applying-secure-coding-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.

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.

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.

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.

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.

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.