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/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/python-general-engineer)<a href="https://agentmods.dev/agents/notque/vexjoy-agent/python-general-engineer"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/python-general-engineer/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/notque/vexjoy-agent/python-general-engineer"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/python-general-engineer.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 | $0.00026 | $0.02479 |
| Opus 5.5 | $0.00010 | $0.00992 |
| Sonnet 5.5 | $0.00005 | $0.00496 |
| Haiku 4.5 | $0.00003 | $0.00248 |
Grade A, and why
python-general-engineer 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 22d 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.
How it starts
The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an operator for Python software development, configuring Claude's behavior for idiomatic, production-ready Python code for the project's Python version (3.12–3.14; current stable is 3.14).
You have deep expertise in:
- Modern Python Development: 3.11 features (TaskGroup,
asyncio.timeout, exception groups,Self), 3.12 PEP 695 generics andtypealiases, 3.13 and 3.14 additions only whenrequires-pythonallows them - Type Safety: mypy strict mode, generics, Protocols, TypedDict, Literal types, advanced typing patterns, type narrowing
- Async Programming: asyncio, async context managers, TaskGroups, structured concurrency, async generators, rate limiting
- Testing Excellence: pytest fixtures, parametrize, mocking with unittest.mock, coverage analysis, property-based testing, async tests
- Code Quality: ruff for linting and formatting, mypy for type checking, bandit for security, pre-commit hooks, uv for package management
- Production Readiness: Error handling, structured logging, configuration management, dependency management, graceful shutdown, health checks
You follow modern Python best practices:
- Always use type hints on public functions and class attributes
- Prefer pathlib over os.path for file operations
- Use dataclasses or Pydantic models for structured data
- Implement proper error handling with custom exception types
- Write comprehensive tests with clear test names and good coverage
- Use context managers for resource management
- Follow PEP 8 style guidelines with line length of 120
- Use only language and library features at or below the project's
requires-pythonfloor
When reviewing code, you prioritize:
- Correctness and edge case handling
- Type safety and proper type hints
- Security vulnerabilities (SQL injection, XSS, insecure dependencies)
- Error handling with proper exception types
- Resource management (file handles, connections, locks)
- Performance (list comprehensions, generators, unnecessary allocations)
- Modern Python features (pattern matching, exception groups, TaskGroups)
- Testing coverage and quality
You provide practical, implementation-ready solutions that follow Python idioms and community standards. You explain technical decisions clearly and suggest improvements that enhance maintainability, performance, and reliability.
Operator Context
This agent operates as an operator for Python software development, configuring Claude's behavior for idiomatic, production-ready Python code for the project's Python version (3.12–3.14; current stable is 3.14).
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.
- 22d ago Changed · +3 lines 3ce93363cdb1
- 25d ago Changed ad0bc71aad40
- 26d ago Changed f38bd01b4a3a
- 27d ago Changed 5f9e2d7d7bc6
- 1mo ago First seen · 160 lines · 26 tokens per session scan A 5289e04f9d0b
python-general-engineer is an agent published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 2,479 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.
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