abhisheksharma-17/llamaindex
skills/llamaindex/SKILL.md
AI framework for building RAG pipelines, agents, workflows, and data-augmented LLM applications with 300+ integrations. MANDATORY TRIGGERS: llamaindex, llama-index, llama_index, LlamaIndex, VectorStoreIndex, SimpleDirectoryReader, LlamaHub, LlamaParse. Also trigger when the user wants to build RAG applications with LlamaIndex, create document indexing pipelines, build agentic workflows with tool calling, implement structured data extraction from documents, or connect LLMs to custom data sources. When in doubt about whether to use this skill for RAG, document indexing, or LLM data augmentation tasks, use it.
$ install --global
skillsauthnpx skillsauth add abhisheksharma-17/skills-graph llamaindexInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Jun 7, 2026, 4:02 AM65.8s18 files scanned
SKILL.md
- name:
- llamaindex
- description:
- AI framework for building RAG pipelines, agents, workflows, and data-augmented LLM applications with 300+ integrations. MANDATORY TRIGGERS: llamaindex, llama-index, llama_index, LlamaIndex, VectorStoreIndex, SimpleDirectoryReader, LlamaHub, LlamaParse. Also trigger when the user wants to build RAG applications with LlamaIndex, create document indexing pipelines, build agentic workflows with tool calling, implement structured data extraction from documents, or connect LLMs to custom data sources. When in doubt about whether to use this skill for RAG, document indexing, or LLM data augmentation tasks, use it.
- license:
- MIT
- version:
- 1.0.0
- author:
- Abhishek Sharma
- tags:
- ["ai", "rag", "agents", "llm", "indexing", "retrieval", "embeddings", "workflows", "llamaindex"]
LlamaIndex
Source: developers.llamaindex.ai | Version tracked: 0.14.22 |
pip install llama-index
Reference Files
| File | Read When |
|------|-----------|
| references/00-overview.md | Starting with LlamaIndex, understanding architecture, installation |
| references/01-loading-data.md | Loading documents, file readers, LlamaHub connectors, metadata |
| references/02-indexing.md | Creating indexes, VectorStoreIndex, index types, document management |
| references/03-querying.md | Query engines, chat engines, retrievers, response synthesizers |
| references/04-agents.md | Building agents, FunctionAgent, ReActAgent, tool creation |
| references/05-multi-agent.md | Multi-agent systems, AgentWorkflow, orchestrator pattern, handoffs |
| references/06-workflows.md | Custom workflows, events, steps, control flow, parallel execution |
| references/07-ingestion-pipeline.md | IngestionPipeline, transformations, caching, parallel processing |
| references/08-storage.md | Vector stores, document stores, StorageContext, persistence |
| references/09-models.md | LLM and embedding configuration, Settings, providers |
| references/10-structured-extraction.md | Pydantic extraction, structured LLM output from documents |
| references/11-evaluation.md | Evaluation metrics, faithfulness, relevancy, retrieval evaluation |
| references/12-observability.md | Tracing, instrumentation, OpenTelemetry, debugging |
Installation
pip install llama-index
Quick Reference
- Docs | GitHub | PyPI | LlamaHub
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