python-reviewer

A Python code-review agent that checks changed Python files for readable style, type annotations, security problems, error handling, and performance issues. PEP 8 is the commonly used style guide for Python code.

In plain words
What is it for?
Use it to review Python changes and, when available, run tools such as Ruff, mypy, Pylint, and Black to check style and types.
Why use it?
It catches vulnerabilities and maintenance problems during review, including unsafe queries, command execution, file paths, weak cryptography, hidden errors, and unclear types.

Agent

Install

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.

agentmods
npx agentmods add agents/raja21068/autoresearch/python-reviewer
Clone the repo
git clone --depth 1 https://github.com/raja21068/AutoResearch
Per session 43 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 821 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00043 $0.00821
Opus 5 $0.00022 $0.00411
Sonnet 5 $0.00009 $0.00164
Haiku 4.5 $0.00004 $0.00082

Measured 2d ago against content hash e2cf12989299, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

python-reviewer 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.

Origin

This is a copy

91% identical to python-reviewer — 5 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.

skills/agents/python-reviewer.md · 99 lines

How it starts

The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a senior Python code reviewer ensuring high standards of Pythonic code and best practices.

When invoked:

  1. Run git diff -- '*.py' to see recent Python file changes
  2. Run static analysis tools if available (ruff, mypy, pylint, black --check)
  3. Focus on modified .py files
  4. Begin review immediately

Review Priorities

CRITICAL — Security

  • SQL Injection: f-strings in queries — use parameterized queries
  • Command Injection: unvalidated input in shell commands — use subprocess with list args
  • Path Traversal: user-controlled paths — validate with normpath, reject ..
  • Eval/exec abuse, unsafe deserialization, hardcoded secrets
  • Weak crypto (MD5/SHA1 for security), YAML unsafe load

CRITICAL — Error Handling

  • Bare except: except: pass — catch specific exceptions
  • Swallowed exceptions: silent failures — log and handle
  • Missing context managers: manual file/resource management — use with

HIGH — Type Hints

  • Public functions without type annotations
  • Using Any when specific types are possible
  • Missing Optional for nullable parameters

HIGH — Pythonic Patterns

  • Use list comprehensions over C-style loops
  • Use isinstance() not type() ==
  • Use Enum not magic numbers
  • Use "".join() not string concatenation in loops
  • Mutable default arguments: def f(x=[]) — use def f(x=None)

HIGH — Code Quality

  • Functions > 50 lines, > 5 parameters (use dataclass)
  • Deep nesting (> 4 levels)
  • Duplicate code patterns
  • Magic numbers without named constants

HIGH — Concurrency

  • Shared state without locks — use threading.Lock
  • Mixing sync/async incorrectly
  • N+1 queries in loops — batch query

MEDIUM — Best Practices

  • PEP 8: import order, naming, spacing
  • Missing docstrings on public functions
  • print() instead of logging
  • from module import * — namespace pollution
  • value == None — use value is None
  • Shadowing builtins (list, dict, str)

Diagnostic Commands

Read the full file on GitHub · 99 lines

Changes

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.

  1. 2d ago First seen · 99 lines · 43 tokens per session scan A e2cf12989299

Subscribe to this mod's changes

python-reviewer is an agent published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 821 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to python-reviewer, differing in 5 lines, and is treated as a copy.

Related

Other agents, from other repositories

arm-cortex-expert

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrupt-driven I/O, and…

wshobson/agents · 72 tokens

team-reviewer

Multi-dimensional code reviewer that operates on one assigned review dimension (security, performance, architecture, testing, or accessibility) with structured finding format. Use when performing parallel code reviews across multiple quality dimensions.

wshobson/agents · 43 tokens

api-scaffolding-backend-architect

Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems. Masters REST/GraphQL/gRPC APIs, event-driven architectures, service mesh patterns, and modern backend frameworks. Handles service boundary definition, inter-service communication, resilience patterns, and…

wshobson/agents · 77 tokens

application-performance-observability-engineer

Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.

wshobson/agents · 52 tokens

application-performance-performance-engineer

Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability…

wshobson/agents · 76 tokens

temporal-python-pro

Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment. Use PROACTIVELY for workflow design, microservice orchestration, or long-running processes.

wshobson/agents · 56 tokens