skill-python-envv1.0.0

An AI agent running several data-ingestion skills needs a consistent Python 3.11 runtime with pandas, requests, and boto3. Each skill calls ensure_python_env.sh 3.11 with its own package list, reusing the same shared environment instead of rebuilding it per skill. This eliminates duplicate installs and cuts cold-start time across the whole agent workflow.

A research assistant skill chain generates reports using scientific libraries like numpy, scipy, and matplotlib. By pinning everything to a shared ~/.python_env/3.12, results stay reproducible across agent runs and machines. The skill never touches the host Python, avoiding dependency conflicts.

A coding-assistant agent must run Python helper scripts on Linux, macOS, and Windows (Git Bash). skill-python-env handles the platform-specific activate paths (bin/activate vs Scripts/activate) and auto-installs uv if missing. Developers get identical behavior regardless of OS, with zero manual setup.

An agent that scaffolds backend microservices calls this skill to provision a Python 3.10 environment with FastAPI, uvicorn, and pydantic in one step. The idempotent design means re-running the scaffold command is safe and fast after the first setup. Teams avoid Docker overhead for simple internal services.

A CI-style agent validates code by installing pytest, coverage, and linting tools into a shared versioned environment. Using uv pip install, already-present packages are skipped, so test runs start quickly. Failed environments can be wiped with rm -rf ~/.python_env/<version> and rebuilt automatically.

The skill remains free and open-source for individual developers, while enterprises pay for curated bundles that pre-configure approved Python versions, internal package mirrors, and security-audited dependencies. Enterprise customers get priority support and air-gapped installation guides.

Hosted platform sells compute alongside the skill, so agent operators don't manage ~/.python_env directories at all. Environments are cached centrally, scanned for vulnerabilities, and billed per environment-version-hour. The permissive skill remains the on-ramp; the hosted control plane is the monetized layer.

skill-python-env acts as a dependency-resolution primitive inside a marketplace where third-party skill authors publish paid Python tooling. The marketplace takes a revenue share on skill sales and offers a verified tier that guarantees environment compatibility with this skill.