just-vibeML data · plan by default
/ml-frame
Define target, prediction moment, unit of analysis, and objective
Use to define the prediction problem; ml-baseline implements the first comparator after the task is defined.
Make it your own.
In Claude Code, use the slash command and add your context. In Codex, select ml-frame from the just-vibe skill picker, then send the same brief.
Version 0.11.0 also supports /jv ml-frame, /just-vibe ml-frame and /jv:ml-frame in Claude. See shortcut setup and context examples.
/just-vibe:ml-frame Frame churn prediction 30 days before cancellation, including unit and label horizon./just-vibe:ml-frame Frame failure prediction for machines with delayed maintenance labels./just-vibe:ml-frame Define the task without business error costs; keep threshold selection undecided.What the agent does
- State one prediction row's entity (the unit of analysis), timestamp, information available at prediction time, label horizon and the action taken from predictions.
- Compare a rule-based decision or other baseline before choosing a model, and state the cost of each error type.
Inputs
- decision to support, population, available data, prediction timing, and operational objective.
Optional context: scope, references, constraints, successCriteria, environment, mode, budget.
Scope
- Reads
- Define the modeling problem before selecting algorithms.
- Writes
- No source changes in inspect/plan. Save only requested planning artifacts. A separately requested repair uses the relevant implementation workflow.
- Mode
- Plan; decision to support, population, available data, prediction timing, and operational objective.
- Prerequisites
- Task definition, dataset identity, field semantics, entity/time keys, and permission to inspect bounded data. Record prediction moment, label horizon, sampling, and provenance. Preserve held-out evaluation boundaries; no data upload, label alteration, or feature fitting across splits implicitly.
Expected output
- Modeling brief and task card with row grain, prediction moment, outcome horizon, action, error costs, success metrics, eligibility/exclusions, deployment assumptions and unresolved policy choices.
How the work is checked
- Target and prediction time are unambiguous; a proxy label's mismatch with the real objective is explicit.
When to stop or clarify
- Do not force an ML solution when deterministic rules suffice or invent business error costs without input.
Handling missing context
- Infer
- Read prediction moment, label horizon, entity/time keys, split policy and dataset provenance from the task and manifests.
- Assume
- Use explicit synthetic examples for design when raw data is unavailable; do not infer missing labels or fit preprocessing across held-out boundaries.
- Ask
- Ask when unresolved label timing, grouping or target semantics would change the split/features; do not demand a full dataset to explain the method.
Technical guidance
- Evidence
- Establish prediction entity/time, decision being supported, available information, outcome horizon and label maturity.
- Method
- Translate product value into a measurable objective with a naive comparator and deployment population.
- Pitfall
- Optimizing an available label can answer a different question from the real decision; missing follow-up is not a negative outcome.
- Check
- Walk one positive, negative and censored example through feature availability, prediction and eventual label eligibility.
Situational decisions
When label timing or intervention changes the observed outcome: Separate prediction from causal/intervention claims and identify the missing observation process.
The coding agent follows this workflow using its available tools. Installation does not grant service access or guarantee an outcome. Read the compatibility notes.