exam-ingest

Solid

从学生上传的课件/大纲/老师勾的重点/真题,一键初始化并验证备考工作区:解析 PDF、DOCX、PPTX、 XLSX、常见独立图片与 txt/md,建立分章节 LLM Wiki、标准题库、结构化接管队列与进度状态;仅在 Python 确实无法运行时 明确降级为手动写盘。当工作区尚未建立、资料发生变化、或建库 readiness 被阻断时使用。

AI & Automation 260 stars 14 forks Updated 1 weeks ago MIT

Install

View on GitHub

Quality Score: 89/100

Stars 20%
80
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# exam-ingest — validated workspace initialization ## Purpose Convert a confirmed materials folder into a validated cram workspace. Build and repair the knowledge base only; do not teach or grade. The normal path produces structured ingestion facts under `.ingest/`, compiled chapter wiki and bank files, progress state, visual evidence, and an explicit readiness verdict before handing control back to `exam-cram`. This module is the explicit `processing_mode=full` route. A missing, legacy, or `lightweight` processing choice must not activate it; route that learner through `scripts/lightweight_session.py` instead. ## Activation Activate when the confirmed workspace lacks its wiki, bank, or progress state; when the student supplies new/changed course materials; or when `validate_workspace.py` reports ingestion readiness `blocked`. Do not treat the mere existence of generated files as proof that the workspace is ready. ## Inputs - A student-confirmed materials directory containing PDF, DOCX, PPTX, XLSX, common standalone raster images, txt, or Markdown. Scans/images without usable sidecar text, damaged/encrypted files, unsupported features/formats, and ambiguous problem/solution pairs may require evidence-backed AI/human review. - A target workspace directory explicitly confirmed by the student. Never default to the repository, process current directory, or an inferred course folder. The workspace must be separate from the materials tree so reruns cannot ingest generated ou...

Details

Author
ZeKaiNie
Repository
ZeKaiNie/universal-examprep-skill
Created
1 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

Integrates with

Similar Skills

Semantically similar based on skill content — not just same category