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python-ultimatePython ultimate 搜索

Agent Skill

用于辅助 Python 项目开发、测试、依赖管理和常见框架工作流。它适合让 Agent 阅读 Python 代码、定位测试问题、整理运行命令、生成脚本或分析数据处理逻辑。使用时需要确认项目虚拟环境、依赖版本和测试入口;涉及执行脚本、读写文件、访问数据库或调用外部 API 时,应先明确运行目录和输入输出范围,避免误改生产数据。

总安装

667

周安装

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GitHub Stars

公开资料未说明

下载量

210
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:python-ultimate(Python ultimate 搜索)
来源仓库:https://github.com/jr2804/prompts
仓库路径:skills/python-ultimate
安装命令:
npx skills add https://github.com/jr2804/prompts --skill python-ultimate
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jr2804/prompts --skill python-ultimate

简介

聚合 Python 开发全链路高阶知识与资源。

  • 涵盖性能优化、并发模型与架构设计深度内容。
  • 适用于资深开发者突破瓶颈与系统级思考。python-ultimate 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装方式:通过 GitHub 仓库使用 npx 命令添加。
  • 内容广度大,建议按需选取重点方向深入学习。

SKILL.md

Python Ultimate

Complete Python development reference. Covers standards, tooling, workflows, and best practices.

Quick Start

Writing code? → Start with Coding Standards Checking naming? → Go to Naming Conventions Building a CLI? → Go to CLI Development Fixing linter errors? → Go to Linter Rules Writing tests? → Go to Testing Debugging a bug? → Go to Debugging Refactoring? → Go to Refactoring Reviewing code? → Go to Code Review Auditing codebase? → Go to Auditing Documenting? → Go to Documentation Planning a feature? → Go to Planning Bulk operations? → Go to Bulk Operations (10+ files, 90%+ token savings)


Coding Standards

Core Python coding rules. See references/coding-standards.md for full details.

Type Hints

Mandatory everywhere. Use pipe syntax (T | None), never Optional[T]. Python 3.10+ required.

def process(data: str, limit: int | None = None) -> list[str]: ...

Never use TYPE_CHECKING guards. See references/type-checking.md for alternatives.

String Formatting

Use f-strings only. No .format() or % formatting.

Code Size Limits

TargetLimit
Module< 250 lines
Function< 75 lines
Class< 200 lines

Docstrings

Google style. Required for public functions and classes.

Prohibited Patterns


Naming Conventions

Variable naming standards for clarity and consistency. See references/naming-conventions.md for full details.

Files and Directories

PatternSuffixExample
Files_fileoutput_file, config_file
Directories_dircache_dir, output_dir
Unknown type_pathdata_path (exceptional only)

Anti-patterns (always invalid for path variables):

  • Bare generic names: path, file, folder, dir, directory, output, input, source, target, dest
  • Prefix instead of suffix: dir_output, file_config
  • Missing suffix: results, data, config (ambiguous)

See references/naming-conventions.md for the complete anti-pattern list.

Test Naming

  • Files: test_<module>.py
  • Classes: Test<DataProcessor> (PascalCase with Test prefix)
  • Methods: test_<description> (snake_case with test_ prefix)

Automated Validation

# Check a variable name
uv run assets/check_path_naming.py output_file
# Output: is_file

# Scan for violations
uv run assets/check_path_naming.py --check-files src/

CLI Development

Building Python CLIs with Typer or Click. See references/cli-development.md.

Framework Selection

Use Typer for new projects (type-hint driven, less boilerplate). Use Click for complex parameter handling.

Key Patterns

  • Parameter validation with type hints
  • Rich output formatting
  • Environment variable integration
  • Exit codes for error states

Linter Rules

Context-aware fixes for Ruff linter rules. See references/linter-rules.md.

Covered Rules

RuleDescriptionQuick Fix
E402Module-level import not at topMove imports to top
B007Unused loop variablePrefix with _
B008Function call in default argUse None sentinel
S108Hardcoded temp file pathUse tempfile
PLC0415Import not at top-levelMove to module level
NPY002Legacy numpy randomUse numpy.random
S311Standard randomUse secrets for security

Typer Exception

B008 is allowed for Typer Annotated parameters. See references/linter-rules.md.


Testing

Test organization, fixtures, mocking, and TDD. See references/testing.md.

Quick Commands

uv run pytest -v --tb=short
uv run pytest --cov=src --cov-report=term-missing

Key Practices

  • Co-located tests: <module>_test.py alongside implementation
  • 90%+ coverage target
  • Fixtures in conftest.py
  • Parameterized testing for multiple inputs
  • unittest.mock for external dependencies

TDD Cycle

Red → Green → Refactor. No production code without a failing test first. See references/testing.md.


Debugging

Systematic 4-phase debugging process. See references/debugging.md.

Iron Law

No fixes without root cause investigation first.

4-Phase Process

  1. Root Cause — Reproduce, isolate, trace data flow
  2. Pattern Analysis — Identify state changes, timing issues
  3. Hypothesis — Form testable prediction
  4. Implementation — Minimal fix, verify with test

Red Flags

  • "Let me just try changing X"
  • Fixing symptoms without understanding cause
  • Multiple failed fix attempts

Refactoring

Find → Replace → Verify workflow. See references/refactoring.md.

Workflow

  1. Find — Grep for target pattern
  2. Replace — Edit with replace_all for bulk changes
  3. Verify — Run tests, check for regressions

Code Transfer

Line-based code movement between files. See references/refactoring.md.


Code Review

Receiving and evaluating code review feedback. See references/code-review.md.

Workflow

Read → Understand → Verify → Evaluate → Respond → Implement

Key Principles

  • No performative agreement
  • Push back with technical reasoning
  • Verify feedback before implementing
  • Evaluate: is the suggestion correct?

Auditing

6-dimension codebase analysis. See references/auditing.md.

Dimensions

  1. Architecture — Structure, modularity, dependencies
  2. Quality — Readability, complexity, duplication
  3. Security — Input validation, secrets, injection
  4. Performance — Bottlenecks, memory, I/O
  5. Testing — Coverage, quality, edge cases
  6. Maintainability — Documentation, technical debt

Severity Ratings

Critical → High → Medium → Low


Documentation

10-section documentation structure. See references/documentation.md.

Workflow

Explore → Map → Read → Synthesize

Sections

Project Overview, Architecture, Key Components, Data Flow, API Reference, Configuration, Setup Guide, Development Guide, Testing, Deployment

Mermaid Diagrams

Use for architecture, sequence, and flowchart visualizations.


Planning

PLAN.md living document for feature implementation. See references/planning.md.

When to Use

Features spanning 3-15 prompts. Self-contained for fresh sessions.

Structure

Goal → Context → Phases → Validation → Progress → Decisions → Notes


Project Setup

Project structure, dependencies, and imports. See references/project-setup.md.

Key Tools

  • uv for dependency management
  • src layout for packages
  • pyproject.toml for configuration

Import Order

  1. Standard library
  2. Third-party
  3. Local (absolute imports)

File Analysis

Non-destructive file and codebase analysis. See references/file-analysis.md.

Tools

  • stat for metadata
  • wc for line counts
  • Grep for pattern searching
  • Glob for file discovery

Type Checking Alternatives

Never use TYPE_CHECKING guards. See references/type-checking.md.

Alternatives

  1. Extract shared types to dedicated modules
  2. Use protocols for structural typing
  3. Forward references (string literals)
  4. Local imports (last resort)

Bulk Operations

High-efficiency Python execution for 10+ file operations. 90-99% token savings vs. iterative approaches.

When to use:

  • Bulk operations (10+ files)
  • Complex multi-step workflows
  • Iterative processing across many files
  • User mentions efficiency/performance

Workflow pattern:

  1. Analyze locally — Use metadata operations (file counts, grep patterns)
  2. Process locally — Execute all transformations in Python
  3. Return summary — Report counts, not full data

Example patterns:

# Bulk refactor across 50 files
from pathlib import Path
import re

files = list(Path('.').glob('**/*.py'))
modified = 0

for f in files:
    content = f.read_text()
    new_content = re.sub(r'old_pattern', 'new_pattern', content)
    if new_content != content:
        f.write_text(new_content)
        modified += 1

result = {'files_scanned': len(files), 'files_modified': modified}
# Code audit metadata extraction
from pathlib import Path
import ast

files = list(Path('src').glob('**/*.py'))
complexity_issues = []

for f in files:
    tree = ast.parse(f.read_text())
    for node in ast.walk(tree):
        if isinstance(node, ast.FunctionDef):
            # Calculate simple complexity metric
            nested = sum(1 for n in ast.walk(node) if isinstance(n, (ast.If, ast.For, ast.While)))
            if nested > 10:
                complexity_issues.append({'file': str(f), 'function': node.name, 'complexity': nested})

result = {'files_audited': len(files), 'high_complexity': len(complexity_issues)}

Best practices:

  • ✅ Return summaries, not full data
  • ✅ Batch operations where possible
  • ✅ Use pathlib.Path for file operations
  • ✅ Handle errors gracefully, return error counts
  • ❌ Don't read full source into context when metadata suffices
  • ❌ Don't process files one-by-one interactively

Token savings scale with file count:

FilesInteractiveBulk OperationSavings
10~5K tokens~500 tokens90%
50~25K tokens~600 tokens97.6%
100~150K tokens~1K tokens99.3%

Reference Files

All detailed content lives in references/. Load only what you need:

FileContent
coding-standards.mdType hints, formatting, size limits, docstrings, comments
cli-development.mdTyper/Click, parameters, Rich output, env vars
linter-rules.mdRuff rules E402, B007, B008, S108, PLC0415, NPY002, S311
testing.mdFixtures, parameterized, mocking, TDD, coverage
type-checking.mdTYPE_CHECKING alternatives, protocols, forward refs
debugging.md4-phase process, red flags, rationalizations
refactoring.mdBulk operations, code transfer, safety checks
code-review.mdReceiving feedback, push back, evaluation
auditing.md6-dimension analysis, severity ratings
documentation.md10-section structure, Mermaid diagrams
planning.mdPLAN.md template and example
file-analysis.mdMetadata, line counting, pattern searching
project-setup.mdProject structure, uv, imports
verification.mdPre-commit hooks, tox, Makefile targets
imports-optional-dependencies.mdRequired vs optional dependency import patterns

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02

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03

需要对比不同来源的安装命令和来源信息时

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能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.16%
按下载量换算74

Claude

28.75%
按下载量换算60

Cursor

21.13%
按下载量换算44

Gemini CLI

10.11%
按下载量换算21

安全审计

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Socket

通过

Snyk

通过

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敏感数据

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安装前确认

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