jabrena/plinth/052-design-hamburger-method

Claude Code and agent skill by jabrena/plinth

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Use when a large feature, story, plan, or spec idea needs to be split into small vertical slices with the Hamburger Method. This should trigger for requests such as Split this oversized story; Find the smallest useful slice; Break this feature into vertical slices; Apply the Hamburger Method; Turn this broad plan into tracked slices. Part of Plinth Toolkit

Install

npx vskill@latest install jabrena/plinth/052-design-hamburger-method

SKILL.md

Hamburger Method Design

Guide Java teams through the Hamburger Method for splitting oversized work into small, valuable, end-to-end vertical slices. This is an interactive SKILL.

What is covered in this Skill?

  • Recognizing oversized feature, story, plan, or spec work
  • Identifying 3-6 functional or workflow layers that participate in delivering value
  • Generating 4-5 implementation or quality options per layer
  • Challenging scope with the smallest useful version question
  • Filtering options that are too costly, redundant, irreversible, or unnecessary for early learning
  • Composing one first vertical slice and follow-up slices
  • Self-checking value, size, testability, deliverability, and issue-tracking suitability
  • Routing plan, OpenSpec, GitHub issue, and Jira follow-up through the existing planning skills

Constraints

Split oversized work into vertical slices that deliver observable value, not isolated horizontal technical tasks.

  • MUST read references/052-design-hamburger-method.md before applying the method
  • MUST identify 3-6 functional or workflow layers before selecting a slice
  • MUST generate 4-5 options per layer, ordered from simplest acceptable option to richer options
  • MUST challenge scope with the smallest-useful-version question before recommending a first slice
  • MUST filter costly, redundant, irreversible, or unnecessary options before composing the first slice
  • MUST keep recommended work as vertical slices that deliver observable value
  • MUST NOT present isolated technical layers as independently shippable stories

When to use this skill

  • Apply the Hamburger Method
  • Split this oversized story
  • Find the smallest useful slice
  • Break this feature into vertical slices
  • Turn this broad plan into tracked slices
  • This feature is too large

Workflow

  1. Recognize Oversized Work

Read references/052-design-hamburger-method.md, then restate the large feature, story, plan, or spec idea and why ordinary implementation task decomposition would be too broad, risky, or horizontal.

  1. Map Layers and Options

Identify 3-6 functional or workflow layers needed for observable value. For each layer, generate 4-5 implementation or quality options ordered from the simplest acceptable choice to richer choices.

  1. Challenge Scope

Ask: "If this had to ship tomorrow, what would be the smallest useful version?" Use the answer to remove options that are too costly, redundant, irreversible, or unnecessary for early learning.

  1. Compose the First Slice

Select one option from each relevant layer to create the smallest useful end-to-end vertical slice. Explain the user-visible value, validation signal, and why rejected layer options wait for later slices.

  1. Plan Follow-Up Slices

Propose follow-up vertical slices that incrementally improve quality, automation, robustness, reach, observability, operational behavior, or supported workflows without turning them into isolated technical layers.

  1. Self-Check and Route Follow-Up

Check every slice for value, size, testability, deliverability, and issue-tracking suitability. Route implementation planning through 041-planning-plan-mode, spec-level changes through 042-planning-openspec, GitHub issue work through 043-planning-github-issues, Jira work through 044-planning-jira, and Azure DevOps work through 045-planning-azure-devops.

Reference

For detailed guidance, examples, and constraints, see references/052-design-hamburger-method.md.

Security

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  • Model reviewPassed

Quality evaluation

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Source

Popularity

Repository stars
441
Repository forks
94
7-day trend score
30

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