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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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/superlinked/sie --skill extract-entities命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | extract-entities |
| description | 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. |
Use the connected Superlinked MCP server to run zero-shot entity extraction on
the SIE cluster through the MCP extract_entities tool.
Pick labels from the user's request. Lowercase nouns work best, for example:
person, organization, date, amount, contract term.
Do not open, view, attach, or read the whole source document into context.
Call the Superlinked MCP extract_entities tool:
labels as a list of label strings.document_base64 plus filename.content_base64.engine as auto unless the user explicitly asks to force a
conversion engine.Write the returned markdown_table to processed/<source-stem>.entities.md.
Create processed/ if it does not exist.
Your next message must begin with a short receipt:
Extracted <entity_count> entities from <source> -> processed/<source-stem>.entities.md
Then present the entity table or the subset the user asked for.
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.
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.
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.
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.
