regex-vs-llm-structured-text
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
npx skills add mturac/everything-openai-codex --skill regex-vs-llm-structured-text --agent codex
Same command for any agent — swap --agent for claude-code, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Regex vs LLM for Structured Text Parsing A practical decision framework for parsing structured text (quizzes, forms, invoices, documents). The key insight: regex handles 95-98% of cases cheaply and deterministically. Reserve expensive LLM calls for the remaining edge cases. ## When to Activate - Parsing structured text with repeating patterns (questions, forms, tables) - Deciding between regex and LLM for text extraction - Building hybrid pipelines that combine both approaches - Optimizing cost/accuracy tradeoffs in text processing ## Decision Framework ``` Is the text format consistent and repeating? ├── Yes (>90% follows a pattern) → Start with Regex │ ├── Regex handles 95%+ → Done, no LLM needed │ └── Regex handles <95% → Add LLM for edge cases only └── No (free-form, highly variable) → Use LLM directly ``` ## Architecture Pattern ``` Source Text │ ▼ [Regex Parser] ─── Extracts structure (95-98% accuracy) │ ▼ [Text Cleaner] ─── Removes noise (markers, page numbers, artifacts) │ ▼ [Confidence Scorer] ─── Flags low-confidence extractions │ ├── High confidence (≥0.95) → Direct output │ └── Low confidence (<0.95) → [LLM Validator] → Output ``` ## Implementation ### 1. Regex Parser
- When to Activate
- Decision Framework
- Architecture Pattern
- Implementation
- 1. Regex Parser (Handles the Majority)
- 2. Confidence Scoring
- 3. LLM Validator (Edge Cases Only)
- 4. Hybrid Pipeline
- Real-World Metrics
- Best Practices
- Anti-Patterns to Avoid
- When to Use
What does the regex-vs-llm-structured-text skill do?
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
How do I install it?
Run `npx skills add mturac/everything-openai-codex --skill regex-vs-llm-structured-text --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mturac/everything-openai-codex, a repository with 84 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.