帮助学生在临考前进行结构化极速复习：解析课程资料/大纲/重点，按章节生成 wiki 知识库与标准题库，组织针对性刷题与判分，并记录复习进度和错题。当用户即将考试、需要快速复习计划、练习题、错题复盘或考前小抄时使用（关键词：期末/备考/复习/刷题/划重点/错题；exam, cram, study plan, quiz, review）。不适用于长期学习规划、与考试无关的写作或编程任务。
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
帮助学生在临考前进行结构化极速复习：解析课程资料/大纲/重点，按章节生成 wiki 知识库与标准题库，组织针对性刷题与判分，并记录复习进度和错题。当用户即将考试、需要快速复习计划、练习题、错题复盘或考前小抄时使用（关键词：期末/备考/复习/刷题/划重点/错题；exam, cram, study plan, quiz, review）。不适用于长期学习规划、与考试无关的写作或编程任务。
license
MIT
metadata
{"version":"4.3","author":"ZeKaiNie"}
Universal Exam Cram Coach — Root Router
This language-neutral router dispatches last-minute exam prep to the chapter-wiki, bank-only, persistent control layer and its wording packs; it is not a duplicate manual.
Language dispatch
Read the canonical, language-neutral study_state.json.language code and load the matching compatibility entry plus its per-skill wording pack BEFORE emitting any student-visible output:
zh (display choice 中文) → locales/zh/SKILL.md plus the selected sub-skill's zh wording pack under locales/zh/skills/
en (display choice English) → locales/en/SKILL.md plus the selected sub-skill's en wording pack under locales/en/skills/
bilingual (display choice 双语) → compose the zh and en wording block by block, with zh first and a > EN: mirror for each block (composition rules in docs/language-policy.md)
中文, English, and 双语 remain accepted user-facing input aliases. On first contact one combined ask sets mode, budget, and language, then show the independent material-processing choice 轻量按需（推荐） / 完整建库; exam_start.py confirm persists them with the exact workspace/materials receipt. Missing, urgent, accepted-default, and legacy processing choices mean lightweight; only explicit full opens complete ingestion. A later reconfirm with no processing flag preserves an existing canonical choice. Later update_progress.py set --language applies next turn. Default English unless the student opened in Chinese; bilingual is explicit-only.
Under scripts/, use exam_start.py status, then exam_start.py confirm --course <name> --materials <dir> --workspace <ws> --mode <mode> --time-budget <tier> --language <lang> --processing-mode <lightweight|full>. It writes the confirmation/state/runtime receipt. Default lightweight_session.py inventories names and processes only current-phase PDF pages or definitely single-frame PNG/JPEG/BMP sources through host-native vision: at most eight primary pages and one active batch. A single page uses no contact sheet; multi-page overview sheets partition primary pages in groups of at most four at roughly 768 px per tile. New schema-3 visual receipts require the generic component token strategy and enumerate stable teaching-item IDs plus generic text|figure|mixed prompt/answer components. A cross-page item repeats on each page that supplies one of its prompt components, with exact page↔component coverage. Detail calls may combine only same-target prompt components, solution calls only same-target answer components, and every component crop receives a separate semantic review that detects exactly its declared target/context IDs with no unrelated content or student attempt. Only prompt components may be context-only; every answer component contains its target. Page answer provenance prevents student attempts or unknown pages from masquerading as official solutions, and every registered official-solution page must contribute an answer component. Additive register-answer-dependency binds exact answer-locator pages; planned batches may auditably replace/narrow or remove a binding with set-answer-dependency / remove-answer-dependency. All canonical visible evidence is PNG under .lightweight/assets/, with exact model-input receipts and hash/magic/dimension checks. Schema-2 visual receipts and the legacy figure-only token strategy remain read-only history; any legacy-strategy active attempt is restricted to status or auditable abandon and cannot silently become schema 3. An unfinished planned/visual-ready batch may close only with receipt-backed abandon --reason; replace-taught --reason preserves a taught predecessor/event as superseded history, revalidates its dependency revisions, and plans an exact-slice successor with the same dependency pages. After an unabridged walkthrough, mark-taught --taught-item-ids <exact IDs> binds notebook/chNN.md#anchor, distinguishes inspected pages from taught items, and recoverably publishes phase_evidence.lightweight_batches; only current unsuperseded attempts enter the completion denominator. Routine status is generation-stable and read-only; validation checks metadata plus physical identity only. Exact hashes are reserved for state transitions, completion, or explicit . Lightweight additionally requires two revision-bound checkpoints, including one pass, from the immutable stat-only baseline of a quiz bank that pre-existed initialization. It runs no full ingestion, Study Guide, or PDF. Explicit opens ; the orchestrator and lower-level workspace builder/compiler all enforce the same exact-pair/runtime/choices/full gate. Exit 10 routes to typed . owns ; is generated. Official selectors are / .
status --verify-live
verified
full
ingest_course.py
ingest_review.py
update_progress.py
study_state.json
study_progress.md
select_questions.py
select_hard_questions.py
Workspace file contract (wiki / quiz bank / state / asset metadata): docs/file-format.md.
Language policy (single-language purity, EN canonical vocabulary, persisted canonical values): docs/language-policy.md.
Missing/legacy artifact_mode is chat; explicit standing visual or a one-shot chapter artifact invokes exam-study-guide, while cheat-sheet PDF uses exam-cheatsheet. An ambiguous PDF request asks which once. Never infer subscription. Persist with update_progress.py set --artifact-mode chat|visual.
processing_mode and artifact_mode are independent. Lightweight never generates a Study Guide; a saved visual preference remains dormant and effective output stays chat until explicit full. Full does not imply a PDF. MinerU, Docling, and LangGraph are explicit-named-request, remote/cloud-host-only capabilities and are never probed, downloaded, installed, imported, executed, or accepted as callable local runners.
preferences.interaction_style stores only batch|step_by_step. A stored step-by-step choice is effective only in full with no_questions=false; otherwise it is retained but dormant and effective cadence is batch. In effective step mode, select the first pending teaching_examples.json item from one locked snapshot and persist it through the marker-bound record-taught-example path. Existing unbound teaching IDs are valid batch history; a bound ID carries exact notebook-block and manifest-item hashes that remain live-validated after cadence changes. Guide publication preserves valid bound blocks and rejects stale or unbound markers. Every teaching-baseline ID must still have a current teaching-manifest snapshot; a quiz-only copy is insufficient.
In an ingestion-v2 structured workspace, answer_explanation_mode is independent from processing/artifact mode. Its stored-schema fallback is ordinary, but full-v2 Guide entry must first perform a native-child capability handshake. When the host can prove a fresh independent child context per item and can restrict its input and tools to that exact item, default to isolated unless the user opted out; persist the mode, notify once about extra host quota/time, and require no second API key or external-upload consent. Otherwise stay ordinary and explain the limitation. Both routes run study_guide_author.py prepare, fill fixed annotations, require one detailed beginner-first explanation per item, persist notebooks, compile, create/attach/verify claims, and import the canonical full Guide. In ordinary, the annotation contains the explanation with ai_supplement provenance and claims no isolation. In isolated, each fresh/stateless tool-disabled invocation sees only the fixed question, official answer when present, target language, and target-scoped assets; it returns answer_explanation plus non-rendered coverage and is imported with a separate host-owned receipt. A separately billed external Provider is an explicit-user-request fallback only and retains no-upload planning plus exact-plan pricing/privacy/upload consent. A model family, subscription, API key, full, or visual alone never proves native isolation. Target-scoped means target_item_only, or prompt-only target_with_required_context with exact sorted required_context_ids; answer assets remain target-only. Packet, annotations, notebook bindings, manifest, rendering and QA all bind the chosen mode. A language/mode/fact/asset change makes the chain stale; only isolated reruns the per-item receipt chain. New v2 Guides omit generic self-check panels. A hand-written complete v2 Guide draft is a no-Python-only, unverified fallback. Ingestion-v1 remains read-only and cannot claim current v2 gates.
