Source profileQuality 96/100

gaelic-ghost/socket/plugins/python-skills/skills/python-package-workflow/SKILL.md

python-package-workflow

Validate Python package surfaces with pyproject metadata, uv-managed builds, dependency boundaries, local smoke checks, semantic versioning, and release-boundary guidance.

Source repository stars
6
Declared platforms
1
Static risk flags
0
Last source update
2026-08-21
Source checked
2026-08-25

Decision brief

What it does: where it fits

Validate Python package surfaces with pyproject metadata, uv-managed builds, dependency boundaries, local smoke checks, semantic versioning, and release-boundary guidance.

Best for

  • Use this skill when a Python library is intended to become an installable package.
  • Use this skill when pyproject.toml package metadata, versioning, dependencies, optional dependencies, or release notes change.
  • Use this skill when adding package validation to CI.

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexDeclaredSource recordInstall path and trigger
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/gaelic-ghost/socket --skill "plugins/python-skills/skills/python-package-workflow"
Safe inspection promptEditorial

Inspect the Agent Skill "python-package-workflow" from https://github.com/gaelic-ghost/socket/blob/1140bc0b60f2c938b81d67dcee88a5eeb2e2f39d/plugins/python-skills/skills/python-package-workflow/SKILL.md at commit 1140bc0b60f2c938b81d67dcee88a5eeb2e2f39d. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Inspection Workflow

    1. Identify package-bearing projects:

    Identify package-bearing projects:Confirm the intended package boundary:one package per public library project
  2. 02

    Purpose

    Validate a Python package before release or publication.

    Validate a Python package before release or publication.The practical job is to make package metadata explicit, build and test the package, inspect the generated artifact when needed, and keep publishing as an explicit release step rather than an accidental side effect.
  3. 03

    When To Use

    Use this skill when a Python library is intended to become an installable package.

    Use this skill when a Python library is intended to become an installable package.Use this skill when pyproject.toml package metadata, versioning, dependencies, optional dependencies, or release notes change.Use this skill when adding package validation to CI.
  4. 04

    Source Check

    Use repo-local files, checked-out dependency sources, Dash MCP or Dash HTTP for installed docsets, and then official project documentation when Dash/local coverage is missing or stale:

    Python packaging user guideWriting pyproject.tomluv build documentation
  5. 05

    Workspace Notes

    For workspaces, validate package members deliberately:

    For workspaces, validate package members deliberately:Do not assume every workspace member should publish. Services, examples, internal tools, and test fixtures are often intentionally unpublished.

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score96/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars6SourceRepository attention, not individual Skill quality
Compatibility1 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
gaelic-ghost/socket
Skill path
plugins/python-skills/skills/python-package-workflow/SKILL.md
Commit
1140bc0b60f2c938b81d67dcee88a5eeb2e2f39d
License
Apache-2.0
Collected
2026-08-25
Default branch
main
View the original SKILL.md

Python Package Workflow

Purpose

Validate a Python package before release or publication.

The practical job is to make package metadata explicit, build and test the package, inspect the generated artifact when needed, and keep publishing as an explicit release step rather than an accidental side effect.

When To Use

  • Use this skill when a Python library is intended to become an installable package.
  • Use this skill when pyproject.toml package metadata, versioning, dependencies, optional dependencies, or release notes change.
  • Use this skill when adding package validation to CI.
  • Use this skill before package publication, but do not publish unless the user asks for that release step.

Source Check

Use repo-local files, checked-out dependency sources, Dash MCP or Dash HTTP for installed docsets, and then official project documentation when Dash/local coverage is missing or stale:

Translate documentation into the specific package, metadata, and release decision in front of you.

Inspection Workflow

  1. Identify package-bearing projects:
    rg --files -g 'pyproject.toml' -g 'uv.lock' -g 'README*' -g 'LICENSE*' -g 'src/**/*.py' -g '*.py'
    
  2. Confirm the intended package boundary:
    • one package per public library project
    • no accidental service-only package release
    • no hidden machine-local dependencies
    • workspace members packaged only when they are intended package surfaces
  3. Check metadata:
    • name
    • version or repository-owned version source
    • description
    • readme
    • requires-python
    • license
    • authors or maintainers
    • classifiers
    • dependencies
    • optional dependencies
    • project URLs
    • build system
  4. Check dependency boundaries:
    • runtime dependencies in [project].dependencies
    • optional feature dependencies in [project.optional-dependencies]
    • maintainer tools in [dependency-groups]
    • workspace sources in [tool.uv.sources]
  5. Run validation:
    uv sync --dev
    uv run pytest
    uv run ruff check .
    uv run mypy .
    uv build
    
  6. Inspect generated package output when metadata or package contents changed.

Workspace Notes

For workspaces, validate package members deliberately:

uv run --package <package-name> pytest
uv build --package <package-name>

Do not assume every workspace member should publish. Services, examples, internal tools, and test fixtures are often intentionally unpublished.

Local Smoke Checks

When package behavior is public or packaging changed materially, create a temporary consumer outside the package tree and install the built artifact there. Verify both the wheel and sdist when the project publishes both:

uv run --isolated --no-project --with dist/*.whl tests/smoke_test.py
uv run --isolated --no-project --with dist/*.tar.gz tests/smoke_test.py

Keep tests/smoke_test.py deliberately small: import the public package, run one representative public API or CLI command, and fail if required package data is missing. Use a disposable directory instead when the repository does not ship a smoke-test script.

Keep the smoke check small:

  • install the built wheel
  • import the public package
  • call one tiny public function or CLI entry point
  • confirm package metadata when relevant

Do not commit temporary consumer directories or generated artifacts unless the repo intentionally tracks release evidence.

Output Shape

Return:

  1. Package boundary: package name and path.
  2. Metadata: changed or verified package metadata.
  3. Artifacts: built wheel or sdist path if created.
  4. Validation: exact commands run and results.
  5. Publish status: not published, blocked, or explicitly published by user request.
  6. Residual risk: missing smoke checks, external index state, or release notes still needed.

Guardrails

  • Do not publish to PyPI or another index unless the user explicitly asks for publication.
  • Do not add machine-local paths to package metadata, dependencies, lockfiles, docs, examples, or CI.
  • Do not move maintainer tools into runtime dependencies.
  • Do not package app-only or service-only code accidentally.
  • Do not change semantic versioning without checking repo-local release policy.

Frequently asked questions

What to verify before installation and use

What does the python-package-workflow source document cover?

Validate Python package surfaces with pyproject metadata, uv-managed builds, dependency boundaries, local smoke checks, semantic versioning, and release-boundary guidance.

How do I install python-package-workflow?

The source record exposes this install command: npx skills add https://github.com/gaelic-ghost/socket --skill "plugins/python-skills/skills/python-package-workflow". Inspect the command and pinned source before running it.

Which Agent platforms does the source record declare?

The pinned source record declares support for: codex.

Alternatives

Compare before choosing

Computed 97149

UiPath/skills

uipath-coded-apps

UiPath Coded Apps — scaffold, build, run, and deploy Coded Web Apps and Coded Action Apps: React/TypeScript apps that call UiPath Cloud APIs via the `@uipath/uipath-typescript` SDK and ship to Automation Cloud (push/pull to Studio Web, pack, publish, deploy, OAuth-PKCE). Also generates live analytics & governance dashboards from a plain-language request, wired to tenant data via the Insights real-time API, with edit and deploy flows. For RPA→uipath-rpa, Python agents→uipath-agents, Maestro flows

Computed 9618,905

teng-lin/notebooklm-py

notebooklm

Complete API for Google NotebookLM - full programmatic access including features not in the web UI. Create notebooks, add sources, generate all artifact types, download in multiple formats. Activates on explicit /notebooklm or intent like "create a podcast about X"

Computed 961,074

TencentCloudBase/CloudBase-AI-Toolkit

cloudbase-agent-python

Build production-ready AI agent backends using the CloudBase Agent Python SDK — create agents with LangGraph/CrewAI/LlamaIndex, serve them via FastAPI with AG-UI protocol streaming + OpenAI-compatible endpoints, add tools (bash, filesystem, MCP, code execution), memory (in-memory, TDAI, MySQL, MongoDB), observability (OpenTelemetry/Langfuse), and middleware (auth, logging). Use this skill when the user wants to create an AI agent server, build a chatbot backend, set up human-in-the-loop workflow

Computed 9660

almanak-co/sdk

almanak-strategy-builder

Build, test, and deploy DeFi trading strategies using the Almanak SDK. ALWAYS use this skill when the user mentions almanak, DeFi strategy, trading strategy, yield farming, liquidity provision, token swap, borrowing, lending, perpetuals, staking, vault deposit, bridging tokens, backtesting, paper trading, or on-chain execution. Use for writing strategy.py files, composing intents (Swap, LP, Borrow, Supply, Perp, Bridge, Stake, Vault, Prediction), working with config.json strategy parameters, run