data-science-engineering-foundation

# 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](references/job-registry.md) | Before offering or selecting a job, routing to a skill or specialist, or naming a durable output | | [lifecycle-classes.md](references/lifecycle-classes.md) | When starting, pausing, resuming, completing, or re-invoking a job | | [transition-protocol.md](references/transition-protocol.md) | When a topic shift, explicit request, or completion suggests moving between jobs | | [session-state.md](references/session-state.md) | Before initialization, validation, mutation, resume, recovery, or reconstruction of coaching state | | [flow-state.md](references/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.