Fireworks Open Eli5 logo

Fireworks Open Eli5

Organization
yizhiyanhua-ai
fireworks-open-eli5

Create evidence-aware, interactive visual explainers as self-contained offline HTML. Use when a user asks to explain a concept, repository module, engineering tradeoff, or incident with a diagram, walkthrough, ELI5 treatment, data/request trace, failure view, or teach-back. Do not use for a plain short answer, ordinary prose rewrite, generic website implementation, or unsupported claims presented as facts.

Overview

Publisheryizhiyanhua-ai
Repositoryfireworks-open-eli5
Skill namefireworks-open-eli5
Stars
191
Forks
12
Bundled files
31
LicenseApache-2.0
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.

  • 31 bundled files

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

  • Open source

    Published by yizhiyanhua-ai on GitHub. Read the source before you install it.

Installation

Install the Fireworks Open 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/yizhiyanhua-ai/fireworks-open-eli5/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/yizhiyanhua-ai/fireworks-open-eli5.git \
  .claude/skills/fireworks-open-eli5
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fireworks Open 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 Fireworks Open 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 Fireworks Open 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.

Fireworks Open ELI5

Turn a difficult system into a truthful visual story. Produce a portable JSON spec first, then a deterministic, self-contained HTML explainer.

Non-negotiable boundary

  • Evidence comes before visual polish. Label every evidence item verified, inferred, or analogy.
  • Never make an analogy look like implementation truth.
  • A generated local HTML file is only generated. It is not published, uploaded, sent, or destination-verified.
  • Do not publish, upload, send, or change access without the user's explicit request and the destination workflow's own verification.
  • Rendering performs no network calls. A cited URL is a reader-facing link, not a runtime dependency. The optional native Pages action may call only the bundled same-origin helper on 127.0.0.1; it must never contact a remote service.
  • Browser-local history, favorites, and annotations are disabled until the reader opts in. They are convenience state, not evidence and never modify the source spec or generated artifact.
  • Describe history precisely as same-origin explainers previously opened in that browser. Never claim that it enumerates every generated file.
  • PDF export invokes the browser print dialog. PNG exports one scene; PPTX and Pages-compatible DOCX export one scene per slide or page. Native .pages output is allowed only after the installed Pages app really saves it; never rename a DOCX to .pages. Generated downloads are local artifacts only.

Workflow

  1. Identify the audience, question, language, and one thing that should travel through the story: a request, packet, decision, event, or user action.

  2. Choose exactly one story grammar:

    • concept: intuition → mechanism → boundary.
    • module: entry point → transformations → outputs/dependencies.
    • tradeoff: shared goal → competing options → consequences/decision rule.
    • incident: normal path → break and propagation → detection/recovery.
  3. Gather evidence. For repository work, inspect actual files and use local paths. For external claims, use authoritative sources when available. Record uncertainty explicitly.

  4. Create a version 1 JSON spec following references/spec-contract.md. Use 3–7 scenes, 2–6 nodes per scene, a complete truth ladder, trace, glossary, teach-back, evidence map, and the required mode-specific modeData. Add failure information to consequential nodes.

  5. Validate before rendering:

    bash
    node scripts/validate.mjs path/to/spec.json
  6. Render to a new local file:

    bash
    node scripts/render.mjs path/to/spec.json path/to/explainer.html

    The renderer refuses an existing path. Only when the user explicitly wants to replace a known regular file, append --force. Forced rendering uses an atomic replacement and still refuses symbolic links and non-file paths.

  7. Validate the spec and artifact together:

    bash
    node scripts/validate.mjs path/to/spec.json path/to/explainer.html
  8. When browser access is available, inspect the local artifact at desktop width and 390px. Confirm one h1, readable labels, no horizontal overflow, keyboard focus, independent tablists, global and scene-local playback, viewport following, active real-edge and evidence-card synchronization, enter/hold/exit animation phases, failure lens, answer reveals, and zero remote resource requests. Every scene evidence card must expose its status, concise core basis, support scope, and a URL or explicit no-locator boundary.

  9. Exercise the reader workspace: open and dismiss the modal drawer by mouse and keyboard; confirm focus return; enable the library intentionally; reload and verify favorites and a plain-text annotation; and confirm history contains only same-origin explainers that were actually opened. Use a markup-shaped annotation to verify it stays inert text.

  10. Exercise export: confirm the print stylesheet, PNG signature and dimensions, PPTX and DOCX ZIP signatures and required OOXML parts, and the fallback download link when automatic downloads are suppressed. Visually confirm that scene artwork includes a bounded evidence footer with status, core basis, and source locator or boundary. When native Pages is in scope, run through scripts/serve.mjs, verify the .pages package contains Index/Document.iwa, then reopen it in Pages. Fix the spec or template and rerun both validators after any change.

Report language

  • Write the final QA, status, handoff, and generated-artifact report in the language of the user's latest substantive request by default.
  • An explicit report-language request overrides that default.
  • spec.language controls the generated explainer interface only; it does not force the conversation report language.
  • In mixed-language conversations, ignore short acknowledgements when choosing the report language. Preserve code, commands, paths, identifiers, exact errors, and source titles when translation would reduce precision.

Read references/reporting.md for the full language selection and delivery-report contract.

Story construction rules

  • Write the title as a concrete promise, not a category label.
  • Keep the summary to one sentence and every node to one responsibility.
  • Prefer 3–5 scenes. Use 6–7 only when omitting a stage would distort causality.
  • Edges are relationships or actions. Use short verbs on labels.
  • The trace must visit real node IDs in a meaningful order.
  • Use failure data only when impact, visible symptom, and fallback are known.
  • Keep the simple layer accurate enough to survive the technical layer.
  • Put limitations in truthLadder.caveat, not in tiny disclaimers.
  • Use source IDs on the scenes or nodes they support.
  • Do not cite a source merely because it is adjacent to the claim.

Read references/story-grammars.md for mode patterns, references/evidence-rules.md for claim boundaries, and references/visual-system.md before changing the renderer's visual language. Read references/library-and-export.md before changing persistence, navigation, playback, or export behavior.

Quality gate

The work is ready as a local artifact only when:

  • the validator returns ok: true for the spec and rendered HTML;
  • all IDs and references resolve;
  • every required interactive section has content;
  • the selected story mode passes its own semantic contract;
  • the HTML is deterministic and carries the spec SHA-256 meta tag;
  • the artifact byte-matches the supplied spec and uses trusted CSP hashes;
  • the artifact has no external resources, unsafe runtime APIs, or remote fonts;
  • persisted reader state is opt-in, namespaced, bounded, same-origin, and kept outside the deterministic spec hash;
  • annotations are rendered only as text and cannot create executable markup;
  • global and local playback share one state machine and never run together;
  • each trace step activates the real outgoing relationship path (or a sink's incoming path), keeps label and arrow state aligned, and highlights only the evidence that supports that step;
  • scene evidence cards preserve verified/inferred/analogy boundaries and expose core text plus an actionable locator without fetching remote previews;
  • scene PNG, PPTX, and DOCX packages have the expected signatures and structure and contain their evidence footer; a claimed native Pages export has also reopened in Pages;
  • mobile uses the stacked flow rather than shrinking the desktop SVG;
  • the final report follows the user's current interaction language, names the generated spec and HTML paths, and says whether desktop, mobile, persistence, playback, evidence, and export browser QA were run.

If evidence is incomplete, generate only when the evidence map and caveat make the gap unmistakable. Otherwise stop and report the missing evidence.

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 Fireworks Open Eli5 AI skill do?

Create evidence-aware, interactive visual explainers as self-contained offline HTML. Use when a user asks to explain a concept, repository module, engineering tradeoff, or incident with a diagram, walkthrough, ELI5 treatment, data/request trace, failure view, or teach-back. Do not use for a plain short answer, ordinary prose rewrite, generic website implementation, or unsupported claims presented as facts.

Why use Fireworks Open Eli5 on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yizhiyanhua-ai/fireworks-open-eli5/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 Fireworks Open 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 Fireworks Open Eli5?

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

Is the Fireworks Open Eli5 AI skill free?

Yes. It is published on GitHub by yizhiyanhua-ai under the Apache-2.0 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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