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Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
Passed 24 of 24 test cases with the skill, and 12 of 24 without it.
and 16 more in the full report
12 fixed12 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
Show the Python import statement for loading experimental and ExperimentalFeature inside internal implementation files of the agent_framework codebase.
| Checked | Without skill | With skill |
|---|---|---|
| The internal implementation file imports decorators and enums from ._feature_stage. | Missed | Passed |
The instructions your model reads when the skill loads.
Agent Framework uses lifecycle at two different levels:
These are related, but they are not the same thing.
This skill describes staged API behavior within a package. For creating or promoting a package, including sample
placement, meta-package membership, versioning, and lazy-loading namespace decisions, follow the
python-package-management skill.
If a package is still in beta / experimental preview, all public APIs in that package are experimental by default.
@experimental(...) everywhere in that package.Once a package moves forward, you can keep individual features behind:
That is the main use case for feature-stage decorators.
Use for features that are still unstable and may change or be removed without notice.
Feature-level code pattern:
test cases passed, vs 12 / 24 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.