Best use case
llamaindex-query is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Build data-connected LLM applications with LlamaIndex
Teams using llamaindex-query should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/llamaindex-query/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How llamaindex-query Compares
| Feature / Agent | llamaindex-query | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Build data-connected LLM applications with LlamaIndex
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# LlamaIndex – Data Framework for LLMs
LlamaIndex simplifies building RAG pipelines by managing data ingestion,
indexing, and LLM integration for question-answering over your data.
- **GitHub**: github.com/run-llama/llama_index (40 000+ ⭐)
- **License**: MIT
- **Security**: Well-maintained, active community. No malware.
## Environment Variables
| Variable | Description |
|---|---|
| `{{LLAMAINDEX_URL}}` | Base URL of a LlamaIndex-powered API |
## Usage Examples
### Query an index
```bash
curl -s -X POST "{{LLAMAINDEX_URL}}/api/query" \
-H "Content-Type: application/json" \
-d '{"query": "Summarise the quarterly report"}'
```
## AI Agent Tips
- Supports 100+ data connectors via LlamaHub (PDF, Notion, Slack, DBs, etc.).
- Modular: swap embedding models, vector stores, and LLM providers.
- Sub-question engine decomposes complex queries automatically.
- Scalable for processing large document collections.Related Skills
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