AI Agent Change Risk Audit Kit — Pro

Use Cases

• Agency delivery review — Run a full risk audit on AI-generated code before client delivery, with risk scoring and client-ready documentation.
• Team code review gate — Use the expanded prompts and risk scoring to structure every AI code review in your team’s workflow.
• Freelancer quality assurance — When your reputation depends on every commit, the Pro Kit catches security, scope, and data risks that visual scanning misses.
• Compliance documentation — Generate documented risk audits that show stakeholders you reviewed AI-generated changes properly.
• Pre-deployment checkpoint — Use the delivery review templates as a final gate before merging AI code into production branches.

AI Agent Change Risk Audit Kit – Pro

A complete local audit toolkit for reviewing AI-generated code changes. Risk scoring, batch audits, text/JSON reports, pre-commit templates, GitHub Actions and Slack/Teams webhooks. Everything in Basic, plus the tools professionals need.

Why Pro, not just a checklist?

When a client asks for evidence, a checklist is not enough. The Pro Kit gives you structured risk scoring, audit-quality reports, and CI enforcement – so every AI-generated change is reviewed, scored, and documented before it reaches production.

What Pro adds beyond Basic

• Local audit CLI – Run the auditor on any diff and get a structured risk assessment.
• Risk scoring (low/medium/high) – Every change gets an evidence-based risk level, not just pass/fail.
• Structured text and JSON reports – Machine-readable or human-readable results for CI and stakeholder review.
• Batch audit mode – Scan multiple diffs or a whole directory at once.
• Pre-commit hook template – Block risky changes before they are committed.
• CI integration examples – GitHub Actions and CI workflow templates for automated review gates.
• Expanded AI-aware prompts – More detailed review prompts for complex changes.
• Agency checklist – Structured checklist for teams managing multiple AI-assisted projects.

Everything in Basic is included. Pro is a superset – if you already own Basic, Pro covers all the same ground and more.

Basic vs Pro

Who Pro is for

• Freelancers delivering AI-assisted code to clients – prove you reviewed it.
• Agency teams managing multiple AI-assisted projects – consistent review standards.
• Tech founders shipping fast but needing audit trails for due diligence.
• Tech leads enforcing AI code review standards across a team.
• Compliance-adjacent roles who need evidence that AI-generated changes were reviewed.

Real-world scenarios

• Freelancer to client delivery: Run the audit CLI on your diff, generate an HTML report, attach it to the PR. The client sees evidence of a structured review, not just a claim that you checked it.
• Team release gate: Add the pre-commit hook. Every commit of AI-generated code is automatically scored. High-risk changes are flagged before they reach main.
• Agency multi-project: Use batch audit to scan all active projects. Get a single report showing which projects have unreviewed AI changes.

Real Sample Output

Here is what the Pro audit CLI actually outputs when you run it against a risky AI-generated diff:

$ python3 agent_change_risk_auditor.py --diff risky-change.patch --json

{
  "added_lines": 7,
  "files_changed": 4,
  "file_changes": [
    {
      "path": "src/app.py",
      "added": 2, "removed": 1,
      "risk_flags": []
    },
    {
      "path": ".env",
      "added": 3, "removed": 0,
      "risk_flags": ["CONFIG_CHANGE", "SENSITIVE_AREA_CHANGE"]
    },
    {
      "path": "package.json",
      "added": 1, "removed": 0,
      "risk_flags": ["DEPENDENCY_CHANGE"]
    },
    {
      "path": "docker-compose.yml",
      "added": 1, "removed": 0,
      "risk_flags": ["INFRA_CHANGE", "SENSITIVE_AREA_CHANGE"]
    }
  ],
  "score": 100,
  "level": "high",
  "flags": [
    "CONFIG_CHANGE:.env",
    "SENSITIVE_AREA_CHANGE:.env",
    "DEPENDENCY_CHANGE:package.json",
    "INFRA_CHANGE:docker-compose.yml",
    "SENSITIVE_AREA_CHANGE:docker-compose.yml",
    "SOURCE_CHANGED_WITHOUT_TEST_CHANGE",
    "POSSIBLE_SECRET_LITERAL_IN_DIFF"
  ],
  "recommendations": [
    "Add or update tests for changed source files before merge.",
    "Remove secret-like literals and rotate exposed credentials if real.",
    "Review dependency changes manually and run lockfile/security checks.",
    "Verify environment/config changes.",
    "Require human review for auth/payment/security/config paths."
  ]
}

The CLI assigns a risk level (low/medium/high) and lists specific flags so you know exactly what to review before merge. This is the real output from the tool — no mock data, no staged screenshots.

What this is not

• Not a security audit or vulnerability scanner.
• Not a guarantee that AI-generated code is safe.
• A downloadable product whose documented core workflow runs on your machine.
• Developer Safety Kit withdrawn from public sale; existing buyer access retained.

Requirements

The included local auditor requires Python 3.10+ and runs its documented core workflow offline without uploading the reviewed diff. Download, updates, and support use online services.

How to get started

1. Get the Pro module free in the kit.
2. Download and unzip the kit.
3. Run the auditor on any diff.
4. Review the risk score and audit report.
5. Integrate the pre-commit hook or CI template into your workflow.

Related products

• Current CodeRiskTools catalog
• The current local scanner is Scanner 3.0.0, an MIT/free GitHub project for pre-merge checks.
• Compare all products →

How to use this kit

1. Download and extract the Gumroad ZIP into a local client/project operations folder.
2. Start with the README and Quick Start, then choose the batch/audit workflow for the type of change you are reviewing.
3. Run the local checks on a real change set and save the generated evidence/notes with the project.
4. Use the client-facing templates to explain what was reviewed, what risks were found, and what still needs human approval.
5. Repeat the workflow before merges, releases, or client handoff — especially for AI-generated code changes.

Best first use: audit one client-style change and produce a small evidence note before adopting it as a team gate.

Honest limitations

• The Pro Pack includes advanced batch/CI integration, cross-repo diffing, and reporting on top of the Basic kit.
• It does not automatically block deployments or roll back changes — it provides evidence and audit trails.
• Not a replacement for professional change management, security audit, or compliance certification.
• CI/CD integration templates are starting points — adapt them to your pipeline.
• Requires Python 3.10+ and Git for full functionality.