# Subagent Testing

> Test skills via TDD in fresh subagents. Use when validating behavior or preventing bias.

- Skill: `thedixitjain/subagent-testing` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add thedixitjain/subagent-testing`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/subagent-testing/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: thedixitjain (https://skillmd.com/u/thedixitjain)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/thedixitjain/subagent-testing

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# Subagent Testing - TDD for Skills

Test skills with fresh subagent instances to prevent priming bias and validate effectiveness.

## When NOT To Use

- Writing the skill under test (use `abstract:skill-authoring`)
- A static quality audit with no execution (use `abstract:skills-eval`)

## Table of Contents

1. [Overview](#overview)
2. [Why Fresh Instances Matter](#why-fresh-instances-matter)
3. [Testing Methodology](#testing-methodology)
4. [Quick Start](#quick-start)
5. [Detailed Testing Guide](#detailed-testing-guide)
6. [Success Criteria](#success-criteria)

## Overview

**Fresh instances prevent priming:** Each test uses a new Claude conversation to verify
the skill's impact is measured, not conversation history effects.

## Why Fresh Instances Matter

### The Priming Problem
Running tests in the same conversation creates bias:
- Prior context influences responses
- Skill effects get mixed with conversation history
- Can't isolate skill's true impact

### Fresh Instance Benefits
- **Isolation**: Each test starts clean
- **Reproducibility**: Consistent baseline state
- **Measurement**: Clear before/after comparison
- **Validation**: Proves skill effectiveness, not priming

## Testing Methodology

Three-phase TDD-style approach:

### Phase 1: Baseline Testing (RED)
Test without skill to establish baseline behavior.

### Phase 2: With-Skill Testing (GREEN)
Test with skill loaded to measure improvements.

### Phase 3: Rationalization Testing (REFACTOR)
Test skill's anti-rationalization guardrails.

## Quick Start

```bash
# 1. Create baseline tests (without skill)
# Use 5 diverse scenarios
# Document full responses

# 2. Create with-skill tests (fresh instances)
# Load skill explicitly
# Use identical prompts
# Compare to baseline

# 3. Create rationalization tests
# Test anti-rationalization patterns
# Verify guardrails work
```

## Detailed Testing Guide

For complete testing patterns, examples, and templates:
- **[Testing Patterns](modules/testing-patterns.md)** - Full TDD methodology
- **[Test Examples](modules/testing-patterns.md)** - Baseline, with-skill, rationalization tests
- **[Analysis Templates](modules/testing-patterns.md)** - Scoring and comparison frameworks

## Success Criteria

- **Baseline**: Document 5+ diverse baseline scenarios
- **Improvement**: ≥50% improvement in skill-related metrics
- **Consistency**: Results reproducible across fresh instances
- **Rationalization Defense**: Guardrails prevent ≥80% of rationalization attempts

## See Also

- **skill-authoring**: Creating effective skills
- **bulletproof-skill**: Anti-rationalization patterns
- **test-skill**: Automated skill testing command

## Exit Criteria

- [ ] Baseline (RED) phase documents at least 5 diverse scenarios run in fresh Claude instances
  without the skill active, with full response text recorded.
- [ ] With-skill (GREEN) phase uses identical prompts in new fresh instances (not continuations
  of the baseline conversation) and shows >= 50% improvement on skill-related metrics.
- [ ] Rationalization (REFACTOR) phase shows skill guardrails blocking >= 80% of rationalization
  attempts tested across at least 3 pressure scenarios.
- [ ] Results are reproducible: the same prompts in a new fresh instance produce consistent
  outcomes, confirming the effect is not conversation-history priming.

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**Source:** [`athola/claude-night-market`](https://github.com/athola/claude-night-market) → `plugins/abstract/skills/subagent-testing/SKILL.md`