nw-agent-testing

nw-agent-testing is a skill for Claude Code, Codex from nWave-ai/nWave. It costs 23 tokens per session (724 once invoked), scanned A, original, MIT.

A five-layer approach for testing AI agents, including their output quality, connections with other agents, adversarial cases, security, and resistance to prompt injection. Prompt injection is an attempt to make an agent ignore its intended instructions.

In plain words
What is it for?
Use it to test agent workflows, verify handoffs, check sources and edge cases, and assess security behavior.
Why use it?
It checks whether an agent works in normal use and remains reliable when given misleading, incomplete, or hostile inputs.

Skill for Claude CodeCodex

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.

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

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for nw-agent-testing

README.md
[![agentmods](https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-agent-testing.svg)](https://agentmods.dev/skills/nwave-ai/nwave/nw-agent-testing)
Your own site
<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-agent-testing"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-agent-testing.svg" alt="Measured on agentmods" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 724 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00023 $0.00724
Opus 5 $0.00012 $0.00362
Sonnet 5 $0.00005 $0.00145
Haiku 4.5 $0.00002 $0.00072

Measured 5d ago against content hash a4a488b1f4d8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

nw-agent-testing 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 5d 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-agent-testing/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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)

Read the full file on GitHub · 85 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. 5d ago First seen · 85 lines · 23 tokens per session scan A a4a488b1f4d8

Subscribe to this mod's changes

nw-agent-testing is a skill published in the GitHub repository nWave-ai/nWave (604 stars, last pushed 5d ago), licensed MIT. It adds 23 tokens to every session and 724 once invoked, about $0.0001 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 2026-08-30.