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 ratnesh-maurya/cursor-claude-personas --skill production-code-auditgit clone --depth 1 https://github.com/ratnesh-maurya/cursor-claude-personasWrote 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/ratnesh-maurya/cursor-claude-personas/production-code-audit)<a href="https://agentmods.dev/skills/ratnesh-maurya/cursor-claude-personas/production-code-audit"><img src="https://agentmods.dev/badge/skills/ratnesh-maurya/cursor-claude-personas/production-code-audit/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/ratnesh-maurya/cursor-claude-personas/production-code-audit"><img src="https://agentmods.dev/badge/skills/ratnesh-maurya/cursor-claude-personas/production-code-audit.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 | $0.00038 | $0.03887 |
| Opus 5 | $0.00019 | $0.01944 |
| Sonnet 5 | $0.00008 | $0.00777 |
| Haiku 4.5 | $0.00004 | $0.00389 |
Grade A, and why
production-code-audit 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.
How it starts
The opening of the file, as written. The whole thing — 544 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production Code Audit
Overview
Autonomously analyze the entire codebase to understand its architecture, patterns, and purpose, then systematically transform it into production-grade, corporate-level professional code. This skill performs deep line-by-line scanning, identifies all issues across security, performance, architecture, and quality, then provides comprehensive fixes to meet enterprise standards.
When to Use This Skill
- Use when user says "make this production-ready"
- Use when user says "audit my codebase"
- Use when user says "make this professional/corporate-level"
- Use when user says "optimize everything"
- Use when user wants enterprise-grade quality
- Use when preparing for production deployment
- Use when code needs to meet corporate standards
How It Works
Step 1: Autonomous Codebase Discovery
Automatically scan and understand the entire codebase:
- Read all files - Scan every file in the project recursively
- Identify tech stack - Detect languages, frameworks, databases, tools
- Understand architecture - Map out structure, patterns, dependencies
- Identify purpose - Understand what the application does
- Find entry points - Locate main files, routes, controllers
- Map data flow - Understand how data moves through the system
Do this automatically without asking the user.
Step 2: Comprehensive Issue Detection
Scan line-by-line for all issues:
Architecture Issues:
- Circular dependencies
- Tight coupling
- God classes (>500 lines or >20 methods)
- Missing separation of concerns
- Poor module boundaries
- Violation of design patterns
Security Vulnerabilities:
- SQL injection (string concatenation in queries)
- XSS vulnerabilities (unescaped output)
- Hardcoded secrets (API keys, passwords in code)
- Missing authentication/authorization
- Weak password hashing (MD5, SHA1)
- Missing input validation
- CSRF vulnerabilities
- Insecure dependencies
Performance Problems:
- N+1 query problems
- Missing database indexes
- Synchronous operations that should be async
- Missing caching
- Inefficient algorithms (O(n²) or worse)
- Large bundle sizes
- Unoptimized images
- Memory leaks
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 · 544 lines · 38 tokens per session scan A 2b17856786bd
production-code-audit is a skill published in the GitHub repository ratnesh-maurya/cursor-claude-personas (8 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 3,887 once invoked, about $0.0002 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 2026-09-03.
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