← Back to Skills Marketplace
athola

Nm Abstract Subagent Testing

by athola · GitHub ↗ · v1.8.3 · MIT-0
cross-platform ✓ Security Clean
154
Downloads
0
Stars
1
Active Installs
3
Versions
Install in OpenClaw
/install nm-abstract-subagent-testing
Description
Test skills via RED/GREEN/REFACTOR TDD with fresh subagents
README (SKILL.md)

Night Market Skill — ported from claude-night-market/abstract. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Subagent Testing - TDD for Skills

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

Table of Contents

  1. Overview
  2. Why Fresh Instances Matter
  3. Testing Methodology
  4. Quick Start
  5. Detailed Testing Guide
  6. 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

# 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:

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
Usage Guidance
This skill is a methodology guide and appears coherent and low-risk. Before running tests, avoid including real secrets or production data in prompts or captured model outputs (use synthetic/test data). If you install any additional plugins the guide references (e.g., Claude Code), review those plugins separately for permissions and network access. If you prefer the agent not to invoke skills autonomously, adjust your agent settings — the skill itself does not demand elevated privileges.
Capability Analysis
Type: OpenClaw Skill Name: nm-abstract-subagent-testing Version: 1.8.3 The skill bundle provides a structured methodology and documentation for testing OpenClaw skills using a TDD-inspired approach (RED/GREEN/REFACTOR). It focuses on using fresh subagent instances to eliminate priming bias and includes templates for adversarial testing to identify and fix AI rationalization behaviors. The files (SKILL.md and modules/testing-patterns.md) contain only educational content, procedural instructions, and non-executable Python code skeletons intended for developer use, with no evidence of malicious intent, data exfiltration, or harmful execution.
Capability Assessment
Purpose & Capability
Name/description match the content: the files describe a methodology for testing skills with fresh subagents. No unrelated binaries, env vars, or credentials are requested.
Instruction Scope
SKILL.md and modules/testing-patterns.md instruct the agent to create fresh conversations, run baseline/with-skill/rationalization tests, and capture responses. All referenced actions are within the stated testing purpose; there are no instructions to read arbitrary host files, access credentials, or transmit data to unexpected endpoints.
Install Mechanism
No install spec and no code files — instruction-only — so nothing is written to disk or pulled from external URLs. This is the lowest-risk install profile.
Credentials
The skill declares no required env vars, credentials, or config paths. The guidance to load other skills during tests (e.g., 'secure-api-design') is expected for a testing workflow and does not itself request unrelated secrets.
Persistence & Privilege
Skill does not request persistent presence (always:false) or modify other skills. It allows normal autonomous invocation (platform default), which is expected for skills but not elevated here.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install nm-abstract-subagent-testing
  3. After installation, invoke the skill by name or use /nm-abstract-subagent-testing
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.8.3
Release v1.8.3
v1.8.2
Release v1.8.2
vv1.8.2
Release v1.8.2
Metadata
Slug nm-abstract-subagent-testing
Version 1.8.3
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 3
Frequently Asked Questions

What is Nm Abstract Subagent Testing?

Test skills via RED/GREEN/REFACTOR TDD with fresh subagents. It is an AI Agent Skill for Claude Code / OpenClaw, with 154 downloads so far.

How do I install Nm Abstract Subagent Testing?

Run "/install nm-abstract-subagent-testing" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Nm Abstract Subagent Testing free?

Yes, Nm Abstract Subagent Testing is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Nm Abstract Subagent Testing support?

Nm Abstract Subagent Testing is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Nm Abstract Subagent Testing?

It is built and maintained by athola (@athola); the current version is v1.8.3.

💬 Comments