Feasibility

MicrosoftOfficialPublished by Microsoft

Skill
Workflow
Software development
CC-BY-4.0
Low risk
Has scripts

From the author

Author and validate durable data and ML feasibility studies using the Feasibility Study Interchange Profile, constrained YAML authority, UUID URN identity, lifecycle lineage, and evidence traceability. Use when assessing whether available data and technical evidence support a proposed outcome.

— the description by Microsoft

AutoGineer summary

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

The feasibility skill provides a structured approach to assessing the viability of data and machine learning projects. It enforces a rigorous, machine-readable format using a constrained YAML block embedded within a Markdown document. By utilizing UUID-based identity, lifecycle lineage, and explicit evidence traceability, the skill ensures that feasibility studies remain durable, auditable, and ready for downstream consumption by product planning processes. It includes validation scripts to ensure structural and semantic integrity, as well as a mechanism to generate a handoff artifact for PRD authoring.

What it does

Defines a structured format for feasibility studies using Markdown and constrained YAML.
Enforces identity management using UUID URNs for concepts, revisions, and relations.
Supports lifecycle tracking, including lineage for splits, merges, and supersessions.
Provides validation tools to check structural integrity and semantic closure.
Generates a formal handoff artifact for transitioning findings into PRD authoring.
Maintains clear separation between machine-readable facts and narrative evidence.

When to use it

When assessing whether technical evidence and data support a proposed project outcome.
When you need to maintain a durable, auditable record of feasibility decisions.
When preparing to hand off validated findings to a product requirements process.
When you need to track the lineage and provenance of feasibility evidence over time.

Advantages

Ensures consistency and machine-readability across all feasibility studies.
Provides clear traceability from evidence to conclusions.
Prevents ambiguity by requiring explicit status and criteria definitions.
Facilitates reliable downstream integration with product planning workflows.
Maintains a clear audit trail for decisions and revisions.

Usage examples

“Initialize a new feasibility study for the proposed data ingestion pipeline.”: The agent creates a new Markdown file using the feasibility template, ready for the user to define the study scope and allocate initial UUIDs.
“Validate the current feasibility study and generate the PRD handoff.”: The agent runs the validation script to check the YAML block and lineage, then generates the required handoff file if the study is in a Recommend state.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/feasibility/download

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

mkdir -p .agents/skills && unzip feasibility.zip -d .agents/skills

Documentation by Microsoft

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

Confirm the proposed outcome, decision boundary, study scope, and durable output path.
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.
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.
Record lifecycle and provenance. Reclassification keeps conceptual identity and creates a new revision. Split, merge, derivation, withdrawal, and supersession retain explicit lineage.
Write or update the single named FEASIBILITY-STUDY-INTERCHANGE YAML block. Narrative can explain machine facts but cannot redefine them.
Validate constrained YAML, JSON Schema 2020-12 structure, semantic closure, revision lineage, tombstones, and narrative anchors with scripts/validate_feasibility.py.
Present the recommendation and unresolved review gaps. Preserve the study as read-only evidence for downstream consumers.
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

Problem definition, desired outcome, and decision the study must support
Data access, discovery, architecture, exploration, preprocessing, and experiment evidence
Source-authored acceptance criteria, when known
Risk, privacy, Responsible AI, performance, and operational evidence
Prior study revision and item identity registry, when revising an existing study

Success criteria

Exactly one named constrained YAML block declares profile: feasibility-study-interchange and profile_version: 1.0.0.
Study, item, relation, and revision IDs are RFC 9562 UUID URNs. Conceptual and revision IDs are unique and never reused.
Item class, status, alias, planning relevance, relations, provenance, confidence, review, and location remain separate fields.
Revision lineage is closed and acyclic. Current revisions appear in the revision registry.
Withdrawn and superseded items remain as complete tombstones. Split, merge, supersession, and derivation targets resolve.
Every item has one matching narrative anchor, and narrative has no unknown item anchor.
Missing criteria remain explicit. The workflow does not invent acceptance criteria.
Each confirmed Recommend exit produces a handoff carrying the verdict, evidence, constraints, gaps, and the study_revision_id it was generated from.

Constraints

The study does not assign FR-### or NFR-###. display_ref uses the study-local, type-neutral FS-### alias only.
The study's constrained YAML block remains the authority for every machine fact. The handoff is a derived summary that never redefines a study fact; a disagreement resolves in favor of the study.
The handoff carries no version constant and no content hash. Recognition uses kind, and freshness is judged from study_revision_id.
Downstream Functional Planner adoption is a separate workstream. This package does not claim current direct compatibility with Functional Planner.
Downstream consumers remain read-only toward the study. They own their UUID-to-requirement mapping and traceability views.
Do not claim SpecIF, OSLC RM, ReqIF, PROV, DCMI, or JSON-LD conformance. The profile maps selected concepts without implementing those complete standards.
Keep YAML JSON-compatible: string keys, JSON scalar values, arrays, and objects only. Do not use aliases, anchors, merge keys, custom tags, timestamps as native YAML objects, or ordering-dependent meaning.

Stop rules

Stop at a hard decision boundary when the problem or desired outcome remains ambiguous.
Stop and record needs_review when criteria, evidence, lifecycle disposition, or relation meaning cannot be established from sources.
Stop before changing a conceptual ID because a title, class, status, alias, or location changed. Create a new revision instead.
Stop before deleting a withdrawn or superseded concept. Retain a tombstone with reason, effective time, last revision, and explicit successor state.
Stop before any downstream writeback to the study. Record mappings in downstream-owned artifacts.

Package resources

Resource	Use
interchange-profile.md	Read for authority, constrained YAML, identity, lifecycle, compatibility, and producer rules
standards-crosswalk.md	Read for SpecIF, OSLC RM, PROV, and DCMI mappings and non-conformance boundaries
provenance.md	Read for source licensing, attribution, and profile independence
feasibility-to-prd-handoff.md	Read for the sibling handoff's emission trigger, field set, verdict presence rules, and regeneration obligation
feasibility-study.md	Copy when starting a study
valid-study.md	Read as a valid profile fixture with revision, evidence, and dependency relations
`assets/feasibility-study-interchange-1.0.0.schema.json`	Use as the local structural JSON Schema 2020-12 profile
`assets/feasibility-to-prd-handoff.schema.json`	Use through the validator for handoff shape, verdict presence rules, and vocabulary
`scripts/validate_feasibility.py`	Execute with `uv run python scripts/validate_feasibility.py <study.md> [--handoff <handoff.yml>]` before publishing a revision or handoff

Attribution

The Feasibility Study Interchange Profile, schema, template, examples, and validator are independently authored repository content licensed CC BY 4.0.

Selected concepts are mapped to open specifications for interoperability vocabulary only. No upstream schema, example, or substantial prose is reproduced. See provenance.md.

Files

ATTRIBUTION.mdAdded by AutoGineer973 B
LICENSEAdded by AutoGineer16.6 KiB
SKILL.md8.2 KiB
assets/feasibility-study-interchange-1.0.0.schema.json5.9 KiB
assets/feasibility-to-prd-handoff.schema.json3.4 KiB
examples/invalid-fixtures.md1.6 KiB
examples/valid-study.md5.3 KiB
pyproject.toml701 B
references/feasibility-to-prd-handoff.md9.9 KiB
references/interchange-profile.md7.3 KiB
references/provenance.md2.9 KiB
references/standards-crosswalk.md3.9 KiB
scripts/validate_feasibility.pyScript25.1 KiB
templates/feasibility-study.md2.6 KiB
tests/corpus/0_valid_profile130 B
tests/corpus/1_anchor_alias143 B
tests/corpus/2_duplicate_key129 B
tests/corpus/3_unterminated_block66 B
tests/corpus/4_no_block26 B
tests/corpus/README.md1.3 KiB
tests/fuzz_harness.pyScript1.2 KiB
tests/test_validate_feasibility.pyScript24.5 KiB
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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, Needs Python

Security

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Attribution and licence

Published by

Microsoft

Publisher

Microsoft (Official)

Source

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

Licence

Creative Commons Attribution 4.0 International (redistribution allowed)

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

Synced

2026-11-08

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