cleanvibe 1.3.0

A workflow accelerator that scaffolds AI-assisted coding projects with opinionated documentation and launches Claude Code.

cleanvibe

Website · cleanvibe.emmaleonhart.com

A tiny Python CLI that scaffolds AI-assisted coding projects and launches Claude Code.

cleanvibe is not a coding tool. It's a state initializer -- it removes the friction between "I want to build something" and "Claude is working inside a well-structured environment." The real value lives in the CLAUDE.md it injects: an opinionated behavior contract that enforces documentation discipline, meaningful commits, and iterative file-based thinking.

Install

pip install cleanvibe

Usage

Create a new project

cleanvibe new my-project

This will:

1. Create the directory my-project/
2. Write CLAUDE.md (workflow rules for AI-assisted development)
3. Write README.md (starter documentation)
4. Write queue.md (active work queue, pre-seeded with a first-session bootstrap sequence that walks Claude through triaging dropped-in files, inferring the project, interviewing the user, creating todo.md, populating the real queue, and pushing to a private GitHub repo)
5. Write .gitignore (sensible Python defaults)
6. Initialize a git repo with an initial commit
7. Launch Claude Code inside the project

Clone an existing repo — codebase onboarding

cleanvibe clone https://github.com/user/repo

clone is for onboarding an existing codebase, not bootstrapping a blank one. It is deliberately different from new:

1. git clone the repository
2. Create and check out a dedicated cleanvibe-onboarding branch — the default branch is left untouched
3. Prepend-or-write an onboarding CLAUDE.md and queue.md: if the repo already has them, the fresh block goes on top (newest first) and the original content is preserved below — re-running just layers another block
4. Inject .gitignore only if missing. No data_lake/ (it is a real codebase, nothing was dropped in) and no README overwrite
5. Commit the onboarding scaffold on the branch
6. Launch Claude Code inside the project

The onboarding queue.md is small and focused: read & document the repo, make existing docs accurate, rewrite CLAUDE.md to the repo's real development practices, add tests/CI if sparse, then synthesize any existing planning artifacts and hand off to the repo's own todo.md.

Replicate a paper

cleanvibe replicate takes either a paper reference or a folder name:

From an arXiv / alphaxiv paper:

cleanvibe replicate https://arxiv.org/abs/1706.03762
cleanvibe replicate https://www.alphaxiv.org/overview/2201.02177
cleanvibe replicate 1706.03762

Any arXiv/alphaxiv id or URL is accepted — /abs/, /pdf/, /html/, alphaxiv's primary /overview/, /forum/, versioned ids, trailing slugs and query strings all resolve to the bare id. This will:

1. Fetch the paper's metadata from the arXiv API
2. Create replicating-<paper-slug>/ (silently -2/-3 if it already exists)
3. Scaffold a standalone replication project: cleanvibe conventions (CLAUDE.md, queue.md, data_lake/) plus the replication structure — SKILL.md (the agent-executable replication plan), download_paper.py, replication_target/ (the paper itself lives here, gitignored — never in data_lake/; the authors' code is cloned here as a git submodule), paper.json, and .github/workflows/ that build a GitHub Pages findings site, a transportable PDF report, and a downloadable ZIP replication package
4. Initialize a git repo with an initial commit
5. Launch Claude Code inside the project

From a folder you fill yourself (manual drop-in mode):

cleanvibe replicate my-paper-replication

When the argument is not an arXiv/alphaxiv reference it is treated as a folder name and a manual drop-in project is scaffolded — no metadata fetch, no download_paper.py, no paper.json, no network. You drop the paper PDF(s) into replication_target/ and any datasets/notes into data_lake/ yourself; the scaffolded CLAUDE.md / queue.md / SKILL.md / README.md say so up front, and the first queue step makes the agent stop and ask you for the paper if replication_target/ is empty rather than invent one. Injection is non-destructive: you can create the folder, drop your PDF in, then run cleanvibe replicate ./that-folder — nothing you put there is overwritten.

Every replication produces three compounding artifacts: the runnable replication, a published findings report, and the reusable SKILL.md methodology. See docs/replication_framing.md for the full vision.

Options

cleanvibe new my-project --dry-run        # Preview what would be created
cleanvibe new my-project --no-claude      # Skip launching Claude Code
cleanvibe clone REPO path --dry-run       # Preview what would be done
cleanvibe replicate URL --dry-run         # Preview the arXiv replication scaffold
cleanvibe replicate FOLDER --dry-run      # Preview the manual drop-in scaffold
cleanvibe replicate URL --no-claude       # Scaffold without launching Claude
cleanvibe --version                       # Show version

Why?

Most people struggle with blank repo paralysis, poor commit hygiene, and AI assistants that ramble without producing durable artifacts. cleanvibe solves this by injecting a disciplined thinking contract into every project from the start.

The CLAUDE.md template enforces:

• Commit early and often with meaningful messages
• No planning-only modes -- all thinking produces files and commits
• Keep documentation up to date as the project evolves
• Use planning/ directories for exploration instead of internal planning modes

Cross-platform

Works on Windows, Linux, and macOS. Zero dependencies beyond Python 3.9+.

Website

Full walkthrough — what cleanvibe is and what each subcommand does — at the project site (built from pages/ and deployed by GitHub Actions): https://cleanvibe.emmaleonhart.com/

Stability

As of v1.0.0, cleanvibe commits to the following contract (semantic versioning from here on):

• Subcommands new, clone, convert, and replicate are stable. Their core behavior will not change incompatibly within the 1.x line.
• Injected files: new guarantees CLAUDE.md, README.md, queue.md, .gitignore, and data_lake/.gitkeep. replicate always guarantees SKILL.md, CLAUDE.md, queue.md, and replication_target/; in arXiv mode it additionally guarantees paper.json and download_paper.py (these are absent by design in manual drop-in mode — there is no metadata to fetch).
• Non-destructive by contract: clone and convert never overwrite existing files — clone prepends; convert only injects what is missing. replicate in arXiv mode never errors on a name collision (silent -2/-3 suffix); in folder mode it injects only what is missing so a pre-dropped paper is never clobbered.
• Template wording may evolve (improvements to the workflow contract are not breaking); the set of guaranteed files and the subcommand contracts above are what 1.x holds stable.
• Zero runtime dependencies remains a hard guarantee for the 1.x line.

License

MIT