gbasin

gbasin / hand-compute

Public

Skill to force AI coding agents to walk system execution by hand, catching races and state bugs that abstract review would miss.

37
1
100% credibility
Found Apr 19, 2026 at 37 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A skill for AI coding assistants that simulates program execution step-by-step by hand to detect state and timing bugs missed by abstract thinking.

How It Works

1
💡 Discover hand-compute

You hear about a clever trick that makes AI helpers carefully track every step in a program to spot sneaky bugs others miss.

2
➕ Add the skill to your AI

You quickly add this step-by-step thinking method to your AI coding assistant so it's ready to use.

3
Pick your goal
🆕
Plan new work

Walk through the task by hand first to uncover hidden issues before writing code.

🔧
Debug a problem

Trace the broken steps exactly to see where things go wrong, then plan the fix.

📐
Scope a feature

Test the new addition against the current setup to note needed changes.

4
📝 Describe your task

You explain the program or flow to your AI in simple terms, ready for it to simulate.

5
👣 Watch the hand-walk

Your AI carefully simulates each step, writing down states and spotting mismatches that cause bugs.

6
💥 See the issues surface

Tricky timing problems or state mix-ups become obvious, guiding better decisions.

✅ Get reliable results

You end up with solid code, fixes, or plans that actually work without surprises.

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AI-Generated Review

What is hand-compute?

hand-compute is a lightweight skill for AI coding agents, installed via `npx skills add gbasin/hand-compute --all -g` from the github skill marketplace or directory. It forces agents to simulate system execution step-by-step like a "hand computer," tracking concrete state to expose race conditions and bugs that abstract reviews miss. Developers get a structured prompt technique for manual walkthroughs in new work, debugging broken flows, or scoping features against existing state machines.

Why is it gaining traction?

Unlike vague narrative traces or sub-agent fanning, it demands explicit state notation at every transition, surfacing contradictions early—benefits users see in fewer production escapes for stateful logic. The hook is its three clear use cases, plus failure mode docs, making it a quick win over hand-wavy github skill compose alternatives. Ties into the hand computer meme of meticulous computation without needing extra tools.

Who should use this?

AI agent wranglers debugging distributed systems or state machines, backend engineers hand-computing flows in unfamiliar codebases, and teams scoping features on legacy services where races lurk. Ideal for skill force scenarios like securing state invariants before coding.

Verdict

Grab it if state bugs plague your AI-assisted dev workflow—37 stars and 1.0% credibility reflect early days with just README docs, but the concept delivers immediate value for hand computer controller precision. Test on a real bug hunt before committing.

(178 words)

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