> 备考教练的一屏速查卡:工作流、3×4 学习选择、产物偏好、工作区文件、6 大题型、来源规则与子技能路由。 用户问怎么用、有哪些模式、文件用途或支持题型时使用。
npx skills add https://github.com/ZeKaiNie/universal-examprep-skill --skill exam-help
Render one read-only card covering the validated workflow, three modes, four time tiers, lightweight|full, chat|visual, workspace truth/views, six quiz types, provenance, and subskill routing.
Use only when the student asks how the suite works, which modes/types exist, or what workspace files mean.
No files, arguments, or state reads. The caller supplies the selected language.
zh, en, or bilingual, otherwise an explicit ad-hoc request.The card must say:
processing_mode=lightweight is the default/recommended startup choice. Itinventories names, then visually processes only the current-phase PDF pages or
definitely single-frame PNG/JPEG/BMP (maximum eight primary pages and one active
batch). Overview contact sheets group at most four pages at roughly 768 px/tile;
page/prompt/dependency detail and target-answer-only solution calls use
source-qualified locations and canonical PNG evidence under .lightweight/assets/.
Bind exact external answer pages with register-answer-dependency; dependency
pages are locator/detail only. Figure prompt/answer crops stay distinct. An
unfinished batch can close only through receipt-backed abandon --reason; taught
progress cannot be abandoned, while replace-taught --reason preserves it as
superseded history and plans an exact-slice successor. It keeps the page-batch /
progress state machines and creates no Study Guide/PDF. processing_mode=full is
explicit opt-in and opens the complete ingestion/review route. Input-token savings
never shorten the teaching explanation.
artifact_mode is independent from processing intensity and never inferred fromsubscription. Missing/legacy/unknown means chat. In full mode, chat stops
without PDF while standing visual additionally requires typed Guide, render,
receipt hashes, every-page QA, and artifact_ready=ready. A one-shot artifact
leaves stored state unchanged. In lightweight, a saved visual preference is
dormant and effective output remains chat; it never builds a Study Guide.
answer_explanation_mode is also independent. Its stored-schema fallback isordinary, and every full-Guide item still gets a detailed beginner-first
explanation. At full-v2 Guide entry, a verified host-native child with a fresh
independent context plus exact single-item input/tool restrictions makes
isolated the default unless the learner opted out; tell the learner once about
extra host quota/time, with no second API key or external upload. Missing or
incomplete capability stays ordinary and is stated honestly. A separately
billed external Provider is explicit-request-only and retains no-upload planning,
current pricing/privacy disclosure, and exact-plan upload consent. A model name,
subscription, key, full, or visual alone proves neither route.
ingest_course.py is the full-mode build entry; exit 10 is process success withblocked readiness, and teaching remains forbidden. .ingest/ is full-mode
build/review truth, .lightweight/session.json is on-demand page-batch truth,
study_state.json is progress truth, and Markdown is a generated view.
typed Guide/full-build evidence; superseded predecessors/events remain history but
leave the current denominator. verified needs revision-bound checkpoints from an
unchanged bank that pre-existed lightweight initialization; startup captures only
its immutable stat baseline. Routine health checks use metadata/physical identity,
not stream hashes; exact hashes occur at transitions/completion or explicit
status --verify-live. Older taught history is unchecked_historical until that
phase is resumed.
download, install, probe, import, or execute them locally.
Output exactly one help card and mutate nothing. Student prose is English by default, Simplified Chinese for a Chinese opening, or explicit bilingual composition.
中文 → ../../locales/zh/skills/exam-help.mdEnglish → ../../locales/en/skills/exam-help.md双语 → compose both blockwise, zh then > EN:, under docs/language-policy.mdDisplay aliases are normalized to zh, en, or bilingual.
This card is read-only. Route actual review to exam-cram.
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take zekainie/exam-help from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.