Execution

Execution modes, the sandbox model, context collection, and how generated code runs safely.

Execution modes

Each target runs in one of three modes, determined by its directives:

Codegen mode is the core execution path today. The CLI collects repo context, calls the LLM to generate a shell script, and executes it. The generated script is cached for subsequent runs.

Execution sandbox

Coming soon. Scripts currently execute directly on the host machine via bash. A warning is displayed at runtime. Use --dry to review generated scripts before execution.

Context collection

The CLI collects repo context before making the LLM call. The collector is task-aware — it sends only what’s relevant for the declared intent. This is fully implemented.

Always included:

• Top-level file tree (depth 2)
• Contents of Vibefile

Conditionally included (detected from task name/intent):

• package.json / pyproject.toml / go.mod / Cargo.toml — framework and dependency detection
• fly.toml, Dockerfile, railway.json — deploy-type tasks
• Test config files (jest.config.*, pytest.ini, etc.) — test-type tasks
• Schema files — seed/migration tasks
• Existing Makefile or shell scripts — inherit known-good patterns
• git status output — uncommitted changes, current branch

Never included:

• node_modules/, .venv/, build output directories
• Full source file contents unless directly relevant
• Secret or credential files

The goal: the LLM generates correct commands on the first attempt because it understands the repo, without being sent the entire codebase.

The context collector also produces file hashes used for cache invalidation — when a relevant context file changes, the cached script is automatically recompiled.