Skip to content
MMake Prompt AI Get Started
Home > ChatGPT Guide > /codeaudit
Custom prompt shortcut / Software Development

/codeaudit — ChatGPT Prompt Guide

Explore the /codeaudit custom ChatGPT prompt shortcut with a practical template, examples, and review steps. Learn how to refine outputs for your workflow.

What does /codeaudit do?

Purpose: Flask API validation and error-handling audit.

This is a custom prompt convention, not a built-in or official ChatGPT slash command. Entering the slash text alone does not guarantee that ChatGPT will invoke a special tool. Use the complete structured prompt below or configure your own app to interpret this shortcut.

Key decision for this workflow

test extension spoofing, file size, path traversal and permissions.

Copy-ready task brief

/codeaudit Objective: Audit a file-upload endpoint for unsafe input handling. Inputs: route code, accepted MIME types and storage design. Deliverable: risk register with reproducible findings and mitigations.

Reusable prompt template

/codeaudit Objective: Audit a file-upload endpoint for unsafe input handling. Available materials: route code, accepted MIME types and storage design. Required output: risk register with reproducible findings and mitigations. Acceptance test: test extension spoofing, file size, path traversal and permissions. Boundary: Flag missing information and unverified assumptions; do not fabricate evidence. Return the requested deliverable first, then a brief verification checklist and any unresolved questions.

How to use this shortcut

  1. Collect the source inputs: route code, accepted MIME types and storage design.
  2. Run /codeaudit with the copy-ready task brief above.
  3. Request the concrete output: risk register with reproducible findings and mitigations.
  4. Check the result: test extension spoofing, file size, path traversal and permissions.

Quality checks and limitations

Acceptance criteria: test extension spoofing, file size, path traversal and permissions. If inputs are incomplete, mark assumptions and verify the result before deployment.

Related tools and learning

Continue with the 35 prompt generators, the technical AI Guides, and the Image Guides for deeper examples.

/code/debug/refactor/frontend/thumbnailtesting/videoseo

Worked example and expected output

Input materials: route code, accepted MIME types and storage design.

Requested result: risk register with reproducible findings and mitigations.

Acceptance test: test extension spoofing, file size, path traversal and permissions.

Detailed worked example: Flask API validation and error-handling audit

Input brief: A Flask POST /notes endpoint accepts {"title":"Hi","body":"Test"} and writes to a SQLite database.

Example output

Review input size limits, JSON parsing failures, type validation, SQL parameterization, transaction rollback, authentication and response codes. Example finding: an unbounded body field can exhaust storage; cap length at 10,000 characters and return HTTP 413 or a documented 422 response. Add tests for malformed JSON, missing title, oversized body and database exceptions.

What to verify

A code review cannot certify security without examining the implementation and executing tests.

Applied workflow and review criteria

Real-world task: Audit a file-upload endpoint for unsafe input handling.

Required inputs: route code, accepted MIME types and storage design.

Deliverable to request: risk register with reproducible findings and mitigations.

How to validate the result: test extension spoofing, file size, path traversal and permissions.

Scope and limitation: A static audit cannot establish exploitability without context.

Frequently asked questions

Is /codeaudit an official ChatGPT command? No. It is a reusable custom shorthand from this catalog. Does it work without a plugin? You can copy the full prompt into an assistant; automatic slash-command routing requires application logic. Can the output be trusted without review? No. Review accuracy, safety, and suitability before use.