By julep-ai
Vibesafe developer tools: scan, compile, test, save, diff, status, mcp; includes mode toggle.
Show the current VIBESAFE_ENV (mode) visible to this session.
Initialize the Vibesafe plugin session: report mode, list commands, and verify MCP connectivity.
Set VIBESAFE_ENV for subsequent Vibesafe commands (dev|prod).
Run vibesafe CLI commands through the MCP server. Usage: /vibe <subcommand> [args] where subcommand ∈ {scan, compile, test, save, diff, status, mcp}.
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
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Cryptographically-verifiable AI code generation for production Python.
Vibesafe is a developer tool that generates Python implementations from type-annotated specs, then locks them to checkpoints using content-addressed hashing. Engineers write small, doctest-rich function stubs; Vibesafe fills the implementation via LLM, verifies it against tests and type gates, and stores it under a deterministic SHA-256. In dev mode you iterate freely; in prod mode hash mismatches block execution, preventing drift between intent and deployed code.
How do you safely deploy AI-generated code when the model can produce different outputs on identical inputs?
Vibesafe solves this with hash-locked checkpoints: every spec (signature + doctests + model config) computes a deterministic hash, and generated code is verified then frozen under that hash. Runtime loading checks the hash before execution—if the spec changes or the checkpoint is missing, prod mode fails fast. This gives you reproducibility without sacrificing iteration speed in development.
Measured impact: Zero runtime hash mismatches in production across 150+ checkpointed functions over 6 months of internal use; dev iteration loop averages <10s for compilation + test verification; drift detection caught 23 unintended spec changes in CI before merge.
Vibesafe bridges human intent and AI-generated code through a contract system:
raise VibeCoded()Traditional code generation tools either:
Vibesafe gives you both: fast iteration in dev, frozen safety in prod. The checkpoint system ensures what you tested is what runs, while the spec-as-code approach keeps your intent readable and version-controlled.
--freeze-http-deps captures exact runtime package versions into checkpoint metadata, solving the "works on my machine" problem for FastAPI endpoints| Tool | Approach | Vibesafe Difference |
|---|---|---|
| GitHub Copilot | Suggests code in editor | Vibesafe generates complete verified implementations |
| Cursor/Claude Code | AI pair programming | Vibesafe enforces hash-locked reproducibility |
| ChatGPT API | On-demand generation | Vibesafe caches + verifies once, reuses in prod |
| OpenAPI codegen | Schema-driven templates | Vibesafe uses LLMs for flexible logic, not just boilerplate |
Here's vibesafe in action—no configuration, just code:
>>> import vibesafe
>>> from vibesafe import VibeCoded
>>> @vibesafe
... def cowsay(msg: str) -> str:
... """
... >>> cowsay("moo")
... 'moo'
... """
... raise VibeCoded()
...
>>> print(cowsay('moo'))
moo
\ ^__^
\ (oo)\_______
(__)\ )\/\
||----w |
|| ||
That's it. The decorator saw your function name, inferred the intent from "cowsay", and generated an ASCII art implementation. Now let's see how to use it in a real project.
# Clone the repo (for now; PyPI package coming soon)
git clone https://github.com/julep-ai/vibesafe.git
cd vibesafe
# Create virtual environment and install
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv pip install -e ".[dev]"
# Verify installation
vibesafe --version
# or use the short alias:
vibe --version
Troubleshooting:
npx claudepluginhub julep-ai/vibesafe --plugin vibesafeToken-efficient vibe-coding pipeline with hard guardrails: brainstorm, plan, isolate, exec, verify, review, integrate. Halts on 'Expected' mismatches instead of committing bad work.
Pre-deploy security audit for vibe-coded apps. Catches unauthenticated admin APIs, missing RLS, leaked service keys, exposed debug routes.
Non-technical progress summaries for Claude Code work (hides diffs/log noise).
Harness-native ECC operator layer - 67 agents, 271 skills, 92 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.