Borrowing it
Nothing to install: this file belongs to DavidLam-oss/obsidian-wechat-converter. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DavidLam-oss/obsidian-wechat-converter/main/.claude/skills/openprd-test-strategy/SKILL.mdgit clone --depth 1 https://github.com/DavidLam-oss/obsidian-wechat-converterWrote 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/davidlam-oss/obsidian-wechat-converter/openprd-test-strategy)<a href="https://agentmods.dev/skills/davidlam-oss/obsidian-wechat-converter/openprd-test-strategy"><img src="https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-test-strategy/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/davidlam-oss/obsidian-wechat-converter/openprd-test-strategy"><img src="https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-test-strategy.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 | $0.00048 | $0.01181 |
| Opus 5 | $0.00024 | $0.00590 |
| Sonnet 5 | $0.00010 | $0.00236 |
| Haiku 4.5 | $0.00005 | $0.00118 |
Grade A, and why
openprd-test-strategy 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 7d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenPrd Test Strategy
当需求进入实现、任务拆分、验证计划、质量评估或 loop 单任务执行时，使用这份 skill。
核心判断
- 先接住
$openprd-requirement-intake的需求类型，再按风险、触达面、失败后果和证据成本选择测试组合。 - 不把“小需求=单测、中需求=集成、大需求=端到端”写成硬规则；它只是默认起点。
- 70/20/10 只作为健康形状参考，不作为 OpenPrd 的硬性比例门禁。
- 脚本存在只能说明项目具备能力，不能替代本次执行证据。
默认分流
- 局部纯逻辑、格式化、解析、规则函数：优先
test-layer: unit，test-size: small，test-scope: isolated。 - 触达 CLI/API/Agent 契约、生成物、跨模块状态、任务推进、quality/run/loop：使用
unit, integration，test-size: medium，test-scope: cli-contract|api-contract|module。 - 触达用户主路径、页面、发布链路、真实浏览器、登录权限或第三方依赖：升级到
integration, e2e，test-size: large，test-scope: user-flow。 - 触达视觉还原：补
visual或visual-flow；已有参考图时用openprd visual-compare --reference/--actual留证据；没有参考图时先判断新建界面还是修改既有界面，新建界面先按用户目标、信息架构和视觉决策成本判断是否需要 3 方向方案评审，修改既有界面用openprd visual-compare --before/--after留修改前后自检证据；局部细节优先补“局部焦点证据板”，多方向实验优先补“并行实验证据板”。卡片宽度、间距、留白、对齐、颜色、圆角、字号、按钮或图标等轻量 UI 可视优化也需要视觉证据，build、package 和 dev-check 不能替代。新功能或改动包含同构列表、卡片、网格、表格时，或用户反馈排版没对齐时，还要补“对齐辅助线证据板”，同时量测容器轨道和内容槽位轨道；内容槽位包括标题、副标题、描述、标签、状态、价格、按钮、图标或操作区等相同文案类型/相同组件槽位的 x/y/宽高/baseline spread。单个 logo、icon、avatar、badge、按钮图形或图片内部需要居中判定、视觉重心评估或用户反馈偏心时，补“内部居中证据板”，用centering-board量画布中心、主体外接框中心和视觉重心偏移。 - 明确要求微信小程序运行态证据，或改动高风险且只能靠真实运行态确认时：补
weapp和weapp-runtime，并使用当前环境已配置的本地小程序验证能力；默认沿用当前小程序运行态或开发者工具会话连续验证，不要为了验证自动重开应用；普通小改默认先选更轻的验证，不要自动升级到小程序运行态验证。 - 触达性能、成本、额度、并发、滥用、安全、敏感信息：增加
performance或security专项验证和负向场景。 - 纯文档或治理任务：使用
manual，并记录标准校验、review、change validate 或人工审查证据。
任务元数据
OpenPrd 任务可以显式写入：
- test-layer: unit|integration|e2e|manual|smoke|visual|performance|security|weapp|none
- test-size: small|medium|large|manual|advisory|none
- test-scope: isolated|module|contract|cli-contract|api-contract|user-flow|visual-flow|weapp-runtime|performance|security|governance|docs|none
- evidence-plan: 说明本任务准备留下什么验证证据
- evidence: 本次已经产生的证据路径或摘要
- waiver-reason: 不做某层测试时的原因和剩余风险
收尾要求
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.
- 7d ago First seen · 52 lines · 48 tokens per session scan A 3d9e933e24cf
openprd-test-strategy is a skill published in the GitHub repository DavidLam-oss/obsidian-wechat-converter (310 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 1,181 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 2026-09-04.
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