demirelo/implement-skill — explained in plain English
Analysis updated 2026-05-18
Feed in a development plan and let AI models implement it as separate pull requests.
Run multiple AI Builders against automated tests to pick the best working solution.
Automatically repair failed CI runs and merge conflicts without manual intervention.
Use it as a Claude Code plugin or a Codex skill to automate coding workstreams.
| demirelo/implement-skill | 0xustaz/streamgate | a-bissell/unleash-lite | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | Python | Python | Python |
| Setup difficulty | hard | hard | hard |
| Complexity | 4/5 | 4/5 | 4/5 |
| Audience | developer | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires Claude Code or Codex, Python 3.11+, git, gh, a sandbox backend, and API credentials for the chosen AI models.
This project is a tool called /implement that automates software development work. You give it a plan describing a feature to build, along with a list of AI models to use, and it tries to build that feature end to end, opening real GitHub pull requests when it is done. The process runs in stages. First the plan is broken into smaller pull request sized pieces with clear acceptance criteria. Independent pieces then run at the same time in separate isolated copies of the repository, called worktrees. For each piece, the tool has several AI models called Builders compete to write the smallest working solution that passes automated tests, which act as an objective pass or fail check rather than relying on a model's own opinion of whether the code is correct. A separate Reviewer model then checks the result for correctness, security issues, and unnecessary complexity. If tests fail, or the reviewer finds a problem, or there is a merge conflict, the tool automatically sends the issue back to the Builders to fix. When everything passes, it can merge the pull request itself, or hand off to a human if something is uncertain or blocked by a policy. Currently it supports test based checks for Python projects using pytest, TypeScript projects using Vitest, and Lean 4 proof projects using the Lake build tool. Security is a focus throughout: generated code runs inside a sandbox with no network access and restricted file writes, sensitive folders like SSH keys are blocked, and any credentials the tool needs are kept out of the repository and out of reach of the models themselves. It is available both as a plugin for Claude Code and as a skill for Codex, using the same underlying engine either way. It targets developers who want to automate routine or well specified coding tasks by supervising a team of AI models rather than writing every pull request by hand, and it can also be called directly from a Python script.
An autonomous coding tool that has AI models build features, test them, and open pull requests, with a human fallback when unsure.
Mainly Python. The stack also includes Python, pytest, Vitest.
Licensed under MIT, allowing free use, modification, and commercial use as long as the copyright notice is kept.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
double-check against the repo, no cap.