# llm-app-patterns

This skill outlines RAG designs and tool-using agent patterns. Consult it when planning document retrieval or choosing an agent architecture.

- skill · `type=skill` · Claude Code · 129 installs
- Author: davila7
- License: not stated · policy=link-only · description only; the file stays with the author
- Source: https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/llm-app-patterns
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/llm-app-patterns

## What it does

The RAG material follows documents from ingestion through vector retrieval to an answer grounded in context. It contrasts chunking approaches and vector stores, then sketches semantic, hybrid, and multi-query retrieval. One example returns source metadata alongside the generated response.

For agents, the skill presents a ReAct loop: the model selects a tool, receives its result, then continues or produces a final answer. The sample caps the loop at 10 iterations. When that cap is reached, it returns a maximum-iterations message, so this stopping behavior matters if you adapt the pattern.

Install it with `npx claude-code-templates@latest --skill ai-research/llm-app-patterns`. These are design patterns and code examples; selecting a model and storage system for your application remains a project decision.

## Install

### Claude Code (native platform)

1. **claude-code-templates**
```bash
npx claude-code-templates@latest --skill ai-research/llm-app-patterns
```

2. **Skills CLI (Vercel)**
```bash
npx skills add davila7/claude-code-templates --skill llm-app-patterns -a claude-code
```

3. **Manually: put the files in place**
- File: `~/.claude/skills/llm-app-patterns/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/llm-app-patterns/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/llm-app-patterns/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/llm-app-patterns/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install davila7/claude-code-templates llm-app-patterns
```

2. **Manually: put the files in place**
- File: `.github/skills/llm-app-patterns/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/davila7/claude-code-templates --path cli-tool/components/skills/ai-research/llm-app-patterns
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/llm-app-patterns/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/llm-app-patterns

## When to use it
- design a RAG flow for document-based answers
- compare semantic retrieval with hybrid search
- choose a vector store for existing infrastructure
- structure an agent around repeated tool calls
- attach source metadata to generated answers

## Example prompts
- Sketch a RAG flow for our project documents and compare semantic and hybrid retrieval.
- Design a ReAct agent for this task and identify the tools it would call.
- Show how to return a RAG answer with metadata for its retrieved sources.

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From the same set: [davila7/claude-code-templates](https://affpapa.org/en/nevibecode/by/davila7/claude-code-templates.md) — 495 cards

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NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md