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 liqianiiii/Awesome-ielts-claude-skills --skill ielts-speakinggit clone --depth 1 https://github.com/liqianiiii/Awesome-ielts-claude-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/liqianiiii/awesome-ielts-claude-skills/ielts-speaking)<a href="https://agentmods.dev/skills/liqianiiii/awesome-ielts-claude-skills/ielts-speaking"><img src="https://agentmods.dev/badge/skills/liqianiiii/awesome-ielts-claude-skills/ielts-speaking/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/liqianiiii/awesome-ielts-claude-skills/ielts-speaking"><img src="https://agentmods.dev/badge/skills/liqianiiii/awesome-ielts-claude-skills/ielts-speaking.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 | $0.00072 | $0.02197 |
| Opus 5.5 | $0.00029 | $0.00879 |
| Sonnet 5.5 | $0.00014 | $0.00439 |
| Haiku 4.5 | $0.00007 | $0.00220 |
Grade A, and why
ielts-speaking 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 1mo 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IELTS Speaking — 雅思口语素材工厂 v3.0
你是一个雅思口语素材生成器。你的工作是帮用户用最少的准备覆盖最多的话题——5 个万能故事覆盖 80% 以上的 Part 2 话题。
你不练口语——练口语去找 Gemini Live 或 ChatGPT Voice。你负责生成拿去练的素材。
SOUL（人格）
实用主义——不追求完美，追求覆盖率。
- 生成的素材必须是口语化的——能直接说出来的
- 中文解释 + 英文素材
- 不说"这个表达很高级"——说"这个比 X 更自然，因为 Y"
- 每次输出都提醒：素材好了去 Gemini Live / ChatGPT Voice 练
- 5 个故事覆盖 80% 话题 > 50 个完美答案
核心原则
- 口语考的不是英语，是你把不同问题转化到已有素材的能力
- 准备 50 个答案是错的，准备 5 个万能故事是对的
- Part 1 不需要专门准备，2-3 句自然回答就行
- Part 3 靠的是思考能力，不是背答案——但可以准备框架
- 不考口音。中式英语完全没问题，只要清晰、流利、有逻辑
口语评分标准（四维）
| 维度 | 权重 | 6 分标准 | 7 分标准 |
|---|---|---|---|
| Fluency & Coherence | 25% | 能说但有明显停顿和重复 | 流利，偶尔停顿，逻辑清晰 |
| Lexical Resource | 25% | 词汇够用但有限 | 灵活使用不常见词汇和习语 |
| Grammatical Range | 25% | 混合简单句和复杂句，有错误 | 多种句型，错误少 |
| Pronunciation | 25% | 能被理解但有明显口音特征 | 清晰，语调自然 |
6 到 7 分的关键跳跃： 从"能说清楚"到"说得自然 + 有深度"。
三种模式
| 模式 | 触发 | 做什么 |
|---|---|---|
| 话题分组 | 用户给了题库（或说"帮我分组"） | 50 个话题分成 5 组 + 每组一个万能故事 |
| 故事生成 | 用户说"帮我准备某个话题" | 生成完整的 Part 2 回答 + Part 3 预测 |
| 表达升级 | 用户给了自己的回答 | 升级词汇和句型，保持口语自然感 |
Phase 0：读取历史数据
- 读取
~/.ielts/speaking/stories/下所有.md文件 - 统计已准备的素材数量和覆盖的万能故事组
- 显示：「已有 {n} 个口语素材，覆盖 {x}/5 组万能故事」
话题分组模式
Step 1：按主题聚类
把所有话题分成 5 个大类，每类对应一个万能故事：
| 组 | 主题 | 万能故事类型 | 可覆盖话题举例 |
|---|---|---|---|
| 1 | 旅行/地点 | 一次旅行经历 | 城市/地方/旅行/开心经历/和朋友做的事 |
| 2 | 人物 | 一个对你有影响的人 | 朋友/家人/老师/佩服的人/帮助过你的人 |
| 3 | 物品/技能 | 一个你学会的技能或得到的东西 | 礼物/拥有的东西/技能/爱好/有用的 app |
| 4 | 经历/事件 | 一次难忘的经历 | 成功/失败/挑战/改变想法的经历/做过的决定 |
| 5 | 媒体/学习 | 一本书/一部电影/一个节目 | 书/电影/电视节目/了解的话题/新闻 |
Step 2：覆盖映射
## 覆盖映射表
| 话题 | 归属组 | 万能故事 | 需要调整的点 |
|------|--------|--------|-----------|
| Describe a city you visited | 组1-旅行 | 香港旅行 | 直接用 |
| Describe a happy experience | 组1-旅行 | 香港旅行 | 强调"开心"的部分 |
**覆盖率：{x}/50 = {x}%**
**未覆盖话题：** {列出 + 建议额外准备}
故事生成模式
Step 1：生成 Part 2 回答（200-250 词，2 分钟）
## Part 2: {话题}
**话题卡：**
Describe {话题内容}
You should say:
- {要点1}
- {要点2}
- {要点3}
And explain {解释要求}
**回答（目标 7 分）：**
{完整回答}
**时间分配：**
- 开头引入（15 秒）
- 主体描述（60-90 秒）
- 结尾解释（15-30 秒）
**关键表达标注：**
| 表达 | 功能 | 可替换为 |
|------|------|--------|
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.
- 1mo ago First seen · 264 lines · 72 tokens per session scan A 859541253014
ielts-speaking is a skill published in the GitHub repository liqianiiii/Awesome-ielts-claude-skills (63 stars, last pushed 6mo ago), licensed MIT. It adds 72 tokens to every session and 2,197 once invoked, about $0.0003 per session on Opus 5.5. 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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