Data and ML Feasibility Workflow

Goal

Produce one durable Markdown feasibility study that remains useful to people and can be consumed later by a Functional Planner. One constrained YAML block owns machine facts; narrative sections preserve evidence, interpretation, and context.

Flow

1. Confirm the proposed outcome, decision boundary, study scope, and durable output path.
2. Allocate UUIDv4 URNs for the study concept, study revision, each item concept, each item revision, and each relation. Never derive identity from a title, class, path, or content.
3. Capture candidate capabilities, constraints, assumptions, findings, risks, dependencies, decisions, evidence, gaps, and non-goals. Preserve uncertainty and source-authored criteria without promoting every item to a requirement.
4. Record lifecycle and provenance. Reclassification keeps conceptual identity and creates a new revision. Split, merge, derivation, withdrawal, and supersession retain explicit lineage.
5. Write or update the single named FEASIBILITY-STUDY-INTERCHANGE YAML block. Narrative can explain machine facts but cannot redefine them.
6. Validate constrained YAML, JSON Schema 2020-12 structure, semantic closure, revision lineage, tombstones, and narrative anchors with scripts/validate_feasibility.py.
7. Present the recommendation and unresolved review gaps. Preserve the study as read-only evidence for downstream consumers.
8. After the study is final, emit the sibling feasibility-to-PRD handoff described in feasibility-to-prd-handoff.md. Regenerate it after any material study revision, then validate both artifacts with scripts/validate_feasibility.py <study.md> --handoff <handoff.yml>.

Inputs