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/ingest/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/ingest)<a href="https://agentmods.dev/skills/zimoliao/scholaraio/ingest"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/ingest/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/ingest"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/ingest.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 | $0.00038 | $0.02359 |
| Opus 5.5 | $0.00015 | $0.00944 |
| Sonnet 5.5 | $0.00008 | $0.00472 |
| Haiku 4.5 | $0.00004 | $0.00236 |
Grade A, and why
ingest 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
入库文档
将 inbox 中的 PDF、Office 文档（DOCX/XLSX/PPTX）或 Markdown 文件处理入库。支持论文、专利、学位论文、一般文档和论文集（proceedings）。
支持的文件格式
| 格式 | 放入目录 | 处理方式 |
|---|---|---|
.pdf |
data/spool/inbox/ 或 data/spool/inbox-doc/ |
MinerU 转 Markdown |
.pdf / .md |
data/spool/inbox-patent/ |
专利文献（按公开号去重） |
.pdf / .md |
data/spool/inbox-proceedings/ |
论文集准备流程（先生成 proceeding.md + split_candidates.json） |
.docx .xlsx .pptx |
data/spool/inbox-doc/ |
MarkItDown 转 Markdown |
.md |
任意 inbox | 直接入库（跳过转换） |
执行逻辑
-
根据用户意图选择预设：
- 入库新文档（默认）：使用
ingest预设（= mineru, extract, dedup, ingest, embed, index） - 完整处理：使用
full预设（= mineru, extract, dedup, ingest, toc, l3, embed, index） - 仅重建索引：使用
reindex预设（= embed, index） - 仅内容富化：使用
enrich预设（= toc, l3, embed, index）
注意：
inbox-doc/始终使用专用步骤office_convert, mineru, extract_doc, ingest，不受 preset 影响。inbox-patent/和inbox-thesis/也有各自的固定流程。preset 中的 papers 级步骤（toc, l3）和 global 级步骤（embed, index）在处理完所有 inbox 后统一执行。 - 入库新文档（默认）：使用
-
执行流水线命令：
scholaraio pipeline <preset> [--dry-run] [--no-api] [--force] [--inspect]
可用预设：full | ingest | enrich | reindex
常用选项：
--dry-run— 预览处理，不写文件--no-api— 离线模式，跳过外部 API 查询--force— 强制重新处理（toc/l3 等步骤）--inspect— 展示处理详情--steps STEPS— 自定义步骤序列（逗号分隔），如--steps toc,l3,index--list— 列出所有可用步骤和预设
-
pipeline 当前会依次处理五个 inbox 目录：
data/spool/inbox/— 普通论文（有 DOI 才入库，无 DOI 且非 thesis 转 pending）data/spool/inbox-thesis/— 学位论文（跳过 DOI 去重，自动标记 thesis）data/spool/inbox-patent/— 专利文献（按公开号去重，自动标记 patent，跳过 DOI 去重）data/spool/inbox-doc/— 非论文文档（技术报告、讲义、Word/Excel/PPT、标准文档等，跳过 DOI 去重，LLM 生成标题/摘要）data/spool/inbox-proceedings/— 论文集（强制按 proceedings 处理；普通data/spool/inbox/不会当作 proceedings）
旧版
data/inbox*与data/pending/是迁移输入，不是当前正常 runtime 输入。先运行scholaraio migrate upgrade --migration-id <id> --confirm，再执行入库流程。 -
论文类的 Stage-1 元数据提取由
ingest.extractor控制：regex：纯正则，最快，不调用 LLMauto：正则优先，关键字段缺失时再调用 LLMrobust：正则 + LLM 双跑，校正 OCR 错误并处理多 DOI 情况（默认）llm：纯 LLM 提取- 如果用户问“为什么标题 / 作者 / DOI 提取不准”，先检查这里的模式配置
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 Changed 67d540fbfed6
- 2mo ago First seen · 147 lines · 38 tokens per session scan A a7f840efdf53
ingest is a skill published in the GitHub repository ZimoLiao/scholaraio (584 stars, last pushed 10d ago), licensed MIT. It adds 38 tokens to every session and 2,359 once invoked, about $0.0002 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.
ingest compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ingestZimoLiao/scholaraio | Skill | 584 | 10d ago | MIT | A | 38 | Claude Code |
| infrastructure-validationdocxology/template | Skill | 23 | yesterday | Apache-2.0 | A | 54 | Claude Code, Codex |
| template-pitch-deckdocxology/template | Skill | 23 | yesterday | Apache-2.0 | A | 35 | Claude Code, Codex |
| infrastructure-steganographydocxology/template | Skill | 23 | yesterday | Apache-2.0 | A | 55 | Claude Code, Codex |
| template-validation-qualitydocxology/template | Skill | 23 | yesterday | Apache-2.0 | A | 64 | Claude Code, Codex |
| infrastructure-documentationdocxology/template | Skill | 23 | yesterday | Apache-2.0 | A | 38 | Claude Code, Codex |
| template-storybookdocxology/template | Skill | 23 | yesterday | Apache-2.0 | A | 28 | Claude Code, Codex |