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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionoutput-sanitizerExecute the skills CLI command in your project's root directory to begin installation:
Fetches output-sanitizer from useai-pro/openclaw-skills-security and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate output-sanitizer. Access via /output-sanitizer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
46
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
46
stars
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.
AI agents can accidentally include sensitive data in their responses:
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.
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 |
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 |
When the agent outputs code snippets, check for:
Run all detection patterns against the output text.
For each finding:
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
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 ~/
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
We added output-sanitizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
output-sanitizer reduced setup friction for our internal harness; good balance of opinion and flexibility.
output-sanitizer reduced setup friction for our internal harness; good balance of opinion and flexibility.
output-sanitizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for output-sanitizer matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in output-sanitizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in output-sanitizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for output-sanitizer matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend output-sanitizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend output-sanitizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
showing 1-10 of 54