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 skills add tmolavi/mcp-agent-skills-hub --skill performance-testing-review-multi-agent-reviewgit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote 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/skills/tmolavi/mcp-agent-skills-hub/performance-testing-review-multi-agent-review)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/performance-testing-review-multi-agent-review"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/performance-testing-review-multi-agent-review/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/skills/tmolavi/mcp-agent-skills-hub/performance-testing-review-multi-agent-review"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/performance-testing-review-multi-agent-review.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 | $0.00018 | $0.01361 |
| Opus 5 | $0.00009 | $0.00681 |
| Sonnet 5 | $0.00004 | $0.00272 |
| Haiku 4.5 | $0.00002 | $0.00136 |
Grade A, and why
performance-testing-review-multi-agent-review 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 9d 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.
This is a copy
89% identical to error-debugging-multi-agent-review — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Code Review Orchestration Tool
Use this skill when
- Working on multi-agent code review orchestration tool tasks or workflows
- Needing guidance, best practices, or checklists for multi-agent code review orchestration tool
Do not use this skill when
- The task is unrelated to multi-agent code review orchestration tool
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
Role: Expert Multi-Agent Review Orchestration Specialist
A sophisticated AI-powered code review system designed to provide comprehensive, multi-perspective analysis of software artifacts through intelligent agent coordination and specialized domain expertise.
Context and Purpose
The Multi-Agent Review Tool leverages a distributed, specialized agent network to perform holistic code assessments that transcend traditional single-perspective review approaches. By coordinating agents with distinct expertise, we generate a comprehensive evaluation that captures nuanced insights across multiple critical dimensions:
- Depth: Specialized agents dive deep into specific domains
- Breadth: Parallel processing enables comprehensive coverage
- Intelligence: Context-aware routing and intelligent synthesis
- Adaptability: Dynamic agent selection based on code characteristics
Tool Arguments and Configuration
Input Parameters
$ARGUMENTS: Target code/project for review- Supports: File paths, Git repositories, code snippets
- Handles multiple input formats
- Enables context extraction and agent routing
Agent Types
- Code Quality Reviewers
- Security Auditors
- Architecture Specialists
- Performance Analysts
- Compliance Validators
- Best Practices Experts
Multi-Agent Coordination Strategy
1. Agent Selection and Routing Logic
- Dynamic Agent Matching:
- Analyze input characteristics
- Select most appropriate agent types
- Configure specialized sub-agents dynamically
- Expertise Routing:
def route_agents(code_context): agents = [] if is_web_application(code_context): agents.extend([ "security-auditor", "web-architecture-reviewer" ]) if is_performance_critical(code_context): agents.append("performance-analyst") return agents
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.
- 9d ago First seen · 216 lines · 18 tokens per session scan A bc3f560c5684
performance-testing-review-multi-agent-review is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 16d ago), licensed MIT. It adds 18 tokens to every session and 1,361 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to error-debugging-multi-agent-review, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
architecture-review
Before committing to an implementation plan, run this skill to stress-test the proposed architecture. Catches over-engineering, circular dependencies, missing failure modes, security gaps, and scalability cliffs — before any code is written. Acts as a "second eye" on the plan.
code-review
Systematic code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.
github
Manages all git operations (commit, push, branch management, PR creation) in a standardized, safe, and consistent way. Automatically runs the code-review skill before any commit. Enforces Conventional Commits standard. Handles the full lifecycle: branch → review → commit → push → PR.
anti-sycophancy
Rejects false validation; enforces objective critique, risk exposure, and constructive code alternatives. / TR: Yapay zekanın kullanıcı fikirlerini ve hatalı kod yönlendirmelerini körü körüne onaylamasını engeller. Yapıcı itiraz eder, riskleri gösterir ve doğru alternatifi sunar.
Git PR Reviewer
Pull Request (PR) creation and code review standards with constructive feedback and merge strategies. / TR: Pull Request (PR) oluşturma ve kod inceleme (code review) süreçleri için standartlar ve yapıcı geri bildirim.
code-orchestrator
Main orchestrator managing code architecture, refactoring, clean code principles, and adversarial code reviews. / TR: Kod yazma, güvenlik, eleştirel denetim, test ve mimari süreçlerini yöneten ana orkestratör.