---
title: "agentic-bundle-llm-application-developer"
description: "Portable skills-only \"LLM Application Developer\" plugin from Agentic Awesome Skills."
canonical: https://agentpluginsdirectory.com/plugins/agentic-bundle-llm-application-developer--lingxling
last-updated: 2026-10-21
---

# agentic-bundle-llm-application-developer
Portable skills-only "LLM Application Developer" plugin from Agentic Awesome Skills.
- Slug: agentic-bundle-llm-application-developer--lingxling
- Publisher: sickn33 and contributors
- Repository: https://github.com/lingxling/awesome-skills-cn
- Manifest: antigravity-awesome-skills/plugins/agentic-bundle-llm-application-developer/plugin.json
- Version: 18.15.0
- License: MIT
- Category (editorial): other
- Skills: 6 (context-window-management, langfuse, llm-app-patterns, llm-evaluation, prompt-caching, rag-implementation)
- MCP servers: 0
- Stars: 317
- Repository created: 2026-02-13
- Repository last pushed: 2026-10-07
- Publisher type: User
- Listing: https://agentpluginsdirectory.com/plugins/agentic-bundle-llm-application-developer--lingxling
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## Skills

- context-window-management: Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot
- langfuse: Expert in Langfuse: the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.
- llm-app-patterns: Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries.
- llm-evaluation: Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
- prompt-caching: Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)
- rag-implementation: RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.