mgratzer

mgratzer / bloomery

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An agent skill that teaches engineers how to build a coding agent from scratch using raw HTTP calls to an LLM API.

16
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100% credibility
Found Mar 01, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

Bloomery is a guided tutorial skill for AI coding assistants that walks users through creating a basic conversational AI agent with tools using simple web requests in TypeScript, Python, Go, or Ruby.

How It Works

1
💡 Discover Bloomery

You find Bloomery, a hands-on guide that helps you build your own AI coding helper from the ground up to really understand how they work.

2
🧑‍💻 Launch your coding buddy

Open your favorite AI assistant for coding that knows about special guides like this one.

3
🚀 Kick off the Bloomery adventure

Ask your assistant to start the Bloomery session, feeling excited to create something real.

4
🎛️ Pick your style

Choose an AI brain to power it, your go-to programming language, a fun name for your agent, and whether to go guided or fast.

5
📁 Starter project appears

Your assistant sets up a fresh project folder with basic pieces ready for you to expand, like magic.

6
🔨 Build one piece at a time

Follow eight simple steps adding chat, memory, personality, and helpful tools for files and commands, with gentle nudges if needed.

7
📊 Progress saved automatically

It tracks what you've done, lets you resume anytime, and keeps your work organized neatly.

🎉 Your AI agent comes alive

Run your creation, chat with it, use its tools, and suddenly agent magic makes perfect sense – you've done it!

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AI-Generated Review

What is bloomery?

Bloomery is a shell-based agent skill for the Agent Skills standard at agentskills.io—you install it with `npx skills add mgratzer/bloomery` into tools like VS Code with GitHub Copilot, Claude Code, or Gemini CLI. It guides you hands-on to build a ~300-line coding agent from scratch in Python, Go, Ruby, or TypeScript, using raw HTTP calls to LLMs like OpenAI, Anthropic Claude, or Google Gemini. You end up with a conversational agent that handles multi-turn chats, system prompts, and tools for file ops and shell commands via an agentic loop.

Why is it gaining traction?

Unlike agent skills examples or GitHub repos that just demo code, bloomery makes you write every line, with incremental steps, validation, hints, and auto-git commits—building real intuition for agent github claude or agent github openai flows. It scaffolds projects tailored to your LLM provider and language, resumes progress across sessions, and connects steps to how your host agent (like agent github copilot vscode) works internally. Devs dig the "meta moments" that click without frameworks or SDKs.

Who should use this?

AI-curious backend engineers implementing custom agent github actions or agent skills anthropic integrations. Tinkerers debugging agent github copilot reddit complaints by grokking raw tool calls. Teams exploring agent skills marketplace before committing to agent github microsoft or agent github copilot intellij.

Verdict

Try it if you want to demystify coding agents—solid docs and multi-provider support make the 30-90 minute tutorial worthwhile, despite 14 stars and 1.0% credibility score signaling early maturity. Skip for production; it's a learning accelerator, not a deployable agent github repo.

(198 words)

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