本文へ移動
cccskills
無料GitHub で公開

feasibility

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.

インストール方法を見る

含まれるファイル(21)

  • SKILL.md8.2 KB
  • assets/feasibility-study-interchange-1.0.0.schema.json5.9 KB
  • assets/feasibility-to-prd-handoff.schema.json3.4 KB
  • examples/invalid-fixtures.md1.6 KB
  • examples/valid-study.md5.3 KB
  • pyproject.toml701 B
  • references/feasibility-to-prd-handoff.md9.9 KB
  • references/interchange-profile.md7.3 KB
  • references/provenance.md2.9 KB
  • references/standards-crosswalk.md3.9 KB
  • scripts/validate_feasibility.py25.1 KB
  • templates/feasibility-study.md2.6 KB
  • 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 KB
  • tests/fuzz_harness.py1.2 KB
  • tests/test_validate_feasibility.py24.5 KB
  • uv.lock107.7 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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

  • 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

ResourceUse
interchange-profile.mdRead for authority, constrained YAML, identity, lifecycle, compatibility, and producer rules
standards-crosswalk.mdRead for SpecIF, OSLC RM, PROV, and DCMI mappings and non-conformance boundaries
provenance.mdRead for source licensing, attribution, and profile independence
feasibility-to-prd-handoff.mdRead for the sibling handoff's emission trigger, field set, verdict presence rules, and regeneration obligation
feasibility-study.mdCopy when starting a study
valid-study.mdRead as a valid profile fixture with revision, evidence, and dependency relations
assets/feasibility-study-interchange-1.0.0.schema.jsonUse as the local structural JSON Schema 2020-12 profile
assets/feasibility-to-prd-handoff.schema.jsonUse through the validator for handoff shape, verdict presence rules, and vocabulary
scripts/validate_feasibility.pyExecute 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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Consolidated accessibility skill entrypoint for WCAG 2.2, ARIA Authoring Practices, cognitive accessibility, Section 508, EN 301 549, design intent verification, and the Accessibility Planner workflow.

日本語の概要は準備中です。原文の説明を表示しています。

microsoft/hve-core1,5192026年10月11日 更新

Build, refresh, report, or probe an accessibility coverage matrix across criteria, surfaces, and evidence methods. Use when assessing coverage with the accessibility runtime harness and generated evidence bundle.

日本語の概要は準備中です。原文の説明を表示しています。

microsoft/hve-core1,5192026年10月11日 更新

Authoring skill for Architecture Decision Records (ADRs) supporting capture, from-planner-handoff, and adopt-template entry modes with selectable Y-Statement or MADR v4.0.0 output templates, supersession lineage, and ASR trigger evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

microsoft/hve-core1,5192026年10月11日 更新

Authoring conventions for exploratory data analysis notebooks and analytical dashboards, covering section sequence, visualization selection, scale thresholds, caching and state, and dashboard validation budgets. Use when composing or reviewing an EDA notebook, an analytical dashboard, or a dashboard test pass.

日本語の概要は準備中です。原文の説明を表示しています。

microsoft/hve-core1,5192026年10月11日 更新

Architecture diagram authoring for cloud infrastructure and declared data catalogs. Use when rendering Azure IaC or DS_CATALOG_V1 relationships as caller-selected ASCII or Mermaid diagrams.

日本語の概要は準備中です。原文の説明を表示しています。

microsoft/hve-core1,5192026年10月11日 更新

Create a durable Architecture Review Record from a confirmed System Architecture Reviewer scope, evidence, pillar analysis, trade-offs, and dispositions

日本語の概要は準備中です。原文の説明を表示しています。

microsoft/hve-core1,5192026年10月11日 更新

microsoft のスキルをすべて見る

このスキルの問題を報告する