- name
- safe-share
- description
- Sanitize logs, configs, prompts, stack traces, and skill content before they are shared publicly. Use when a user wants a local, low-risk pass to remove API keys, tokens, cookies, passwords, emails, IPs, or other sensitive values from text before posting to GitHub issues, chat, READMEs, demos, or ClawHub.
Safe Share
Use this skill to produce a safe-to-share copy of text. Prefer deterministic local sanitization over model-only guessing.
Workflow
- Confirm the user wants a shareable copy, not a forensic analysis.
- Determine the output mode:
- placeholder: best default for docs, issues, READMEs, and tutorials - redact: best when preserving shape is not important - mask: best when keeping a hint of the original value is useful
- Run
scripts/sanitize_text.pyon the exact text the user provided. - Return:
- sanitized_text - findings_summary - review_notes
- Never echo the original sensitive value back to the user.
Operating Rules
- Default to
placeholdermode unless the user asks for something else. - Treat secrets and credentials as higher priority than general PII.
- Replace with stable labels such as
<OPENAI_API_KEY>or[REDACTED:BEARER_TOKEN]. - Keep summaries high level. Report type and count, not the captured value.
- State clearly that sanitization reduces risk but does not guarantee complete detection.
- Do not scan unrelated files or repositories unless the user explicitly asks for that broader scope.
- Do not send text to external services for classification or validation.
High-Risk Patterns
Prioritize these categories:
- API keys and secret tokens
- Authorization headers and bearer tokens
- Cookies and session identifiers
.env-style credentials and password assignments- Private key blocks and PEM material
- Sensitive URL query parameters
Then handle lower-risk identifiers:
- Email addresses
- Phone numbers
- IP addresses
- National ID or payment-card-like strings when confidence is high
Output Contract
Use the JSON contract from references/output-format.md when returning structured results from the script. If answering in prose, include the same three sections in human-readable form.
Resources
- Detection and replacement behavior:
references/patterns.md - Output structure and reviewer guidance:
references/output-format.md - Smoke-test inputs and expected behavior:
references/test-cases.md - Deterministic local sanitizer:
scripts/sanitize_text.py