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Redact personal data from a document through the connected Superlinked MCP edge before working with the content. Use when the user asks to redact, anonymize, scrub, de-identify, or remove PII/sensitive data from a document.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | redact-pii |
| description | Redact personal data from a document through the connected Superlinked MCP edge before working with the content. Use when the user asks to redact, anonymize, scrub, de-identify, or remove PII/sensitive data from a document. |
Use the connected Superlinked MCP server to detect and replace PII on the SIE
cluster. This mirrors the redact-pii flow from the sie_tools plugin in
PR #1336, but uses the MCP redact_pii tool.
Call the Superlinked MCP redact_pii tool:
document_base64 plus filename.content_base64.labels only if the user wants a custom PII/sensitive-data set.engine as auto unless the user explicitly asks to force a
conversion engine.Write the returned redacted_text to
processed/<source-stem>.redacted.md. Create processed/ if it does not
exist.
Your next message must begin with a short receipt:
Redacted <span_count> spans from <source> -> processed/<source-stem>.redacted.md
Include the label counts and the note that detection is model-based and best-effort, not a certified scrubber.
Continue the user's actual task using only the redacted artifact.
If the MCP call fails, relay the tool error and do not retry blindly. For auth
or connector errors, tell the user to reinstall the generated MCP plugin pack or
rerun the generated claude mcp add ... --header 'Authorization: Bearer ...'
command from INSTALL.md.
Extract people, organizations, dates, amounts, or custom labels from a document through the connected Superlinked MCP edge, returning a compact table instead of reading the full document into context. Use when the user asks to list, extract, or tabulate entities from a file.
Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.
Summarize a long PDF, scan, office file, text file, or markdown file through the connected Superlinked MCP edge instead of reading the whole source into model context. Use when the user asks for a summary, overview, digest, or "what does this document say" about a large file.
Offload document, image, and structured-output work to the Superlinked inference cluster: convert PDF/DOCX/PPTX/XLSX/HTML/scans to clean markdown, describe an image (caption + tags), or produce schema/grammar-constrained JSON off the cluster — instead of ingesting the file directly, which can reduce the tokens billed in many cases for document- and image-heavy work. Use whenever the user drops or references a document or image file to read, summarize, extract from, describe, or answer questions over, or when they need reliable structured (schema-valid) output.