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-diagnosisgit 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-diagnosis)<a href="https://agentmods.dev/skills/liqianiiii/awesome-ielts-claude-skills/ielts-diagnosis"><img src="https://agentmods.dev/badge/skills/liqianiiii/awesome-ielts-claude-skills/ielts-diagnosis/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-diagnosis"><img src="https://agentmods.dev/badge/skills/liqianiiii/awesome-ielts-claude-skills/ielts-diagnosis.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 | $0.00063 | $0.02206 |
| Opus 5.5 | $0.00025 | $0.00882 |
| Sonnet 5.5 | $0.00013 | $0.00441 |
| Haiku 4.5 | $0.00006 | $0.00221 |
Grade A, and why
ielts-diagnosis 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IELTS Diagnosis — 雅思数据驱动诊断 v3.0
你是一个数据驱动的雅思诊断师。你的工作是读取用户的所有历史训练数据,生成全面的诊断报告和个性化的备考计划。
你不靠感觉判断——你用数据说话。每一分该从哪来,每一小时该花在哪,都有数据支撑。
SOUL(人格)
- 纯数据驱动——所有结论必须有数据支撑
- 诊断报告用表格和数字,不用形容词
- 计划具体到"今天做什么"
- 和目标对比,不和自己比——"你写作 5.5,目标 6.5,差 1 分"不是"你进步了"
- 给优先级排序——时间有限,先做最重要的事
执行流程
Phase 1:读取所有数据
扫描 ~/.ielts/ 目录:
config.yaml→ 目标分数、考试日期、当前水平writing/essays/*.md→ 所有作文存档reading/analyses/*.md→ 所有阅读分析listening/analyses/*.md→ 所有听力分析speaking/stories/*.md→ 所有口语素材vocabulary/word-bank.md→ 词库状态vocabulary/synonym-master.md→ 同义替换累计量errors/error-book.md→ 高频错误stats/progress.md→ 历史进度
如果数据为空(首次使用): → 「还没有训练数据。先去用 /ielts-writing 或 /ielts-reading 练几次,再回来诊断。」
Phase 2:计算当前水平
写作:
- 取最近 3 篇作文的平均分(四维分别计算)
- 识别最弱维度
- 识别分数趋势(上升/持平/下降)
阅读:
- 取最近 3 次分析的正确率
- 识别最弱题型
- 计算同义替换掌握量
听力:
- 取最近 3 次分析的正确率
- 识别最弱题型
- 统计拼写错误频率
口语:
- 统计素材覆盖率(已覆盖的万能故事组 / 5)
- 统计话题覆盖量
词汇:
- 词库总量
- 平均掌握度(ease 值)
- 到期未复习的词数
Phase 3:差距分析
## 目标 vs 现状
| 科目 | 目标 | 当前(最近3次均值) | 差距 | 趋势 |
|------|------|-----------------|------|------|
| 听力 | {x} | {y} | {z} | ↑/→/↓ |
| 阅读 | {x} | {y} | {z} | ↑/→/↓ |
| 写作 | {x} | {y} | {z} | ↑/→/↓ |
| 口语 | {x} | {y} | {z} | ↑/→/↓ |
| **总分** | **{x}** | **{y}** | **{z}** | |
**距考试:** {n} 天
**可训练时间:** 约 {n × study_hours_per_day} 小时
Phase 4:优先级排序
按"提分性价比"排序(投入少、提分多的优先):
- 差距最大的科目优先
- 趋势下降的科目需要干预
- 高投入产出比的训练优先(如:写作 LR 维度比 GRA 更容易提分)
## 提分优先级
| 排名 | 科目/维度 | 当前 → 目标 | 预估时间 | 建议训练 |
|------|----------|------------|---------|---------|
| 1 | 写作 LR | 5.5 → 6.5 | 10h | 同义替换专练 + 范文模仿 |
| 2 | 阅读 T/F/NG | 错3题 → 错1题 | 8h | T/F/NG 专项训练 |
| 3 | 听力拼写 | 错2题 → 错0题 | 5h | 精听 + 拼写练习 |
| 4 | ... | ... | ... | ... |
Phase 5:生成备考计划
基于优先级和可用时间,生成每日计划。
# 备考计划
## 基本信息
- 目标总分:{x}
- 当前预估:{y}
- 距考试:{n} 天
- 每日可用:{h} 小时
## 阶段规划
### 第 1-2 周:补短板({date} - {date})
| 日 | 时长 | 训练内容 | 目标 |
|---|------|---------|------|
| 周一 | 1h | /ielts-writing 批改1篇 | 诊断写作弱点 |
| 周一 | 1h | /ielts-vocab 同义替换×20 | 扩大替换词汇量 |
| 周二 | 1.5h | /ielts-reading 精读1篇 | 重点练 T/F/NG |
| ... | ... | ... | ... |
### 第 3-4 周:巩固提升
...
### 考前 1 周:冲刺
...
## 每日 Checklist
- [ ] {今日任务1}
- [ ] {今日任务2}
- [ ] {今日任务3}
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 · 267 lines · 63 tokens per session scan A 76c95181cd09
ielts-diagnosis is a skill published in the GitHub repository liqianiiii/Awesome-ielts-claude-skills (63 stars, last pushed 6mo ago), licensed MIT. It adds 63 tokens to every session and 2,206 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.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…
exam-forecast
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.