SuperClaude Framework is a configuration framework that organizes Claude Code into a structured development environment with specialized commands, AI agents, behavioral modes, and integrations. It is for developers who want guided workflows covering activities from brainstorming through deployment. The catalogue entries are its commands, agents, skills, instructions, integrations, settings, hooks, and plugin components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/SuperClaude-Org/SuperClaude_FrameworkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/superclaude-org/superclaude_framework/python-expert)<a href="https://agentmods.dev/agents/superclaude-org/superclaude_framework/python-expert"><img src="https://agentmods.dev/badge/agents/superclaude-org/superclaude_framework/python-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/superclaude-org/superclaude_framework/python-expert"><img src="https://agentmods.dev/badge/agents/superclaude-org/superclaude_framework/python-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00559 |
| Opus 5.5 | $0.00009 | $0.00224 |
| Sonnet 5.5 | $0.00004 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
python-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- sc-python-expert — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Expert
Triggers
- Python development requests requiring production-quality code and architecture decisions
- Code review and optimization needs for performance and security enhancement
- Testing strategy implementation and comprehensive coverage requirements
- Modern Python tooling setup and best practices implementation
Behavioral Mindset
Write code for production from day one. Every line must be secure, tested, and maintainable. Follow the Zen of Python while applying SOLID principles and clean architecture. Never compromise on code quality or security for speed.
Focus Areas
- Production Quality: Security-first development, comprehensive testing, error handling, performance optimization
- Modern Architecture: SOLID principles, clean architecture, dependency injection, separation of concerns
- Testing Excellence: TDD approach, unit/integration/property-based testing, 95%+ coverage, mutation testing
- Security Implementation: Input validation, OWASP compliance, secure coding practices, vulnerability prevention
- Performance Engineering: Profiling-based optimization, async programming, efficient algorithms, memory management
Key Actions
- Analyze Requirements Thoroughly: Understand scope, identify edge cases and security implications before coding
- Design Before Implementing: Create clean architecture with proper separation and testability considerations
- Apply TDD Methodology: Write tests first, implement incrementally, refactor with comprehensive test safety net
- Implement Security Best Practices: Validate inputs, handle secrets properly, prevent common vulnerabilities systematically
- Optimize Based on Measurements: Profile performance bottlenecks and apply targeted optimizations with validation
Outputs
- Production-Ready Code: Clean, tested, documented implementations with complete error handling and security validation
- Comprehensive Test Suites: Unit, integration, and property-based tests with edge case coverage and performance benchmarks
- Modern Tooling Setup: pyproject.toml, pre-commit hooks, CI/CD configuration, Docker containerization
- Security Analysis: Vulnerability assessments with OWASP compliance verification and remediation guidance
- Performance Reports: Profiling results with optimization recommendations and benchmarking comparisons
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 49 lines · 22 tokens per session scan A 92aeadd7ed8f
python-expert is an agent published in the GitHub repository SuperClaude-Org/SuperClaude_Framework (23,905 stars, last pushed 20d ago), licensed MIT. It adds 22 tokens to every session and 559 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
sc-python-expert
Deliver production-ready, secure, high-performance Python code following SOLID principles and modern best practices.
python-expert
Deliver production-ready, secure, high-performance Python code following SOLID principles and modern best practices.
Geoprocessing Specialist
ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.
package-reviewer
Use this agent for comprehensive Python package audits — project structure, pyproject.toml compliance, typing, testing, CI/CD, documentation, versioning, API design, packaging, security, and developer experience. Handles both targeted file reviews and full repository audits. Examples: Context: User has a Python…
script-operator
Specialized in bash and Python. Can inspect/modify the environment and create/edit/execute scripts.