Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/854771076/oh-my-claude-rolesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/854771076/oh-my-claude-roles/run-prompt-unit-test)<a href="https://agentmods.dev/commands/854771076/oh-my-claude-roles/run-prompt-unit-test"><img src="https://agentmods.dev/badge/commands/854771076/oh-my-claude-roles/run-prompt-unit-test/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/854771076/oh-my-claude-roles/run-prompt-unit-test"><img src="https://agentmods.dev/badge/commands/854771076/oh-my-claude-roles/run-prompt-unit-test.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.00283 |
| Opus 5 | $0.00010 | $0.00142 |
| Sonnet 5 | $0.00004 | $0.00057 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
run-prompt-unit-test scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
命令内容
执行指定提示词的单元测试，生成符合企业规范的测试报告。
测试文件：$ARGUMENTS
执行步骤：
- 读取测试文件和关联提示词配置
- 使用Vitest执行单元测试，覆盖正常场景、边界场景、异常场景、注入攻击场景
- 收集测试结果：格式合规率、业务准确率、异常处理覆盖率
- 对照企业验收标准，判断测试是否通过
- 生成完整测试报告，包含通过率、失败用例详情、修复建议
检查要点：
- 必须遵循企业测试规范要求，所有测试用例必须全部执行
- 格式合规率要求100%，异常场景处理覆盖率要求100%
- 必须明确标注不符合验收标准的测试项
- 测试报告必须结构化，可归档作为上线审批材料
- 测试不通过必须给出清晰的失败原因和修复方向
---
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 30 lines · 19 tokens per session scan A 1f4d7d4dc9cb
run-prompt-unit-test is a command published in the GitHub repository 854771076/oh-my-claude-roles (22 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 283 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
test-prompt
Test an AI prompt against multiple scenarios to verify consistent, quality output.
dados-distribuidos
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caracterizar-prompt
Characterization de prompts/tools LLM em produção — temperature=0 + seed fixo + sanitização específica. Trata prompts como código legacy. Modernização 2026 sem precedente em 2004.
prompt-eval-debug
Debug any prompt with a tiny eval suite (control, edge, boundary), failure diagnosis, and smallest next change, no blind rewrite.
prompt-tuner
Improve embedded LLM system prompt based on evaluation test failures.
data_refactor-validate
Validate refactored dbt project against real data.