google-agents-cli-scaffold
Project scaffolding, deployment configuration, and CI/CD setup for Google ADK agents.
large-skill
Large skill with extensive content and supplementary files
Full skill instructions
This skill contains a longer narrative that resembles the kind of internal field guide teams use when designing new systems. It is not intended to be exhaustive, but it is intentionally verbose so that token-counting tests have a substantial body of text to work with.
Every system design effort should begin with a careful restatement of the problem. Engineers should write down:
Capturing these items up front makes it easier to evaluate trade‑offs later. When the design diverges from the original problem statement, the team can either adjust expectations or update the requirements.
System diagrams are useful, but they must be backed by well-defined boundaries. Each component should have a crisp responsibility such as "serves read‑only product data" or "handles authenticated user sessions." When responsibilities are vague, coupling tends to increase and it becomes difficult to reason about failures.
Consider documenting:
These notes rarely appear in user-facing documentation, but they are precisely the kind of content that internal skills can capture.
Choosing the right data model is often more important than picking a particular database product. For each major entity, describe:
During testing, this section serves simply as additional text that increases the size of the large skill. The token counter does not need to understand the semantics; it only needs to see that this content is substantially larger than the metadata header used at progressive disclosure level one.
Performance planning starts with realistic assumptions about traffic. Design documents should include back‑of‑the‑envelope calculations for:
Engineers can use these estimates to decide whether to introduce queues, caching layers, background workers, or read replicas. When documenting these decisions in a skill, it is helpful to explain the reasoning in plain language so that future maintainers can understand why certain trade‑offs were made.
No system is perfect, so designs must include a plan for failure. This section should answer questions such as:
Operational guidance may also cover runbooks, on‑call expectations, and playbooks for common incidents. Again, from the perspective of token efficiency tests, these paragraphs simply provide more natural language for the encoder to process.
Longer design documents often refer to additional material such as sequence diagrams, API schemas, or architectural decision records. In this skill we model that pattern using a separate reference file:
See the extended notes in the reference document for more detailed examples, diagrams, and checklists.
The loader should treat this as a normal markdown link. Tests that exercise progressive disclosure at level three can assert that the content of the reference file is not automatically inlined when the skill is loaded; instead, only the path to the document is expanded to an absolute filesystem path.
Project scaffolding, deployment configuration, and CI/CD setup for Google ADK agents.
Set up tracing, logging, and monitoring for deployed ADK agents across Cloud Trace, BigQuery, and third-party platforms.
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