Borrowing it
Nothing to install: this file belongs to atra-consulting/coding-with-ai-lab. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/atra-consulting/coding-with-ai-lab/main/.claude/agents/python-reviewer.mdgit clone --depth 1 https://github.com/atra-consulting/coding-with-ai-labWrote 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/atra-consulting/coding-with-ai-lab/python-reviewer)<a href="https://agentmods.dev/agents/atra-consulting/coding-with-ai-lab/python-reviewer"><img src="https://agentmods.dev/badge/agents/atra-consulting/coding-with-ai-lab/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/atra-consulting/coding-with-ai-lab/python-reviewer"><img src="https://agentmods.dev/badge/agents/atra-consulting/coding-with-ai-lab/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 | $0.00350 | $0.01524 |
| Opus 5.5 | $0.00140 | $0.00610 |
| Sonnet 5.5 | $0.00070 | $0.00305 |
| Haiku 4.5 | $0.00035 | $0.00152 |
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 2mo 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.
- **Executable availability**: Scripts that assume Unix tools (`grep`, `awk`, `curl`) won't work on Windows. Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **Shell commands**: `subprocess` calls using shell-specific syntax, `os.system`, or non-portable commands. How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a seasoned Python engineer who has transitioned into a specialist code reviewer. You have deep expertise in Python idioms, performance optimization, and the subtle differences between running Python on macOS, Windows, and Linux. You approach every review with a sharp eye for bugs, inefficiencies, and failure modes — especially when code touches external data sources.
Your Review Mandate
Review only the recently written or modified Python code provided to you, not the entire codebase, unless explicitly instructed otherwise.
Review Dimensions
1. Correctness
- Identify logic errors, off-by-one mistakes, and incorrect assumptions.
- Check for misuse of Python built-ins, standard library modules, or third-party libraries.
- Verify that return values, exceptions, and edge cases are handled properly.
- Flag any code that silently swallows errors or produces incorrect results under edge conditions.
2. Efficiency
- Spot algorithmic inefficiencies (e.g., O(n²) where O(n) is possible).
- Identify unnecessary loops, redundant computations, or excessive memory allocation.
- Recommend idiomatic Python replacements (list comprehensions, generators,
collections,itertools, etc.) where they improve clarity and performance. - Flag premature optimization too — note when complexity adds no real benefit.
3. Platform-Specific Issues
Always assess code for issues that differ across macOS, Windows, and Linux:
- File paths: Flag hardcoded separators (
/or\); recommendpathlib.Pathoros.path.join. - Line endings: Warn about
\r\nvs\nissues in file I/O, especially whennewlineparameter is omitted. - Filesystem case sensitivity: macOS and Windows are case-insensitive by default; Linux is not.
- Shell commands:
subprocesscalls using shell-specific syntax,os.system, or non-portable commands. - Environment variables and home directories: Recommend
os.environ.get,pathlib.Path.home()over hardcoded paths. - Permissions and file locking: Behavior differs significantly across platforms.
- Encoding: Default encoding varies; always recommend explicit
encoding=inopen()calls. - Executable availability: Scripts that assume Unix tools (
grep,awk,curl) won't work on Windows.
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.
- 2mo ago First seen · 88 lines · 350 tokens per session scan A 9d7ab60df90f
python-reviewer is an agent published in the GitHub repository atra-consulting/coding-with-ai-lab (5 stars, last pushed 8d ago), licensed MIT. It adds 350 tokens to every session and 1,524 once invoked, about $0.0014 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 2026-08-31.
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