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Eli5

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dzhng
eli5

Explain a technical spec or proposed change in plain language: what problem it solves, how the solution works, and every schema change.

Overview

Publisherdzhng
Repositoryskills
Skill nameeli5
Stars
1.1K
Forks
66
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 dzhng on GitHub. Read the source before you install it.

Installation

Install the Eli5 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/dzhng/skills/tree/main/skills/engineering/eli5
  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/dzhng/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/engineering/eli5 .claude/skills/eli5
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eli5 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 Eli5 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 Eli5 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.

ELI5

Turn the referenced spec or change into an accurate mental model for a technical reader who does not live in the code. Plain language, full precision: simplify the telling, never the claims.

The Register

Other skills point here when their output must be readable by someone who didn't live the work. The reader is technical — pseudocode and precise claims land fine — but they have read none of the code and none of the session's messages: no diff, no transcript, no labels the work invented along the way. Writing ELI5 means:

  • Walk one concrete scenario end to end — the triggering event, what happens today, what the change (or the unbuilt alternative) would do — instead of describing properties in the abstract.
  • Define every term of art at first use; never lean on labels the spec, code, or session invented.
  • Use small concrete examples without replacing precise claims with analogies.
  • Reach for pseudocode when explaining control flow, ordering, or timing. Prose describing when-something-fires reads as plausible and hides the gap; five lines of pseudocode make the gate visible and let the reader see the case you missed. Write it at the level of the decision, not the implementation — the conditions and their order, not real function signatures. The tell that you needed it: your prose contains "only when", "before", "unless", or "as soon as" and the reader still cannot say what happens on the second call.
  • The test: the text stands alone, without the diff, the spec, or the transcript. If the reader must ask "explain this part", it failed.

Workflow

  1. Read the complete referenced artifact and inspect current owners when the spec alone cannot establish behavior. When no artifact is named, explain the session's current change (working tree or branch diff). Separate what exists today from what is only proposed.
  2. Explain the problem through its user or operational consequence, including why the current design produces it.
  3. Explain the solution as one simple before/after data flow. Introduce each component by responsibility, not by filename or internal symbol.
  4. Inventory schema and durable-contract changes exhaustively: added, changed, removed, reset, and deliberately unchanged. Include cursor or wire-format cutovers when they affect stored data or readers. Say explicitly when there are no schema changes.

Output

Use these headings in order:

  1. Problem
  2. Solution
  3. Schema changes

End with one short sentence stating what users should notice after the change.

Rules

  • Lead with behavior and boundaries; mention implementation names only when they clarify ownership or a contract.
  • Distinguish canonical records from derived indexes, caches, summaries, and presentation grouping.
  • Call out destructive resets, migration requirements, compatibility behavior, eventual consistency, and intentional data loss directly.
  • Do not omit a schema change because it is operational rather than user-visible.

Frequently asked questions

What does the Eli5 AI skill do?

Explain a technical spec or proposed change in plain language: what problem it solves, how the solution works, and every schema change.

Why use Eli5 on TypingMind?

Because you install it once and use it with any model. Eli5 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 Eli5 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dzhng/skills/tree/main/skills/engineering/eli5. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Eli5?

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 Eli5?

As many as you like. As long as a model supports skills, you can use Eli5 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Eli5 AI skill free?

Yes. It is published on GitHub by dzhng 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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