow:

  1. Write or receive a spec — stored in specs/<feature>.md
  2. Extract acceptance criteria — each criterion becomes one or more test cases
  3. Write failing tests (RED) — one test per acceptance criterion
  4. Implement minimal code (GREEN) — satisfy each test in order
  5. Refactor — clean up while all tests stay green
Spec Directory Convention
project/
├── specs/
│   ├── user-auth.md          # Feature spec with acceptance criteria
│   ├── payment-processing.md
│   └── notification-system.md
├── tests/
│   ├── test_user_auth.py     # Tests derived from specs/user-auth.md
│   ├── test_payments.py
│   └── test_notifications.py
└── src/
Extracting Tests from Specs

Each acceptance criterion in a spec maps to at least one test:

Spec CriterionTest Case
"User can log in with valid credentials"test_login_valid_credentials_returns_token
"Invalid password returns 401"test_login_invalid_password_returns_401
"Account locks after 5 failed attempts"test_login_locks_after_five_failures

Tip: Number your acceptance criteria in the spec. Reference the number in the test docstring for traceability (# AC-3: Account locks after 5 failed attempts).

Cross-reference: See engineering/spec-driven-workflow for the full spec methodology, including spec templates and review checklists.


Red-Green-Refactor Examples Per Language

TypeScript / Jest
typescript
// test/cart.test.ts
describe("Cart", () => {
  describe("addItem", () => {
    it("should add a new item to an empty cart", () => {
      const cart = new Cart();
      cart.addItem({ id: "sku-1", name: "Widget", price: 9.99, qty: 1 });

      expect(cart.items).toHaveLength(1);
      expect(cart.items[0].id).toBe("sku-1");
    });

    it("should increment quantity when adding an existing item", () => {
      const cart = new Cart();
      cart.addItem({ id: "sku-1", name: "Widget", price: 9.99, qty: 1 });
      cart.addItem({ id: "sku-1", name: "Widget", price: 9.99, qty: 2 });

      expect(cart.items).toHaveLength(1);
      expect(cart.items[0].qty).toBe(3);
    });

    it("should throw when quantity is zero or negative", () => {
      const cart = new Cart();
      expect(() =>
        cart.addItem({ id: "sku-1", name: "Widget", price: 9.99, qty: 0 })
      ).toThrow("Quantity must be positive");
    });
  });
});
Python / Pytest (Advanced Patterns)
python
# tests/conftest.py — shared fixtures
import pytest
from app.db import create_engine, Session

@pytest.fixture(scope="session")
def db_engine():
    engine = create_engine("sqlite:///:memory:")
    yield engine
    engine.dispose()

@pytest.fixture
def db_session(db_engine):
    session = Session(bind=db_engine)
    yield session
    session.rollback()
    session.close()

# tests/test_pricing.py — parametrize for multiple cases
import pytest
from app.pricing import calculate_discount

@pytest.mark.parametrize("subtotal, expected_discount", [
    (50.0, 0.0),       # Below threshold — no discount
    (100.0, 5.0),      # 5% tier
    (250.0, 25.0),     # 10% tier
    (500.0, 75.0),     # 15% tier
])
def test_calculate_discount(subtotal, expected_discount):
    assert calculate_discount(subtotal) == pytest.approx(expected_discount)
Go — Table-Driven Tests
go
// cart_test.go
package cart

import "testing"

func TestApplyDiscount(t *testing.T) {
    tests := []struct {
        name     string
        subtotal float64
        want     float64
    }{
        {"no discount below threshold", 50.0, 0.0},
        {"5 percent tier", 100.0, 5.0},
        {"10 percent tier", 250.0, 25.0},
        {"15 percent tier", 500.0, 75.0},
        {"zero subtotal", 0.0, 0.0},
    }

    for _, tt := range tests {
        t.Run(tt.name, func(t *testing.T) {
            got := ApplyDiscount(tt.subtotal)
            if got != tt.want {
                t.Errorf("ApplyDiscount(%v) = %v, want %v", tt.subtotal, got, tt.want)
            }
        })
    }
}

Bounded Autonomy Rules

When generating tests autonomously, follow these rules to decide when to stop and ask the user:

Stop and Ask When
  • Ambiguous requirements — the spec or user story has conflicting or unclear acceptance criteria
  • Missing edge cases — you cannot determine boundary values without domain knowledge (e.g., max allowed transaction amount)
  • Test count exceeds 50 — large test suites need human review before committing; present a summary and ask which areas to prioritize
  • External dependencies unclear — the feature relies on third-party APIs or services with undocumented behavior
  • Security-sensitive logic — authentication, authorization, encryption, or payment flows require human sign-off on test scenarios
Show full SKILL.md (389 more words)Show less
Continue Autonomously When
  • Clear spec with numbered acceptance criteria — each criterion maps directly to tests
  • Straightforward CRUD operations — create, read, update, delete with well-defined models
  • Well-defined API contracts — OpenAPI spec or typed interfaces available
  • Pure functions — deterministic input/output with no side effects
  • Existing test patterns — the codebase already has similar tests to follow

Property-Based Testing

Property-based testing generates random inputs to verify invariants instead of relying on hand-picked examples. Use it when the input space is large and the expected behavior can be described as a property.

Python — Hypothesis
python
from hypothesis import given, strategies as st
from app.serializers import serialize, deserialize

@given(st.text())
def test_roundtrip_serialization(data):
    """Serialization followed by deserialization returns the original."""
    assert deserialize(serialize(data)) == data

@given(st.integers(), st.integers())
def test_addition_is_commutative(a, b):
    assert a + b == b + a
TypeScript — fast-check
typescript
import fc from "fast-check";
import { encode, decode } from "./codec";

test("encode/decode roundtrip", () => {
  fc.assert(
    fc.property(fc.string(), (input) => {
      expect(decode(encode(input))).toBe(input);
    })
  );
});
When to Use Property-Based Over Example-Based
Use Property-BasedExample
Data transformationsSerialize/deserialize roundtrips
Mathematical propertiesCommutativity, associativity, idempotency
Encoding/decodingBase64, URL encoding, compression
Sorting and filteringOutput is sorted, length preserved
Parser correctnessValid input always parses without error

Mutation Testing

Mutation testing modifies your production code (creates "mutants") and checks whether your tests catch the changes. If a mutant survives (tests still pass), your tests have a gap that coverage alone cannot reveal.

Tools
LanguageToolCommand
TypeScript/JavaScriptStrykernpx stryker run
Pythonmutmutmutmut run --paths-to-mutate=src/
JavaPITmvn org.pitest:pitest-maven:mutationCoverage
Why Mutation Testing Matters
  • 100% line coverage != good tests — coverage tells you code was executed, not that it was verified
  • Catches weak assertions — tests that run code but assert nothing meaningful
  • Finds missing boundary tests — mutants that change < to <= expose off-by-one gaps
  • Quantifiable quality metric — mutation score (% mutants killed) is a stronger signal than coverage %

Recommendation: Run mutation testing on critical paths (auth, payments, data processing) even if overall coverage is high. Target 85%+ mutation score on P0 modules.


Cross-References

SkillRelationship
engineering/spec-driven-workflowSpec → acceptance criteria → test extraction pipeline
engineering-team/focused-fixPhase 5 (Verify) uses TDD to confirm the fix with a regression test
engineering-team/senior-qaBroader QA strategy; TDD is one layer in the test pyramid
engineering-team/code-reviewerReview generated tests for assertion quality and coverage completeness
engineering-team/senior-fullstackProject scaffolders include testing infrastructure compatible with TDD workflows

Limitations

ScopeDetails
Unit test focusIntegration and E2E tests require different patterns
Static analysisCannot execute tests or measure runtime behavior
Language supportBest for TypeScript, JavaScript, Python, Java
Report formatsLCOV, JSON, XML only; other formats need conversion
Generated testsProvide scaffolding; require human review for complex logic

When to use other tools:

  • E2E testing: Playwright, Cypress, Selenium
  • Performance testing: k6, JMeter, Locust
  • Security testing: OWASP ZAP, Burp Suite

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

Files

SKILL.md and 17 other files (scripts, references, assets) in engineering-team/skills/tdd-guide of alirezarezvani/claude-skills.

  • SKILL.md
  • HOW_TO_USE.md
  • README.md
  • assets/expected_output.json
  • assets/sample_coverage_report.lcov
  • assets/sample_input_python.json
  • assets/sample_input_typescript.json
  • references/ci-integration.md
  • references/framework-guide.md
  • references/tdd-best-practices.md
  • scripts/coverage_analyzer.py
  • scripts/fixture_generator.py
  • scripts/format_detector.py
  • scripts/framework_adapter.py
  • scripts/metrics_calculator.py
  • scripts/output_formatter.py
  • scripts/tdd_workflow.py
  • scripts/test_generator.py

Open the folder on GitHubat commit 19392f7

Compare with similar skills

TDD Guide 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 Guide compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
TDD Guide this skillalirezarezvani/claude-skills28k—~3.4kAutomated safety check: PassMIT
TDD GuideLeoYeAI/openclaw-master-skills2.2k—~1.4kAutomated safety check: PassMIT
TDD GuideaAAaqwq/AGI-Super-Team1052 repos~1.1kAutomated safety check: PassMIT
TDD Guideborghei/Claude-Skills874—~1.7kAutomated safety check: PassMIT
MoAI TDD Workflowmodu-ai/moai-adk1.2k—~3.1kAutomated safety check: PassApache-2.0
Code Testing Agentmicrosoft/testfx1k—~2.7kAutomated safety check: PassMIT

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Categories

Questions about TDD Guide

What does TDD Guide do?

Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and…. TDD Guide is an agent skill from alirezarezvani/claude-skills. Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and Mocha.

When should I use TDD Guide?

TDD Guide fits situations like: the user asks to write tests; improve test coverage; mentions testing frameworks like Jest.

How do I install TDD Guide in Claude Code?

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

How do I install TDD Guide in Codex?

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

Can I use TDD Guide 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 alirezarezvani/claude-skills --skill tdd-guide -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-guide, .gemini/skills/tdd-guide, .github/skills/tdd-guide and .opencode/skills/tdd-guide in your project.

What does TDD Guide need to run?

Going by SKILL.md and its folder, TDD Guide needs Python for the scripts in its folder and the command-line tools its instructions call (python, npm, npx and mvn). Our summary lists: Python 3; Node.js.

Does TDD Guide access the network?

SKILL.md contains no URLs. Its commands use npm and npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is TDD Guide 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does TDD Guide use?

TDD Guide 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 Guide use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3k tokens, read only when the agent opens those files.

What are the alternatives to TDD Guide?

Skills that share tags, products or a category with TDD Guide: TDD Guide (LeoYeAI/openclaw-master-skills, 2.2k stars), TDD Guide (aAAaqwq/AGI-Super-Team, 105 stars), TDD Guide (borghei/Claude-Skills, 874 stars) and MoAI TDD Workflow (modu-ai/moai-adk, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains TDD Guide?

alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,788 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.

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