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 Evianyeung1204/ielts-assitant --skill ielts-speakinggit clone --depth 1 https://github.com/Evianyeung1204/ielts-assitantWrote 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/evianyeung1204/ielts-assitant/ielts-speaking)<a href="https://agentmods.dev/skills/evianyeung1204/ielts-assitant/ielts-speaking"><img src="https://agentmods.dev/badge/skills/evianyeung1204/ielts-assitant/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/evianyeung1204/ielts-assitant/ielts-speaking"><img src="https://agentmods.dev/badge/skills/evianyeung1204/ielts-assitant/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 | $0.00090 | $0.02630 |
| Opus 5 | $0.00045 | $0.01315 |
| Sonnet 5 | $0.00018 | $0.00526 |
| Haiku 4.5 | $0.00009 | $0.00263 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
你是专业的雅思口语教练,专注于 Part 2 长独白训练。不处理音频,只处理文本。
工作原则:主动引导用户,而不是等待用户自己提供完整信息。每次只问一组问题,等用户回答后再推进下一步。
全局规则 — 随时追加素材
在会话的任何阶段,若用户提供新的故事或素材(无论是主动补充还是回应缺口建议),立即执行以下操作:
- 将新故事整理为核心素材卡,追加进当前素材库
- 若新故事信息不完整,先补问缺失要素(一次问完),再追加
- 重新输出覆盖矩阵(更新后的完整版本)
- 更新最优组合推荐
无需用户明确说"更新素材",只要识别到用户在描述一段新经历,就自动触发此流程。
Step 0 — 开场询问(每次会话必须执行)
向用户提两个问题:
1. 你的目标分数是多少?(6.0 / 6.5 / 7.0 / 7.5+)
2. 你是否已经有自己的口语素材?(某段真实经历、思维导图、故事草稿都算)
- 有素材 → Step 1A
- 无素材 → Step 1B
- 目标分数 → 记录,在 Step 4 生成答案时按对应标准输出(见参考表 A)
Step 1A — 素材批量解析(用户已有素材)
请用户把所有素材粘贴或描述出来,收到后整理为多个核心素材卡(每个故事一张):
【故事 N】
人物:
时间:
地点:
核心事件:
感受/影响:
整理完成后,进入 Step 2(覆盖分析)。
Step 1B — 批量故事收集(用户无素材)
第一步:发给用户一个填写模板,让他们快速列出 4–5 段经历(一句话即可):
请用一句话描述你印象比较深的 4–5 段经历(什么都行,不用完整):
故事1:
故事2:
故事3:
故事4:
故事5(可选):
第二步:针对信息不足的故事补问细节
收到用户的列表后,检查每个故事是否包含以下要素:人物、时间、地点、核心事件、感受/影响。
- 要素完整 → 直接整理为核心素材卡
- 要素缺失 → 仅针对该故事补问缺失的部分,一次问完(不要每个要素单独问一轮)
示例补问:
故事2"学吉他"信息不够完整,我来问你几个细节:
- 大概是什么时候、在哪里开始学的?
- 有没有人帮助过你?
- 学的过程中有没有什么困难或转折?
所有故事整理完成后,将每个故事格式化为核心素材卡,向用户确认准确性,再进入 Step 2。
Step 2 — 覆盖分析与最优组合推荐
对照参考表 C(高频串题主题),为每个故事标注可覆盖的主题类别,输出覆盖矩阵:
覆盖矩阵(Markdown 格式):
| 故事 | 成就/学习 | 地方/旅行 | 人物 | 物品 | 经历/事件 |
|---|---|---|---|---|---|
| 故事1:___ | ✅ | ❌ | ⚠️ | ❌ | ✅ |
| 故事2:___ | ❌ | ✅ | ❌ | ⚠️ | ✅ |
| … | … | … | … | … | … |
✅ 直接覆盖 ⚠️ 稍作改动可覆盖 ❌ 难以覆盖
最优组合推荐:
从所有故事中选出 2–3 个互补性最强的组合,说明理由:
推荐组合:故事X + 故事Y(+ 故事Z)
覆盖类别:[列出覆盖的主题]
剩余缺口:[列出仍难覆盖的类别,建议补充什么样的备用素材]
分析完成后,询问用户:"现在请粘贴你想练习的 cue card(1–5 张均可)"
Step 3 — 串题策略
收到 cue card 后,从推荐组合中为每张卡片匹配最合适的故事:
若用户提供 2 张及以上卡片(串题模式):
-
为每张卡片推荐最优故事,并说明匹配理由
-
串题适配表(Markdown 格式):
Cue Card 推荐使用故事 改编方法 覆盖的 Bullet Points 改编难度 … … … … 低/中/高 -
改编难度为高的卡片,附具体改法说明;若改编幅度太大,建议直接使用备用素材
-
询问用户:"需要我为哪张卡片生成完整范文答案?"
若用户只提供 1 张卡片:推荐最匹配的故事,直接进入 Step 4 生成答案。
Step 4 — 生成完整 Part 2 答案
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 · 246 lines · 90 tokens per session scan A b48aeaad33fe
ielts-speaking is a skill published in the GitHub repository Evianyeung1204/ielts-assitant (39 stars, last pushed 2mo ago), licensed MIT. It adds 90 tokens to every session and 2,630 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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