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/TestAny-io/testany-agent-skillsWrote 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/hld-writer)<a href="https://agentmods.dev/commands/testany-io/testany-agent-skills/hld-writer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/hld-writer/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/hld-writer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/hld-writer.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 | $0.00421 |
| Opus 5 | $0.00005 | $0.00211 |
| Sonnet 5 | $0.00002 | $0.00084 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
hld-writer 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
HLD Writer
启动 HLD 撰写流程。基于 PRD 和 API Contract，做出架构级技术决策。
使用方式
提供 PRD 和 API Contract 文件路径：
$ARGUMENTS
为什么需要 API Contract？
API Contract（来自 api-writer）是接口定义的唯一事实源：
- HLD 直接引用契约中的接口，不重新设计
- 确保前后端/跨团队对接口理解一致
- 避免 HLD 与契约产生漂移
支持的 HLD 类型
- 新功能（有 UI） - 涉及前后端的新功能
- 新功能（无 UI / 后端） - 纯后端服务
- 第三方集成 - 接入外部服务
- 功能重构 - 内部架构重构
- 性能/安全优化 - 非功能性改进
HLD 聚焦内容
- 高成本决策：技术选型、架构模式
- 接口引用：引用 API Contract，不重新定义
- 风险决策：安全、性能、兼容性
- 复用决策：复用 vs 新建
工作流程
- 上下文收集：读取 PRD + API Contract，扫描技术文档
- 需求映射：PRD 需求 → HLD 章节
- 结构规划：选择合适的模板
- 内容撰写：技术决策 + 理由（接口引用契约）
- 强制审查：PRD 覆盖率、契约一致性检查
请提供 PRD 和 API Contract 路径开始撰写。
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 · 47 lines · 11 tokens per session scan A 0e9b6f93cb14
hld-writer 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 421 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.