Borrowing it
Nothing to install: this file belongs to ZimoLiao/scholaraio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ZimoLiao/scholaraio/main/.claude/skills/topics/SKILL.mdgit clone --depth 1 https://github.com/ZimoLiao/scholaraioWrote 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/zimoliao/scholaraio/topics)<a href="https://agentmods.dev/skills/zimoliao/scholaraio/topics"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/topics/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/zimoliao/scholaraio/topics"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/topics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00553 |
| Opus 5 | $0.00016 | $0.00277 |
| Sonnet 5 | $0.00007 | $0.00111 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
topics 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 10d 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
主题探索
探索论文库的主题分布，发现跨领域关联。基于 BERTopic 聚类。
执行逻辑
-
判断用户意图：
- "建模"、"重建主题" → 构建/重建
- "合并主题"、"压缩到N个" → 智能合并
- "可视化"、"画图" → 生成 HTML
- 查看某主题详情 → 主题查询
- 查看 outlier → topic -1
- 默认展示概览
-
执行命令：
构建/重建主题模型：
scholaraio topics --build
scholaraio topics --rebuild [--min-topic-size N] [--nr-topics N]
手动合并指定主题（格式: 逗号分隔同组ID，+分隔不同组）：
scholaraio topics --merge "1,6,14+3,5"
算法合并到 N 个主题：
scholaraio topics --reduce <N>
查看主题概览：
scholaraio topics
查看指定主题的论文：
scholaraio topics --topic <ID> [--limit N]
生成 HTML 可视化（6 张图表）：
scholaraio topics --viz
- 智能合并流程（当用户要求合并/压缩主题时）：
a. 先执行
topics获取所有主题概览 b. 分析每个主题的关键词，判断哪些主题在学术上属于同一研究方向 c. 生成合并方案 d. 用--merge执行合并
示例
用户说："帮我看看库里的主题分布"
→ 执行 topics
用户说："主题2里有哪些论文"
→ 执行 topics --topic 2
用户说："帮我把相似的主题合并一下"
→ 先 topics 查看概览，分析关键词，再 topics --merge "1,6,14+3,5"
用户说："给我画个主题分布图"
→ 执行 topics --viz
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.
- 10d ago First seen · 71 lines · 33 tokens per session scan A 93b2a117b90a
topics is a skill published in the GitHub repository ZimoLiao/scholaraio (570 stars, last pushed 10d ago), licensed MIT. It adds 33 tokens to every session and 553 once invoked, about $0.0002 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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