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About regex-vs-llm-structured-text
regex-vs-llm-structured-text
Decision framework for parsing structured text
regex-vs-llm-structured-text is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It is a decision framework for parsing structured text such as quizzes, forms, invoices, receipts and tables. Its central claim is that a hybrid pipeline should start with regular expressions, which handle the large majority of repeating patterns cheaply and deterministically, around 95 to 98 per cent in the listing's examples, and reserve calls to a language model for the remaining edge cases. The skill helps decide between regex and an LLM for a given extraction job, designs hybrid pipelines that use both, and weighs cost against accuracy for text processing. It applies when text has repeating structure such as questions, form fields or table rows, and when a team is deciding whether a model call is worth its cost. A skill is a SKILL.md file of instructions that an AI agent loads when a task calls for it. It is free and open source, and the skills.sh listing shows all three security audits passing.
- Price
- Free
- Made by
- affaan-m
- Category
- AI Skills
- Website
- github.com/affaan-m/ecc/regex-vs-llm-structured-text