just-vibeML deployment · apply by default
/ml-package
Package preprocessing, artifacts, dependencies, and interfaces
Use to create a reproducible inference artifact; ml-serving implements the serving boundary.
Make it your own.
In Claude Code, use the slash command and add your context. In Codex, select ml-package from the just-vibe skill picker, then send the same brief.
Version 0.11.0 also supports /jv ml-package, /just-vibe ml-package and /jv:ml-package in Claude. See shortcut setup and context examples.
/just-vibe:ml-package Package the model with preprocessing, schema, dependencies, and parity fixtures./just-vibe:ml-package Package a model whose categorical encoder and feature order were saved separately./just-vibe:ml-package Inspect an artifact manifest without loading untrusted executable serialization.What the agent does
- Verify artifact provenance.
- Bundle preprocessing, feature order/schema, model identity and pinned compatible dependencies, recording versions and checksums.
- Validate fresh-load parity with known-input fixtures in an isolated supported environment.
Inputs
- model/preprocessing artifacts, runtime, interface, and output location.
Optional context: scope, references, constraints, successCriteria, environment, mode, budget.
Scope
- Reads
- Reproducible inference package and metadata; no registry upload or deployment implicitly.
- Writes
- Apply: only the requested local changes and relevant isolated verification. Inspect/plan requests remain inspection/planning. External actions require their exact action and target in session authorization.
- Mode
- Apply; model/preprocessing artifacts, runtime, interface, and output location.
- Prerequisites
- Versioned model and preprocessing artifacts, input/output schema, runtime/dependencies, operating targets, and authorized environment. Validate artifact trust before loading formats that can execute code. Packaging or writing monitoring configuration does not deploy a model or enable a hosted service.
Expected output
- Package and manifest with checksums, input/output schema, loading instructions and fresh-load expected-output fixture results.
How the work is checked
- A fresh supported environment reproduces fixture outputs within tolerance; incompatible artifact/schema versions fail clearly.
When to stop or clarify
- Do not load untrusted executable serialization or bundle training data/secrets unnecessarily. Missing preprocessing prevents a complete package claim.
Handling missing context
- Infer
- Read artifact format/trust, preprocessing schema, serving runtime, compatibility and existing rollout controls.
- Assume
- Prepare packaging/configuration and isolated checks without treating them as a live deployment.
- Ask
- Resolve the target, rollback compatibility and operating limits before rollout or load generation; missing production access does not block packaging.
Technical guidance
- Evidence
- Inventory model/preprocessor, ordered feature schema, versions, provenance and artifact format.
- Method
- Package the complete inference contract with known-input expectations; inspect executable serialization and trusted origin before loading.
- Pitfall
- Pickle-style loading can execute code; matching a model filename does not establish trusted provenance or preprocessing parity.
- Check
- Fresh-load a trusted fixture in isolation and compare exact feature order, missing/unseen handling and expected output within tolerance.
Situational decisions
When serialization may execute code and provenance is untrusted: Inspect provenance and use a safe supported loading path or stop before loading.
The coding agent follows this workflow using its available tools. Installation does not grant service access or guarantee an outcome. Read the compatibility notes.