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Decoy redact
Redact sensitive data from PDFs and images locally using regex patterns and pixelated decoy phrases.
Free
Decoy redact
Example session with this skill installed
Redact the IBAN and email from 'contract.pdf' using a corporate mood.
- Read your context and instructions
- Compiled the decoy redact
- Generated the document
I have identified and redacted the sensitive information in contract.pdf. I used the decoy tool to find the text coordinates, applied the redaction with a corporate mood, and verified that the text layer and metadata were successfully removed.
Redaction Report
| Field | Page | Match Found | Status |
|---|---|---|---|
| 1 | john.doe@example.com | Redacted | |
| IBAN | 3 | ES12 3456 7890 1234 5678 9012 | Redacted |
| Metadata | N/A | Document Properties | Stripped |
The redacted file has been saved as contract-decoyed.pdf. The secrets have been replaced with pixelated corporate-themed phrases. Verification confirms that no searchable text remains in the output file and the original patterns no longer return matches.
Next steps
- Review
contract-decoyed.pdfto ensure the mosaic blending meets your visual requirements. - Run
decoy verify contract-decoyed.pdfwith the original file if you require a secondary checksum proof. - Delete the original
contract.pdfif it is no longer needed on this local environment.
contract.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
Manually masking sensitive data in documents is error-prone and slow. This skill automates the redaction of PDFs and images using the decoy CLI, ensuring secrets are covered by pixelated phrases rather than simple black boxes.
What it does
- Pattern matching identifies sensitive strings like IBANs, emails, or names using JavaScript regex.
- Visual masking replaces secrets with a mosaic generated from decoy phrases to prevent reconstruction.
- Automated verification confirms that redacted files contain no residual text layers, metadata, or readable secrets.
- Local processing performs all operations on the host machine without uploading files to external servers.
- Visual blending samples surrounding ink and paper colors so redactions integrate naturally with the document.
How it works
- Find involves scanning the PDF text layer to identify specific strings and their coordinates.
- Redact applies regex patterns or explicit pixel coordinates to generate a flattened, masked version of the file.
- Verify compares the output against the original to prove no sensitive data remains and only intended areas were altered.
- Report generates a summary of covered pages, boxes, and the final output path.
Frameworks & tools
This skill utilizes the decoy CLI tool via Node.js/npm and supports integration through MCP. It relies on pdf.js for text layer analysis and standard JavaScript regular expressions for pattern matching.
Why this beats prompting it yourself
Standard LLM prompting cannot reliably modify binary file structures like PDFs or guarantee the removal of hidden text layers. This skill uses a specialized CLI to ensure the underlying data is physically removed and replaced, providing a verifiable audit trail that simple image generation lacks.
Use cases
- Redact bank account numbers and IBANs from financial statements.
- Anonymize PII like names and email addresses in legal discovery documents.
- Mask sensitive areas in screenshots or scanned images using coordinate boxes.
- Sanitize internal reports for public distribution while maintaining visual layout.
Known limitations
Scanned PDFs and images require explicit coordinate boxes as they lack a text layer. The size of the redaction box remains proportional to the hidden text, potentially revealing secret length.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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