← 返回 Skills 市场
athola

Nm Abstract Subagent Testing

作者 athola · GitHub ↗ · v1.8.3 · MIT-0
cross-platform ✓ 安全检测通过
154
总下载
0
收藏
1
当前安装
3
版本数
在 OpenClaw 中安装
/install nm-abstract-subagent-testing
功能描述
Test skills via RED/GREEN/REFACTOR TDD with fresh subagents
使用说明 (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
安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install nm-abstract-subagent-testing
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /nm-abstract-subagent-testing 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.8.3
Release v1.8.3
v1.8.2
Release v1.8.2
vv1.8.2
Release v1.8.2
元数据
Slug nm-abstract-subagent-testing
版本 1.8.3
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 3
常见问题

Nm Abstract Subagent Testing 是什么?

Test skills via RED/GREEN/REFACTOR TDD with fresh subagents. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 154 次。

如何安装 Nm Abstract Subagent Testing?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install nm-abstract-subagent-testing」即可一键安装,无需额外配置。

Nm Abstract Subagent Testing 是免费的吗?

是的,Nm Abstract Subagent Testing 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Nm Abstract Subagent Testing 支持哪些平台?

Nm Abstract Subagent Testing 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Nm Abstract Subagent Testing?

由 athola(@athola)开发并维护,当前版本 v1.8.3。

💬 留言讨论