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/enrich/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/enrich)<a href="https://agentmods.dev/skills/zimoliao/scholaraio/enrich"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/enrich/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/enrich"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/enrich.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 | $0.00034 | $0.00791 |
| Opus 5 | $0.00017 | $0.00396 |
| Sonnet 5 | $0.00007 | $0.00158 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
enrich 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 11d 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
富化论文内容
通过 LLM 提取论文的目录结构（TOC）或结论段（L3），丰富论文元数据。
注意：
import-endnote/import-zotero导入时默认自动执行 toc + l3 + abstract backfill。以下命令用于选择性富化（如重新提取、补充特定论文、或处理全库）。引用量补查：使用
/citationsskill 中的scholaraio refetch命令。
执行逻辑
-
解析用户意图：
- 提取目录：使用
enrich-toc - 提取结论：使用
enrich-l3 - 补全摘要：使用
backfill-abstract（从 .md 提取 + LLM 校验）
- 提取目录：使用
-
确定处理范围：
- 指定论文 ID → 处理单篇
- 用户说"全部" → 使用
--all - 可选
--force覆盖已有结果
批量模式说明：
--all会按config.llm.concurrency做多篇并发处理- 并发只发生在“论文之间”，单篇内部提取逻辑不拆分并发
- 批量模式会对单篇失败自动做指数退避重试
- 执行命令：
提取目录：
scholaraio enrich-toc [<paper-id> | --all] [--force] [--inspect]
提取结论：
scholaraio enrich-l3 [<paper-id> | --all] [--force] [--inspect] [--max-retries N]
补全摘要：
scholaraio backfill-abstract [--dry-run] [--doi-fetch]
参数说明：
--inspect— 展示提取过程详情（调试用）--max-retries N— L3 单篇提取最大重试次数（默认 2）；--all时也作为每篇论文的批量重试预算--doi-fetch— 从出版商网页抓取官方 abstract（覆盖现有，需联网）
- 展示处理结果。
enrich-toc现在会显示开始提取、是否成功、以及提取出的 TOC 节数- 单篇处理不再只是打印论文名
- 批量处理会显示并发 worker 数，以及最终的成功 / 失败 / 跳过汇总
示例
用户说："帮我提取所有论文的结论"
→ 执行 enrich-l3 --all
用户说："重新提取 Smith-2023-Survey 的目录"
→ 执行 enrich-toc "Smith-2023-Survey" --force
用户说："帮我看看这篇论文 TOC 提取成功没有"
→ 执行 enrich-toc "<paper-id>" --force，并根据终端输出确认 TOC 提取完成: N 节
用户说："补全摘要"
→ 执行 backfill-abstract，然后提示 embed --rebuild
用户说："补查引用量"
→ 转交 /citations skill（使用 refetch 命令）
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.
- 11d ago First seen · 73 lines · 34 tokens per session scan A f1ffe37edf84
enrich is a skill published in the GitHub repository ZimoLiao/scholaraio (570 stars, last pushed 11d ago), licensed MIT. It adds 34 tokens to every session and 791 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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