This is the hybrid parsing strategy you wish someone had told you about earlier. Use regex to handle the 95-98% of structured text that follows predictable patterns (quiz questions, forms, invoices), then add confidence scoring to flag the edge cases, and only call an LLM for those low-confidence extractions. The source shows real production metrics: 410 quiz items parsed, only 8 flagged for LLM review, 95% cost savings versus sending everything to Claude. The key insight is that regex is deterministic and nearly free, so you should always try it first and treat LLM calls as expensive exception handling rather than your primary parser.
npx -y skills add affaan-m/ecc --skill regex-vs-llm-structured-text --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
sickn33/antigravity-awesome-skills
moizibnyousaf/ai-agent-skills
github/awesome-copilot