agentleFS
jscott3201jscott3201/yet-more-skills/skills/python-workflow/SKILL.md

python-workflow

Skill4 starsChanged 2 months agoApache-2.0View on GitHub

Orient a substantial Python implementation or refactor: inspect interpreter, packaging, public contracts, tests, and native boundaries. Explicit entry point; not needed for tiny edits.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read AGENTS.md and the relevant package before editing.

    line 9
  2. Distinctive:

    Read orientation notes only when the environment or scope is unclear.

    line 31

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Python Workflow

Read AGENTS.md and the relevant package before editing. Treat scripts, installed libraries, services, notebooks, and extension packages as different products. Use the configured agent roles and review rules; this skill does not create another gate.

Establish the execution context

Start with the owning package and relevant pyproject.toml, source, test, and command definitions. Establish the interpreter and package origin when execution or an environment mismatch makes them relevant. Inspect dependency, build-backend, typing, and pytest-plugin settings as needed; locating a lockfile does not mean reading it in full. Reuse known environment facts instead of collecting every dependency or every test for each edit. Do not infer a global 3.14 minimum from one repository or assume an activated environment is the one the command uses. Expand to nested projects and Rust binding crates when the boundary crosses them.

For a mixed package, map Python orchestration -> binding -> Rust core. Keep wire parsing, durable storage, and established native invariants in their owning layer. Avoid implementing a second protocol engine in the Python wrapper. A missing tool is an explicit limitation, not permission to install globally or silently change the stack.

Make a small, meaningful change

State the behavior to change, consumers it affects, and one useful regression case. Preserve input validation, exception types, resource ownership, cancellation, and documented output semantics. Read an existing implementation and its callers before inventing an abstraction. Prefer ordinary functions, small classes, context managers, and existing dependencies over a generic framework.

Choose only the needed specialists: python-api-typing for interfaces; python-pytest for runner selection; python-test-design for behavioral evidence; python-performance for measurement; python-async-concurrency or python-threading-workers for lifecycle work; python-packaging-environments for environments; python-data-boundaries for domain values. Use rust-python-bindings for a mixed-language change when installed, or inspect the boundary directly. No skill requires all others to be loaded.

Validate without competing work

Reuse repository commands. Begin with changed tests and consumers, then the appropriate local gate. Check formatting and the configured type checker where relevant. Do not treat import success or compileall as behavioral validation. Report Python tests, Rust nextest/doctests, and installed-wheel checks separately.

Keep a single writer unless the existing workflow permits more. Independent readers may inspect contracts or tests while implementation proceeds. Never run competing benchmarks or rebuild the same extension in a shared environment from multiple agents. One agent owns each virtual environment and build directory during a task.

Finish with the behavior changed, commands/results, interpreter and package origin, and remaining gaps. A few paragraphs are enough; no mandatory plan file or new tracking service.

Read orientation notes only when the environment or scope is unclear.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.