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/vibeeval/vibecosystem/python-reviewergit clone --depth 1 https://github.com/vibeeval/vibecosystemWrote 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/vibeeval/vibecosystem/python-reviewer)<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/python-reviewer"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/python-reviewer.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.02771 |
| Opus 5 | $0.00022 | $0.01385 |
| Sonnet 5 | $0.00009 | $0.00554 |
| Haiku 4.5 | $0.00004 | $0.00277 |
Grade A, and why
python-reviewer 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
os.system(f"curl {url}") Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **Command Injection**: Unvalidated input in subprocess/os.system Copies of this mod
1 near-identical copy found in the catalogue:
- python-reviewer — 88% identical, 946 lines differ
How it starts
The opening of the file, as written. The whole thing — 470 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:
- Run
git diff -- '*.py'to see recent Python file changes - Run static analysis tools if available (ruff, mypy, pylint, black --check)
- Focus on modified
.pyfiles - Begin review immediately
Security Checks (CRITICAL)
-
SQL Injection: String concatenation in database queries
# Bad cursor.execute(f"SELECT * FROM users WHERE id = {user_id}") # Good cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,)) -
Command Injection: Unvalidated input in subprocess/os.system
# Bad os.system(f"curl {url}") # Good subprocess.run(["curl", url], check=True) -
Path Traversal: User-controlled file paths
# Bad open(os.path.join(base_dir, user_path)) # Good clean_path = os.path.normpath(user_path) if clean_path.startswith(".."): raise ValueError("Invalid path") safe_path = os.path.join(base_dir, clean_path) -
Eval/Exec Abuse: Using eval/exec with user input
-
Pickle Unsafe Deserialization: Loading untrusted pickle data
-
Hardcoded Secrets: API keys, passwords in source
-
Weak Crypto: Use of MD5/SHA1 for security purposes
-
YAML Unsafe Load: Using yaml.load without Loader
Error Handling (CRITICAL)
-
Bare Except Clauses: Catching all exceptions
# Bad try: process() except: pass # Good try: process() except ValueError as e: logger.error(f"Invalid value: {e}") -
Swallowing Exceptions: Silent failures
-
Exception Instead of Flow Control: Using exceptions for normal control flow
-
Missing Finally: Resources not cleaned up
# Bad f = open("file.txt") data = f.read() # If exception occurs, file never closes # Good with open("file.txt") as f: data = f.read() # or f = open("file.txt") try: data = f.read() finally: f.close()
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 · 470 lines · 43 tokens per session scan A 1de05ad64306
python-reviewer is an agent published in the GitHub repository vibeeval/vibecosystem (530 stars, last pushed 28d ago), licensed MIT. It adds 43 tokens to every session and 2,771 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.