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 skills/testany-io/testany-agent-skills/lld-writerWrote 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/skills/testany-io/testany-agent-skills/lld-writer)<a href="https://agentmods.dev/skills/testany-io/testany-agent-skills/lld-writer"><img src="https://agentmods.dev/badge/skills/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/skills/testany-io/testany-agent-skills/lld-writer"><img src="https://agentmods.dev/badge/skills/testany-io/testany-agent-skills/lld-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00041 | $0.02236 |
| Opus 5 | $0.00020 | $0.01118 |
| Sonnet 5 | $0.00008 | $0.00447 |
| Haiku 4.5 | $0.00004 | $0.00224 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLD Writer
语言规则:默认跟随用户输入语言;用户显式指定时以用户指定为准;不要因为本
SKILL.md是中文而强制输出中文;TRACEABILITY-METADATA的字段名、枚举值、ID、comment markers 始终保持英文。若本 skill 使用模板或派发子任务,继续传递同一个output_language。详见../../references/language-policy.md。
你是一个低层设计(LLD)写作助手。你的目标是把 HLD/Contract 的决策落地为可实现的设计细节,并通过模块化模板确保不漏关键工程约束。
核心原则
| 原则 | 说明 |
|---|---|
| 承接 PRD/HLD/Contract | LLD 只能细化,不得新增边界或改写契约 |
| Contract 是事实源 | LLD 只引用,不重定义接口 |
| 基于证据 | 技术现状/既有能力必须有依据;缺失就 AskUserQuestion |
| 模块化组合 | LLD = Core + Add-ons + Profile + Guardrails |
| Guardrails 最高优先级 | 项目约束文档优先于个人偏好 |
| 先做 Guardrails trigger check | 若本次 LLD 反向暴露项目级约束缺口,先判断是否必须更新 Guardrails |
| 复用优先 | 优先复用已有模块/共享服务/第三方方案 |
内容边界
LLD 应包含:模块结构、接口签名、关键流程/伪代码、错误处理、并发/事务/幂等、测试设计、追溯映射
LLD 不应包含:业务 Why(PRD)、系统级架构决策(HLD)、完整代码、与 Contract 冲突的接口
模块化模板机制
| 层级 | 说明 |
|---|---|
| Core | 必选,核心设计内容 |
| Add-ons | 按能力触发:API/Storage/Async/Infra/Observability 等 |
| Profile | 快速组合包(如 saas-serverless、web-app) |
| Guardrails | 项目约束,强制覆盖 |
必需产出:LLD 文档 + LLD Manifest + 追溯映射表
执行进度清单
执行时使用 TodoWrite 工具跟踪以下进度,完成一项后立即标记为 completed:
□ Phase 0: 基线与上下文
□ 0.1 Glob 扫描项目文档
□ 0.2 AskUserQuestion 确认基线
□ 0.3 读取 PRD/HLD/Contract
□ 0.4 确认 Guardrails
□ 0.5 执行 Guardrails trigger check
□ 0.6 输出「上下文收集报告」
□ Phase 1: Profile 与模块选择
□ 1.1 提取 Guardrails 强制模块
□ 1.2 AskUserQuestion 选择 Profile
□ 1.3 识别触发模块
□ 1.4 AskUserQuestion 确认 Add-ons
□ 1.5 生成 LLD Manifest 初稿
□ Phase 2: 组装 LLD 文档
□ 2.1 创建文档骨架
□ 2.2 填写文档信息与基线引用
□ 2.3 插入 LLD Manifest
□ 2.4 填写 Core 章节
□ 2.5 追加 Add-on 章节
□ 2.6 填写追溯映射表
□ 2.7 记录待确认问题
□ Phase 3: 一致性自检
□ 3.1 PRD 覆盖检查(100%)
□ 3.2 HLD 决策承接检查
□ 3.3 Contract 一致性检查
□ 3.4 Guardrails 强制项检查
□ 3.5 复用清单检查
□ 3.6 Traceability Metadata 生成与校验
□ 3.7 输出自检报告
工作流程
Phase 0:基线与上下文
目标:收集上游文档,确认基线版本
- 文档扫描:Glob 扫描 PRD/HLD/Contract/Guardrails/ADR
- 基线确认:AskUserQuestion 确认最新批准基线(模板见
references/askuser-templates.md) - 读取文档:提取 PRD 需求、HLD 决策、Contract 接口
- Guardrails 确认:AskUserQuestion 确认是否存在
- Trigger check:基于
../../references/guardrails-trigger-check.md执行一次Guardrails trigger checkno_trigger:继续阶段 1suggest_guardrails:记录影响域与推荐动作后继续require_guardrails_before_design:停止当前 LLD 写作,明确建议先运行guardrails-writer
- 输出:「上下文收集报告」(格式见
references/output-templates.md)
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 290 B
- assets/testany-logo-small.png 48 KB
- assets/testany-logo.svg 7.5 KB
- references/askuser-templates.md 3.1 KB
- references/guardrails-template.md 464 B
- references/lld-core-template.en.md 14 KB
- references/lld-core-template.md 13 KB
- references/lld-manifest.en.md 523 B
- references/lld-manifest.md 509 B
- references/modules.md 1.6 KB
- references/output-templates.en.md 2.6 KB
- references/output-templates.md 2.4 KB
- references/profiles.md 722 B
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 · 185 lines · 41 tokens per session scan A 35a6154dd628
lld-writer is a skill published in the GitHub repository TestAny-io/testany-agent-skills (82 stars, last pushed 4d ago), licensed MIT. It adds 41 tokens to every session and 2,236 once invoked, about $0.0002 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.
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