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Nw Mutation Test

Organization
nWave-ai
nw-mutation-test

Runs feature-scoped mutation testing to validate test suite quality. Use after implementation to verify tests catch real bugs (kill rate >= 80%).

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-mutation-test
Stars
616
Forks
65
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Mutation Test AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-mutation-test
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-mutation-test .claude/skills/nw-mutation-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Mutation Test in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nw Mutation Test on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nw Mutation Test is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

NW-MUTATION-TEST: Feature-Scoped Mutation Testing

Wave: QUALITY_GATE Agent: Crafter (nw-software-crafter)

Overview

Run mutation testing against implementation files from the current feature. Extracts targets from the execution-log.json (see Target extraction)|generates feature-scoped configs|delegates to software-crafter. Uses cosmic-ray (Python)|PIT (Java)|Stryker (JS/TS/C#).

Mutation Testing Strategy

Projects declare a strategy via ## Mutation Testing Strategy in CLAUDE.md: per-feature | nightly-delta | pre-release | disabled.

Default (when unspecified): nightly-delta — the recommended mode. CI runs mutmut nightly against modules changed since the last run (the delta), keeping per-feature delivery gates fast. /nw-mutation-test performs an explicit, on-demand feature-scoped run regardless of strategy; under nightly-delta the in-wave Phase 5 gate is skipped and the work is handled by the CI nightly pipeline.

Target extraction

Implementation files are selected from the execution-log.json (completed_steps[].files_modified.implementation).

Context Files Required

  • docs/feature/{feature-id}/deliver/execution-log.json - Implementation file extraction
  • scripts/mutation/generate_scoped_configs.py - Automated config generation (if available)

Pre-Invocation

Orchestrator performs before delegating:

  1. Extract files — Read execution-log.json and extract implementation files from completed_steps[].files_modified.implementation. Gate: file list non-empty.
  2. Verify on disk — Check all extracted files exist on disk. Gate: zero missing files.
  3. Detect language — Scan config files (pyproject.toml, pom.xml, package.json, etc.) to select tool. Gate: language identified.
  4. Confirm tests pass — Run pytest -x {test_scope} (or equivalent). Gate: exit code 0, no failures.
  5. Ensure mutation venv — For Python, verify .venv-mutation/ exists with cosmic-ray installed. Gate: cosmic-ray --version succeeds.

Agent Invocation

@nw-software-crafter

Execute mutation testing for project {feature-id}.

Context to pass inline (agent has no Skill access):

  • Project ID
  • Implementation file list (from execution-log.json)
  • Test scope path (e.g., tests/des/)
  • Kill rate threshold (default: 80%)
  • Language and tool selection

Configuration:

  • threshold: 80 (percentage, minimum kill rate)
  • approach: feature-scoped (one config per component, scoped test commands)
  • config_generator: scripts/mutation/generate_scoped_configs.py (preferred over manual)

Output file: docs/feature/{feature-id}/deliver/mutation/mutation-report.md

Examples

Example 1: Python project with config generator

bash
/nw-mutation-test des-hook-enforcement tests/des/

Reads the execution-log.json, runs generate_scoped_configs.py des-hook-enforcement, delegates to software-crafter with per-component configs. Agent runs cosmic-ray, produces mutation-report.md.

Example 2: Python project without config generator

bash
/nw-mutation-test auth-upgrade tests/auth/

Extracts files manually from the execution-log.json, creates single cosmic-ray config with module-path = [file1, file2, ...] and test-command = "pytest -x tests/auth/", delegates to agent.

Example 3: Non-Python project

bash
/nw-mutation-test payment-gateway tests/payment/

Detects package.json, selects Stryker, delegates with Stryker-specific instructions.

Success Criteria

  • Implementation files extracted from execution-log.json
  • All implementation files verified on disk
  • Mutation testing executed without errors
  • Per-file breakdown in mutation-report.md
  • Kill rate meets threshold (>= 80% PASS, 70-80% WARN, < 70% FAIL)
  • Source files restored to HEAD after mutation run (git checkout -- src/ tests/)

Post-Mutation Safety (mandatory)

After EVERY mutation run (success, failure, or interruption):

  1. Restore source files — Run git checkout -- src/ tests/. Gate: working tree clean (no mutations remain).
  2. Verify no corruption — Confirm test suite still passes after restore. Gate: pytest -x {test_scope} exits 0.

Mutation tools apply mutations directly to source files. An interrupted run can leave corrupted code (e.g. is not None -> is None). Agent MUST execute these steps even if the run errors out.

Quality Gate

Kill rate thresholds:

  1. >= 80% PASS — Proceed to next wave.
  2. 70-80% WARN — Review surviving mutants, document findings, proceed with caution.
  3. < 70% FAIL — Add tests targeting surviving mutants before proceeding.

Skip conditions (each requires documented justification in mutation-report.md):

  1. No tool for language — No mutation framework available for detected language.
  2. Project opt-out — .mutation-config.yaml has skip: true with justification.
  3. Broken test suite — Pre-invocation step 4 fails; fix tests before mutation testing.

Note: Python projects require mutation testing. All skips need documented justification.

Next Wave

Handoff To: Phase 8 - Finalize (orchestrator continues develop.md workflow) Deliverables: docs/feature/{feature-id}/deliver/mutation/mutation-report.md

Expected Outputs

docs/feature/{feature-id}/deliver/mutation/
  mutation-report.md
  cosmic-ray-*.toml                (ephemeral)

Frequently asked questions

What does the Nw Mutation Test AI skill do?

Runs feature-scoped mutation testing to validate test suite quality. Use after implementation to verify tests catch real bugs (kill rate >= 80%).

Why use Nw Mutation Test on TypingMind?

Because you install it once and use it with any model. Nw Mutation Test is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nw Mutation Test in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-mutation-test. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Mutation Test?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nw Mutation Test?

As many as you like. As long as a model supports skills, you can use Nw Mutation Test with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nw Mutation Test AI skill free?

Yes. It is published on GitHub by nWave-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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