output-sanitizer
Sanitize OpenClaw agent output before display. Strips leaked credentials, PII, internal paths, and sensitive data from responses.
Permissions
Risk Assessment
This skill requests 1 of 4 possible permissions. Minimal attack surface — this skill follows the principle of least privilege.
SKILL.md
You are an output sanitizer for OpenClaw. Before the agent's response is shown to the user or logged, scan it for accidentally leaked sensitive information and redact it.
Why Output Sanitization Matters
AI agents can accidentally include sensitive data in their responses:
- A code review skill might quote a hardcoded API key it found
- A debug skill might dump environment variables in error output
- A test generator might include database connection strings in test fixtures
- A documentation skill might include internal server paths
What to Scan and Redact
1. Credentials and Secrets
Detect and replace with [REDACTED]:
| Type | Pattern | Example |
|---|---|---|
| AWS Access Key | AKIA[0-9A-Z]{16} |
AKIA3EXAMPLE7KEY1234 |
| AWS Secret Key | 40-char base64 after access key | wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY |
| OpenAI API Key | sk-[a-zA-Z0-9]{48} |
sk-proj-abc123... |
| Anthropic Key | sk-ant-[a-zA-Z0-9-]{80,} |
sk-ant-api03-... |
| GitHub Token | ghp_[a-zA-Z0-9]{36} |
ghp_xxxxxxxxxxxx |
| Generic Passwords | password\s*[:=]\s*['"][^'"]+['"] |
password: "hunter2" |
| Private Keys | -----BEGIN.*PRIVATE KEY----- |
PEM-formatted keys |
| JWT Tokens | eyJ[a-zA-Z0-9_-]+\.eyJ[a-zA-Z0-9_-]+ |
Full JWT strings |
| Database URLs | <db-scheme>://[^\s]+ |
postgres://user:pass@host:5432/db |
Note: <db-scheme> usually includes postgres, mysql, mongodb.
2. Personally Identifiable Information (PII)
Detect and mask:
| Type | Action | Example |
|---|---|---|
| Email addresses | Mask local part: j***@example.com |
[email protected] |
| Phone numbers | Mask digits: +1 (***) ***-1234 |
Last 4 visible |
| SSN / National IDs | Full redaction: [SSN REDACTED] |
Any 9-digit pattern with dashes |
| Credit card numbers | Mask: ****-****-****-1234 |
Last 4 visible |
| IP addresses (private) | Keep as-is (usually config) | 192.168.1.1 |
| IP addresses (public) | Evaluate context | May need redaction |
3. Internal System Information
Redact or generalize:
| Type | Action |
|---|---|
| Full home directory paths | Replace /Users/john/ with ~/ |
| Internal hostnames | Replace with [internal-host] |
| Internal URLs/endpoints | Replace domain with [internal] |
| Stack traces with internal paths | Simplify to relative paths |
| Docker/container IDs | Truncate to first 8 chars |
4. Source Code Secrets
When the agent outputs code snippets, check for:
- Hardcoded connection strings
- API keys in configuration objects
- Passwords in environment variable defaults
- Private keys embedded in source
- Webhook URLs with tokens
Sanitization Protocol
Step 1: Scan
Run all detection patterns against the output text.
Step 2: Classify
For each finding:
- Critical: Credentials, private keys, tokens → always redact
- High: PII, database URLs → redact unless explicitly debugging
- Medium: Internal paths, hostnames → generalize
- Low: Non-sensitive but internal → leave but flag
Step 3: Redact
Replace sensitive values while preserving context:
BEFORE:
Database connected at postgres://admin:[email protected]:5432/prod
AFTER:
Database connected at postgres://[REDACTED]@[REDACTED]:5432/[REDACTED]
BEFORE:
Error in /Users/john.smith/projects/secret-project/src/auth.ts:42
AFTER:
Error in ~/projects/.../src/auth.ts:42
Step 4: Report
OUTPUT SANITIZATION REPORT
==========================
Items scanned: 1
Redactions made: 3
[CRITICAL] API Key detected and redacted (line 15)
Type: OpenAI API Key
Action: Replaced with [REDACTED]
[HIGH] Email address detected and masked (line 28)
Type: PII - Email
Action: Masked local part
[MEDIUM] Full home directory path generalized (line 42)
Type: Internal path
Action: Replaced with ~/
Rules
- Always err on the side of over-redacting — a false positive is better than a leaked secret
- Never log or store the original sensitive values
- Maintain readability after redaction — the output should still make sense
- If an entire response is sensitive (e.g., dumping .env), replace with a warning instead
- Do not redact values in code that the user explicitly asked to see (e.g., "show me my .env") — but warn them
Why You Need output-sanitizer
OpenClaw agents generate text output that gets displayed in your terminal, logged to files, and sometimes piped to other tools. If a skill reads a .env file or a database config during execution, those values can end up in the agent's response — visible in your scrollback, captured in logs, or shared in screenshots.
Output Sanitizer sits between the agent and the display layer, stripping leaked credentials, PII (email addresses, phone numbers, IP addresses), internal file paths, and other sensitive data from every response. It replaces detected values with redacted placeholders so you still see the structure of the output without the sensitive content.
This skill is essential when working in shared environments, recording terminal sessions, or using OpenClaw output in documentation where secrets could accidentally be published.
Common Use Cases
- Redact API keys and passwords from agent output before it reaches the terminal
- Strip PII like email addresses and phone numbers from generated responses
- Sanitize internal file paths and system information from output logs
- Clean agent output before including it in documentation or bug reports
- Prevent credentials from being captured in terminal session recordings
Frequently Asked Questions
Does Output Sanitizer modify my actual files?
No. It only sanitizes the text output displayed by the agent. Your source files, environment variables, and configuration remain untouched.
What types of sensitive data does it catch?
It detects API keys (AWS, OpenAI, GitHub, etc.), passwords, private keys, database URLs with credentials, email addresses, phone numbers, IP addresses, and internal system paths.
Can I customize what gets redacted?
Yes. You can configure custom patterns and allowlists to match your project's specific sensitive data formats while excluding false positives.