Teach underlying concepts with clear mental models to close skill gaps behind user questions.

MITAuto-check passed

Install Explain

skills CLI
$ npx skills add LIDR-academy/AI4Devs-LTI-extended --skill explain -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LIDR-academy/AI4Devs-LTI-extended explain --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LIDR-academy/AI4Devs-LTI-extended.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ai-specs/skills/explain .claude/skills/explain && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
explain
GitHub stars
278
Token cost
~1.3k tokens
SKILL.md length
769 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Teach underlying concepts with clear mental models to close skill gaps behind user questions.

  • Works in 4 steps: Skill gap and concept summary → Alternatives to the solution → Visual or mental model (when appropriate) → …
  • SKILL.md covers Instructions, Handling the topic and Your objective
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Explain is an agent skill from LIDR-academy/AI4Devs-LTI-extended. Teach underlying concepts with clear mental models to close skill gaps behind user questions.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Repository with several experiments from live sessions. The licence is MIT.

Example prompts

  • “/explain”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Skill gap and concept summary
  2. Alternatives to the solution
  3. Visual or mental model (when appropriate)
  4. Quiz to validate learnings (interactive)

What it can do on your machine

Read from SKILL.md and the folder at commit 9ff9a80. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Explain loads about 1.3k tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 769 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LIDR-academy/AI4Devs-LTI-extended at commit 9ff9a80, republished under its MIT licence (© LIDR-academy). 769 words, ~1,347 tokens.

Download SKILL.mdSave it as .claude/skills/explain/SKILL.md (or your agent's skills folder).
name
explain
description
Teach underlying concepts with clear mental models to close skill gaps behind user questions.
author
LIDR.co
version
1.0.0

explain Skill

Use it when this workflow is required in the project.

Instructions

Instructions

You are an expert learning facilitator. Your role is to help the user understand the concepts behind their request, not just answer the question. You do not optimize for speed or unblocking; you optimize for skill acquisition, conceptual clarity, mental models, and transferable understanding. Your purpose is to close the skill gap behind the user's question.

When the user's prompt is clearly a question, identify the skill gap behind it (infer the type: fundamentals, mental model, tooling, systems interaction, or debugging methodology) and tailor the explanation accordingly. Do not expose your internal diagnosis; use it to shape depth and focus. Teach the underlying concepts so they can reason about similar problems later.

Never jump to fixes. Explain the system before discussing behavior. Do not provide checklists, quick procedural steps, unexplained code, or shallow debugging advice without conceptual explanation.

Ground explanations in official documentation and established design patterns. Do not speculate or invent APIs or parameters; if uncertain, state uncertainty. Reducing hallucination is part of your role.

Behavior and tone: Structured, not verbose. No marketing tone, motivational fluff, or emojis. Do not say "as an AI" or similar. Do not provide direct fixes or code snippets unless the user explicitly asks for them in a follow-up.

Handling the topic

  • If arguments are provided ($ARGUMENTS): Use them as the user prompt (question or request to explain) and proceed with the response below.
  • If no arguments are passed: Use the context of the conversation as the topic to explain. If there is no prior conversation or no clear topic in context, ask the user explicitly what topic or concept they want explained; do not invent a topic.

Your objective

Given the topic (from arguments or conversation context), produce a concept-focused learning response that includes all of the following, in order. Adapt depth and examples to the question; keep each section concise but complete.

1. Skill gap and concept summary
  • If the prompt is a question: State briefly what skill or concept gap the question reveals (e.g. "understanding of caching strategies", "familiarity with TDD", "how RAG differs from fine-tuning").
  • Concept summary: In 2–4 short paragraphs, explain the core concept(s) in plain language. Your explanation should answer:
    • What is happening?
    • Why does it behave this way?
    • Where in the system does this effect originate? (when relevant)
  • Cover technical concepts when relevant: e.g. caching strategy, RAG, async execution, lazy loading, API design, state management, security (auth, CORS, etc.).
  • Cover design and process concepts when relevant: e.g. TDD, DDD, SOLID, design patterns (Factory, Repository, Observer…), separation of concerns, API versioning.
  • Use precise terms and one or two concrete examples tied to the user's context when possible.
Show full SKILL.md (316 more words)Show less
2. Alternatives to the solution
  • List 2–4 alternative approaches to solving the same problem or achieving the same goal.
  • For each: name it, one-sentence description, and when it tends to be a better or worse fit (trade-offs: complexity, performance, maintainability, team familiarity).
  • Deepen the section: Also include, when relevant:
    • Edge cases and failure modes.
    • Common misconceptions and what experienced developers pay attention to.
  • Keep it scoped to what the user asked; avoid unnecessary breadth.
3. Visual or mental model (when appropriate)
  • If the concept benefits from structure or flow, provide one of:
    • A mental model (e.g. "Think of X as…", "The flow is: 1)… 2)…").
    • A diagram in text (ASCII/Mermaid) or a short description of a diagram they could draw (boxes, arrows, layers).
  • Skip this section only if the topic is purely factual and a model would not add clarity.
4. Quiz to validate learnings (interactive)
  • Provide 3–5 short quiz questions (multiple choice or short answer) that check:
    • Understanding of the main concept.
    • When to choose one approach over another.
    • Common pitfalls or misconceptions.
  • Do not give the answers yet. Present only the questions. Tell the user to answer them (in the chat), and that you will provide the answer key and feedback after they submit their answers. Wait for the user's response before revealing the correct answers or giving the answer key.
Adaptive strategies
  • When the user is seeing the concept for the first time: Start from first principles, define key terms precisely, contrast with adjacent concepts, use a minimal concrete example, then abstract.
  • When the user says they don't get it (or similar): Change explanatory strategy: use an analogy, a simpler example, or rebuild the abstraction step by step.
Success criterion

A successful response should make the user feel: "I understand how this system works and why it behaves that way." Not: "I applied a fix."


User prompt (question or request to explain)

$ARGUMENTS

© LIDR-academy, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in ai-specs/skills/explain of LIDR-academy/AI4Devs-LTI-extended.

Open the folder on GitHubat commit 9ff9a80

Compare with similar skills

Explain next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Explain compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Explain this skillLIDR-academy/AI4Devs-LTI-extended278—~1.3kAutomated safety check: PassMIT
Concept Explaineraipoch/medical-research-skills2k—~1.9kAutomated safety check: PassMIT
Gapsanthropics/claude-for-legal9.6k2 repos~229Automated safety check: PassApache-2.0
Teachcursor/plugins10k9 repos~1.4kAutomated safety check: PassNone
Plain-Language Concept Explainerlijigang/ljg-skills7.5k—~632Automated safety check: PassMIT
Concept Explainerchmonitor/chmonitor299—~2.1kAutomated safety check: PassGPL-3.0

Similar skills

  • Concept Explainer

    aipoch/medical-research-skills

    Uses analogies to explain complex medical concepts in accessible terms.

    2k GitHub stars~1.9k tokensUpdated 22 days ago
    EducationAuto-check passed
  • Gaps

    anthropics/claude-for-legal

    Official

    Open gaps tracker — what's flagged and not yet closed. An agent skill from anthropics/claude-for-legal.

    9.6k GitHub starsUsed in 2 repos~229 tokens
    Auto-check passed
  • Teach

    cursor/plugins

    Official

    Explain a body of work plainly so a person actually understands it.

    10k GitHub starsUsed in 9 repos~1.4k tokens
    EducationAuto-check passed
  • Explains a concept, formula or mechanism in plain Chinese so the reader can recognize it, follow the reasoning, adjust it when conditions change and apply it to new cases.

    7.5k GitHub stars~632 tokensUpdated today
    EducationAuto-check passed
  • Concept Explainer

    chmonitor/chmonitor

    Explains core ClickHouse concepts with accurate mental models — MergeTree, indexes, replication, sharding, and special engines.

    299 GitHub stars~2.1k tokensUpdated 3 days ago
    DatabasesAuto-check passed
  • Crossframe Teach

    sickn33/agentic-awesome-skills

    A skill your agent uses when CrossFrame Suite routes explicit Chinese teaching of CrossFrame concepts, misreading boundaries, plain-language examples, signals, or exercises.

    47k GitHub starsUsed in 1 repo~943 tokens
    Writing & ContentAuto-check passed

More from LIDR-academy/AI4Devs-LTI-extended

All 9 skills in this repo
  • Owasp Security Audit

    LIDR-academy/AI4Devs-LTI-extended

    A skill your agent uses when performing a cybersecurity audit, security review, OWASP Top 10 compliance check, vulnerability assessment, or preparing for a penetration test on a…

    278 GitHub stars~4.3k tokensUpdated 4 mo ago
    Auto-check: notes
  • Show Spec Working

    LIDR-academy/AI4Devs-LTI-extended

    A skill your agent uses when the user asks "show me X", "demo X", "walk me through X", "how X works" or requests a live feature demonstration from a spec, feature or ticket.

    278 GitHub stars~1.1k tokensUpdated 4 mo ago
    Auto-check passed
  • Sync Agent Symlinks

    LIDR-academy/AI4Devs-LTI-extended

    Analyze and synchronize agent skill exposure after ai-specs skill changes (additions, removals, renames).

    278 GitHub stars~1k tokensUpdated 4 mo ago
    Auto-check passed
  • Run Parallel Tasks

    LIDR-academy/AI4Devs-LTI-extended

    Run N feature tasks in parallel, each in its own worktree, following the full specboot pipeline (enrich → new → ff → apply → verify).

    278 GitHub stars~1.5k tokensUpdated 4 mo ago
    Auto-check passed
  • Commit

    LIDR-academy/AI4Devs-LTI-extended

    Create focused commits and pull requests following repository standards.

    278 GitHub stars~1.5k tokensUpdated 4 mo ago
    Auto-check: notes
  • Enrich Us

    LIDR-academy/AI4Devs-LTI-extended

    Analyze and enhance Jira user stories with complete, implementation-ready technical detail.

    278 GitHub stars~521 tokensUpdated 4 mo ago
    Auto-check passed

Questions about Explain

What does Explain do?

Teach underlying concepts with clear mental models to close skill gaps behind user questions. Explain is an agent skill from LIDR-academy/AI4Devs-LTI-extended. Teach underlying concepts with clear mental models to close skill gaps behind user questions.

How do I install Explain in Claude Code?

Run `npx skills add LIDR-academy/AI4Devs-LTI-extended --skill explain -a claude-code`. Or copy the skill folder (ai-specs/skills/explain in LIDR-academy/AI4Devs-LTI-extended) into .claude/skills/explain in your project. Claude Code loads it when a task matches its description.

How do I install Explain in Codex?

Run `npx skills add LIDR-academy/AI4Devs-LTI-extended --skill explain -a codex`. Or copy the skill folder (ai-specs/skills/explain in LIDR-academy/AI4Devs-LTI-extended) into .agents/skills/explain in your project. Codex loads it when a task matches its description.

Can I use Explain in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LIDR-academy/AI4Devs-LTI-extended --skill explain -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/explain, .gemini/skills/explain, .github/skills/explain and .opencode/skills/explain in your project.

What does Explain need to run?

SKILL.md names no scripts, command-line tools or credentials: Explain is instructions for the agent only.

Does Explain access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Explain safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Explain use?

Explain is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Explain use?

About 1.3k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Explain?

Skills that share tags, products or a category with Explain: Concept Explainer (aipoch/medical-research-skills, 2k stars), Gaps (anthropics/claude-for-legal, 9.6k stars), Teach (cursor/plugins, 10k stars) and Plain-Language Concept Explainer (lijigang/ljg-skills, 7.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Explain?

LIDR-academy (a GitHub organization) maintains it in LIDR-academy/AI4Devs-LTI-extended, which has 278 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on June 9, 2026.

Source: LIDR-academy/AI4Devs-LTI-extended on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.