Specify a webhook-driven task for OpenClaw
Describe event inputs, validation, deduplication, and intended actions before integration work.
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Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python ...
--- name: python-packaging-9c67cdb33175 description: "Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python ..." risk: unknown source: community date_added: "2026-02-27" --- # Python Packaging Comprehensive guide to creating, structuring, and distributing Python packages using modern packaging tools, pyproject.toml, and publishing to PyPI. ## Use this skill when - Creating Python libraries for distribution - Building command-line tools with entry points - Publishing packages to PyPI or private repositories - Setting up Python project structure - Creating installable packages with dependencies - Building wheels and source distributions - Versioning and releasing Python packages - Creating namespace packages - Implementing package metadata and classifiers ## Do not use this skill when - The task is unrelated to python packaging - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Resources - `resources/implementation-playbook.md` for detailed patterns and examples. --- <!-- AGI-INTEGRATION-START --> ## AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Memory-First Protocol Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting. ```bash # Check for prior backend/API context before starting python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Python Packaging" ``` ### Storing Results After completing work, store backend/API decisions for future sessions: ```bash python3 execution/memory_manager.py store \ --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \ --type decision --project <project> \ --tags python-packaging backend ``` ### Multi-Agent Collaboration Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage. ```bash python3 execution/cross_agent_context.py store \ --agent "<your-agent>" \ --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \ --project <project> ``` ### Agent Team: Code Review After implementation, dispatch `code_review_team` for two-stage review (spec compliance + code quality) before merging. <!-- AGI-INTEGRATION-END -->--- name: python-packaging-9c67cdb33175 description: "Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python ..." risk: unknown source: community date_added: "2026-02-27" --- # Python Packaging Comprehensive guide to creating, structuring, and distributing Python packages using modern packaging tools, pyproject.toml, and publishing to PyPI. ## Use this skill when - Creating Python libraries for distribution - Building command-line tools with entry points - Publishing packages to PyPI or private repositories - Setting up Python project structure - Creating installable packages with dependencies - Building wheels and source distributions - Versioning and releasing Python packages - Creating namespace packages - Implementing package metadata and classifiers ## Do not use this skill when - The task is unrelated to python packaging - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Resources - `resources/implementation-playbook.md` for detailed patterns and examples. --- <!-- AGI-INTEGRATION-START --> ## AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Memory-First Protocol Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting. ```bash # Check for prior backend/API context before starting python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Python Packaging" ``` ### Storing Results After completing work, store backend/API decisions for future sessions: ```bash python3 execution/memory_manager.py store \ --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \ --type decision --project <project> \ --tags python-packaging backend ``` ### Multi-Agent Collaboration Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage. ```bash python3 execution/cross_agent_context.py store \ --agent "<your-agent>" \ --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \ --project <project> ``` ### Agent Team: Code Review After implementation, dispatch `code_review_team` for two-stage review (spec compliance + code quality) before merging. <!-- AGI-INTEGRATION-END -->
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.