DenisSergeevitch

DenisSergeevitch / repo-task-proof-loop

Public

Spec driven skill with subagents spawning

512
32
89% credibility
Found Apr 01, 2026 at 509 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A tool that creates structured folders and guides within a project to manage AI-driven coding tasks through a repeatable loop of planning, building, testing, and fixing.

How It Works

1
📰 Discover the task organizer

You hear about a helpful tool that keeps AI-assisted coding projects neat and provable by creating organized folders right in your work area.

2
📂 Add it to your project

Simply copy the tool's folders into your project's special AI helpers section so it's ready to use.

3
✨ Start a new task

Give it a short name for your coding job and describe what you want done, like 'add login security'.

4
📋 Special folder appears

It instantly sets up a dedicated spot with ready-made notes, checklists, and guides for your AI friends to follow.

5
Pick your next move
▶️
Continue task

Let your AI helpers plan, build, test, and fix until it's right.

🔍
Check status

See what's done, what's pending, and if everything checks out.

6
✅ Review and validate

Look over the proofs, tests, and notes to confirm the task is solid and complete.

🎉 Task proven and saved

Your coding task is fully verified with all evidence stored safely in your project, ready to build on.

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Star Growth

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AI-Generated Review

What is repo-task-proof-loop?

Repo Task Proof Loop is a Python tool for GitHub repos that sets up spec driven development workflows for complex coding tasks. It generates durable task folders with specs, evidence logs, JSON verdicts, and raw outputs like build results or screenshots, while installing subagents for Claude Code and Codex to drive a tight loop: freeze spec, build, collect evidence, verify, fix minimally, verify again. This keeps all proof repo-local, making tasks easy to pause, resume, or audit without losing context.

Why is it gaining traction?

As a github spec kit alternative, it excels in spec driven development ai by enforcing separation of building from verification via subagents, unlike loose prompt chains in spec github copilot or Cursor setups. Developers hook on the CLI commands (init, status, validate) and ready prompts for starting or continuing tasks, plus auto-updates to repo guides like AGENTS.md or CLAUDE.md. The loop's durability beats one-off spec driven development tools, turning chaotic AI sessions into auditable processes.

Who should use this?

AI-augmented backend devs tackling feature hardening or refactors in team repos. Solo full-stack engineers using Claude Code or Codex for spec driven design on non-trivial loops. GitHub power users wanting spec driven development frameworks that persist state across sessions.

Verdict

Worth adopting for spec driven development github fans in Claude/Codex flows—501 stars, strong docs, and built-in smoke tests show maturity, though the 0.9% credibility score suggests testing in your stack first. Pairs well as a spec kit claude code or codex upgrade.

(198 words)

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