Now included free
This module is part of the CodeRiskTools Developer Safety Kit
The individual paid listing has been retired. The former bundle has been withdrawn from public sale; existing buyers retain access.
What’s Included
- Expanded Review Prompts — Structured prompts for scope, security, data, runtime, and rollback checks, going deeper than the free 5-point checklist
- Risk Scoring Template — Rate each finding by severity and likelihood so you can prioritize fixes
- Workflow Template — A repeatable AI code review workflow you can follow for every pull request
- Quick-Reference Card — One-page summary of all review steps for fast on-screen reference
- README with Quick Start — Get started in minutes
FAQ
- How is this different from just using GitHub Copilot or Cursor?
- Copilot and Cursor help you write code. This workflow pack helps you review AI-generated code for security risks, secret leaks, and config drift — before it reaches production.
- Does it require a specific AI tool?
- No. The workflow works with any AI code generator — Copilot, Cursor, Claude, ChatGPT, or others. It reviews the output, not the tool.
- Does it upload my code anywhere?
- No. All reviews run locally. No code or data leaves your machine.
- Can I use this in my team’s CI pipeline?
- Yes. The pack includes CI templates and pre-commit hooks. Integrate it into any pipeline that supports shell commands.
- What if I need a refund?
- Refund requests handled under the published Terms and applicable law. No questions asked.
AI Code Review Workflow Pack
v1.1.2 clean package: the live Gumroad buyer ZIP was downloaded and hash-verified after deployment; 169/169 tests pass. SHA256: 4de2f96ff3c2af5763163ea57e69f88dfbc37b25b7b8884ac4d02f3a646307f8.
How to use this kit
- Download the ZIP from Gumroad and extract it locally.
- Read the README/usage notes and pick the workflow rule/checklist that matches your review process.
- Copy the policy/checklist examples into your repository or review workspace.
- Run the included validator/checklist workflow against an AI-generated diff or pull request.
- Use the evidence logs and CI/pre-commit examples to block unclear, risky, or under-tested AI changes before merge.
Best first use: test it on a single pull request and tune the checklist before making it mandatory.
Executable CLI validators, review checklists, evidence logs, Cursor/Claude rules, CI integration, and policy config for reviewing AI-generated code changes. Not just prompts – real local tools for safer AI-assisted development.
What you get
- Review checklist generator – Generate structured checklists for unified diffs.
- Review log validator – Validate review logs with pass/fail exit codes.
- Evidence log generator – Create audit-quality JSON/HTML records for every review.
- Cursor rules and Claude Code project rules generators – Enforce AI code review rules in your IDE.
- Policy-driven review enforcement – JSON config that defines what must be reviewed.
- Pre-commit hook and GitHub Actions review gate – Automated enforcement in CI.
- 169 passing tests and full source code – Transparent, verifiable, no hidden behavior.
How it works
Feed a unified diff into the review checklist generator. It produces a structured checklist. Run the validator on your completed review log. If any required items are missing, it exits with a failure code – perfect for CI gates. Generate audit-quality evidence logs for compliance trails.
Best for
Solo developers, small teams, agencies, and tech leads who use AI coding agents but still need structured review evidence before merge.
What this is not
- Not a security audit tool or vulnerability scanner.
- Not a code review replacement – it structures your review process.
- Not a SaaS – local tools you run on your machine.
- Developer Safety Kit withdrawn from public sale; existing buyer access retained.
Requirements
Python 3.10+. Runs locally and offline. No SaaS required.
Related products
- Current CodeRiskTools catalog
- CodeRiskTools Scanner 3.0.0 is the current public scanner: MIT-licensed and free on GitHub for local secret and configuration-change review.
- Compare all products →
Proof & Sample Output
See Workflow Pack Sample Output in action:
https://gist.github.com/9batalion/c448e4ab15c60162904ee5fb76e87c00
https://gist.github.com/9batalion/c448e4ab15c60162904ee5fb76e87c00
Real Checklist Output
Feed a unified diff into the review checklist generator and get a structured, risk-flagged checklist. Here is the actual output:
$ python3 -m src.review_workflow.cli checklist --diff risky-change.patch
# Code Review Checklist
**Date:** 2026-06-30
**Files changed:** 4
**Lines added:** 11
**Lines removed:** 3
## Files Changed
- [ ] `src/auth/login.py` (+4/-1)
- [ ] `config/settings.json` (+2/-1)
- [ ] `requirements.txt` (+2/-1)
- [ ] `src/utils/helpers.py` (+3/-0)
## Risk Flags
### High Risk
- [ ] **POSSIBLE_SECRET_LITERAL_IN_DIFF** in `config/settings.json`
### Medium Risk
- [ ] **AUTH_CHANGE** in `src/auth/login.py`
- [ ] **DEPENDENCY_CHANGE** in `requirements.txt`
### Low Risk
- [ ] **TODO_FIXME_MARKER** in `src/utils/helpers.py`
### Security
- [ ] No SQL injection or XSS vulnerabilities
- [ ] Auth/security changes are safe
## Sign-off
- [ ] I have reviewed all changes listed above
- [ ] I approve this change for merge
**Verdict:** Approve / Approve with comments / Request changesThe checklist is generated from your actual diff — not a template, not a mockup. Every risk flag corresponds to a real pattern in the code change.
My team uses Cursor and Copilot for vibe coding. How does the Workflow Pack help catch AI-specific risks?
Vibe coding tools generate code fast, but a 2026 IBM analysis found AI-assisted teams ship 10x more security flaws despite 4x faster output. The Workflow Pack gives you a structured review process specifically for AI-generated changes: scope checking, security pattern detection, data handling verification, runtime testing, and rollback readiness. It catches the logic and intent errors that AI tools introduce and that traditional dependency scanners miss.
Comparing CodeRiskTools with Alternatives?
See how we compare:
- CodeRiskTools vs Snyk — dependency scanning vs AI code risk review
- CodeRiskTools vs GitGuardian — secret monitoring vs local AI code review
- CodeRiskTools vs SonarQube — static analysis vs AI-specific risk detection
Honest limitations
- This pack provides workflow templates, checklists, and a review pipeline — it does not perform automated AI code review itself.
- You need to bring your own AI code review tool (Cursor, Claude Code, Copilot, etc.) or use the included templates with your existing workflow.
- It does not automatically fix code — it structures your review process and generates evidence.
- Not a replacement for human code review or professional security audits.
- Templates and examples are starting points — adapt them to your team’s needs.
🎁 Retired: 5-Point AI Code Review Checklist
The retired 5-Point AI Code Review Checklist is no longer available through a public checkout; existing buyers retain access.
Safety Kit retired — current catalog
This legacy public checkout has been withdrawn; existing buyers retain access.
📖 Related Reading
Git Diff Security: How to Spot Hidden Risks in Code Changes Before They Ship — Learn how to catch secret leaks, config drift, and AI-generated risks in your git diffs before they reach production.
Use Cases
- Solo developer code review workflow — Systematic process for reviewing AI-generated changes before merge
- Small team code review standard — Consistent checklists and automated checks for every AI-assisted pull request
- Code review gate for CI/CD — Ensure every AI-generated change is reviewed with structured checklists and automated scanning
- Compliance documentation — Record your review process for audit requirements
- Pre-commit hook integration — Block secrets and risky configs before they reach your repository
What’s Included
- Full source code (Python stdlib-only)
- AI code review workflow document
- Secret detection CLI tool
- Config drift detection CLI tool
- Review checklist templates
- CI integration templates (GitHub Actions, GitLab CI)
- Sample project for testing
- Customer support; refund requests handled under the published Terms and applicable law
Use Cases
- Developers who want a complete AI code review workflow, not just a checklist
- Teams enforcing code review policies before merging AI-generated changes
- CI/CD pipelines that need to flag risky AI changes automatically
- Developers who want scanner + checklist + policy enforcement in one kit
What’s Included
- Review workflow CLI — combines scanning, checklists, and policy enforcement
- Policy enforcement engine — define rules for what AI-generated changes must pass
- Evidence logging — track every review decision with timestamps and reasoning
- JSON and HTML reports — structured review outputs for teams and compliance
- CI integration templates — GitHub Actions and GitLab CI examples
- AI code review checklist — structured 5-point review framework
- Full source code — Python package, runs locally, no API keys required
Comparing Code Security Tools?
See the full feature-by-feature breakdown: AI Code Security Hub | CodeRiskTools vs Snyk, GitGuardian, Semgrep, and SonarQube — pricing, privacy, and local-first workflow comparison.
Frequently Asked Questions
How is this different from the Pro Kit?
The Pro Kit focuses on risk scoring and audit reports. The Workflow Pack adds policy enforcement and evidence logging — it is designed for developers who want a complete, enforceable review pipeline. Many teams use both together.
Does my code leave my machine?
The Workflow Pack runs its documented core workflow on your machine without uploading selected project files to CodeRiskTools. Purchase, download, updates, support, and optional integrations remain online activities.
Can I use this in my CI pipeline?
Yes. The kit includes CI integration templates for GitHub Actions and GitLab CI. You can run the review workflow as a pre-merge check and fail the pipeline if risky AI changes are detected.
FAQ
Is AI-generated code really less secure than human-written code?
A 2026 Cloud Security Alliance research note found 35 CVEs in a single month (March 2026) directly attributable to AI coding tools, with the true count estimated at 5x higher. A Q1 2026 assessment of over 200 vibe-coded applications found that 91.5% contained at least one vulnerability traceable to AI hallucination, and AI-generated code exhibited a 2.74x higher XSS vulnerability rate compared to human-written code. Reviewing AI code before merge is not optional — it is essential.
Do I need a security tool if my team is small?
The retired 5-Point AI Code Review Checklist is no longer available through a public checkout; existing buyers retain access.
Pair It With
If you use the Workflow Pack for structured code review, pair it with:
- Use Scanner 3.0.0 on GitHub when you need MIT/free local review of secrets and configuration changes.
- Safety Kit retired — current catalog — full risk audit for AI agent changes with severity classification
Honest Limitations
- Runs its documented core workflow on your machine without uploading source code to CodeRiskTools.
- Workflow Pack provides templates and checklists — you still need a human reviewer to make final decisions.
- Focuses on AI code review workflow — it does not scan for secrets or config drift by itself.
- JSON output is designed for CI integration — setup is required for automated pipelines.