Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/TestAny-io/testany-agent-skillsnpx agentmods add commands/testany-io/testany-agent-skills/prd-reviewerWrote 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/testany-io/testany-agent-skills/prd-reviewer)<a href="https://agentmods.dev/commands/testany-io/testany-agent-skills/prd-reviewer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/prd-reviewer/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/testany-io/testany-agent-skills/prd-reviewer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/prd-reviewer.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 | $0.00011 | $0.00401 |
| Opus 5 | $0.00005 | $0.00200 |
| Sonnet 5 | $0.00002 | $0.00080 |
| Haiku 4.5 | $0.00001 | $0.00040 |
Grade A, and why
prd-reviewer 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 11d 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
PRD Reviewer
启动 PRD 审查流程。作为"需求评审会议的 AI 化"，对 PRD 进行全方位审查，确保质量达到准出标准。
使用方式
提供需要审查的 PRD 文件路径：
$ARGUMENTS
启动后，reviewer 必须先执行：
python3 plugins/testany-eng/scripts/trace_lint.py --format json <PRD文件路径>
审查维度
- 结构完整性 - 必填章节是否完整
- 业务逻辑（PM 视角） - 业务背景、用户故事、业务规则
- 需求清晰度（开发视角） - 是否能据此编写 HLD
- 可测试性（QA 视角） - 验收标准是否可测试
- 业务方视角 - 业务现状、变更影响
- 内容边界 - 是否越界到 HLD 领域
- 证据可追溯性 - 相关能力识别是否有来源
- 一致性 - 术语、需求描述是否一致
- Traceability Metadata -
prd-profile-v1元数据是否完整、可解析、可追溯
问题分级
- P0 阻塞：必须修复才能准出
- P1 严重：强烈建议修复，累计 ≥2 个不放行
- P2 建议：可选优化，不阻塞放行
请提供 PRD 文件路径开始审查；reviewer 会同时检查正文内容和 TRACEABILITY-METADATA block。
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.
- 11d ago First seen · 41 lines · 11 tokens per session scan A dd674b7d7b88
prd-reviewer is a command published in the GitHub repository TestAny-io/testany-agent-skills (82 stars, last pushed 3d ago), licensed MIT. It adds 11 tokens to every session and 401 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.