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 | $0.02360 |
| Opus 5 | $0.00019 | $0.01180 |
| Sonnet 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 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.
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.
- 10d ago First seen · 147 lines · 38 tokens per session scan A a7f840efdf53
ingest is a skill published in the GitHub repository ZimoLiao/scholaraio (570 stars, last pushed 11d ago), licensed MIT. It adds 38 tokens to every session and 2,360 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.
Other skills, from other repositories
infrastructure-validation
Skill for the validation infrastructure module providing PDF validation, markdown validation, output integrity checks, link verification, documentation audits, issue categorization, and repository scanning. Use when validating research outputs, checking document quality, running audits, or verifying cross-references.
infrastructure-steganography
Skill for the steganography infrastructure module providing QR code generation with dynamic mailto links, hash manifests, metadata payloads, and document-wide overlay processing. Use this module to insert opt-in cryptographic and steganographic provenance data onto PDFs.
template-pitch-deck
Pitch-deck generation exemplar — short/medium/long PDF+PPTX decks from one token-resolved, diligence-cited content source.
infrastructure-documentation
Skill for the documentation infrastructure module providing figure management, image handling, markdown integration, and API glossary generation. Use when managing research figures, inserting images into manuscripts, auto-numbering figures, or generating API documentation.
template-validation-quality
Run validation CLI, prerender, markdown/PDF/integrity gates, and QA workflows for the Research Project Template. USE WHEN validate manuscript, check PDF for ?? refs, prerender gate, link checker, output integrity, or pre-commit validation — even without validationquality prompt.
template-textbook
Modular fillable textbook scaffold — parts/chapters/labs/question banks from config.yaml, auto-numbering, deterministic figures, structural contract enforcement.