backend
無料Backend Java — Spring Boot controllers/services, JUnit controller tests, MakeMe. Read this first for any backend work. Use when working on the Java Spring backend.
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。
Prepare, refine, order, and assess a real-product backlog for an upcoming LeSS in Action (LIA) class. Use when selecting class-sized product features, investigating how much of an idea already exists, turning feature themes into vertical interdependent user stories, adding high-value items, or facilitating Product Backlog refinement for teams working on Donut or another production product.
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。
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Keep two outputs separate:
Do not put selection criteria, training rationale, evaluation tables, team assignments, process notes, or research summaries in the Product Backlog artifact. </objective>
<context> LeSS in Action is a five-day experiential developer training in which feature teams collaborate on one real product and run a real Sprint. Product work is the medium for learning; the product domain and implementation technology are not the learning objective.Participants should encounter requirements discovery, Specification by Example, ATDD, TDD, pairing, continuous integration, refactoring, emergent design, shared ownership, and cross-team coordination through the work itself. Do not add training-only behavior or contrive architectural work merely to exercise a technology. </context>
<feature_criteria> Select a feature set that:
<item_criteria> Require each Product Backlog Item to:
Treat capacity as a hypothesis. Aim for a stop-safe core plus pull-forward work, not a promise that every item will be completed. </step>
<step name="understand_users_and_outcomes"> Identify the real users, their present workflow, their pain or opportunity, and an observable outcome they would value.Write a one-sentence feature outcome before generating items. Reject themes whose primary justification is novelty, curriculum coverage, or technology fashion. </step>
<step name="audit_reality"> Inspect the live product, source, tests, history, APIs, CLI behavior, and current documentation before treating a feature as new.Classify relevant behavior as:
Fact-check external standards, formats, products, and APIs against current primary sources. Distinguish compatibility, storage, transport, and product integration instead of treating them as one feature.
Produce a concise gap map: current capability, reusable foundation, missing behavior, and unresolved decision. </step>
<step name="set_boundaries"> Make the smallest coherent release boundary explicit. Resolve or flag:Prefer standard platform mechanisms when they deliver the outcome. For example, using an existing Git remote may provide GitHub value without repository creation, token storage, or a GitHub-specific API. </step>
<step name="form_a_walking_skeleton"> Choose the narrowest useful end-to-end behavior that establishes the central product contract. It must be valuable on its own and production-safe enough to ship if the Sprint ends there.Do not start with schema, framework, parser, API, or infrastructure items. </step>
<step name="slice_vertically"> Grow from the walking skeleton by varying user scenarios and policies:Split by user-observable behavior, workflow step, business rule, data variation, or operational boundary. Do not split by technical layer or assign stories to component teams.
Before retaining a story, ask: once earlier stories exist, can the user already achieve this outcome directly with a standard tool? If yes, treat it as a supported workflow, documentation need, or acceptance constraint—not as a product feature. Keep an integration story only when the product adds differentiated behavior such as domain-aware mapping, managed synchronization, conflict handling, or recovery. </step>
<step name="order_for_value_learning_and_dependencies"> Order items using all three lenses:Insert high-value, low-effort items as soon as their minimum trustworthy prerequisite exists. Do not delay them behind a theoretically complete feature.
Create purposeful cross-team interaction through product dependencies, shared contracts, and integration—not through separate frontend/backend ownership. </step>
<step name="size_for_the_class"> Build:Use half to one-and-a-half team days as a refinement heuristic, not an estimate to impose. Split likely two-team-day items. Account for learning, pairing, integration, refinement, and retrospectives rather than converting eight people into forty uninterrupted person-days. </step>
<step name="write_acceptance_examples"> For each item, add a small set of concrete examples covering:Use examples to expose domain decisions. Avoid turning them into a technical task checklist. </step>
<step name="assess_and_refine"> Assess the whole feature set against every feature criterion and each story against the item criteria. Use evidence and cautions, not unsupported pass/fail labels.Look especially for:
Refine the items or recommend a different feature theme when material weaknesses remain. </step>
<step name="separate_the_outputs"> Present the facilitation analysis in the conversation or a dedicated facilitation artifact. Include the criteria assessment, gap map, dependency shape, core and pull-forward boundary, sizing cautions, and decisions still needed.Write the Product Backlog separately using only this form:
# Feature name
## User stories
### 1. Observable outcome
As a [user], I want [outcome] so that [reason].
Acceptance examples:
- [Normal example]
- [No-change or repeat example, when relevant]
- [Failure, boundary, or conflict example]
Keep the ordered stories and acceptance examples in the backlog. Keep all selection criteria, process, evaluations, class logistics, and team allocation outside it. </step>
</process><facilitation_prompts> Use questions like these during refinement:
<lessons_from_prior_use> Retain these lessons:
<success_criteria>
<out_of_scope>
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Backend Java — Spring Boot controllers/services, JUnit controller tests, MakeMe. Read this first for any backend work. Use when working on the Java Spring backend.
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。
Backend Java production code, Spring Boot controllers/services/repositories, controller return values, imports, schema ERD pointer. Use when editing backend production Java.
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。
Backend Java tests, controller-level stable-boundary tests, JUnit, @Nested, MakeMe builders, @Transactional, parameterized tests. Use when writing or changing backend JUnit tests.
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。
related to donut CLI. Use when working on donut-cli.
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。
Set up and run the Donut dev environment on Cursor Cloud VM (no Nix). Use when working on Cloud VM, running tests on Cloud VM, or encountering command errors related to missing nix. Triggers on: cloud VM, cloud agent, no nix available, /workspace path.
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。
Regenerate docs/database-erd.md from MySQL information_schema (Mermaid ERD: foreign keys and key columns). Use after Flyway schema changes, new migrations, or when the diagram is stale. Triggers on: database ERD, database-erd.md, export_database_erd, schema diagram, ER diagram after migration.
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。