confusion-tracker
教学过程中自动捕获和记录学习者的概念疑难点("为什么/是什么/怎么推/什么意思"类型的问题),保存到进度文件的"概念疑难点记录"区,形成考前回顾清单。
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。
$ npx -y skills add ZeKaiNie/universal-examprep-skill --skill exam-ingest --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/exam-ingestContext preview
The summary Claude sees to decide when to auto-load this skill.
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。
name: exam-ingest description: > 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。 license: MIT
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.
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.
1. **Pass the executable start gate, then use the official ingestion entry.** The exact materials/workspace pair, all three learning choices, and explicit `processing_mode=full` must already have been persisted with `exam_start.py confirm` as specified by `exam-cram`; a bare registry row or `update_progress.py set` is insufficient. Verify read-only with `exam_start.py status --materials <dir> --workspace <ws> --json`; require `ready_to_ingest=true`, then run from the package root:
python scripts/ingest_course.py --materials <dir> --workspace <ws> --json [--course-name <name>] [--lang zh|en] [--artifact-mode chat|visual]
The default `core` orchestrator performs dependency preflight, deterministic extraction, provenance-preserving structured compilation, state initialization, visual indexing/repair, and canonical workspace validation. It never installs a dependency. Pass `--artifact-mode` only for an explicit standing student choice; omit it to retain the existing preference (or the default `chat` on a new workspace). An ordinary `exam_start.py confirm` with no `--processing-mode` likewise preserves an existing canonical processing choice; nevertheless this subskill still requires the effective choice to be explicit/current `full`. 2. **Interpret process and readiness separately.** Exit `0` means the engineering process completed and the JSON readiness is `ready` or `usable_with_gaps`; preserve and report any warnings in the latter. Exit `10` means `process_success=true` but `readiness=blocked`: do not teach, quiz, or claim completion. Any other nonzero is a dependency, input, or operation failure. For a missing required capability, ask once with the active language pack's consent line, install only on yes, then rerun the same command. A business/data failure is never evidence that Python is absent. 3. **Require ingestion-v2 parser receipts.** The regular path writes `.ingest/parser_receipts.json` with one receipt for every discovered source. Each row binds canonical source path, exact source SHA-256/media type, adapter/module/distribution/version, requested and produced location anchors, config SHA-256, result status, and the exact policy `{network:false, upload:false, install:false}`. Missing/duplicate rows, source or page drift, a policy mismatch, or a receipt referring to an unknown source blocks validation. A legacy ingestion-v1 payload remains readable only as legacy and must not be described as having v2 receipts. Unit language comes only from its payload: `zxx` is formula/symbol-only, never inherited, and never zh/en Guide support; otherwise review. Automatic layout crops remain available as unreceipted legacy `crop_image` assets for ordinary tutoring/quiz ingestion; geometry alone must never mint a current Study Guide receipt. Every new strict crop requires receipt schema v2 plus semantic-review schema v2, exact crop-hash binding, `unrelated_content_present=false`, and `student_attempt_present=false`. Target-only is the default (`verdict=target_item_only`, `isolation=target_item_only`, empty `required_context_ids`, detected IDs exactly the target); a dependent prompt instead uses `verdict=target_with_required_context` plus the distinct `isolation=target_with_required_context`, declares sorted unique prerequisite item/theorem/example IDs, and detects the target followed by exactly those contexts. Historical receipt schema v1 and semantic-review v1 stay read-only; existing v2 single-region receipts remain readable without hash/ID migration. For a completed ingestion-v2 workspace, `scripts/backfill_crop_receipts.py validate|apply --workspace <ws> --annotations <jsonl> --json` supports `upgrade_existing`, `create_from_parent`, and prompt-only `create_composite_from_parent` without rerunning the PDF builder. The composite is an explicit compatible v2 receipt variant: 2–32 non-overlapping regions from one exact parent/source/page are stacked without scaling using fully specified order, gap, RGBA background, and horizontal alignment; every pixel/PDF bbox, content ID, parent/target/candidate h
Drop your course folder on a coding agent. Get a tutor that teaches from your own slides, shows the figures, quizzes you with your own homework, and remembers where you stopped.
教学过程中自动捕获和记录学习者的概念疑难点("为什么/是什么/怎么推/什么意思"类型的问题),保存到进度文件的"概念疑难点记录"区,形成考前回顾清单。
只读检查一个已生成的备考工作区是否健康并报告问题,默认不做任何修改。核对 .ingest 原材料版本、 内容单元、接管队列与派生产物完整性,以及 wiki、题库、视觉证据、计划和进度的一致性。当用户怀疑 工作区有问题、建库 readiness 被阻断、或想在开始复习前体检时使用。
全员通关后把 错题本+笔记本+知识点窗口+wiki 编译成考前速记小抄 cheatsheet.md(每条要点带可溯源 锚点),并在视觉产物模式或用户明确要求 PDF/打印版时按指定页数渲染成打印级 PDF:按「必背结论/公式 → 有难度例题(必要时含题面图)→ 例题解答(代入公式、保留基础过程)→…
临考前的极速备考总教练。把课件、大纲、重点与真题建成分章 wiki 和标准题库,再组织惰性授课、 题库判分、错题与疑难复盘及可选考前小抄,并持久化进度。用于期末、备考、突击、刷题、划重点、 错题与考前复习;不用于长期规划或与考试无关的写作/编程。