python-feature-lifecycle Skill
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. Published by microsoft in agent-framework.
What is python-feature-lifecycle Skill?
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. Published by microsoft in agent-framework. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Developers using agent-framework
Technical details
- Install or run with Copy skill directory
When to use python-feature-lifecycle Skill
- Use it for developers using agent-framework.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/agent-framework
- Skill file: python/.github/skills/python-feature-lifecycle/SKILL.md
What it does
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.
Skill instructions
Python Feature Lifecycle Two lifecycle levels Agent Framework uses lifecycle at two different levels: 1. Package lifecycle — the maturity of the package as a whole 2. Feature lifecycle — the maturity of a specific API or feature inside that package These are related, but they are not the same thing. - The package stage is the default for everything in the package. - Feature-stage decorators are only for exceptions when a feature is behind the package's default stage. - Do not decorate every class or function just because the package is experimental or release candidate. Important default If a package is still in beta / experimental preview, all public APIs in that package are experimental by default. - Do not add @experimental(...) everywhere in that package. - The package stage already communicates that default. Once a package moves forward, you can keep individual features behind: - If a package moves to release candidate, a feature may remain experimental - If a package moves to rel
Explore related resources
Frequently asked questions
What is python-feature-lifecycle?
python-feature-lifecycle is a open-source AI agent skill with Copy skill directory. 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.
Who is python-feature-lifecycle best for?
python-feature-lifecycle is best for reusing agent instructions, scripts, and references.
How do I install python-feature-lifecycle?
Install or run python-feature-lifecycle using Copy skill directory. Check python-feature-lifecycle for the latest setup command.
Is python-feature-lifecycle actively maintained?
python-feature-lifecycle may need a closer maintenance check before production use.
Auto-fetched from GitHub.
