Eazyr logo

Eazyr

Organization
buildfastwithai
eazyr

Make an unfamiliar or undocumented repo easy to run. Use when someone cannot get a project running, when onboarding a new developer, or when asked to write a QUICKSTART, a setup/prerequisite check ("doctor") script, or a documented .env.example. Also use when setup instructions are missing, stale, or spread across a wiki, a chat thread, and someone's head — or to audit whether a repo is onboardable at all.

Overview

Publisherbuildfastwithai
Repositorygen-ai-experiments
Skill nameeazyr
Stars
785
Forks
225
Bundled files
16
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.

  • 16 bundled files

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

  • Open source

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

Installation

Install the Eazyr 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/buildfastwithai/gen-ai-experiments/tree/main/apps/eazyr-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/buildfastwithai/gen-ai-experiments.git /tmp/gen-ai-experiments
mkdir -p .claude/skills
cp -r /tmp/gen-ai-experiments/apps/eazyr-main .claude/skills/eazyr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eazyr 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 Eazyr 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 Eazyr 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.

eazyr

Turn "clone it and good luck" into a five-minute path from clone to a running app.

You produce an onboarding kit that lives in the repo:

ArtifactPurpose
QUICKSTART.mdThe shortest correct path from clone to "it works"
scripts/doctor.sh + scripts/doctor.ps1Runnable checks that fail loudly before setup wastes an hour
.env.exampleEvery variable the app reads, documented, with safe defaults

The one rule

Never write a step you have not verified against the repo. Invented steps are worse than absent ones — they cost the reader the time it takes to discover they're fake.

This ships with a scanner so you don't have to rely on memory. eazyr scan --json returns detected facts with the file each one came from. Prefer it over reading twenty files by hand, and cite its provenance rather than guessing.

If a fact is missing, write > **TODO:** and say what you'd need. Never fill the hole with something plausible.

Workflow

1. Scan

bash
npx eazyr scan --json

Returns: ecosystems (stack, package manager, version ranges, real commands from the manifest), env.vars (each with required, secret, and sources: ["file:line"]), compose and services, ports, verify (health endpoint or test command), steps (the ordered setup path), and gaps (what's missing).

Read the human summary first if you just want orientation:

bash
npx eazyr scan

The scanner is a strong first pass, not an oracle. It is regex-based, skips node_modules, and caps at 4000 files. Verify anything surprising by opening the file it cites. If ecosystems is empty or truncated is true, fall back to reading the repo yourself — references/detect.md is the manual lookup table.

2. Generate the first draft

bash
npx eazyr init          # add --force to overwrite, --dry-run to preview

Writes all four files from the scan. Existing files are skipped unless --force, so this is safe to run in a repo that already has some docs.

This is a draft, not a deliverable. It knows the facts; it does not know your project.

3. Edit it into something true

This is the part only you can do. Work through the generated files and:

  • Resolve every TODO. The generator emits one wherever it couldn't determine a fact.
  • Read the sources. For each sources: ["src/config.ts:14"], open that line and write what the variable actually does. "Stripe API key" beats a repeated variable name.
  • Name where secrets come from. STRIPE_SECRET_KEY → "Create a test-mode key at dashboard.stripe.com/apikeys". The generator cannot know this; a reader is stuck without it.
  • Check CI. .github/workflows/ is the setup that provably works. If it does something the quickstart doesn't, the quickstart is wrong.
  • Fix the ordering if the project has a real constraint the scanner missed — a seed step that must precede first boot, a service that needs a warm-up.
  • Cut what isn't on the critical path. Under 120 lines. Everything else is README material.

references/quickstart.md has the section-by-section spec, references/doctor.md the check conventions, references/detect.md the manual detection table.

4. Verify

bash
npx eazyr doctor        # runs the derived checks against this machine, writes nothing
bash scripts/doctor.sh  # and confirm the committed script agrees

Then run the install and start commands if you can. Best effort, in this order:

  1. doctor passes, and fails informatively when you unset a required variable.
  2. The install step completes.
  3. The app starts and the verify step returns what the quickstart claims.

Report honestly: what you ran, what passed, what you could not check. If you couldn't run the app, say so plainly rather than implying the path is proven.

When the scanner comes up empty

Unrecognised stack, or a repo too unusual to detect? Fall back to the manual workflow — the artifacts are still the goal:

  1. Read CI, then the task runner, then the container files. CI has to pass, so it doesn't lie.
  2. Reconstruct: prerequisites → clone → install → configure → services → run → verify.
  3. Copy assets/doctor.sh and assets/doctor.ps1 into scripts/, and edit only the CHECKS block — the runner is complete.
  4. Write QUICKSTART.md from assets/QUICKSTART.template.md.

Scope

Write the kit and the scripts that support it. Don't refactor the project, change its dependencies, or rewrite the main README — QUICKSTART.md links from the README, it doesn't replace it. Never copy a real value out of an existing .env; use a placeholder and say you did.

Files

  • references/detect.md — manual stack detection, per ecosystem
  • references/quickstart.md — QUICKSTART.md section spec
  • references/doctor.md — check catalog and script conventions
  • assets/QUICKSTART.template.md, assets/doctor.sh, assets/doctor.ps1, assets/env.example.template — the templates init fills in

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 Eazyr AI skill do?

Make an unfamiliar or undocumented repo easy to run. Use when someone cannot get a project running, when onboarding a new developer, or when asked to write a QUICKSTART, a setup/prerequisite check ("doctor") script, or a documented .env.example. Also use when setup instructions are missing, stale, or spread across a wiki, a chat thread, and someone's head — or to audit whether a repo is onboardable at all.

Why use Eazyr on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/buildfastwithai/gen-ai-experiments/tree/main/apps/eazyr-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 Eazyr?

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 Eazyr?

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

Is the Eazyr AI skill free?

Yes. It is published on GitHub by buildfastwithai 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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