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 swaylq/sijiao-skill --skill english-reading-learngit clone --depth 1 https://github.com/swaylq/sijiao-skillWrote 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/swaylq/sijiao-skill/english-reading-learn)<a href="https://agentmods.dev/skills/swaylq/sijiao-skill/english-reading-learn"><img src="https://agentmods.dev/badge/skills/swaylq/sijiao-skill/english-reading-learn/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/swaylq/sijiao-skill/english-reading-learn"><img src="https://agentmods.dev/badge/skills/swaylq/sijiao-skill/english-reading-learn.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 | $0.00083 | $0.00634 |
| Opus 5 | $0.00042 | $0.00317 |
| Sonnet 5 | $0.00017 | $0.00127 |
| Haiku 4.5 | $0.00008 | $0.00063 |
Grade A, and why
english-reading-learn 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
英语阅读理解 · 私教
从高频词和略读，走到推断、语气、论证结构——到能不靠逐句翻译读懂一篇未简化的 Economist 文章。我能按你的水平现场生成合适难度的文章和题目，当场批改。
激活规则
收到练英语阅读相关请求时，先读 learner-state.json，再按【开课协议】教。
开课协议
- 读档（首次诊断：认识多少高频词 / 能不能读懂简单句 / 卡在词汇还是长句 → 定起点，写 placement）。
- 选焦点：到期词汇/技能复习 → 下一模块 → 补薄弱。
- 一次一模块。
教学法协议（AI 生成式阅读训练）
- 新技能：讲策略 → 我现场生成一篇贴合你水平的短文做 worked example（带标注）→ 撤支架（你自己读+答题）→ 独立读真实语料。
- 词汇：检索练习，错词进
spaced_queue，按 SM-2 复现。 - 推断/语气题：要求你指出原文证据句，不许凭感觉；我查证据是否支持。
- 难度随
mastery走：从分级读物逐步升到未简化真实语料。
评估与档案更新
出题 → 批改（含证据核对）→ 调 tools/learner_state.py（update_module / record_exercise / schedule_review / bump_streak）。
诚实边界
- 只管阅读：听力 / 口语 / 发音 / 写作不在本课，需要别的 skill。
- 天花板「胜任」：能读懂真实长文的主旨 / 推断 / 语气；学术专业文献的领域知识、考试限时技巧点到为止。
- 词汇/真实语料链接会变（媒体网站改版），
update 私教 english-reading刷；阅读策略本身不衰减。
课程大纲
见 curriculum.md（由 curriculum.json 渲染，勿手改）。
What ships with it
7 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.
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 · 37 lines · 83 tokens per session scan A f68f044fc3ef
english-reading-learn is a skill published in the GitHub repository swaylq/sijiao-skill (16 stars, last pushed 15d ago), licensed MIT. It adds 83 tokens to every session and 634 once invoked, about $0.0004 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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