Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jabrena/plinth --skill 052-design-hamburger-methodgit clone --depth 1 https://github.com/jabrena/plinthWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jabrena/plinth/052-design-hamburger-method)<a href="https://agentmods.dev/skills/jabrena/plinth/052-design-hamburger-method"><img src="https://agentmods.dev/badge/skills/jabrena/plinth/052-design-hamburger-method/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jabrena/plinth/052-design-hamburger-method"><img src="https://agentmods.dev/badge/skills/jabrena/plinth/052-design-hamburger-method.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.00853 |
| Opus 5 | $0.00038 | $0.00426 |
| Sonnet 5 | $0.00015 | $0.00171 |
| Haiku 4.5 | $0.00008 | $0.00085 |
Grade A, and why
052-design-hamburger-method scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.mdbefore 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
- 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.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 74 lines · 76 tokens per session scan A 759b09bfe10c
052-design-hamburger-method is a skill published in the GitHub repository jabrena/plinth (437 stars, last pushed yesterday), licensed Apache-2.0. It adds 76 tokens to every session and 853 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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