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Final Review Slides

Community
chowkayee11
final-review-slides

Create Chinese-, English-, or bilingual final-exam review packs and source-grounded mock exams from PDF/PPTX lecture decks, including mixed-language slides, Revision/Review decks, syllabi, assignments, quizzes, and past exams. Use for 期末复习, 课件总结, 考点整理, 模拟题, final review notes, formula/concept sheets, misconception guides, or source-cited exam preparation materials. Preserve lecture and slide order, inspect visual evidence, retain LaTeX sources, and apply strict coverage, depth, assessment, and rendering gates.

Overview

Publisherchowkayee11
Repositoryfinal-review-slides
Skill namefinal-review-slides
Stars
56
Forks
4
Bundled files
11
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by chowkayee11 on GitHub. Read the source before you install it.

Installation

Install the Final Review Slides AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chowkayee11/final-review-slides/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chowkayee11/final-review-slides.git \
  .claude/skills/final-review-slides
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Final Review Slides in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Final Review Slides on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Final Review Slides is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Final Review Slides

Build a source-traceable exam pack without reordering or silently omitting course material. Use the pdf and presentations skills for format-specific intake and rendering; follow their render-and-verify requirements.

Required deliverables

Unless the user explicitly changes the outputs, create:

  • final-review.tex and final-review.pdf: complete lecture-by-lecture review in source order.
  • quick-reference.tex and quick-reference.pdf: formulas, symbols, conditions, concepts, and fast lookup tables.
  • mistakes-and-distinctions.tex and mistakes-and-distinctions.pdf: misconceptions, confusable concepts, invalid shortcuts, and diagnostic comparisons.
  • coverage-report.md: source inventory, ordering basis, page/slide coverage, visual-check status, exclusions, uncertainties, and quality-gate verdicts.
  • course-map.json and course-map.mmd, plus a rendered course map used in final-review.pdf.

When Revision/Review, assignments, quizzes, midterms, past exams, or an explicit mock-exam request supplies enough assessment evidence, also create:

  • mock-exam.tex and mock-exam.pdf: new questions or source-grounded variants without answers.
  • mock-exam-solutions.tex and mock-exam-solutions.pdf: model solutions, marking points, common errors, difficulty, assessed concepts, and citations.
  • mock-exam-blueprint.md: scope, evidence basis, topic/type/difficulty/mark allocation, and validation results.

Keep generated files and intermediates in one dedicated output directory. Preserve .tex sources. Do not claim completion unless all required files exist, all PDFs compile successfully, and rendered PDFs pass visual inspection.

Intake contract

Accept one or more .pdf or .pptx lecture decks. Also ingest, when supplied:

  • Revision/Review/Final Review decks
  • syllabus or course outline
  • assignments and homework
  • quizzes and midterms
  • past exams and answer keys

Accept language: auto | zh | en | bilingual. Do not block when the user omits it: default auto to Chinese explanations while retaining English definitions, technical terms, proper names, abbreviations, variables, and exam-relevant original wording. If the user requests “follow the source”, use the detected dominant language. Record the resolved mode in coverage-report.md.

Accept depth: concise | comprehensive | deep; default to comprehensive. final-review is a teaching document, not a compressed outline. The depth option does not authorize omissions.

Read bilingual-writing.md when deciding terminology. Read source-and-priority.md before assigning exam importance. Read output-schemas.md before drafting. Read build-and-render.md before creating full drafts. Read quality-gates.md before verification.

Workflow

0. Preflight the build environment

Before deep extraction or drafting, copy and compile assets/build-smoke-test.tex. It tests Chinese, English, mathematics, tables, and hyperlinks. Select a working PDF backend using build-and-render.md, record the result, and test Mermaid or its PDF-native fallback at the same time.

Do not process the full course before discovering that the requested deliverables cannot be built. If no PDF backend works, stop early and ask whether the user accepts a source-only delivery; do not silently downgrade.

1. Inventory and classify

Create a source inventory before summarizing. For every file record its path, type, apparent role, lecture identifier, title, page/slide count, language, and ordering evidence.

Classify files as lecture, review, syllabus, assignment, quiz/exam, answer key, or unknown. Search case-insensitively for revision, review, final review, recap, summary, 复习, 总复习, 考试重点, and equivalent content signals. Confirm candidates from their contents rather than filenames alone.

Resolve lecture order using, in priority order: explicit syllabus schedule, lecture numbering inside the deck, dates, internal prerequisite statements, then filenames. Report missing, duplicate, or ambiguous items. Never silently guess an uncertain order.

2. Establish assessment evidence

Read the syllabus and review materials before deep chapter drafting. Extract explicit scope, exclusions, weights, formats, named topics, instructor warnings, and emphasized formulas. Then inspect assignments, quizzes, and past exams for recurring assessed skills.

Separate evidence labels:

  • Explicit: directly stated by the instructor or assessment material.
  • Observed: recurring in assignments, quizzes, or past exams.
  • Inferred: predicted from repetition, examples, summaries, or dependencies.

Never present an inferred exam point as instructor-confirmed. If sources conflict, report the conflict with citations instead of choosing silently.

3. Build a global map

Read all decks broadly before writing chapter summaries. Identify major themes, subtopics, prerequisites, formula-heavy regions, examples, and assessment links.

Create one canonical course-map.json, then derive course-map.mmd as a Mermaid mind map showing course → major themes → subtopics → lecture identifiers. The map may show conceptual dependencies, but the written review must retain original lecture order.

Use a privacy-safe rendering ladder: (1) use a trusted local Mermaid renderer when its browser runtime is permitted; (2) otherwise derive a PDF-native LaTeX hierarchy/tree from the same JSON and embed it, while preserving .mmd; (3) use a remote renderer only with explicit user consent because it transmits course structure. Never bypass the sandbox. Mermaid image rendering is conditional; an embedded map consistent with the canonical JSON is mandatory.

4. Build the coverage ledger

Create one ledger row for every source page or PPTX slide. At minimum record:

source_id, lecture_id, page_or_slide, topic, concept_id, importance, disposition, summary_location, explanation_depth, visual_check, citation, notes.

Allowed dispositions are covered, duplicate, administrative, blank, and uncertain. Every row must have a disposition. duplicate, administrative, and blank require a short reason; uncertain blocks completion until resolved or explicitly accepted by the user.

Use scripts/validate_coverage.py to validate the machine-readable CSV ledger before finalization.

5. Summarize in strict source order

Write final-review by lecture order, then by page/slide order within each lecture. Do not reorganize the main chapters for thematic elegance. Handle cross-links with “see Lecture X” references rather than moving content.

For each chapter include, where supported:

  • learning focus and assessment relevance
  • definitions and core concepts
  • formulas with symbol meanings, assumptions, and conditions
  • methods or derivations in their presented order
  • representative worked-example patterns
  • links to assignments, quizzes, or past exams
  • likely traps and distinctions
  • tight page/slide citations

For every substantive core concept, explain rather than merely name it: definition or preserved original wording; intuition and purpose; mechanism, derivation, or steps; assumptions and boundary conditions; a representative example or exam application; failure/attack mode where relevant; distinctions from nearby concepts; and tight citations. Formula entries must define variables and conditions and include an application. Algorithms and protocols must preserve their sequence or message flow. Charts and tables must explain axes/headers, encodings, trend or relationship, and the supported conclusion.

Every explicit Revision topic needs a complete explanation. A ledger row is not “covered” merely because its title or keywords appear. Use the selected depth consistently and record it in the ledger.

Compress duplicates but retain citations to every occurrence. Never drop later lectures because of context limits; segment the work and merge only after every ledger range has been processed.

6. Inspect visual evidence

Render and inspect source pages/slides when they contain formulas, derivations, tables, charts, diagrams, spatial annotations, low extracted-text density, OCR anomalies, or layout-dependent meaning.

Verify subscripts, superscripts, operators, matrices, arrows, axes, legends, table row/column relations, color semantics, and animation-like incremental slides. Mark each inspected item in the ledger. Do not reconstruct unreadable mathematics or diagrams from guesswork.

7. Derive the companion documents

Generate quick-reference and mistakes-and-distinctions from the verified chapter notes and source evidence, not from memory alone. These documents may reorganize content for lookup, but every entry must retain lecture/page citations.

Do not omit non-formula courses from quick-reference; use definitions, frameworks, cases, dates, vocabulary, or comparison tables as appropriate.

8. Generate a source-grounded mock exam when applicable

Read mock-exam.md. Build the blueprint before drafting questions. Use evidence in this order for assessment shape: explicit Revision/syllabus rules, past exams, quizzes/assignments, then lecture examples. Preserve stated format and marks; disclose missing information rather than inventing instructor policy.

Create new questions or meaningful variants by default. Keep answers separate. Cite sources in the solution document, not the question paper. Validate answerability, uniqueness of single-choice answers, numerical/protocol correctness, mark totals, topic balance, difficulty balance, and absence of unsupported content.

9. Verify and build

Run all gates in quality-gates.md. A verifier must compare drafts against source pages and the coverage ledger, not merely review the prose.

Use the backend selected during preflight. Prefer the Codex built-in LaTeX compiler when available, then a proven installed LaTeX engine. If dependencies fail, retry with the minimal template; if LaTeX remains unavailable, use a supported direct-PDF path through the PDF skill while preserving equivalent .tex sources. Never attempt network package installation without authorization.

Render every generated PDF to images and inspect for missing glyphs, overflow, clipping, overlap, broken formulas, unreadable tables, and course-map placement. Fix and re-run checks until all blocking gates pass. Record the backend, template mode, missing dependencies, fallback attempts, and visual-QA evidence in coverage-report.md.

Citation format

Use stable citations such as [Lecture 04, p.17], [Lecture 04, slides 17–19], [Revision, p.8], or [Quiz 2, Q3]. Prefer exact pages; use tight ranges only for genuinely continuous material. Cite all sources when synthesizing across documents.

Completion rules

Do not claim “no omissions” as an unsupported absolute. Report verified coverage numerically and disclose exclusions and uncertainties. Completion requires:

  • every source inventoried and ordered or explicitly unresolved
  • every page/slide represented in the coverage ledger
  • zero uncovered or uncertain ledger rows
  • all explicit review topics represented
  • all required visual pages checked
  • all citations sampled and validated
  • the three core .tex files and three successfully compiled, visually checked PDFs
  • mock-exam artifacts when assessment evidence or the user request triggers them
  • canonical map JSON, Mermaid source, and a successfully embedded Mermaid or PDF-native fallback map
  • a complete coverage-report.md with PASS/FAIL results

Report one overall delivery state:

  • PASS: all requested PDFs exist and pass visual QA.
  • PARTIAL: sources are complete but PDFs are absent, only when the user explicitly accepted source-only delivery after preflight.
  • FAIL: required PDFs or blocking verification remain unavailable.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Final Review Slides AI skill do?

Create Chinese-, English-, or bilingual final-exam review packs and source-grounded mock exams from PDF/PPTX lecture decks, including mixed-language slides, Revision/Review decks, syllabi, assignments, quizzes, and past exams. Use for 期末复习, 课件总结, 考点整理, 模拟题, final review notes, formula/concept sheets, misconception guides, or source-cited exam preparation materials. Preserve lecture and slide order, inspect visual evidence, retain LaTeX sources, and apply strict coverage, depth, assessment, and rendering gates.

Why use Final Review Slides on TypingMind?

Because you install it once and use it with any model. Final Review Slides is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Final Review Slides in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chowkayee11/final-review-slides/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Final Review Slides?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Final Review Slides?

As many as you like. As long as a model supports skills, you can use Final Review Slides with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Final Review Slides AI skill free?

It is published on GitHub by chowkayee11. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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