opendatahub-io/docs-skills/docs-review-security
Claude Code and agent skill by opendatahub-io/docs-skills
- community
Checking that the SKILL.md file is still in the repository.
Scan documentation for sensitive data before publication — real IP addresses, credentials, internal hostnames, email addresses, and MAC addresses. Use this skill when asked to check for PII, security issues, customer-sensitive data, or before publishing documentation. Runs a deterministic regex scanner first, then applies agent judgment for patterns regex cannot catch.
Install
npx vskill@latest install opendatahub-io/docs-skills/docs-review-securitySKILL.md
Security and PII review skill
Scan documentation for sensitive data that must not appear in published content: real IP addresses, credentials, internal hostnames, customer-identifying information, and other PII.
This skill has two layers:
- Deterministic scan — run
pii_scanner.pyfor regex-based detection - Agent analysis — review for patterns regex cannot catch
Layer 1: Run the scanner
Run the PII scanner against the target files:
Or for a docs directory:
Parse the JSON output. If findings exist, include them in the report grouped by severity (critical first, then warning).
Layer 2: Agent analysis checklist
After the scanner runs, review the content for patterns that regex cannot reliably detect:
Customer-sensitive data in YAML/JSON examples
- [ ] CR
metadata.namefields use generic names (example-<kind>) not customer-specific names - [ ] CR
metadata.namespacefields use generic namespaces, not customer project names - [ ]
resourceName,hostname, and similar fields do not contain real customer identifiers - [ ] No customer application names appear — use "the custom application" or a generic name
Internal references
- [ ] No internal Jira project keys or ticket numbers that expose internal tooling
- [ ] No internal wiki, Confluence, or Google Doc URLs
- [ ] No internal Slack channel names or references
- [ ] No references to internal build systems, CI/CD pipelines, or staging environments
Sensitive patterns in prose
- [ ] No real person names associated with accounts, cases, or examples
- [ ] No real organization or company names in examples (use "Example Corp" or similar)
- [ ] No internal Red Hat team names or org chart references in customer-facing content
- [ ] No case numbers, account numbers, or subscription IDs from real customers
LUN WWIDs (if applicable)
- [ ] World Wide IDs use the generic pattern:
3600000000000000000aaaaaaaaaaaaaa - [ ] If differentiation is needed, vary the end characters:
...bbbbbbbbbbbbbb
How to report
For each finding, report:
- File and line number
- Category (ip, email, credential, url, mac, internal_hostname, or agent-detected)
- Severity:
critical(credentials, private keys) orwarning(IPs, emails, URLs) - Suggestion: the safe replacement (RFC 5737 IP,
@example.comemail, etc.)
Example invocations
- "Scan this file for PII and sensitive data"
- "Run a security review on the installation guide"
- "Check these docs for credentials and internal hostnames before we publish"
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/docs-review-security/SKILL.md
Popularity
- 7-day trend score
- 0
Not refreshed yet.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
Embed badge
[](https://verified-skill.com/skills/opendatahub-io/docs-skills/docs-review-security)