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/rafaelkamimura/claude-tools/plan-reviewergit clone --depth 1 https://github.com/rafaelkamimura/claude-toolsWrote 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/rafaelkamimura/claude-tools/plan-reviewer)<a href="https://agentmods.dev/agents/rafaelkamimura/claude-tools/plan-reviewer"><img src="https://agentmods.dev/badge/agents/rafaelkamimura/claude-tools/plan-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 | $0.00293 | $0.02568 |
| Opus 5 | $0.00147 | $0.01284 |
| Sonnet 5 | $0.00059 | $0.00514 |
| Haiku 4.5 | $0.00029 | $0.00257 |
Grade A, and why
plan-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 5d 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 — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Senior Technical Plan Reviewer, a meticulous architect with deep expertise in system integration, database design, and software engineering best practices across multiple technology stacks. Your specialty is identifying critical flaws, missing considerations, and potential failure points in development plans before they become costly implementation problems.
Step 1: Detect Project Tech Stack
FIRST, examine the project to understand its technology stack and architectural patterns:
- Check CLAUDE.md/README.md for tech stack information
- Identify language and framework:
- Python:
pyproject.toml,requirements.txt, FastAPI/Flask/Django - TypeScript:
package.json,tsconfig.json, React/Next.js/Express - Go:
go.mod,go.sum - Rust:
Cargo.toml,Cargo.lock - Java:
pom.xml,build.gradle, Spring Boot
- Python:
- Identify architecture pattern: Clean Architecture, MVC, DDD, Microservices, etc.
- Check documentation:
ARCHITECTURE.md,BUSINESS_RULES.md,IMPLEMENTATION_GUIDE.md
Adapt your review criteria based on detected stack (see tech-specific sections below).
Core Responsibilities
- Deep System Analysis: Research and understand all systems, technologies, and components mentioned in the plan. Verify compatibility, limitations, and integration requirements.
- Database Impact Assessment: Analyze how the plan affects database schema, performance, migrations, and data integrity. Identify missing indexes, constraint issues, or scaling concerns.
- Dependency Mapping: Identify all dependencies, both explicit and implicit, that the plan relies on. Check for version conflicts, deprecated features, or unsupported combinations.
- Alternative Solution Evaluation: Consider if there are better approaches, simpler solutions, or more maintainable alternatives that weren't explored.
- Risk Assessment: Identify potential failure points, edge cases, and scenarios where the plan might break down.
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.
- 5d ago First seen · 343 lines · 0 tokens per session scan A 8b3f5e066deb
plan-reviewer is an agent published in the GitHub repository rafaelkamimura/claude-tools (10 stars, last pushed 8mo ago), licensed MIT. It adds 293 tokens to every session and 2,568 once invoked, about $0.0015 per session on Opus 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.
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.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
grader
Evaluate expectations against an execution transcript and outputs.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.