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 agentmods add skills/guoqiaozhou/study-with-claude-code/weaknpx skills add guoqiaoZhou/study-with-claude-code --skill weakgit clone --depth 1 https://github.com/guoqiaoZhou/study-with-claude-codeWrote 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/guoqiaozhou/study-with-claude-code/weak)<a href="https://agentmods.dev/skills/guoqiaozhou/study-with-claude-code/weak"><img src="https://agentmods.dev/badge/skills/guoqiaozhou/study-with-claude-code/weak.svg" alt="Measured on agentmods" 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 | $0.00067 | $0.01266 |
| Opus 5 | $0.00034 | $0.00633 |
| Sonnet 5 | $0.00013 | $0.00253 |
| Haiku 4.5 | $0.00007 | $0.00127 |
Grade A, and why
swcc-weak 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 5d 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.
What it actually says
swcc · weak — 薄弱点专项强化
只针对薄弱点做针对性考核(到期的优先),不是全节点复习。本质是 go 的「只打薄弱点 + test 取向」变体,复习完同样用 /swcc-stop 归档。
开始前先读数据契约:
${CLAUDE_PLUGIN_ROOT}/skills/_shared/data-contract.md。本技能只读不写——评分与进度落盘由/swcc-stop完成。
参数:$ARGUMENTS —— 可选 topic(默认 activeTopic)。
核心原则
- 只打薄弱点,不展开全节点。 围绕
weakPoints里的具体概念考核,不顺手把整个知识树过一遍。 - 直接考(test 取向)。 薄弱点是已学过的,默认直接出题;只有用户明确说"先讲讲"才补讲。
- 到期优先。 按
nextReview升序,逾期最久的先打。 - 只读;评分交给 stop。 本技能不打掌握度、不改文件;收尾输出结构化小结供
/swcc-stop评估。
借口对照表(出现这些念头=跑偏,照右栏纠正)
| 借口 | 现实 |
|---|---|
| 「顺便把这个节点其他点也复习了吧」 | weak 只打薄弱点;想全面复习用 /swcc-go。别扩散。 |
| 「这个薄弱点我先给他讲一遍」 | 默认直接考(test);讲解只在用户明确要求时做,否则先考、卡了再补。 |
| 「答对一题就把这个薄弱点划掉」 | 是否移除由 stop 按 consecutivePass ≥ 2 判定,weak 不自行决定。 |
流程
1. 加载
- topic 缺省 → 读
config.json的activeTopic(缺失按数据契约第九节兜底)。专题不存在 → 提示先/swcc-plan,停止。 - 读
progress.json、knowledge-system.md、references.json。 - 读全局
learner-profile.md(若存在),据其中讲解偏好与盲区调整出题/追问风格——只调风格,不放松"只打薄弱点、默认直接考"的纪律。见 data-contract 第十四节。
2. 取薄弱点队列
- 读
weakPoints[],按nextReview升序;到期的(≤今天)排最前。 weakPoints为空 → 告诉用户「当前没有薄弱点,可/swcc-go正常复习或/swcc-stats看进度」,停止。
宣布:
🔴 薄弱点强化:<topic> 共 <n> 个(到期 <d> 个)
3. 逐个强化(test 取向,一次一个薄弱点)
对每个薄弱点,从 knowledge-system.md 找到它所属节点的相关内容作为出题/点评依据(挂了资料按数据契约第八节读相关章节)。然后:
| 用户回答 | 你的下一步 |
|---|---|
| 答得准且深 | 认可 → 追问一层边界/反例确认,再换下一个薄弱点 |
| 答得对但浅 | 追问「为什么」「换个场景还成立吗」逼出原理 |
| 卡壳 / 答错 | 给小提示再追;仍卡 → 简要补讲,记为本次仍薄弱 |
- 一次一个薄弱点、一次一题;点评指出对/错/缺漏。
- 用户主动要求"先讲讲这个" → 就这一个薄弱点做 blog 级补讲(参照 go 阶段 4),再继续考。
4. 收尾交接(喂给 stop)
📝 薄弱点强化小结(供 stop 评估用)
- 专题:<topic>
- 本次强化的薄弱点:
- <概念>: ✅这次答好了 / ⚠️有进步但仍浅 / ❌仍卡壳
- 仍需保留/加重的薄弱点:<…>
复习完用 /swcc-stop 归档(掌握度由系统独立评估;达标的薄弱点会按规则移除)。
⚠️ /swcc-weak 与 /swcc-stop 需在同一轮对话里使用。
质量基准
- 全程只围绕
weakPoints里的概念,没扩散到无关节点。 - 到期薄弱点优先;每个都做了"为什么/边界"层面的追问,不是问一句就过。
- 收尾小结如实标注每个薄弱点这次的表现,stop 能据此判断是否达标移除。
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.
- 5d ago First seen · 81 lines · 67 tokens per session scan A 9d3798dd56a4
swcc-weak is a skill published in the GitHub repository guoqiaoZhou/study-with-claude-code (2 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 1,266 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
textbook-distillation
Turn a textbook or long-form source into a self-paced learning track: intake the material, build a chapter map, draft a lesson plan, then generate self-contained HTML lecture notes in a style the human specifies (layout, palette, emphasis), each lesson carrying worked examples, exercises, and checkpoint questions.…
researchers-historical
Researches archives, contemporary accounts, and timeline reconstruction. Use when the album subject involves historical events that need primary source verification.
Interview Prep Generator
Generate STAR stories, practice questions, and talking points from resume.
obsidian-to-clew-import
Convert an Obsidian vault or wiki-linked markdown graph into a validated structured-learning graph package for Clew. Use when the user wants to inspect a vault, preview whether it imports cleanly, preserve explicit relation markers, choose only the few import settings that matter, and produce a fail-closed package…
interview-simulator
Simulate role-specific mock interviews, score each answer, and provide concrete feedback and model responses for interview preparation.
Automation Interview Prep
Prepare for SDET and automation interviews round by round, covering coding screens, framework design, API testing tasks, scenario questions, and STAR stories built from real testing work.