nw-agent-testing
This testing framework provides a five-layer validation approach for Claude Code agents, progressing from output quality checks through integration testing, adversarial output validation, peer review, and security hardening. Use it when developing multi-agent workflows to ensure agents produce correct outputs, integrate properly with other agents, withstand scrutiny of their reasoning and recommendations, avoid design flaws, and resist prompt injection attacks through both platform-level configuration and structured review processes.
git clone --depth 1 https://github.com/nWave-ai/nWave /tmp/nw-agent-testing && cp -r /tmp/nw-agent-testing/nWave/skills/nw-agent-testing ~/.claude/skills/nw-agent-testingSKILL.md
# 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
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating architecture styles.
Comprehensive architecture patterns, methodologies, quality frameworks, and evaluation methods for solution architects. Load when designing system architecture or selecting patterns.
Canonical AT completeness gate — research-anchored 7-category taxonomy (C1-C7) + 15-item mechanical checklist. Paradigm-neutral. Drives acceptance-designer reviewer verdict deterministically.
Domain-specific authoritative source databases, search strategies by topic category, and source freshness rules