Nw Agent TestingSAFE
AI agents that guide you from idea to working code, with you in control at every step.
Overview
AI agents that guide you from idea to working code, with you in control at every step.
1a864ec81cdeOBSERVED · 2026-10-14Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: nw-agent-testing description: 5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance user-invocable: false disable-model-invocation: true --- # 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
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
1a864ec81cdefull audit observations/trust-audit/skill/nwave-ai__nw-agent-testing.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-14 | 1a864ec81cde | SAFE | B | 89 | first audit |
Questions
What does the Nw Agent Testing skill do?
AI agents that guide you from idea to working code, with you in control at every step.
Is Nw Agent Testing safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Nw Agent Testing access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Nw Agent Testing work with?
Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (1a864ec81cde), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.