gbasin

gbasin / clanker-discipline

Public

An agent skill designed to discipline coding agents against state explosion, grab-bag models, and mutation ambiguity.

12
0
89% credibility
Found Mar 24, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

Clanker Discipline trains AI assistants that write code to use simpler, safer ways of handling information and functions, reducing errors and mess.

How It Works

1
🔍 Discover AI coding helpers

You hear about smart AI assistants that can write computer programs for you, but they often make things messy and hard to manage.

2
đź’ˇ Find Clanker Discipline

You come across Clanker Discipline, a simple way to teach those AI helpers better habits for cleaner, more reliable code.

3
🧠 Equip your AI with discipline

You easily add these smart rules to your AI helper so it learns to keep data simple and organized from now on.

4
đź’¬ Give your AI a task

You describe what you want built, like a fun app or useful tool, in everyday words.

5
✨ See the AI code perfectly

Your AI now creates neat programs that avoid confusion, with everything in the right place and no hidden surprises.

🎉 Build amazing projects effortlessly

You end up with smooth-running creations that work right the first time, saving you headaches and letting creativity flow.

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AI-Generated Review

What is clanker-discipline?

Clanker-discipline is an agent skill you install via `npx skills add gbasin/clanker-discipline --all -g` to train AI coding agents—like those powered by GitHub Copilot, Claude, or OpenAI—on lean state management. It tackles state explosion in agent-generated code, where bugs spawn extra flags and features bloat with optional fields, by enforcing rules like deriving values instead of storing them and using discriminated unions over nullable bags. Developers get cleaner, more predictable outputs from their agent GitHub actions or repos without refactoring nightmares.

Why is it gaining traction?

In the crowded agent skills GitHub scene—think Anthropic skills, VSCode extensions, or Copilot IntelliJ integrations—this stands out by targeting mutation ambiguity and grab-bag models head-on, unlike generic agent skills libraries or MCP alternatives. Users notice agents producing pure functions, branded primitives, and table-like data shapes that cut debugging time on stateful code. Its concise principles, pulled from event sourcing ideas and self-documenting code patterns, hook devs experimenting with agent GitHub code who want production-ready outputs fast.

Who should use this?

Backend devs wrangling AI-assisted state machines in Node or TypeScript apps, especially those using agent skills io for GitHub repos with complex data flows. Frontend teams building forms or UIs with Copilot Reddit workflows will appreciate impossible wrong states via unions. It's ideal for anyone scaling agent GitHub Microsoft or OpenAI setups against antigravity-style bloat.

Verdict

Try it if you're deep into agent skills examples on GitHub—its 12 stars and single-doc setup scream early alpha, backed by a 0.9% credibility score—but the focused rules deliver immediate wins on code hygiene. Watch for updates; pair with mature agent skills repos for best results.

(198 words)

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