Extract domain-specific entities from documents using regex and LLM methods.
npx skills add https://github.com/memect/kc --skill entity-extraction-memect
Please help me install this Agent Skill. Skill: entity-extraction Source: https://github.com/memect/kc/tree/main/template/skills/zh/entity-extraction Command: npx skills add https://github.com/memect/kc --skill entity-extraction-memect
Extract domain-specific entities from documents to support verification rules, enabling reliable extraction of numbers, dates, names, amounts, clauses, and other domain data after the relevant section has been located.
Extract a numeric amount and a date from the provided document using the defined extraction schema.
High-intent search queries and answers about installing and using this skill.
To extract entities for compliance verification, apply regex or LLM methods to target document sections, using a structured schema to capture values, source locations, and confidence scores for downstream rules.
Choosing between regex and LLM approaches for entity extraction depends on your document structure: regex offers precise pattern matching for standardized formats, while LLM methods handle complex clauses and contextual data within a post-processing pipeline.
Yes, you can integrate extracted document entities into an automated QC pipeline by defining a structured output schema that captures value, raw text, and confidence annotations to feed downstream worker LLM workflows.
Yes, you need a design-ready schema before extracting domain-specific data from text, as it dictates how values, units, source locations, and confidence scores are structured for reliable downstream decision making.
Confidence annotation during regulatory clause extraction works by scoring the reliability of the extracted text against the defined schema, providing clear annotations that feed downstream verification rules and automated decision making.
Regex for domain-specific entity extraction from unstructured text struggles with contextual ambiguity and complex phrasing, making it necessary to apply robust post-processing or optional LLM methods to achieve reliable verification outputs.
guillempucheAccess Airparser document parsing via REST API with JSON schemas.
memectEvaluate extracted entities against verification rules and return structured judgments.
wmoreinExtract cited content from large documents via outline and targeted section reads.
Sake-TeamRoutes structured text parsing decisions between regex and LLM validation.
