| name | data-science-engineering-foundation |
| description | State, resume, reconstruction, job-lifecycle, transition, and flow-state mechanics for the Data Science and Engineering Coach. Loaded by the coach; not a user entry point. |
| license | CC-BY-4.0 |
| user-invocable | false |
| metadata | {"authors":"microsoft/hve-core","spec_version":"1.0","last_updated":"2026-08-03"} |
Data Science and Engineering Foundation
Goal
Keep Data Science and Engineering Coach orchestration consistent across job changes and
sessions without duplicating job-specific guidance. The coach loads this index
at initialization and resume, then reads the reference for the current
coaching moment.
Reference index
| Reference | When to read |
|---|
| job-registry.md | Before offering or selecting a job, routing to a skill or specialist, or naming a durable output |
| lifecycle-classes.md | When starting, pausing, resuming, completing, or re-invoking a job |
| transition-protocol.md | When a topic shift, explicit request, or completion suggests moving between jobs |
| session-state.md | Before initialization, validation, mutation, resume, recovery, or reconstruction of coaching state |
| flow-state.md | Before interrupting work, crossing a gate, writing a durable artifact, or offering post-job choices |
Success criteria
- Each orchestration rule has one owner in this package.
- State and lifecycle mechanics remain independent of job-specific methods.
- The coach reads the applicable reference before acting on its contract.
Constraints
- Keep catalog guidance in
data-catalog, the durable data-catalog workflow for
entities, declared relationships, lineage, and ERD-ready model semantics.
- Keep feasibility guidance in
feasibility, the evidence-led data and ML
feasibility-study workflow with lifecycle and interchange traceability.
- Keep pipeline and testing guidance in
dataops, the DataOps reference for
tier behavior, pipeline invariants, validation placement, tests, drift, and
operational signals.
- Keep general experiment guidance in
experiment-design, the reusable
workflow for candidate selection, hypotheses, vetting, minimum scope, and
result evaluation.
- Keep ML-specific experiment guidance in
ml-experimentation, the reference
for ML environments, reproducibility, tracking, evaluation, abstractions,
and production readiness.
- Keep notebook and dashboard guidance in
analysis-authoring, the
reference for EDA notebook and analytical dashboard composition,
visualization selection, and dashboard validation.
- Keep AI-system evaluation guidance in
evaluation-design, the reference
for evaluation dataset design, difficulty balance, metric selection, and
tooling fit.
- Treat this package as internal foundation knowledge, not a user-selectable
workflow.
- Preserve the skill authority boundaries and cross-cutting concerns defined in
the job registry.
Stop rules
- Stop before a state mutation when the session-state protocol has not been
loaded or project identity is uncertain.
- Stop before a job switch when the user has not confirmed the transition.
- Stop and report the missing owner when requested behavior belongs to neither
this foundation nor a registered job capability.
Attribution
This package is repository-original orchestration guidance licensed under
CC BY 4.0. It does not reproduce or summarize an external standard.
