Topic Explainer logo

Topic Explainer

Organization
pnp
topic-explainer

Create an adaptive interactive explainer dashboard for a user-specified topic, using a clean professional card-based structure with concept cards, comparisons, topic-fit sections, optional decision guides, use cases, governance notes, and source notes. Use when the user says: - "create an explainer of a topic" - "create an explainer dashboard for <topic>" - "make an explainer about <topic>" - "turn this topic into an explainer" - "build a dashboard that explains <topic>" - "create a topic explainer"

Overview

Publisherpnp
Repositorysharepoint-skills
Skill nametopic-explainer
Stars
141
Forks
60
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Topic Explainer 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/pnp/sharepoint-skills/tree/main/Skills/topic-explainer/topic-explainer
  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/pnp/sharepoint-skills.git /tmp/sharepoint-skills
mkdir -p .claude/skills
cp -r /tmp/sharepoint-skills/Skills/topic-explainer/topic-explainer .claude/skills/topic-explainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Topic Explainer 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 Topic Explainer 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 Topic Explainer 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.

Topic Explainer

When to use

Use this skill whenever the user asks to create an explainer, explainer page, or explainer dashboard for a topic. The output should be a polished, interactive HTML dashboard that explains the topic clearly for a business audience.

Use a clean, professional card-based design as the content model, but adapt the sections to fit the topic and content. Do not force every section into every dashboard.

Inputs

  • Required: the topic to explain.
  • Optional: target audience, organization/team context, preferred tone, source files/pages, and whether the dashboard should be saved as a SharePoint file or shown inline.
  • If the topic needs current, enterprise, or document-specific information, retrieve or summarize the relevant content first. Do not invent source-specific claims.

Steps

  1. Identify the explainer topic, audience, and main user need from the request.
  2. Decide which sections fit the topic before building the dashboard. Use the strongest-fit sections only:
    • Always consider: hero/header, shortest version, core concept cards, active-detail panel, comparison table, use cases/examples, governance or watch-outs, and source notes.
    • Include a decision guide only when the topic asks users to choose between options, make a recommendation, follow a path, or decide “when to use what”.
    • Include a Without vs With section only when the topic naturally compares adopting a capability, method, or maturity step. Skip it for side-by-side product comparisons where it feels artificial.
    • Include a context overload / complexity explanation only when reusable instructions, knowledge context, complexity, or prompt burden is central to the topic.
    • Include implementation, rollout, adoption, governance, or operating model sections only when relevant.
  3. If a section is skipped, rebalance the layout. Do not leave an empty adjacent panel. Expand the neighboring section to full width, replace the skipped section with a better-fit section, or use a single-column layout for that row.
  4. Build the dashboard content around the selected sections. Common section options:
    • Hero/header with a short title, subtitle, and “shortest version”.
    • 3–5 core concept cards for the most important ideas in the topic.
    • Active concept detail panel containing: what it is, analogy, when to use it, example prompts/scenarios, and watch-outs.
    • Decision guide with practical questions and recommendations, when relevant.
    • Clear comparison table across the core concepts, platforms, methods, or options.
    • “Without vs with” section, only when it creates real explanatory value.
    • Topic-specific section such as channel selection, operating principles, adoption playbook, risks, anti-patterns, stakeholder map, maturity model, or examples.
    • Searchable use cases and examples by team, role, or scenario.
    • Governance notes, risks, or implementation considerations.
    • Source notes listing the sources or assumptions used.
  5. Create the dashboard as standalone HTML/CSS/JavaScript rather than raw React unless the environment explicitly supports React build output.
  6. Preserve a clean professional design spirit:
    • Clean card-based layout.
    • Interactive concept selection.
    • Search/filter for use cases where useful.
    • Professional, clear tone with plain-language explanations, analogies, practical examples, and clear distinctions between concepts, benefits, limitations, and governance.
  7. Use this content schema when adapting the original code:
js
const concepts = [
  {
    key: 'short-key',
    label: 'Concept name',
    plain: 'One-sentence plain-language definition.',
    analogy: 'Simple analogy.',
    useWhen: 'When to use this concept or approach.',
    examples: ['Example 1', 'Example 2', 'Example 3'],
    limits: 'Watch-out or limitation.'
  }
];

const comparison = [
  ['Question', 'Concept A answer', 'Concept B answer', 'Concept C answer'],
  ['Primary purpose', '...', '...', '...'],
  ['Best for', '...', '...', '...'],
  ['Context model', '...', '...', '...'],
  ['Example', '...', '...', '...'],
  ['Governance need', '...', '...', '...']
];

const useCases = [
  { team: 'Team or role', task: 'Task or scenario', choose: 'Recommended option', value: 'Business value' }
];

const decisionTree = [
  { q: 'Practical decision question?', a: 'Recommended answer.' }
];

Optional schema when the topic benefits from it:

js
const withoutWith = [
  { area: 'Area', without: 'What happens without this approach.', with: 'What improves with it.', why: 'Why it matters.' }
];

const topicSections = [
  { title: 'Section title', cards: [{ heading: 'Card heading', body: 'Card body' }] }
];
  1. If saving the dashboard, create an .html file with a descriptive filename such as <topic>-explainer-dashboard.html.
  2. If a tool fails or returns empty, say so plainly. Do not fabricate retrieved content, file URLs, or source notes.

Output format

Return a concise confirmation with:

  • The dashboard title.
  • Where it was created or how it was delivered.
  • Any assumptions or missing sources, if relevant.

When presenting the dashboard content inline, provide only the final HTML or a short preview plus the saved file link, depending on the user's request.

Frequently asked questions

What does the Topic Explainer AI skill do?

Create an adaptive interactive explainer dashboard for a user-specified topic, using a clean professional card-based structure with concept cards, comparisons, topic-fit sections, optional decision guides, use cases, governance notes, and source notes. Use when the user says: - "create an explainer of a topic" - "create an explainer dashboard for <topic>" - "make an explainer about <topic>" - "turn this topic into an explainer" - "build a dashboard that explains <topic>" - "create a topic explainer"

Why use Topic Explainer on TypingMind?

Because you install it once and use it with any model. Topic Explainer 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 Topic Explainer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pnp/sharepoint-skills/tree/main/Skills/topic-explainer/topic-explainer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Topic Explainer?

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 Topic Explainer?

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

Is the Topic Explainer AI skill free?

Yes. It is published on GitHub by pnp under the MIT license. 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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