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-quality/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-quality)<a href="https://agentmods.dev/skills/davidlam-oss/obsidian-wechat-converter/openprd-quality"><img src="https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-quality/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-quality"><img src="https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-quality.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 | $0.00046 | $0.01855 |
| Opus 5 | $0.00023 | $0.00928 |
| Sonnet 5 | $0.00009 | $0.00371 |
| Haiku 4.5 | $0.00005 | $0.00186 |
Grade A, and why
openprd-quality 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 6d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenPrd Quality
当实现就绪、日志、链路追踪、免费额度、业务成本、滥用防护、评估执行环境、冒烟测试、性能基线、压力数据或项目级经验 Skill 在范围内时，使用这份 skill。
命令
openprd quality . --init：初始化.openprd/quality/config.json和.openprd/knowledge/openprd quality . --verify：在.openprd/quality/reports/下生成 JSON 和 HTML 质量评估报告openprd quality . --learn --from <report-id-or-json>：把已修复或已审查的质量问题沉淀为项目级经验 Skillopenprd grow . --review：审查执行中发现的可复用配置、规则候选或 user-local 偏好；和quality --learn互补，前者沉淀操作配置，后者沉淀已验证质量经验。若quality --learn --review命中项目经验候选，先在最终回复结尾用人话说明“本次情况 / 计划保留的经验 / 以后怎么复用 / 只保留在当前项目里”，再询问用户是否保留。
审查契约
- 场景画像：先判断当前变更是基础、前端、桌面端、后端、成本、安全、性能、极端数据还是发布交付场景，再确定必需 EVO 门禁。
- 可观测性：确认中心化 logs / traces / errors、共享 trace/request/task/error id、脱敏、保留期和查询示例。
- 业务护栏：涉及免费用户、额度、AI 调用、第三方 API、生成、存储或下载时，确认成本来源、用户级限制、负向验证、监控、报警和止损动作。
- 评估执行环境：确认冒烟测试、任务到功能覆盖、正常性能基线和极端数据压力场景；脚本存在只代表能力，不能替代本次运行证据。
- 大界面改动证据：先按用户目标、信息架构变化、视觉决策成本和验证风险判断是否需要方案评审；需要时确认候选效果图已经给用户看过，并且每个方向有清楚的用途、受众、气质端点、审美主张和记忆点；用户已明确确认哪个方向、整张图或哪些子图纳入后续对比；只有确认后的 reference-set 才应出现在
.openprd/harness/visual-reviews/。 - 视觉评审证据：涉及界面视觉实现且已有确认参考效果图时，确认
.openprd/harness/visual-reviews/下存在本次openprd visual-compare输出的“效果图 / 实现截图”JPG，并且 Agent 已基于合成图复核差异；如果参考图来自整板、网格图或多对象候选图，确认已完成openprd visual-prepare产出的 reference-set、contact sheet 或 board 模板审查。没有参考图时先按场景区分新建界面和修改既有界面：新建界面确认实现前 3 方向方案评审已完成，修改既有界面确认存在“修改前 / 修改后”JPG，并已检查预期变化和未改区域漂移；若验收关注局部细节，确认存在“局部焦点证据板”；若并行跑了多个优化方向，确认存在“并行实验证据板”；若使用普通截图或 Computer/Browser/Playwright 实测截图作为证据，确认存在“截图实测证据板”；若新功能或改动包含同构列表、卡片、网格、表格，或用户反馈排版没对齐，确认存在“对齐辅助线证据板”，并同时包含容器轨道 spread 和标题/副标题/描述/标签/状态/价格/按钮/图标等内部内容槽位 spread 量测；若用户反馈或任务目标是单个素材/图标/头像/徽标/按钮图形/图片内部居中、视觉重心或偏心，确认存在“内部居中证据板”，并同时包含主体外接框中心偏移和视觉重心偏移。这些证据不只查有无图片，还要查气质、层级、字体/色彩/动效/表面角色和记忆点是否成立。 - 轻量 UI 可视优化证据：卡片宽度、间距、留白、对齐、颜色、圆角、字号、按钮或图标等小改仍属于用户可见变化。质量审查时至少确认存在修改前后视觉对比、局部焦点证据板、截图实测证据板、对齐辅助线证据板或内部居中证据板，并且本轮审美意图、气质、层级和记忆点已被复核；build、package、dev-check、单元测试或单张原始截图都不能替代视觉收口证据。
- HTML 报告：把
.openprd/quality/reports/*.html当成面向人的评审产物，而不是次级导出。 - 知识沉淀：当某个已验证修复具备重复性、高影响、隐藏性或由 agent 误判引发时，把模式抽象到
.openprd/knowledge/skills/<skill>/SKILL.md。 - 自我成长：当问题来自配置缺口、文件识别、命令习惯或用户偏好时，优先记录为
.openprd/growth候选，经用户确认后固化；不要把个人偏好混进项目共享质量经验。
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.
- 6d ago First seen · 46 lines · 46 tokens per session scan A cd29a8e6318f
openprd-quality is a skill published in the GitHub repository DavidLam-oss/obsidian-wechat-converter (307 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 1,855 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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