Llm Patterns
This is about building apps where LLMs do the actual business logic, not just generate text. You get patterns for wrapping LLM calls with Zod schemas, organizing prompts as versioned functions, and a three-tier testing approach: mocked unit tests for speed, fixture tests for parsing validation, and eval suites that run nightly in CI to catch accuracy regressions. The core idea is LLM for logic, code for plumbing. It includes cost tracking utilities and shows how to structure projects so prompts live in their own directory instead of scattered through your codebase. Useful when you're building classification systems, extraction pipelines, or anything where the LLM is making actual decisions rather than just filling in templates.
- skills.sh installs
- 190
- GitHub stars
- 685
- last updated
- Jun 3, 2026
Install
$ npx -y skills add alinaqi/claude-bootstrap --skill llm-patterns --agent claude-codeInstalls into .claude/skills of the current project.
Files
LLM Patterns Skill
For AI-first applications where LLMs handle logical operations.
Core Principle
LLM for logic, code for plumbing.
Use LLMs for:
- Classification, extraction, summarization
- Decision-making with natural language reasoning
- Content generation and transformation
- Complex conditional logic that would be brittle in code
Use traditional code for:
- Data validation (Zod/Pydantic)
- API routing and HTTP handling
- Database operations
- Authentication/authorization
- Orchestration and error handling
Project Structure
Sponsors
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- Repository
- github.com/alinaqi/claude-bootstrap
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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