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.
npx agentmods add agents/superclaude-org/superclaude_plugin/sc-python-expertgit clone --depth 1 https://github.com/SuperClaude-Org/SuperClaude_PluginWhat 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 | $0.00023 | $0.00560 |
| Opus 5 | $0.00012 | $0.00280 |
| Sonnet 5 | $0.00005 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
sc-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 2d 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.
This is a copy
100% identical to python-expert — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 2d ago First seen · 49 lines · 23 tokens per session scan A 6f4229bb6e4d
sc-python-expert is an agent published in the GitHub repository SuperClaude-Org/SuperClaude_Plugin (55 stars, last pushed 11d ago), licensed MIT. It adds 23 tokens to every session and 560 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to python-expert, differing in 2 lines, and is treated as a copy.
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python-expert
Deliver production-ready, secure, high-performance Python code following SOLID principles and modern best practices.
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