Nw Optimize Tests logo

Nw Optimize Tests

Organization
nWave-ai
nw-optimize-tests

Minimizes test count while preserving coverage. Detects byte-identical pairs, parametrize-inflation, language-guarantee tests, AST-shape tests, stale migration nets. Approval gate before any change.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-optimize-tests
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 Optimize Tests 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-optimize-tests
  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-optimize-tests .claude/skills/nw-optimize-tests
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Optimize Tests 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 Optimize Tests 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 Optimize Tests 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-OPTIMIZE-TESTS: Test Suite Optimization

Wave: CROSS_WAVE Agent: Trim (nw-test-optimizer) Reviewer: Trim Review (nw-test-optimizer-reviewer)

Overview

Dispatches Trim to inventory a test scope, detect duplication and anti-patterns, propose a consolidation plan, and apply it after explicit approval. Coverage is preserved; production code is never modified. Use after a feature lands, when a suite feels slow or noisy, on weekly audit, or whenever overtesting is suspected.

Context Files Required

  • The scope path (passed as argument or auto-detected)
  • ~/.claude/skills/nw-test-optimization/SKILL.md — methodology (loaded by agent)

Timing Baseline

Trim compares against the wall-clock figures recorded in the execution-log.json.

Agent Invocation

@nw-test-optimizer

Execute test optimization for {scope}.

Configuration:

  • scope: <path | feature-id | empty for full unit suite>
  • approval_required: true # always; gate is non-negotiable
  • mutation_validation: false # set true for critical scopes (financial, safety, infra)
  • reviewer_chain: false # set true to dispatch nw-test-optimizer-reviewer after apply

Approval Gate

Trim presents the plan as a markdown table after Phase 3 (PLAN). The orchestrator (or invoking user) responds with one of:

  1. APPROVE — full plan, all rows
  2. APPROVE WITH EXCLUSIONS — list row IDs to skip
  3. REJECT — abort, return findings as deferred report
  4. REPLAN — provide new scope or constraint

No changes are applied without one of these responses. Trim never assumes approval.

Reviewer Chain (optional)

If reviewer_chain: true:

@nw-test-optimizer-reviewer

Validate the optimization output for {scope}.

Reviewer hard-blocks on: production drift, coverage drop without justification, unmapped removal, missing approval gate evidence.

Success Criteria

  • Baseline numbers recorded (passed count, coverage %)
  • Plan presented before any change
  • Explicit approval received
  • Production files in diff: 0
  • Coverage % preserved (or drop documented per skill 5.3)
  • Atomic commits per consolidation pattern
  • Final report with deltas and SHAs

Examples

Example 1: Full unit suite audit

/nw-optimize-tests

Trim inventories the unit suite, runs md5sum cross-check, scans for anti-patterns, produces a leverage-sorted plan covering byte-identical pairs and parametrize-inflated files.

Example 2: Scoped to a feature

/nw-optimize-tests lean-wave-documentation

Trim resolves to tests/<feature-id>/ paths from the execution-log.json if available, otherwise scopes to test files referencing the feature-id.

Example 3: Single fat file

/nw-optimize-tests tests/build/unit/test_skill_restructuring.py

Trim probes the single file (315 collected tests), checks migration-collapse lifecycle (skill 3.5), proposes collapse to ~3 tests.

Example 4: With reviewer

/nw-optimize-tests tests/des/unit/ --reviewer

Trim runs the workflow, then dispatches Trim Review for adversarial validation. Reviewer issues YAML verdict.

Out of Scope

  • Authoring new tests (crafter scope, DELIVER wave)
  • Production code refactoring (/nw-refactor, crafter scope)
  • Test infrastructure changes (platform-architect, troubleshooter)

Expected Outputs

git log --oneline {base}..HEAD                  (atomic commits per pattern)
<scope>                                          (test files modified or deleted)
report (returned inline by agent)                (baseline, after, deltas, SHAs)

Frequently asked questions

What does the Nw Optimize Tests AI skill do?

Minimizes test count while preserving coverage. Detects byte-identical pairs, parametrize-inflation, language-guarantee tests, AST-shape tests, stale migration nets. Approval gate before any change.

Why use Nw Optimize Tests on TypingMind?

Because you install it once and use it with any model. Nw Optimize Tests 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 Optimize Tests in TypingMind?

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

Which AI models can use Nw Optimize Tests?

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 Optimize Tests?

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

Is the Nw Optimize Tests 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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