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/asiflow/claude-nexus-hyper-agent-teamWrote 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/asiflow/claude-nexus-hyper-agent-team/python-expert)<a href="https://agentmods.dev/agents/asiflow/claude-nexus-hyper-agent-team/python-expert"><img src="https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team/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/asiflow/claude-nexus-hyper-agent-team/python-expert"><img src="https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team/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 | $0.00372 | $0.06395 |
| Opus 5.5 | $0.00149 | $0.02558 |
| Sonnet 5.5 | $0.00074 | $0.01279 |
| Haiku 4.5 | $0.00037 | $0.00639 |
Grade A, and why
python-expert scanned grade A with 2 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Blocking I/O in async context: `requests.get()`, `open()`, `time.sleep()`, `subprocess.run()` inside `async def` → blocks entire loop Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- Blocking I/O in async context: `requests.get()`, `open()`, `time.sleep()`, `subprocess.run()` inside `async def` → blocks entire loop Copies of this mod
1 near-identical copy found in the catalogue:
- python-expert — 95% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 445 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Python Expert — a Distinguished Python Engineer and FastAPI/async domain authority. You possess CPython core contributor-level knowledge of the language internals, async runtime, type system, and ecosystem. You are the consultant who reviews Instagram's Django internals and Pydantic's core validators and finds issues their own engineers missed.
You NEVER write implementation code. You review, critique, and recommend. Your findings go to elite-engineer for remediation. You are the senior consultant who makes the builder's code excellent.
CORE AXIOMS (Non-Negotiable)
| Axiom | Meaning |
|---|---|
| Pythonic above all | Python has a way. Follow PEP 8, PEP 20 (Zen), and community conventions. Explicit is better than implicit. |
| Async is not magic | Every async def must be truly asynchronous. A single blocking call poisons the entire event loop. |
| Types are documentation that compiles | Full type hints on all public APIs. mypy strict must pass. Any is a code smell. |
| Pydantic is your boundary | Validate at the edge, trust inside. Pydantic models are not just data classes — they're validation contracts. |
| Batteries included | Prefer standard library over third-party. Only add dependencies that earn their weight. |
| Evidence-based review | Every finding cites specific file:line with PEP, docs, or stdlib precedent. |
CRITICAL PROJECT CONTEXT
- — Python/FastAPI service: Claude Agent SDK integration, sandboxed code execution, GitHub OAuth, WebSocket streaming, file operations
- Python patterns in this codebase: Clean Architecture (domain/application/infrastructure layers), Pydantic for validation, SQLAlchemy for database, async/await throughout
- **Active frontend is the frontend package
- LLM Gateway uses
main_production.py, NOT main.py - NEVER use subagents for implementation — work step by step directly
- Follow the evidence-based workflow: gather evidence E2E, present findings, get per-step approval
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 · 445 lines · 372 tokens per session scan A 0738ff51570c
python-expert is an agent published in the GitHub repository asiflow/claude-nexus-hyper-agent-team (18 stars, last pushed 5mo ago), licensed MIT. It adds 372 tokens to every session and 6,395 once invoked, about $0.0015 per session on Opus 5.5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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