ricoyudog

ricoyudog / Coding_Corgi_flow

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OpenSpec GitFlow — structured AI engineering workflows with issue tracking

17
1
100% credibility
Found May 08, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

Coding Corgi Flow is a community extension of OpenSpec that adds structured workflows for AI-assisted coding, including issue tracking, checkpoint reviews, and cross-session memory on GitHub or GitLab.

How It Works

1
🔍 Discover Coding Corgi Flow

You find a friendly tool that turns your AI coding helper into organized, reliable steps for building projects.

2
Pick your easy setup
🔌
Plugin install

One command in your AI chat adds it automatically.

📋
Paste prompt

Copy a ready message into your AI chat to set it up.

3
🐕 Assistant prepares everything

Your AI grabs the tools, sets up folders, and gets ready to guide your work.

4
🧠 Add ongoing memory

Create simple notes so your AI remembers decisions and progress across chats.

5
💡 Propose a change

Tell it what you want to build, and it makes plans, lists steps, and starts tracking progress.

6
🔧 Build and check step-by-step

Work through small groups of tasks, test quality, review results, and move forward safely.

🎉 Projects flow smoothly

Enjoy reliable coding with automatic tracking, quality checks, and everything organized.

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Star Growth

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AI-Generated Review

What is Coding_Corgi_flow?

Coding Corgi Flow extends Fission AI's OpenSpec GitHub tool into a full GitFlow for AI coding agents, enforcing schema-driven planning (proposals, specs, designs, tasks), checkpoint-based implementation, and automatic GitHub or GitLab issue tracking. TypeScript-based, it plugs into tools like Claude Code or OpenCode via simple slash commands (/corgi-propose, /corgi-apply) to generate artifacts, run verify gates (lint/test coverage), and handle 5-axis reviews before merging. Developers get structured AI engineering workflows that sync progress to issues, beating ad-hoc API spec vs GitHub Speckit or OpenAPI GitHub Copilot CLI setups.

Why is it gaining traction?

It stands out by layering issue-driven state machines (backlog to done) and worktree isolation on OpenSpec's core, letting AI handle one task group at a time with pauses for human review—unlike vanilla OpenSpec's all-at-once apply. Plugin marketplace installs (Claude /plugin install corgispec) make it dead simple, plus cross-session memory keeps AI context alive. For GitHub OpenSpec AI or OpenSpec GitHub CN users, it's the missing GitFlow bridge to disciplined coding flows.

Who should use this?

AI-heavy engineering teams on GitHub/GitLab building complex features, like backend devs crafting OpenAPI GitHub specs or full-stack folks using GitHub Copilot API spec workflows. Ideal for solo devs or small squads wanting structured tracking without losing AI speed—think "add JWT auth" turning into tracked issues, deltas, and merges.

Verdict

Promising OpenSpec extension for disciplined AI flows, but 17 stars and 1.0% credibility signal early-stage maturity—docs are solid, but expect rough edges in production. Try it if you're on Claude/OpenCode and tired of chaotic AI coding; skip for battle-tested GitFlow alternatives.

(198 words)

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