LangChain · Prompt engineering

Reference for designing and tuning production LLM prompts: few-shot examples, chain-of-thought, structured outputs, templates and system prompts.

wshobson/agents

Comprehensive guide for designing, refining, and auditing system prompts for autonomous AI agents based on Anthropic's production practices.

agentailor/fullstack-langgraph-nextjs-agent

This skill should be used when a learner is working through Module 1 ("Build an Agent") of the Build-an-Agent workshop and wants help understanding the concepts, notebooks, or code — e.g.

brevdev/workshop-build-an-agent

This skill should be used when a learner is working through Module 1 ("Build an Agent") of the Build-an-Agent workshop and wants help understanding the concepts, notebooks, or code — e.g.

brevdev/workshop-build-an-agent

Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models.

agentsope/SkillAlchemy

Tool skill — the first move in any LM-debugging session: dump the actual rendered prompt the framework sent to the model, before changing anything else.

agentsope/SkillAlchemy

Manage LangChain 1.0 prompts like code — LangSmith prompt hub versioning, XML-tag conventions for Claude, few-shot example selection, discriminated-union extraction schemas, and A/B test wiring.

jeremylongshore/tons-of-skills-marketplace

Compose LangChain 1.0 Python runnables with the production defaults the docs do not warn about: parallel batching, narrow fallbacks, and brace-safe prompts.

jeremylongshore/tons-of-skills-marketplace