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Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.
Passed 22 of 22 test cases with the skill, and 6 of 22 without it.
and 21 more in the full report
16 fixed6 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 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
A new developer just cloned the repository and needs to initialize their python virtual environment, install local dependencies, and configure git pre-commit hooks. Which automated repository command should they run?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends uv run poe setup for complete environment and hook initialization. | Missed | Passed |
The instructions your model reads when the skill loads.
python/
├── pyproject.toml # Root package (agent-framework)
├── packages/
│ ├── core/ # agent-framework-core (main package)
│ ├── foundry/ # agent-framework-foundry
│ ├── lab/ # Standalone uv project, excluded from the root workspace
│ ├── anthropic/ # agent-framework-anthropic
│ └── ... # Other connector packages
agent-framework-core contains core abstractions and OpenAI/Azure OpenAI built-inagent-framework depends on agent-framework-core[all]. Despite the existing extra name, this is a
curated standard install set, not every package at a particular lifecycle stage.agent-framework-core[all] carry lower and upper bounds. Workspace integrations use the version
released with the current Core cohort as their lower bound so pip does not backtrack across old cyclic
core[all] -> integration -> core combinations.packages/lab is intentionally excluded from the root uv workspace. It has its own
pyproject.toml, uv.lock, environment, CI matrix, and dependency updates so experimental
dependencies cannot constrain released provider packages. Lab resolves released Core and provider
distributions rather than local root-workspace packages.Uses uv for dependency management and poethepoet for task automation.
# Full setup (venv + install + prek hooks)
uv run poe setup
# Install dependencies from lockfile (frozen resolution with prerelease policy)
uv run poe install
# Create venv with specific Python version
test cases passed, vs 6 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.