Borrowing it
Nothing to install: this file belongs to nerds-odd-e/doughnut. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nerds-odd-e/doughnut/main/.agents/skills/prepare-product-backlog-for-lia/SKILL.mdgit clone --depth 1 https://github.com/nerds-odd-e/doughnutWrote 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/nerds-odd-e/doughnut/prepare-product-backlog-for-lia)<a href="https://agentmods.dev/skills/nerds-odd-e/doughnut/prepare-product-backlog-for-lia"><img src="https://agentmods.dev/badge/skills/nerds-odd-e/doughnut/prepare-product-backlog-for-lia/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/nerds-odd-e/doughnut/prepare-product-backlog-for-lia"><img src="https://agentmods.dev/badge/skills/nerds-odd-e/doughnut/prepare-product-backlog-for-lia.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 | $0.00082 | $0.02700 |
| Opus 5 | $0.00041 | $0.01350 |
| Sonnet 5 | $0.00016 | $0.00540 |
| Haiku 4.5 | $0.00008 | $0.00270 |
Grade A, and why
prepare-product-backlog-for-lia 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 10d 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 — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prepare a Product Backlog for LeSS in Action
Keep two outputs separate:
- A facilitation analysis containing facts, criteria, trade-offs, ordering, dependencies, risks, and class sizing.
- A clean Product Backlog artifact containing only user stories and their acceptance examples.
Do not put selection criteria, training rationale, evaluation tables, team assignments, process notes, or research summaries in the Product Backlog artifact.
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.
<feature_criteria> Select a feature set that:
- Delivers authentic value to real users.
- Is interesting, visible, demonstrable, and suitable for participant dogfooding.
- Contains slightly more work than the class can finish, while leaving a useful and safe product if only the core is completed.
- Has an accessible domain with common-sense entry points and enough depth to require genuine domain learning.
- Has moderate technical difficulty: meaningful design and integration work without depending on specialist research.
- Crosses multiple technical seams across the backlog, such as user interface or CLI, application APIs, persistence, formats, external systems, and tests.
- Supports vertical, interdependent Product Backlog Items rather than frontend, backend, database, or infrastructure work packages.
- Meets production expectations for data integrity, failure handling, security, observability, and maintainability.
- Creates strategic leverage without making speculative future technology a prerequisite for current user value.
- Prefers high user value per unit of effort and reuses established platform mechanisms where appropriate.
- Can be developed and tested in the class environment and CI without fragile shared credentials or unavailable external services. </feature_criteria>
<item_criteria> Require each Product Backlog Item to:
- Name a user, an observable outcome, and why the outcome matters.
- Deliver one coherent end-to-end behavior that can be demonstrated.
- Be a vertical slice, not an internal component or architectural layer.
- Normally fit within roughly half to one-and-a-half team days; split an item likely to exceed two team days before Sprint Planning.
- Include concrete acceptance examples for the normal case, a no-change case where relevant, and at least one failure, boundary, or conflict case.
- Leave the product useful and safe even if later items are never completed.
- Expose real product dependencies without assigning permanent component ownership to a team.
- Express behavior and constraints without prescribing implementation unnecessarily.
- Address credentials, partial failure, concurrency, deletion, and recovery wherever relevant.
- Be testable through automation at the cheapest appropriate level.
- Encourage shared code ownership and feature-team collaboration.
- Avoid shortcuts or code that would be unacceptable outside the class. </item_criteria>
Treat capacity as a hypothesis. Aim for a stop-safe core plus pull-forward work, not a promise that every item will be completed.
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.
- 10d ago First seen · 332 lines · 82 tokens per session scan A 4c848f4ed6c0
prepare-product-backlog-for-lia is a skill published in the GitHub repository nerds-odd-e/doughnut (49 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 2,700 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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