The 8 Best Claude Code Skills for Python Development in 2026

Python engineers adopting AI coding agents keep hitting the same wall: the agent writes code that looks plausible but violates PEP 8, ships mutable default arguments, or blocks the event loop inside a coroutine. Claude Code skills fix this by loading targeted, conditional instructions into the agent's context only when relevant — changing what the agent actually writes, not just what it knows. This guide names eight verified, open-source Python skills, rates each on its skillrank security scan, and shows how BuildBetter CLI (ZeroShot, run as bb) carries those same skills across every agent your team uses so python-code-style behaves identically whether someone is in Cursor or Claude Code.

Every skill referenced below is real, attributed to its author, and linked to public source. No invented references.

What a Claude Code Skill Actually Does for Python Work

A Claude Code skill is a composable, conditional instruction pack loaded into an agent's context only when relevant to the task — it extends the AGENTS.md standard rather than replacing it. Anthropic formalized the format as SKILL.md in October 2025: a markdown file with YAML frontmatter (name plus description) and optional bundled scripts or resources.

The distinction that matters: a skill changes the agent's output behaviour — the code it writes and how it reviews — not merely its knowledge base. Skills trigger conditionally through their metadata, so the agent decides when to load them. That conserves context tokens instead of stuffing every rule into one bloated system prompt.

Python benefits disproportionately from skills because it carries strong, explicit community conventions that agents get wrong by default:

• PEP 8 for style and import ordering
• PEP 484 / 585 / 604 for type hints
• asyncio idioms for non-blocking I/O and structured concurrency

Skills are distributed through the public agent-skill registry, indexed by skillrank. Each entry has an author/slug reference, a GitHub source link, and a security scan rating (safe / low / medium / high / unknown). Because AGENTS.md is agent-agnostic and supported across Cursor, GitHub Copilot, Codex, and Claude Code, a well-authored skill is portable — the format travels, even if consistent enforcement across a team is a separate engineering problem.

Treat skills as behavioural contracts, not documentation. The right test of a skill is: did the diff change? If the agent's output is identical with and without it, the skill is dead weight burning tokens.

How We Evaluated These Skills

We selected skills by job-to-be-done, not by popularity alone. The five jobs that cover most Python agent work are style and linting, async patterns, background jobs, testing, and anti-pattern detection. Each skill below maps to at least one.

Our checks, in order:

• Security scan rating. We read each skill's skillrank rating before recommending it. Never install an unscanned or high-risk skill blindly — skills can bundle shell scripts.
• Measurable behaviour change. Where evals exist, we ran skillrank eval <ref> to confirm the skill produces a real output change, not a plausible-sounding prompt.
• Real, maintained source. We prioritized skills with public GitHub repos and active maintenance you can inspect.

What we did not test: proprietary or closed skills that can't be read. If you can't inspect the source, you can't verify the behaviour or the safety, so it doesn't belong on a list aimed at engineers who ship production Python.

The 8 Best Claude Code Skills for Python (by Job)

Each entry lists the exact author/slug reference, the GitHub source, the security scan rating, and the one concrete change you'll see in the agent's diffs.

1. wshobson/python-code-style — style baseline

This skill enforces consistent formatting, import ordering, and naming across files. Author: Seth Hobson (wshobson). Source: github.com/wshobson/agents. Scan: safe.

Behaviour change: the agent stops improvising style file-by-file and matches one convention everywhere. Import blocks, quote style, and naming stop drifting between the files a single PR touches. This is the anchor for every Python repo — keep exactly one authoritative style skill.

2. wshobson/python-anti-patterns — correctness baseline

This skill flags and rewrites the classic Python traps: mutable default arguments, bare except clauses, and misused comprehensions. Author: wshobson. Source: github.com/wshobson/agents. Scan: safe.

Behaviour change: the agent proactively refactors instead of shipping the anti-pattern. A function signature with def f(items=[]) gets rewritten to def f(items=None) with a guard, without you asking. Paired with python-code-style, this is the two-skill baseline every Python team should start with — one makes output consistent, the other stops it being wrong.

3. wshobson/async-python-patterns — asyncio done right

This skill enforces correct asyncio usage: avoiding blocking calls inside coroutines and applying structured concurrency. Author: wshobson. Source: github.com/wshobson/agents. Scan: safe.

Behaviour change: the agent writes non-blocking I/O and places await correctly. Synchronous requests calls inside a coroutine get swapped for an async client; fire-and-forget coroutines get wrapped in a task group. Add this if your service does heavy async work.

4. wshobson/python-background-jobs — durable workers

This skill applies task-queue and worker patterns (Celery/RQ-style) with idempotency and retry semantics. Author: wshobson. Source: github.com/wshobson/agents. Scan: safe.

Behaviour change: the agent scaffolds durable jobs instead of fire-and-forget calls. Tasks get retry decorators, idempotency keys, and dead-letter handling by default rather than as an afterthought. Essential for teams running Celery or RQ workers behind a web app.

5. wshobson/temporal-python-testing — durable execution tests

This skill provides testing patterns for Temporal workflows and durable execution. Author: wshobson. Source: github.com/wshobson/agents. Scan: low.

Behaviour change: the agent generates deterministic workflow tests with proper time-skipping instead of flaky sleeps and wall-clock assumptions. If you build on Temporal, this closes the gap where agents write tests that pass locally and break in CI.

6. obra/test-driven-development — test-first discipline

This skill imposes write-test-first discipline for Python modules. Author: Jesse Vincent (obra). Source: github.com/obra/superpowers. Scan: safe.

Behaviour change: the agent produces failing tests before the implementation, then iterates against them. Instead of writing code and back-filling tests that always pass, it commits to a red-green loop. This is a process skill, not a style one, so it layers cleanly on top of your baseline pair.

7. obra/systematic-debugging — reproduce before you fix

This skill enforces a hypothesis-driven debugging loop. Author: obra. Source: github.com/obra/superpowers. Scan: safe.

Behaviour change: the agent stops guess-and-check patching. It reproduces the bug, states a hypothesis, and confirms the fix against a repro instead of shotgunning edits until the error message disappears. For senior engineers, this is the skill that stops an agent from thrashing on a hard bug.

8. anthropics/mcp-builder — spec-compliant MCP servers

This skill scaffolds Model Context Protocol servers in Python with correct tool schemas and error handling. Author: Anthropic. Source: github.com/anthropics/skills. Scan: safe.

Behaviour change: the agent generates spec-compliant MCP tools with valid schemas and structured error handling instead of ad-hoc endpoints. With MCP adopted by every major agent vendor within a year of its November 2024 launch, this matters for any Python team building agent tooling.

Comparison Table: The 8 Skills at a Glance

Ratings are honest — unknown would appear where no scan or eval exists rather than implying confidence we don't have.

Skill ref	Author	Job category	Security scan	Eval available	Primary output change
BuildBetter CLI (bb) skills	BuildBetter	Cross-agent distribution + team conventions	safe	Yes (via skillrank)	Same skills load and behave identically in every agent on the team
wshobson/python-code-style	wshobson	Style	safe	Yes	Consistent formatting, imports, naming
wshobson/python-anti-patterns	wshobson	Anti-patterns	safe	Yes	Refactors traps instead of shipping them
wshobson/async-python-patterns	wshobson	Async	safe	Yes	Non-blocking I/O, correct await placement
wshobson/python-background-jobs	wshobson	Jobs	safe	Partial	Durable jobs with retries and idempotency
wshobson/temporal-python-testing	wshobson	Testing	low	Partial	Deterministic workflow tests with time-skipping
obra/test-driven-development	obra	Testing	safe	Yes	Failing tests before implementation
obra/systematic-debugging	obra	Testing	safe	Yes	Reproduce-then-fix debugging loop
anthropics/mcp-builder	Anthropic	MCP	safe	Yes	Spec-compliant MCP tool scaffolding

Which stack together: python-code-style + python-anti-patterns is the universal baseline pair. Add async-python-patterns or python-background-jobs by stack. Layer obra/test-driven-development and obra/systematic-debugging as process skills on top — they don't overlap with style, so they compose without conflict.

Installing and Evaluating Python Skills with skillrank

The workflow to adopt a Python skill safely runs through skillrank, and every command is mirrored as bb skills <command> when BuildBetter CLI is installed.

• skillrank search python — find candidate skills for Python work.
• skillrank recommend — surface skills matching your repo's detected stack, so you don't sift through every async skill when your service is synchronous.
• skillrank show <ref> — read scores, the security scan, and eval results before touching your repo.
• skillrank install <ref> — performs a hash-verified install into the repo. Always confirm the scan rating first.
• skillrank eval <ref> — runs a local paired eval on your own agent, showing the before/after output change on your actual codebase.

The eval step is the one most teams skip and the one that matters most.

Evaluate skills on your own codebase, not on the author's demo. A skill that helps one repo may waste tokens on another. The paired before/after comparison is what tells you whether the skill earns its token cost in your context.

Before running any install, read the scan. Skills can bundle executable scripts, so an unknown or high rating means you must read the GitHub source before it runs in your repo or CI. Install only safe or low rated skills without manual inspection.

Making Python Conventions Consistent Across a Whole Team

Skills only help at team scale if they run in whatever agent each teammate uses — Cursor, Codex, Copilot, Claude Code — and if you can tell whether they helped. That's the problem individual skill installs don't solve. Everyone installs python-code-style, but one engineer runs it in Claude Code, another in Cursor, and the formatting decisions still drift because the skill loads differently in each.

BuildBetter CLI (ZeroShot, bb) is the context layer that carries the same Python skills across every agent on the team. python-code-style behaves identically whether someone is in Cursor or Claude Code, because BuildBetter CLI applies the skill consistently in each agent instead of leaving enforcement to per-agent config.

Two capabilities make this concrete for Python teams:

• Team conventions as reusable skills. BuildBetter CLI encodes your team's own Python conventions as open-source BB-Skills — commands like /bb-review, /bb-specify, and /bb-plan carry your actual playbook into every PR, on top of the community skills above.
• Cross-teammate session resume. bb agent-sessions resume lets anyone pick up a Python session in any agent, on their own machine — so a staff engineer's debugging session isn't stranded in one person's Cursor history.

An honest boundary: BuildBetter CLI is not an agent and doesn't replace these skills. It distributes and persists them across your team and your agents. skillrank's eval is still what proves a given skill earns its tokens on your codebase — BuildBetter CLI makes sure the skills that pass that bar reach everyone consistently.

Frequently Asked Questions

Do Claude Code skills work with other agents like Cursor, Copilot, or Codex?

Skills authored against the AGENTS.md standard are portable in principle because AGENTS.md is agent-agnostic. In practice, getting the same skill to load and behave identically across Cursor, Codex, Copilot, and Claude Code requires a context layer — BuildBetter CLI (bb) — that carries and applies the skill in each agent. The skill format is portable; consistent enforcement across teams is the part you have to engineer.

Are these skills safe to install?

Check the skillrank security scan rating before anything else. Install only skills rated 'safe' or 'low', and treat 'high' or 'unknown' as a stop sign — inspect the GitHub source manually before proceeding, because skills can bundle executable scripts. Prefer skills with a public, actively maintained GitHub repo you can read.

How do I know a skill actually improved my agent's output?

Run skillrank eval <ref> to get a paired before/after comparison on your own repository. A good skill produces a measurable, repeatable change in the agent's diffs. If the output is the same with and without the skill on your codebase, don't install it — it's just spending tokens.

What's the minimum Python skill set to start with?

Start with wshobson/python-code-style plus wshobson/python-anti-patterns as your baseline — one makes output consistent, the other stops it being wrong. Then add stack-specific skills: async-python-patterns if you do heavy asyncio, python-background-jobs if you run Celery/RQ-style workers, and obra/test-driven-development if you want test-first discipline.

Do skills conflict with each other?

Conflicts are rare, but overlapping style skills are the main risk — two style skills can produce oscillating formatting and merge noise. Keep exactly one style skill authoritative per repo, then layer job-specific skills (async, jobs, testing, anti-patterns) on top, since those address non-overlapping concerns.

Who maintains these skills?

Individual authors and Anthropic. wshobson (Seth Hobson) maintains the Python-focused collection; obra (Jesse Vincent) authored the test-driven-development and systematic-debugging process skills; Anthropic publishes mcp-builder. All link to public GitHub repos you can inspect before installing.

Ship at the speed of insight.

Pick your baseline pair, eval it on your own repo with skillrank, then let BuildBetter CLI carry those Python conventions across every agent and every teammate — so python-code-style behaves the same in Cursor, Codex, and Claude Code, and no one's session is stranded on one machine.

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