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/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/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/vibeeval/vibecosystem/python-reviewer"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/python-reviewer.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 | $0.00043 | $0.02771 |
| Opus 5.5 | $0.00017 | $0.01108 |
| Sonnet 5.5 | $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 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.
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.
- 1mo ago First seen · 470 lines · 43 tokens per session scan A 1de05ad64306
python-reviewer is an agent published in the GitHub repository vibeeval/vibecosystem (541 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 2,771 once invoked, about $0.0002 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 2026-09-03.
Other agents, from other repositories
security-reviewer
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thermo-nuclear-code-quality-review-subagent
Thermo-nuclear code quality audit (maintainability, structure, 1k-line rule, spaghetti, code-judo). Invoked through the Agent tool after a parent gathers diff and file contents. Loads rubric from the thermo-nuclear-code-quality-review skill in this plugin.
predictive-analyst
Precognition agent. Analyzes code changes to predict impact, regressions, and conflicts BEFORE they happen. Uses dependency graphs and historical data.
atomic-auditor
Final gate for a finished implementation. Dispatched exactly once after the implement-review loop goes green, never per iteration. Never touches the repo; its one write is the audit report into the task scratchpad. Audits the delivered work as a whole: cumulative spec compliance, cross-iteration coherence…
verification-subagent
Verifies if a task from the implementation plan has been completed by checking source files.
verifier
Verification agent for /craft:research-verify. Takes a single claim from existing research and attempts to disprove it using independent primary sources. Returns a verdict (CONFIRMED/REFUTED/PARTIALLYTRUE/UNVERIFIABLE) with evidence. NOT a researcher. Does not discover new topics or cast a wide net. Takes one claim…