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Nw Agent Testing

Organization
nWave-ai
nw-agent-testing

5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-agent-testing
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 Agent Testing 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-agent-testing
  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-agent-testing .claude/skills/nw-agent-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Agent Testing 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 Agent Testing 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 Agent Testing 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.

Agent Testing Framework

5-Layer Testing Approach

Layer 1: Output Quality (Unit-Level)

Validate agent produces correct, well-structured outputs for typical inputs.

Test: Agent follows workflow phases | Outputs match expected format/structure | Domain-specific rules correctly applied | Token efficiency within bounds

How: Manual invocation with representative inputs. Check against acceptance criteria in agent description.

Layer 2: Integration / Handoff Validation

Validate correct input/output between agents in workflows.

Test: Input parsing handles upstream format | Output format matches downstream expectations | Error signals propagate correctly | Subagent mode activation works (skip greet, execute autonomously)

How: End-to-end workflow execution through full agent chain (e.g., DISCUSS -> DESIGN -> DELIVER).

Layer 3: Adversarial Output Validation

Challenge validity of agent outputs rather than accepting at face value.

Test: Source verification (cited sources real and accurate?) | Bias detection (favors one approach without evidence?) | Edge case coverage | Completeness (required sections present?)

How: Peer review by -reviewer agent using structured critique dimensions.

Layer 4: Adversarial Verification (Peer Review)

Independent review to catch biases and blind spots in agent design.

Test: Definition follows validation checklist? | Redundant Claude default instructions? | Over/under-specified? | Could simpler agent achieve same results?

How: @nw-agent-builder validates via 11-point checklist or @agent-builder-reviewer runs structured review.

Layer 5: Security Validation

Test resilience against misuse and prompt injection.

Test: Tool restriction enforcement | maxTurns respected | Permission mode correctly scoped | Agent stays within declared scope

How: Frontmatter fields enforce at platform level. Verify configuration.

Prompt Injection Resistance

Claude Code platform provides injection resistance through: subagent isolation (own context, no sub-subagents) | Tool restriction via frontmatter tools | Permission modes via permissionMode | Hook-based validation (PreToolUse, PostToolUse)

Do NOT add prose-based injection defense. Configure platform features:

yaml
---
tools: Read, Glob, Grep           # Only tools this agent needs
maxTurns: 30                       # Prevents runaway execution
permissionMode: default            # User approves dangerous actions
---

Security Validation Checklist

  • tools restricted to minimum necessary (least privilege)
  • maxTurns set to prevent runaway execution
  • permissionMode appropriate for risk level
  • No Bash unless agent requires command execution
  • No Write unless agent creates/modifies files
  • Description accurately describes scope
  • Subagent mode handles autonomous execution correctly
  • No sensitive data hardcoded in definition

Testing Workflow for New Agents

  1. Create with minimal definition
  2. Layer 1: Invoke with 2-3 representative inputs, check outputs
  3. Layer 2: Run in workflow chain if applicable
  4. Fix failures observed
  5. Validate: Run 11-point checklist
  6. Iterate: Add instructions only for observed failure modes

Frequently asked questions

What does the Nw Agent Testing AI skill do?

5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance

Why use Nw Agent Testing on TypingMind?

Because you install it once and use it with any model. Nw Agent Testing 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 Agent Testing in TypingMind?

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

Which AI models can use Nw Agent Testing?

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 Agent Testing?

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

Is the Nw Agent Testing 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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