ielts-speaking

ielts-speaking is a skill for Claude Code, Codex from Evianyeung1204/ielts-assitant. It costs 90 tokens per session (2,630 once invoked), scanned A, original, MIT.

A text-only IELTS Speaking Part 2 coach for the English exam's two-minute individual speaking task. It helps organize personal stories into reusable answer material and connect one story to several cue-card topics.

In plain words
What is it for?
Use it to collect and structure experiences, map them to common topics, choose useful story combinations, and draft or improve Part 2 answers.
Why use it?
It helps learners prepare efficiently when different exam prompts can be answered with related experiences. It also identifies missing story details and adapts answers to a chosen target score.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to collect and structure experiences, map them to common topics, choose useful story combinations, and draft or improve Part 2 answers.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/evianyeung1204/ielts-assitant/ielts-speaking
Install

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.

Any agent
npx skills add Evianyeung1204/ielts-assitant --skill ielts-speaking
Clone the repo
git clone --depth 1 https://github.com/Evianyeung1204/ielts-assitant

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ielts-speaking

README.md
[![agentmods](https://agentmods.dev/badge/skills/evianyeung1204/ielts-assitant/ielts-speaking/github.svg)](https://agentmods.dev/skills/evianyeung1204/ielts-assitant/ielts-speaking)
Your own site
<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.

agentmods 80×15 button for ielts-speaking

Your own site · 80×15
<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>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,630 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.02630
Opus 5.5 $0.00036 $0.01052
Sonnet 5.5 $0.00018 $0.00526
Haiku 4.5 $0.00009 $0.00263

Measured 1mo ago against content hash b48aeaad33fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

ielts-speaking/SKILL.md · 246 lines

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:

  1. 将新故事整理为核心素材卡,追加进当前素材库
  2. 若新故事信息不完整,先补问缺失要素(一次问完),再追加
  3. 重新输出覆盖矩阵(更新后的完整版本)
  4. 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):

  1. 为每张卡片推荐最优故事,并说明匹配理由

  2. 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
  3. 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

  4. 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

Read the full file on GitHub · 246 lines

Changes

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.

  1. 1mo ago First seen · 246 lines · 90 tokens per session scan A b48aeaad33fe

Subscribe to this mod's changes

ielts-speaking is a skill published in the GitHub repository Evianyeung1204/ielts-assitant (42 stars, last pushed 4mo ago), licensed MIT. It adds 90 tokens to every session and 2,630 once invoked, about $0.0004 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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