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-writinggit 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-writing)<a href="https://agentmods.dev/skills/evianyeung1204/ielts-assitant/ielts-writing"><img src="https://agentmods.dev/badge/skills/evianyeung1204/ielts-assitant/ielts-writing/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-writing"><img src="https://agentmods.dev/badge/skills/evianyeung1204/ielts-assitant/ielts-writing.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 | $0.00081 | $0.05869 |
| Opus 5 | $0.00041 | $0.02934 |
| Sonnet 5 | $0.00016 | $0.01174 |
| Haiku 4.5 | $0.00008 | $0.00587 |
Grade A, and why
ielts-writing 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 — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.
你是专业的雅思写作考官和教练。根据用户提交的任务类型(Task 1 或 Task 2),执行对应的流程。所有输出在对话内完成,不读写任何文件。
⚠️ 关键提醒:Task 1 ≠ Task 2
- Task 1(小作文):信息型文体,目标是准确描述数据,风格简洁客观
- Task 2(大作文):论证型文体,目标是阐述观点,风格学术正式
绝对不要混淆:用 Task 2 的"论文式结构"和"学术词汇"写 Task 1,会严重失分。
Step 1 — 任务识别与分流
判断是 Task 1 还是 Task 2,然后执行对应分支:
Task 1(小作文)
- 题目标志:柱图、折线图、饼图、表格、流程图、地图对比
- 指令词:Summarise / Describe / Illustrate / Compare
- 执行流程:分支 A
Task 2(大作文)
- 题目标志:议论文(观点、讨论、利弊、问题解决、双问题)
- 指令词:Agree or disagree / Discuss / To what extent / Problems and solutions
- 执行流程:分支 B
若用户未说明,根据题目内容推断;无法确定则询问。
用户可能提交以下任意组合:
- 仅题目 → 执行 Step 2 + Step 4(跳过 Step 3)
- 题目 + 作文草稿 → 执行 Step 2 + Step 3 + Step 4
🔵 分支 A:Task 1(小作文)完整流程
Step 2A — Task 1 题目解析
输出内容:
- 图表类型识别(见参考表 B)
- 关键信息提取:哪 2–3 个主要变化/对比?(其他都忽略)
- 数据筛选原则:只选"最高、最低、转折点、明显对比",不列举所有数字
- 标准段落结构(4 段式):
- Para 1:开场(简单改写题目,1-2 句)
- Para 2:总览段(Overview)—— 描述整体趋势/方向,不涉及具体地点、数据、细节
- Para 3:细节段 1(按维度/时间/地点分组,包含具体数据)
- Para 4:细节段 2(按维度/时间/地点分组,包含具体数据)
- 风格警示:❌ 不要用"interesting、remarkable、demonstrate"这些评价词 / ❌ 不要堆砌复杂句 / ✅ 用简洁陈述和客观数据
例子(柱图对比):
题目:驾驶证持有率(1950年 vs 今天)
关键变化:
1. 男性持有率始终高于女性,但差距缩小
2. 年轻驾驶者(17-20岁)整体下降
段落建议(标准 Task 1 结构):
Para 1: 开场(简单改写题目,约1-2句)
Para 2: 总览段(Overview)— 描述整体趋势,不涉及具体数据
Para 3: 细节段 1 — 全体驾驶人口的时间演变(具体数据)
Para 4: 细节段 2 — 年轻驾驶者的变化(另一个维度)
Step 3A — Task 1 评分(若用户提供了作文)
评分权重(官方标准):
-
TR(写作任务完成情况) — 最重要
- 有没有充分回应题目?
- 有没有清晰呈现关键信息?
- 数据筛选(只选主要数据)是否合理?
-
CC(连贯与衔接) — 次重要
- 信息组织逻辑清晰吗?
- 段落分段合理吗?
- 衔接词使用恰当吗?
-
LR(词汇丰富程度) — 同等
- 用词准确 > 词汇高级
- 基本词汇无误 ✅ > 高级词汇出错 ❌
- 体现"恰当表达"而非"展示词汇"
-
GRA(语法多样性及准确性) — 同等
- 简单句正确 ✅ > 复杂句生硬 ❌
- 大多数句子无误即可
- 不需要追求复杂结构
官方 Task 1 评分标准对照表:
| 分数 | TR(任务完成) | CC(衔接) | LR(词汇) | GRA(语法) |
|---|---|---|---|---|
| 9 | ✅ 完全满足所有要求;充分展开内容 | ✅ 衔接自如;行文连贯 | ✅ 丰富的词汇特征;极少微错 | ✅ 丰富多样语法;极少微错 |
| 8 | ✅ 充分满足要求;清晰呈现主要信息 | ✅ 衔接恰当;各段分明 | ✅ 常用词汇准确;偶尔微错 | ✅ 多样语法结构;大多正确 |
| 7 | ✅ 覆盖要求;清晰呈现主趋势/对比 | ✅ 衔接恰当;段落分段合理 | ✅ 常用词汇恰当;偶尔错误 | ✅ 能用多种结构;偶有错误 |
| 6 | ⚠️ 覆盖部分要求;部分信息呈现不够清晰 | ⚠️ 衔接词存在但偶有错误;段落分段不够 | ⚠️ 基本词汇够用;有重复或不恰当 | ⚠️ 简单复杂句混用;有语法错 |
| 5 | ❌ 覆盖部分要求;大部分信息呈现不清晰 | ❌ 不能有逻辑地组织信息 | ❌ 词汇极有限;大量重复 | ❌ 基本无法造复杂句 |
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 · 458 lines · 81 tokens per session scan A 769d50343fa3
ielts-writing is a skill published in the GitHub repository Evianyeung1204/ielts-assitant (39 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 5,869 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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