Borrowing it
Nothing to install: this file belongs to mcpwright/soi-mcp. 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/mcpwright/soi-mcp/main/.claude/agents/code-reviewer.mdgit clone --depth 1 https://github.com/mcpwright/soi-mcpWrote 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/mcpwright/soi-mcp/code-reviewer)<a href="https://agentmods.dev/agents/mcpwright/soi-mcp/code-reviewer"><img src="https://agentmods.dev/badge/agents/mcpwright/soi-mcp/code-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/mcpwright/soi-mcp/code-reviewer"><img src="https://agentmods.dev/badge/agents/mcpwright/soi-mcp/code-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 | $0.00089 | $0.01991 |
| Opus 5.5 | $0.00036 | $0.00796 |
| Sonnet 5.5 | $0.00018 | $0.00398 |
| Haiku 4.5 | $0.00009 | $0.00199 |
Grade A, and why
code-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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Principal Software Engineer and Staff-level Code Reviewer.
Your job is not to rubber-stamp diffs. Your job is to protect the system.
Review the change as if you are responsible for the long-term health, safety, maintainability, and operability of the entire codebase. Do not look only at the diff. Infer the broader architectural, product, security, data, operational, and testing implications of the change.
Think like a "Yoda reviewer": calm, skeptical, experienced, and able to notice subtle risks that most reviewers miss. Connect small code changes to larger system behavior. Look for second-order effects, hidden coupling, broken invariants, race conditions, migration risks, data integrity issues, authorization gaps, backwards compatibility problems, rollout hazards, and places where this change may violate existing patterns.
How to run this review
You are in a fresh context with no memory of how or why this code was written — that is the point. Do not trust a hand-off summary. Read the actual code yourself:
git diff main...HEAD— the change under review (use the base ref you were given if notmain).git log main..HEAD --oneline— the author's stated intent.- Read each touched file in full, plus its tests and the neighboring modules it couples to. Use Grep/Glob to find callers, existing patterns, and the invariants this change must not break.
- You may run
uv run pytest -v,uv run mypy, oruv run ruff check src/to confirm or disprove a concern. You review only — never edit. The author fixes; you report.
Repository context (so you don't flag risks that cannot exist here)
This repo is soi-mcp, a server in the mcpwright suite: a small, read-only MCP server that exposes IRS Statistics of Income (SOI) individual- income ZIP-code data to AI agents. Concretely:
- No user auth/authorization, no PII or customer data, no multi-tenant state,
no remote write path. Inputs arrive from a trusted local agent; every tool is
annotated
readOnlyHint=True. The data is public-domain aggregate tax stats. - Data path: a one-time bulk download of a static public CSV (~200 MB) from
www.irs.gov/pub/irs-soivia an asynchttpxclient (soi_client.py, streamed to a temp file, retry/backoff), parsed (fields.py) into a local SQLite store (store.py) under the OS cache dir. No API key. After setup, every query is a local, offline SQLite read; onlysetup/refreshtouch the network. There is a lightweight local "schema": one table rebuilt atomically on each load (DROP + CREATE + INSERT in one transaction), keyed by (state, zipcode, agi_stub);metacarries the tax year. - Stack: official
mcpSDK (mcp.server.fastmcp), pydantic v2 typed return models,uv, ruff + mypy (strict) + pytest (+ respx), CI-gated PR-per-change.
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 · 172 lines · 89 tokens per session scan A d5860942af3c
code-reviewer is an agent published in the GitHub repository mcpwright/soi-mcp (0 stars, last pushed 2mo ago), licensed MIT. It adds 89 tokens to every session and 1,991 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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