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/lld-writer)<a href="https://agentmods.dev/commands/testany-io/testany-agent-skills/lld-writer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/lld-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/lld-writer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/lld-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 | $0.00563 |
| Opus 5 | $0.00005 | $0.00282 |
| Sonnet 5 | $0.00002 | $0.00113 |
| Haiku 4.5 | $0.00001 | $0.00056 |
Grade A, and why
lld-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 10d 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
LLD Writer
启动 LLD 撰写流程。基于 PRD/HLD/API Contract，将架构决策细化为可实现的设计细节。
使用方式
提供上游文档路径：
$ARGUMENTS
LLD 在研发流程中的位置
PRD → API Contract → HLD → Guardrails → LLD → 代码实现
LLD 承接 HLD 的架构决策，输出可直接指导编码的设计细节。
模块化设计
LLD 采用模块化组合方式，根据功能需求选择模块：
- Core（必选）：模块结构、接口签名、流程、错误处理、测试设计
- Add-ons（按需）：API Contract、Storage、Async/Event、Infra、Observability 等
- Profile（快速组合）：saas-serverless、web-app、data-pipeline 等预设组合
- Guardrails（约束）：项目级工程约束，优先级最高
LLD 聚焦内容
- 模块/包结构：目录结构、依赖关系
- 接口与函数签名：核心类/接口/方法定义
- 关键流程/伪代码：Happy path + 异常分支
- 错误处理：错误分类、处理策略
- 并发/事务/幂等：并发模型、幂等设计
- 测试设计：单测、集成测试、Mock 策略
LLD 不包含
- 完整代码实现（属于代码阶段）
- 系统级架构决策（属于 HLD）
- 与 Contract 冲突的接口定义
工作流程
- 基线收集：读取 PRD/HLD/Contract/Guardrails
- 模块选择：选择 Profile 和 Add-ons
- 文档组装：按模板生成 LLD + Manifest
- 一致性自检：覆盖率 100%、无冲突、Guardrails 全覆盖
必需产出
- LLD 文档（Core + Add-ons）
- LLD Manifest（模块选择与理由）
- 追溯映射表（PRD/HLD/Contract → LLD）
请提供 PRD、HLD、API Contract 路径，若有 Guardrails 请一并提供。
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.
- 10d ago First seen · 62 lines · 11 tokens per session scan A 3e50f8123ff0
lld-writer is a command published in the GitHub repository TestAny-io/testany-agent-skills (81 stars, last pushed yesterday), licensed MIT. It adds 11 tokens to every session and 563 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.