nw-tdd-methodology

nw-tdd-methodology is a skill for Claude Code from nWave-ai/nWave. It costs 35 tokens per session (7,823 once invoked), scanned A, original, MIT.

A guide to outside-in test-driven development (TDD), a method of writing tests before or alongside code while starting from user-visible behavior. It covers connecting high-level acceptance tests to lower-level code tests.

In plain words
What is it for?
Use it when designing a test-first development process, including integration tests, test doubles, and a basic working version of a feature. It supports projects in languages such as Python, TypeScript, Rust, Go, Java, and others.
Why use it?
It helps teams structure tests around real behavior instead of internal implementation details. It also explains how to adapt the method to different programming languages and test tools.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

Good fit Use it when designing a test-first development process, including integration tests, test doubles, and a basic working version of a feature. It supports projects in languages such as Python, TypeScript, Rust, Go, Java, and others.

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Install with agentmods
npx agentmods add skills/nwave-ai/nwave/nw-tdd-methodology
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add nWave-ai/nWave --skill nw-tdd-methodology
Clone the repo
git clone --depth 1 https://github.com/nWave-ai/nWave

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,823 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.07823
Opus 5 $0.00017 $0.03911
Sonnet 5 $0.00007 $0.01565
Haiku 4.5 $0.00003 $0.00782

Measured 3d ago against content hash 3a1dc0eaf81c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

nw-tdd-methodology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

nWave/skills/nw-tdd-methodology/SKILL.md · 596 lines

How it starts

The opening of the file, as written. The whole thing — 596 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Outside-In TDD Methodology

LANGUAGE CONVENTION FRAME (read FIRST — overrides all examples below)

Code examples in this skill use Python syntax for illustration only. They are NOT prescriptive about target language. nWave is language-agnostic per the "genericity and agnosticism" mandate (2026-05-24).

Before applying TDD cycle, detect the target project's language from manifest files: package.json → TypeScript/JS (jest/vitest); Cargo.toml → Rust (cargo test/proptest); go.mod → Go (testing/ginkgo); pyproject.toml/setup.py/Pipfile → Python (pytest/hypothesis); pom.xml/build.gradle → Java/Kotlin (JUnit5/jqwik); *.csproj/*.fsproj → C#/F# (xUnit/FsCheck); Gemfile → Ruby (RSpec); Package.swift → Swift (XCTest/swift-testing).

When the target language is NOT Python: adapt EVERY code example — replace Python imports, type hints, class/function syntax, test-framework specifics with target equivalents. Project conventions ALWAYS WIN over skill examples.

Empirical anchor: skill examples being Python-only caused LLM to emit Python code in greenfield TS project despite language-agnostic mandate. Fix per F-SKILL-EXAMPLES-LANGUAGE-LEAK. Connects [[feedback_language_adapter_plugin_architecture_2026_05_24]].

TDD cycle — 3-phase canonical (ADR-025, 2026-05-07)

Current canonical: DELIVER cycle is 3-phase: RED → GREEN → COMMIT.

  • RED absorbs PREPARE + RED_ACCEPTANCE + RED_UNIT (legacy 5-phase). Writes PBT unit tests targeting production code; unskips the corresponding AT scenario authored upstream by DISTILL. Exits via the fail-for-right-reason gate — both PBT unit + AT must fail with a semantically-correct error (AssertionError / expected-exception-not-thrown), not a collection error / import error / skip marker. The gate preserves RED→GREEN discipline atomically without separate phase boundaries.
  • GREEN: implement minimum production code making PBT unit + AT pass. Exit gate all-tests-pass.
  • COMMIT: commit this step's owned files via des-commit (carries the Step-Id: trailer and is parallel-safe — see issue #51 / ADR-027), not raw git add/git commit. F-DES-COMMIT-PHASE-CRAFTER-DEAD-PATH guidance in commit.yaml addresses adapter-probe annotation requirement.

Read the full file on GitHub · 596 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 596 lines · 35 tokens per session scan A 3a1dc0eaf81c

Subscribe to this mod's changes

nw-tdd-methodology is a skill published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 3d ago), licensed MIT. It adds 35 tokens to every session and 7,823 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-06.