applying-secure-coding-rules

Name: applying-secure-coding-rules
Availability: InStock
Author: Rock Lambros

Apply user-supplied secure-coding rule corpora to target repositories with per-finding citations.

2|Updated May 23, 2026

Search Tags:#code review #security #fastapi #langchain #semgrep #sarif #rule corpus

Ready to run

npx skills add https://github.com/rocklambros/rcs --skill applying-secure-coding-rules

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: applying-secure-coding-rules
Source: https://github.com/rocklambros/rcs/tree/main/skills/security/applying-secure-coding-rules
Command: npx skills add https://github.com/rocklambros/rcs --skill applying-secure-coding-rules

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill audits a repository against a user-supplied secure-coding rule corpus, so findings are grounded in an explicit policy instead of improvised from memory.

Core Features & Use Cases

Detects the project stack and applies only the rules that are relevant to that codebase.
Produces per-finding output with file, line, rule id, severity, and a recommended fix.
Separates skipped rules with reasons, making coverage gaps and unmet preconditions visible.
Fits security reviews for Python, FastAPI, LangChain, JavaScript, React, Flask, Django, and mixed polyglot repositories.

Quick Start

Ask the skill to apply your supplied rule corpus to a repository and return the stack manifest, findings, skipped rules, open questions, and provenance.

Frequently Asked Questions about applying-secure-coding-rules

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I audit my FastAPI or LangChain repository against custom security rules?▼

To audit a FastAPI or LangChain repository against custom security rules, apply a user-supplied secure-coding rule corpus. The process detects the project stack, indexes the corpus, separates applicable rules from skipped ones, and returns findings with file and line citations.

What is a secure-coding rule corpus and how does it structure code review findings?▼

A secure-coding rule corpus is a collection of markdown rule sheets, semgrep packs, SARIF packs, and YAML packs. It structures code review findings by grounding them in explicit policy, providing per-finding file, line, rule id, severity, and recommended fixes for FastAPI, LangChain, and polyglot codebases.

How do I run a security code review that skips irrelevant rules for a polyglot codebase?▼

Running a security code review for a polyglot codebase requires stack detection to separate applicable rules from skipped ones. This makes coverage gaps visible by identifying skipped rules with reasons, ensuring only relevant security rules are applied to your JavaScript, React, Flask, or Django code.

Does this code review approach work with semgrep and SARIF packs for Flask and Django?▼

Yes, this code review approach works with semgrep and SARIF packs for Flask and Django. It applies these rule packs to the detected project stack, generating reproducible provenance reporting and per-finding citations for your Python web frameworks.

What is the best way to get reproducible provenance reporting from a semgrep rule corpus?▼

The best way to get reproducible provenance reporting from a semgrep rule corpus is to apply the corpus to the target repository using an automated auditing process. This indexes the rules and generates a stack manifest tracking applicable rules, findings, skipped rules, and open questions.

Why does my security code review show skipped rules and coverage gaps?▼

Your security code review shows skipped rules and coverage gaps because the stack detection mechanism separates applicable rules from skipped ones based on the detected codebase technologies. This separation makes unmet preconditions visible, ensuring rules are only applied when relevant to the target repository.