| name | grimoire |
| description | Grimoire — turn a PDF/document/raw text into reading notes + a reusable skill pack in one parse. Auto-classifies book/paper/document, scaffolds type-specific notes for an Obsidian vault, and mines per-source skills merged per book/course. Built on the MinerU parsing API (OCR, formula/table, batch). Scripts never call an LLM and never write the vault; skill install is OPT-IN (--install / scripts/skill-install.sh) and deliberately crosses the candidate-only boundary by explicit user request, recorded in the manifest. Raw text → skill via --from-text; cross-agent install + management via the grimoire-manage companion skill. |
| triggers | ["grimoire","魔典","把这本书炼成魔典","pdf 转笔记","pdf to notes","帮我读这个 pdf","帮我读这个 pdf 写笔记并提炼 skill","读书笔记","论文笔记","文档笔记","这个 pdf 是书还是论文","reading notes","book to skill","书籍转 skill","从书中提炼 skill","技能提炼","mineru","pdf解析","文档解析","document parsing","pdf to markdown","extract pdf","文本转 skill","text to skill","from-text","把技能装到其它 agent","跨 agent 安装技能","install skill across agents","skill 没出现在某个 agent","重新同步技能","skill 确认门"] |
Grimoire · 魔典 — Document → Notes + Skill Pack
One source, parsed once, woven into typed reading notes (→ Obsidian) and a
per-source skill pack. Primary entry point: scripts/grimoire.sh.
Full product overview and bilingual docs: see README.md.
The sections below are the underlying MinerU parsing API reference that the
grimoire toolchain is built on.
Flow: Markdown first, the grimoire is opt-in · 先转 MD,魔典是可选续跑
The pipeline is gated, not automatic:
- Always first → Markdown. A source becomes Markdown via
pdf2md /
mineru-local (or any MinerU parse). For many requests this is the whole
job — stop here. Do not auto-run notes/skill mining.
- Opt-in → the grimoire. Only when the user actually wants reading notes
and/or a packaged skill, continue from that Markdown with
grimoire.sh --from-markdown <md>. Nothing is re-uploaded; the parse is
skipped. --only notes|skills|both is the second opt-in: notes only,
the skill (engineering-prompt) packaging only, or both.
--only both is two-stage, not parallel. Stage 1 writes the
type-specific notes from the source. Stage 2 is a deliberate
re-learning pass (重复学习): the agent re-reads the notes it just
wrote and mines the skill pack from those notes' knowledge points,
not the raw source (source-markdown/ is then only for evidence
anchors). --only skills (no notes) keeps mining from the source.
默认只到 MD;笔记 / 把内容封装成 skill 工程提示属于用户主动选择的下游,
用 --from-markdown 从已有 MD 续跑,不重新上传、不重复解析。
--only both 是两段式:先写笔记,再「重复学习」从笔记里挖技能包,
不是同段并行。
This keeps the live pdf2md / mineru-local path unchanged — Grimoire does
not steal its triggers; it is the deliberate next step after Markdown.
Overview
MinerU converts PDF, DOC, DOCX, PPT, PPTX, PNG, JPG, JPEG, HTML into machine-readable Markdown/JSON. Supports OCR (109 languages), formula/table recognition, cross-page table merging, and batch processing.
This skill can also stage a parsed long-form source as a source skill pack:
a workspace that a large language model can read to extract candidate agent
skills, grouped by source first and then by chapter, lesson, section, or note.
The workflow does not call an LLM and does not install generated skills
automatically.
It can additionally run a source-to-notes pipeline: parse a document,
auto-classify it as book / paper / document (hybrid heuristic + AI
confirmation), scaffold the matching note discipline, and stage the final note
for the Obsidian Knowledge-Hub vault. The agent does the reading and writing;
the scripts never call an LLM and never write into the vault directly.
Two modes:
- Cloud API —
https://mineru.net/api/v4 (no GPU required, token-based)
- Local API —
mineru-api --port 8000 (self-hosted, requires GPU or CPU backend)
Authentication (Cloud API)
mkdir -p ~/.config/mineru
echo "YOUR_TOKEN" > ~/.config/mineru/token
chmod 600 ~/.config/mineru/token
Limits (Cloud API)
| Item | Limit |
|---|
| Single file size | 200MB max |
| Single file pages | 600 pages max |
| Daily priority pages | 2000 pages/account |
| Batch upload | 200 files/request |
| Token validity | 90 days |
Model Versions
| Model | Use Case | Speed | Notes |
|---|
vlm | Default. MinerU2.5, complex layouts, highest accuracy | Slower | Cloud-recommended; needs GPU locally |
pipeline | General documents, CPU-friendly | Fast | Pure CPU support, lower accuracy |
MinerU-HTML | HTML output, preserves formatting | Medium | For web content |
hybrid | Default pre-2026-04 | — | RETIRED on the cloud API — returns code -10002 "version field invalid". Do not use against mineru.net. |
API Endpoints (Cloud)
Base URL: https://mineru.net/api/v4
1. Create Extraction Task (Single File)
POST /extract/task
| Param | Type | Required | Default | Description |
|---|
| url | string | yes | - | File URL (no direct upload) |
| model_version | string | no | vlm | vlm / pipeline / MinerU-HTML (hybrid retired, returns -10002) |
| is_ocr | bool | no | false | Enable OCR |
| enable_formula | bool | no | true | Formula recognition |
| enable_table | bool | no | true | Table recognition |
| language | string | no | ch | Document language |
| data_id | string | no | - | Custom identifier |
| page_ranges | string | no | - | e.g. "2,4-6" |
| callback | string | no | - | Callback URL for async results |
| extra_formats | array | no | - | ["docx"], ["html"], ["latex"] |
Response:
{"code": 0, "data": {"task_id": "xxx"}, "msg": "ok"}
2. Get Task Results
GET /extract/task/{task_id}
States: pending → running → done / failed / converting
Done response: includes full_zip_url (download link)
3. Batch Upload Local Files
POST /file-urls/batch
Returns presigned upload URLs (valid 24h). System auto-submits extraction after upload.
4. Batch URL Extraction
POST /extract/task/batch
Submit multiple URLs at once, returns batch_id.
5. Batch Results
GET /extract-results/batch/{batch_id}
Local API (Self-Hosted)
Start Server
mineru-api --host 0.0.0.0 --port 8000
mineru-gradio --server-name 0.0.0.0 --server-port 7860
mineru-openai-server --port 30000
Environment Variables
| Variable | Description | Default |
|---|
MINERU_MODEL_SOURCE | Model source: modelscope / huggingface | huggingface |
MINERU_API_MAX_CONCURRENT_REQUESTS | Max concurrent API requests | Unlimited |
MINERU_API_ENABLE_FASTAPI_DOCS | Enable /docs page | true |
Local API Docs
Access at http://127.0.0.1:8000/docs after starting.
Use CLI with Remote Server
mineru -p input.pdf -o output/ -b hybrid-http-client -u http://server:30000
Error Codes
| Code | Issue | Fix |
|---|
| A0202 | Token invalid | Check Bearer prefix and token |
| A0211 | Token expired | Recreate at mineru.net |
| -60002 | Unrecognized format | Check file extension |
| -60005 | File too large | Max 200MB |
| -60006 | Too many pages | Max 600, split document |
| -60008 | URL timeout | Check URL accessibility |
| -60012 | Task not found | Verify task_id |
Helper Script
~/.claude/skills/mineru/scripts/mineru-parse.sh — full-featured CLI wrapper.
mineru-parse.sh https://example.com/doc.pdf
mineru-parse.sh /path/to/file.pdf --model vlm --ocr --output /tmp/result
mineru-parse.sh doc.pdf --format docx --format latex
mineru-parse.sh doc.pdf --pages "1-5,8" --output ./results
mineru-parse.sh doc.pdf --output ./results --extract
mineru-parse.sh book.pdf --output ./results --extract --no-print-md --manifest ./results/parse_manifest.json
Source-to-Skill Workflow
Use this when the user uploads a book, course, paper, manual, article
collection, or other long-form text and wants the agent to learn which reusable
skills can be extracted from it.
Commands
~/.claude/skills/mineru/scripts/mineru-source-to-skill.sh /path/to/book.pdf \
--title "Book Title" \
--type auto \
--output ./source-workspaces \
--cloud-ok
~/.claude/skills/mineru/scripts/source-skill-pack.sh ./mineru-extracted/book \
--title "Book Title" \
--type auto \
--output ./source-skill-packs
Compatibility wrappers are still available for older automation:
mineru-book-to-skill.sh, book-skill-pack.sh, and
vivo-agent-workspace.sh.
Output Contract
The wrapper creates a source-scoped workspace:
source-workspaces/
└── sources/
└── <source-slug>/
├── README.md
├── source/
├── mineru/
│ ├── parse_manifest.json
│ ├── *_result.zip
│ └── <extracted markdown files>
└── analysis/
└── source-skill-pack/
├── README.md
├── manifest.json
├── LLM_EXTRACTION_PROMPT.md
├── BOOK_SKILL_INDEX.md
├── MANAGE_SKILLS.md
├── MINDMAP.md
├── source-markdown/
├── segments/
│ ├── manifest.json
│ └── 001-<chapter-or-section>.md
├── chapter-skills/
│ └── 001-<chapter-or-section>/
│ ├── CHAPTER_SKILL_INDEX.md
│ └── skills/
├── whole-book/
│ └── WHOLE_BOOK_SUMMARY.md
└── skills/
Give LLM_EXTRACTION_PROMPT.md, segments/, and chapter-skills/ to the
active agent. The agent should fill:
chapter-skills/*/CHAPTER_SKILL_INDEX.md: what each segment contributes before whole-source synthesis.chapter-skills/*/skills/*.md: one narrow candidate skill per segment-level capability.whole-book/WHOLE_BOOK_SUMMARY.md: source-level capability summary after segment extraction.MINDMAP.md: topic classification and skill-family map.BOOK_SKILL_INDEX.md: what this source can help the agent do, when to reference it, and a table of skill candidates.skills/*.md: reviewed cross-segment candidates only, using the generated _skill-template.md.
Extraction Criteria
Extract only content that can become an operational skill:
- procedures and workflows
- checklists
- diagnostics
- decision rules
- reusable prompt patterns
- coding or analysis patterns
- frameworks with clear trigger situations
Keep broad concepts and background theory as reference-only material unless the source gives a concrete procedure.
Manage Skills Boundary
Long-form skill packs are candidates. Do not automatically install, enable, or sync generated skills. After human review, promote only stable skills into the managed skills repository, then run the local skills manager separately, for example:
skills enable <promoted-skill-name>
The generated pack must not store API tokens, authorization headers, remote result URLs, or private account data.
Source Type Classification
Use --type auto by default. The packer records detected type metadata for
books, courses, papers, manuals, article collections, videos, web sources,
mixed source sets, and project notes. Override with --type book,
--type course, --type paper, --type manual, --type article-collection,
--type video, --type audio, --type web, --type mixed, or
--type project-notes when the automatic classifier is wrong.
Source-to-Notes Workflow
Use this when the user gives a PDF/document and wants the agent to read it
and write notes (not extract agent skills). The pipeline auto-decides the
note discipline from the document itself.
Commands
~/.claude/skills/mineru/scripts/mineru-to-notes.sh /path/to/source.pdf \
--title "Source Title" \
--type auto \
--cloud-ok
~/.claude/skills/mineru/scripts/reading-notes-pack.sh ./mineru-extracted/doc \
--title "Source Title" --type auto
~/.claude/skills/mineru/scripts/grimoire.sh \
--from-markdown ./mineru-extracted/doc \
--title "Source Title" \
--only both
Classification (hybrid, autonomous)
scripts/lib/reading-types.sh scores filename + parsed-Markdown structure into
three buckets and a confidence band:
| Type | Trigger signals | Note discipline |
|---|
book | many 第N章/Chapter N, TOC, ISBN, long page count | chapter-by-chapter complete notes |
paper | abstract, DOI/arXiv, references, related work, venue | detailed structured reading / excerpt notes |
document | API ref / install / config / report / spec / slides, short | content-adaptive structured notes |
- High/medium confidence → the scaffold proceeds for that type.
- Low confidence → AI fallback: the pack writes
AI_CLASSIFY.md with a text
sample and a strict book/paper/document decision contract. The agent confirms
the type (Step 0) before reading; if it disagrees with the heuristic it
re-runs the packer with the correct --type.
Manual override is always available with --type book|paper|document.
Output Contract
reading-workspaces/sources/<slug>/
README.md
source/
mineru/parse_manifest.json + extracted Markdown
notes/reading-notes-pack/
classification.json detected type / confidence / signals
AI_CLASSIFY.md only when confidence is low
AI_READING_TASK.md the reading contract (entry point)
OBSIDIAN_PLAN.md exact vault target + house-style rules
manifest.json
source-markdown/
segments/ chapter/section split (book & document)
notes/<slug>.md scaffold from the matching template
Give the agent AI_READING_TASK.md. It reads source-markdown/ (and
segments/ for books/documents), fills notes/<slug>.md using the
type-specific template, passes a quality self-check (> 2KB real synthesis,
evidence anchors, [[wikilinks]], no placeholders), then writes the final
note into the Obsidian vault path from OBSIDIAN_PLAN.md and appends a
log.md line.
Obsidian Knowledge-Hub Boundary
The vault target folder is book → Books/, paper → Papers/,
document → Documents/. Vault root defaults to $MINERU_OBSIDIAN_VAULT or
~/Documents/Obsidian-Vaults/Knowledge-Hub and is overridable with --vault.
The scripts never create or modify a vault file — the agent writes the
note after the quality self-check, matching the house style of an existing
sibling note. Generated packs store no API tokens, auth headers, or remote
result URLs.
Vivo Agent Workflow
Use this when the user gives text or source metadata and wants Codex, Claude
Code, or another agent to first identify the object type, write the matching
notes, then distill candidate skills from books, papers, courses, web pages,
videos, audio transcripts, PDFs, or existing notes.
~/.claude/skills/mineru/scripts/vivo-workspace.sh \
--title "Knowledge Source" \
--type mixed \
--agent "Claude Code" \
--output ./vivo-workspaces
Vivo creates AGENT_TASK.md, SOURCES.md, notes/live-notes.md,
notes/source-type-confirmation.md, notes/topic-classification.md,
notes/skill-discovery-coverage.md, captured-markdown/, and packs/. The
script does not call OpenAI, Claude, or any model. The active agent performs
capture and reasoning with its available tools:
- web pages: browser, fetch, web search, or OpenAI-backed web capability
- videos: subtitles or transcript tools
- audio: transcript or speech-to-text tools when available
- PDFs: MinerU parsing or another local converter
- courses: lesson/module/topic classification
- notes: first source-type confirmation, then typed notes while capture happens
After type confirmation and typed notes, the agent runs source-skill-pack.sh
over captured-markdown/ and fills segment skills,
whole-book/WHOLE_BOOK_SUMMARY.md, MINDMAP.md,
SKILL_DISCOVERY_COVERAGE.md, and BOOK_SKILL_INDEX.md before promoting
reviewed candidates. Stop only when a full pass over every source unit finds no
new supported operational skills.
Quick Parse (Python)
import requests, time
TOKEN = open("~/.config/mineru/token").read().strip()
BASE = "https://mineru.net/api/v4"
HEADERS = {"Authorization": f"Bearer {TOKEN}"}
def parse_document(url, model="vlm", ocr=False, extra_formats=None):
"""Parse a document from URL, return download link."""
body = {
"url": url, "model_version": model,
"is_ocr": ocr, "enable_formula": True, "enable_table": True,
}
if extra_formats:
body["extra_formats"] = extra_formats
resp = requests.post(f"{BASE}/extract/task", headers=HEADERS, json=body)
task_id = resp.json()["data"]["task_id"]
while True:
result = requests.get(f"{BASE}/extract/task/{task_id}", headers=HEADERS).json()
state = result["data"]["state"]
if state == "done":
return result["data"]["full_zip_url"]
elif state == "failed":
raise Exception(f"Task failed: {result}")
time.sleep(5)
Quick Parse (curl)
TOKEN=$(cat ~/.config/mineru/token)
curl -s -X POST "https://mineru.net/api/v4/extract/task" \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d '{"url":"https://example.com/doc.pdf","model_version":"vlm"}'
curl -s "https://mineru.net/api/v4/extract/task/{task_id}" \
-H "Authorization: Bearer $TOKEN"
Installation (Local Mode)
pip install uv
uv pip install -U "mineru[all]"
Requirements: Python 3.10-3.13, 16GB+ RAM, 20GB+ SSD. GPU optional (Volta+ or Apple Silicon).
