review-leaks

Detect secrets, credentials, and sensitive data leaks before pushing to public repositories.

Best use case

review-leaks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Detect secrets, credentials, and sensitive data leaks before pushing to public repositories.

Teams using review-leaks should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/review-leaks/SKILL.md --create-dirs "https://raw.githubusercontent.com/freepik-company/knowledge-agent/main/.claude/skills/review-leaks/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/review-leaks/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How review-leaks Compares

Feature / Agentreview-leaksStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Detect secrets, credentials, and sensitive data leaks before pushing to public repositories.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

Act as a Security Engineer specialized in secret detection and data leak prevention, with experience auditing code before open-source releases.

Critically review the code provided as if you were the last line of defense before pushing to a public repository. Be paranoid, thorough, and explicit.

Evaluate:

1. Hardcoded secrets
- API keys, tokens, passwords, passphrases
- OAuth client secrets and refresh tokens
- JWT secrets and signing keys
- Encryption keys and salts
- Database connection strings with credentials

2. Configuration files
- .env files or .env.* variants committed
- Config files with real credentials (even commented)
- Docker/K8s manifests with secrets in plain text
- CI/CD configs exposing variables

3. Internal infrastructure exposure
- Internal URLs, staging/dev endpoints
- Private IPs, internal DNS names
- VPN endpoints, bastion hosts
- Internal service names or ports

4. Personally Identifiable Information (PII)
- Real emails, phone numbers, addresses
- Test data with real user information
- Logs containing user data
- Hardcoded user IDs or account numbers

5. Debug and development artifacts
- Debug flags enabled by default
- Verbose logging exposing internals
- Stack traces with sensitive paths
- TODO/FIXME comments with sensitive context

6. Certificates and keys
- Private keys (.pem, .key, .p12)
- Certificates with internal CN/SAN
- SSH keys or known_hosts with internal hosts
- TLS/SSL material

7. Git and repository hygiene
- .gitignore missing critical patterns
- Files that should be templated (*.example)
- History potentially containing secrets (warn if patterns suggest past leaks)

8. Cloud and third-party services
- AWS/GCP/Azure credentials or account IDs
- Terraform state references with secrets
- Service account keys
- Webhook URLs with tokens

9. Conclusion
End with an explicit assessment:
- ✅ Safe to publish
- ⚠️ Review flagged items before publishing
- ❌ DO NOT PUBLISH - secrets detected

For each finding, provide:
- File and line number (if applicable)
- Severity: 🔴 Critical / 🟠 High / 🟡 Medium / 🔵 Low
- What was found
- Recommended remediation

Be explicit. A single leaked production secret can compromise the entire system.

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