yaojingang/yao-open-skills is a public collection of reusable AI skills for research, decision-making, business analysis, learning, and document creation. It serves people who want repeatable, maintainable AI workflows instead of isolated prompts, and catalogued add-ons are published skills from this collection.
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.
npx skills add yaojingang/yao-open-skills --skill yao-weread-skillgit clone --depth 1 https://github.com/yaojingang/yao-open-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/skills/yaojingang/yao-open-skills/yao-weread-skill)<a href="https://agentmods.dev/skills/yaojingang/yao-open-skills/yao-weread-skill"><img src="https://agentmods.dev/badge/skills/yaojingang/yao-open-skills/yao-weread-skill/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/yaojingang/yao-open-skills/yao-weread-skill"><img src="https://agentmods.dev/badge/skills/yaojingang/yao-open-skills/yao-weread-skill.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 | $0.00091 | $0.01096 |
| Opus 5 | $0.00046 | $0.00548 |
| Sonnet 5 | $0.00018 | $0.00219 |
| Haiku 4.5 | $0.00009 | $0.00110 |
Grade A, and why
yao-weread-skill 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.
What it actually says
Yao WeRead Skill
从微信读书账号数据生成精排中文 HTML 报告。默认范围为截至今天的最近 24 个月。
输入
- 环境变量
WEREAD_API_KEY,格式遵循微信读书 skill 的要求。 - 可选报告范围:
--years、--start或--end。 - 可选笔记深度:
--max-note-books;省略或传0时处理微信读书返回的全部笔记书籍。 - 可选输出目录。
- 可选示例模式:
--sample-ai-founder --sample-scale 5,不需要WEREAD_API_KEY。
输出
流程会生成:
weread-report.html:参考 kami 排版风格的交互式 HTML 报告。weread-report-data.json:聚合后的图表数据。weread-raw-summary.json:不含密钥的 API 结构和计数摘要,便于复核。
报告还会生成一个读者画像模块:顶部诗性总结、1 条画像金句,以及从近两年划线中筛选出的最多 20 条高价值句子。原始划线和想法仅用于聚合分析与画像筛选。除非用户明确要求分享或发布,否则报告产物应视为私有内容。
工作流
- 调用 API 前先阅读微信读书 skill 文档:
shelf.md:书架计数、公开/私密规则。readdata.md:阅读时长单位、周期规则、年度/月度字段。notes.md:笔记分页、笔记数计算、划线/想法文本。book.md:仅在需要书籍详情或阅读进度时使用。
- 运行
scripts/generate_weread_report.py。 - 检查生成的 HTML 至少包含 20 个图表面板,没有
TODO、占位文本或内嵌 API key。 - 检查矩形树图、热力图、横向条形图等高密度图表没有被默认边距压缩,没有右侧空白导致标签截断。
- 检查读者画像模块:金句必须来自画像划线清单第一条;真实账号模式不得编造划线,划线不足 20 条时如实少展示。
- 如果用户要求视觉验证,或报告排版有实质变化,使用浏览器打开生成的 HTML,并检查桌面、平板宽度和窄屏宽度。
命令
python3 scripts/generate_weread_report.py --output reports/generated
常用选项:
python3 scripts/generate_weread_report.py \
--years 2 \
--max-note-books 0 \
--output reports/generated
AI 创业者示例报告:
python3 scripts/generate_weread_report.py \
--years 2 \
--sample-ai-founder \
--sample-scale 5 \
--output reports/generated/ai-founder-sample
报告设计
- 视觉系统遵循
references/report-design.md。 - 图表模块遵循
references/chart-catalog.md。 - API 字段语义和降级规则遵循
references/data-contract.md。 - 高密度图表必须显式设置容器占满、标签换行/隐藏策略和 resize 监听,避免 ECharts 默认布局留下空白。
- 内容画像必须基于真实划线文本生成,顶部总结可以使用统计口径生成温暖、诗性但不冒充用户原文的描述。
边界
- 真实账号模式下,不编造微信读书响应中不存在的阅读事件、笔记文本、评分或分类。
- AI 创业者示例模式用于在不接入真实账号时生成可复用示例报告。
- 不导出书籍全文;只使用用户自己的划线/想法,以及微信读书 skill 可访问的元数据。
- 不存储或打印
WEREAD_API_KEY。 - 无法获得精确滚动日期边界时,必须清楚标注月度或年度近似口径。
What ships with it
12 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/interface.yaml 884 B
- examples/ai-founder-report/README.md 456 B
- examples/ai-founder-report/weread-report.html 94 KB
- manifest.json 464 B
- README.md 3.4 KB
- references/chart-catalog.md 2.9 KB
- references/data-contract.md 3.0 KB
- references/report-design.md 2.5 KB
- reports/artifact-design-profile.md 1.2 KB
- reports/output-risk-profile.md 1.6 KB
- requirements.txt 17 B
- scripts/generate_weread_report.py 99 KB runs code
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 · 89 lines · 91 tokens per session scan A 95cb00ed6154
yao-weread-skill is a skill published in the GitHub repository yaojingang/yao-open-skills (1,314 stars, last pushed 15d ago), licensed MIT. It adds 91 tokens to every session and 1,096 once invoked, about $0.0005 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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