Install in seconds
Install this skill
Copy the command and run it in your terminal. You can review the source before installing.
terminal
git clone https://github.com/tt-a1i/matt-skills-with-to-goal

Works with Git. The repository opens in your current directory.

๐Ÿงช
QualityShell

Test-Driven Development

by tt-a1i

Guides test-driven development for AI coding agents, ensuring tests are spec-like, seams are confirmed, and the red-green loop produces maintainable tests. Use when building features or fixing bugs test-first.

14 stars3 forksAdded 2026/07/16
agent-skillsai-agentsclaude-codecodexcoding-agentsdeveloper-toolsworkflow

Documentation

README

Test-Driven Development

TDD is the red โ†’ green loop. This skill is the reference that makes that loop produce tests worth keeping: what a good test is, where tests go, the anti-patterns, and the rules of the loop. Every section applies on every cycle โ€” consult them before and during the loop, not after.

When exploring the codebase, read CONTEXT.md (if it exists) so test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.

What a good test is

Tests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't. A good test reads like a specification โ€” "user can checkout with valid cart" tells you exactly what capability exists โ€” and survives refactors because it doesn't care about internal structure.

See tests.md for examples and mocking.md for mocking guidelines.

Seams โ€” where tests go

A seam is the public boundary you test at: the interface where you observe behavior without reaching inside. Tests live at seams, never against internals.

Test only at pre-agreed seams. Before writing any test, write down the seams under test and confirm them with the user. No test is written at an unconfirmed seam. You can't test everything โ€” agreeing the seams up front is how testing effort lands on the critical paths and complex logic instead of every edge case.

Ask: "What's the public interface, and which seams should we test?"

Anti-patterns

  • Implementation-coupled โ€” mocks internal collaborators, tests private methods, or verifies through a side channel (querying the database instead of using the interface). The tell: the test breaks when you refactor but behavior hasn't changed.
  • Tautological โ€” the assertion recomputes the expected value the way the code does (expect(add(a, b)).toBe(a + b), a snapshot derived by hand the same way, a constant asserted equal to itself), so it passes by construction and can never disagree with the code. Expected values must come from an independent source of truth โ€” a known-good literal, a worked example, the spec.
  • Horizontal slicing โ€” writing all tests first, then all implementation. Bulk tests verify imagined behavior: you test the shape of things rather than user-facing behavior, the tests go insensitive to real changes, and you commit to test structure before understanding the implementation. Work in vertical slices instead โ€” one test โ†’ one implementation โ†’ repeat, each test a tracer bullet that responds to what the last cycle taught you.

Rules of the loop

  • Red before green. Write the failing test first, then only enough code to pass it. Don't anticipate future tests or add speculative features.
  • One slice at a time. One seam, one test, one minimal implementation per cycle.
  • Refactoring is not part of the loop. It belongs to the review stage (see the code-review skill), not the red โ†’ green implementation cycle.

More from tt-a1i

Other Claude Code skills by this author in the directory.

๐Ÿงญ
2w ago

Ask Matt

A router that helps determine which skill or workflow to use for a given task. Use when you need guidance picking the right tool from a suite of AI agent skills.
AI Engineering
+17%143
๐Ÿ”
2w ago

Code Review

Reviews code changes since a fixed point along two independent axes: coding standards compliance and spec/issue fidelity. Runs both reviews in parallel sub-agents and reports findings side by side. Ideal for reviewing branches, PRs, or work-in-progress changes.
Quality
+17%143
๐Ÿงฑ
2w ago

Codebase Design

A shared vocabulary and principles for designing deep, testable modules with small interfaces and high leverage. Use when designing or improving module interfaces, deciding where to place seams, or making code more maintainable and AI-navigable.
Quality
+17%143
๐Ÿ”
2w ago

Diagnosing Bugs

Guides systematic debugging of hard bugs and performance regressions through building a tight feedback loop, reproducing, minimizing, hypothesizing, and instrumenting. Use when encountering difficult or flaky bugs.
Quality
+17%143
๐Ÿ—๏ธ
2w ago

Domain Modeling

Actively builds and sharpens a project's domain model, challenging vague terms, stress-testing concepts with edge cases, and capturing glossaries and architectural decisions (ADRs) as they crystallize.
Writing
+17%143
๐ŸŽฏ
2w ago

Goal Crafter

Craft verifiable goal prompts for AI coding agents like Claude Code, Codex, or Pi. Turns vague tasks into precise goals with checkable completion criteria, ensuring agents run unattended.
AI Engineering
+17%143