Data Science Engineering Foundation

MicrosoftOfficialPublished by Microsoft

Skill
Meta (authoring, orchestration, planning, memory)
Agent tooling
CC-BY-4.0
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From the author

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.

— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Data Science and Engineering Foundation serves as the core orchestration layer for the Data Science and Engineering Coach. It provides a centralized set of protocols for managing session state, job lifecycles, and flow-state mechanics. This foundation ensures consistency across different coaching sessions and job transitions by defining how the coach should initialize, resume, and navigate between various tasks. It acts as an internal reference index rather than a user-facing tool, enforcing rules for when to interrupt, how to handle durable writes, and how to maintain session integrity.

What it does

Defines protocols for session initialization, state reconstruction, and resumption.
Provides a registry for routing tasks to specific skills and specialists.
Implements a flow-state protocol to manage interruptions and user gates.
Enforces a durable-write gate to scan for sensitive content before creating artifacts.
Maintains clear boundaries between general orchestration and job-specific logic.
Standardizes the lifecycle management of coaching jobs.

When to use it

When the coach needs to initialize or resume a session.
When transitioning between different data science or engineering jobs.
Before performing a durable write of a customer artifact.
When validating the state of a coaching session.
When determining the appropriate skill or specialist to handle a specific task.

Advantages

Ensures consistent behavior across different coaching sessions.
Provides a structured approach to handling sensitive data during artifact creation.
Decouples orchestration logic from job-specific implementation details.
Improves reliability by enforcing clear state and lifecycle rules.
Facilitates modularity by routing tasks to specialized skills.

Usage examples

“Initialize the coaching session for a new data engineering project.”: The coach loads the session-state protocol, validates the project identity, and prepares the environment for the selected job.
“I want to save this new data catalog document.”: The coach triggers the flow-state protocol, performs a sensitive content scan on the document, and proceeds with the write only if the scan passes.
“Switch from the current feasibility study to a new pipeline design task.”: The coach follows the transition protocol to pause the current job, confirm the switch with the user, and route to the dataops skill.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/microsoft/data-science-engineering-foundation/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip data-science-engineering-foundation.zip -d .agents/skills

Documentation by Microsoft

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.

Files

ATTRIBUTION.mdAdded by AutoGineer1021 B
LICENSEAdded by AutoGineer16.6 KiB
SKILL.md3.9 KiB
references/flow-state.md5.3 KiB
references/job-registry.md17.8 KiB
references/lifecycle-classes.md4.0 KiB
references/session-state.md6.9 KiB
references/transition-protocol.md3.0 KiB

references/lifecycle-classes.md

title: Data Science and Engineering Lifecycle Classes description: Discriminated lifecycle semantics for episodic, bounded, and continuous jobs in a persistent coaching session

Data Science and Engineering Lifecycle Classes

Purpose

Classify every registered job as exactly one of episodic, bounded, or continuous. The class determines valid state fields, outgoing disposition, resume behavior, and completion behavior. Job completion never ends the coaching session automatically.

Class contract

Class	Termination	Valid state	Resume behavior
`episodic`	One invocation ends after Frame, Execute, Confirm	`status`, `invocations`, artifact and summary pointers	List prior invocations; run again only after a new explicit request
`bounded`	Ends at its recommendation or terminal gate	`status`, `phase`, `phase_gates`, artifact pointer	Restore phase and gates; completed work stays terminal unless explicitly revised
`continuous`	Does not terminate during the project	`status`, artifact pointer, `last_enriched_at`	Read the durable artifact and restore it as active background context

Episodic jobs

Use this sequence for each invocation:

Frame: confirm the target, relevant catalog context, and expected output.
Execute: route to the owning skill or specialist and perform the confirmed work.
Confirm: summarize the output and connections, append an invocation and artifact record, and mark that invocation complete.

An episodic invocation may be abandoned before a durable output exists. Record the outgoing disposition as discarded-cleanly. A completed invocation remains in history and is not repeated on resume. A later explicit request creates a new invocation rather than reopening the old one.

Bounded jobs

Allowed statuses are never, active, paused, and complete.

Starting sets active and records the initial phase and gates.
Leaving before the terminal recommendation records the current phase and gate states, then sets paused.
Resuming restores the recorded phase and gates before any new question.
Reaching the terminal recommendation sets complete, persists the terminal state, and offers choices without advancing to another job.
A completed job is not re-entered during a later resume. Revision requires an explicit user request and a recorded rationale.

Continuous jobs

Allowed statuses are never and active.

Starting creates or adopts the durable artifact and sets active.
Enrichment events update the artifact and last_enriched_at after the durable-write gate succeeds.
Leaving the foreground flushes confirmed pending enrichment before recording flushed as the outgoing disposition.
Resume reads the artifact itself to restore current catalog context.

Session completion boundary

The session is unbounded. An episodic invocation or bounded job can complete inside it, while continuous work remains available. After completion:

Name what completed and its durable outputs.
Persist terminal or invocation state.
List paused bounded work and active continuous work.
Offer user-selected next actions, including close, without auto-selecting another job.

Invalid states

An episodic block with phase or phase_gates
A continuous block with phase, phase_gates, or complete
A bounded block marked paused without a resumable phase
A completed bounded job silently reset to active
Any class change without an explicit user-confirmed correction and log entry

Provenance

This lifecycle model is repository-original guidance licensed under CC BY 4.0. It does not reproduce or summarize an external standard.

Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter

Security

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Attribution and licence

Published by

Microsoft

Publisher

Microsoft (Official)

Source

microsoft/hve-core · .github/skills/data-science-engineering/data-science-engineering-foundation · commit f425e94

Licence

Creative Commons Attribution 4.0 International (redistribution allowed)

LICENSE (written by AutoGineer)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by Microsoft or Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download data-science-engineering-foundation.zip