Build LLM applications with the LlamaIndex framework. An agent skill from magnus919/agent-skills.
$ npx skills add magnus919/agent-skills --skill llamaindex -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install magnus919/agent-skills llamaindex --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/llamaindex .claude/skills/llamaindex && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "llamaindex" agent skill from https://github.com/magnus919/agent-skills/tree/main/llamaindex into .claude/skills/llamaindex/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llamaindex", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/magnus919/agent-skills/tree/main/llamaindexType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add magnus919/agent-skills --skill llamaindex -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install magnus919/agent-skills llamaindex --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/llamaindex .agents/skills/llamaindex && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "llamaindex" agent skill from https://github.com/magnus919/agent-skills/tree/main/llamaindex into .agents/skills/llamaindex/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llamaindex", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add magnus919/agent-skills --skill llamaindex -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install magnus919/agent-skills llamaindex --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/llamaindex .cursor/skills/llamaindex && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "llamaindex" agent skill from https://github.com/magnus919/agent-skills/tree/main/llamaindex into .cursor/skills/llamaindex/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llamaindex", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/magnus919/agent-skills.git --path llamaindex--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add magnus919/agent-skills --skill llamaindex -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install magnus919/agent-skills llamaindex --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/llamaindex .gemini/skills/llamaindex && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "llamaindex" agent skill from https://github.com/magnus919/agent-skills/tree/main/llamaindex into .gemini/skills/llamaindex/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llamaindex", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install magnus919/agent-skills llamaindexInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add magnus919/agent-skills --skill llamaindex -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/llamaindex .github/skills/llamaindex && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "llamaindex" agent skill from https://github.com/magnus919/agent-skills/tree/main/llamaindex into .github/skills/llamaindex/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llamaindex", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add magnus919/agent-skills --skill llamaindex -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install magnus919/agent-skills llamaindex --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/llamaindex .opencode/skills/llamaindex && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "llamaindex" agent skill from https://github.com/magnus919/agent-skills/tree/main/llamaindex into .opencode/skills/llamaindex/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llamaindex", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
llamaindexBuild LLM applications with the LlamaIndex framework. An agent skill from magnus919/agent-skills.
Llamaindex is an agent skill from magnus919/agent-skills. Build LLM applications with the LlamaIndex framework. Use when working with LlamaIndex or comparing RAG and agent orchestration frameworks. Do not use this skill for unrelated requests; route to the nearest named specialist.
Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 22 other files, including scripts and reference files (for example `README.md`, `evals/evals.json` and `references/agent-patterns.md`).
It sits in AI & LLM Engineering, covering Multi-agent orchestration and Retrieval-augmented generation. It works with LlamaIndex. The repository describes itself as: Curated collection of AI agent skills for Hermes and other agent frameworks. The licence is MIT.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 22b4723. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python, from the files we listed), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Llamaindex loads about 3k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 59 tokens; SKILL.md has 1,208 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from magnus919/agent-skills at commit 22b4723, republished under its MIT licence (© magnus919). 1,208 words, ~2,999 tokens.
.claude/skills/llamaindex/SKILL.md (or your agent's skills folder). This skill also uses 18 other files; get the full folder from GitHub.LlamaIndex is an MIT-licensed Python framework for building LLM applications over your data. In 2026, it has evolved from a RAG indexing library into an event-driven workflow framework with integrated production runtime (llama-deploy), agent orchestration (AgentWorkflow), knowledge graph construction (PropertyGraphIndex), and OpenTelemetry-native observability.
The framework is organized around seven core primitives: Reader (data loaders), Document/Node (chunked content model), Index (data structures over Nodes), Retriever (relevant Node selection), Query Engine (retriever + synthesis), Agent (LLM with tools), and Workflow (event-driven orchestration).
These principles govern every decision when building with LlamaIndex. Read them before proceeding to the reference guides.
SentenceWindowNodeParser + MetadataReplacementNodePostProcessor for this pattern.FunctionAgent and AgentWorkflow are pre-configured Workflows. Drop to raw Workflow when you need custom control flow.PropertyGraphIndex adds structural path traversal that vector similarity cannot provide — combine both for maximum retrieval quality.The pipeline has 9 phases from Ingest to Deploy. If you're joining mid-stream with existing work, find your entry point:
| You already have... | Start at phase | What to do |
|---|---|---|
| Nothing — blank project | Ingest | Set up data loading, then proceed through the full pipeline |
| Documents in a directory | Chunk | Choose a chunking strategy, build your index |
| A working vector index | Retrieve | Add hybrid search, reranking, metadata filters |
| An existing RAG pipeline to harden | Deploy | Add observability, llama-deploy, production debugging |
| A need to measure and improve quality | Evaluate | Set up evaluators, ParamTuner, span-attached scoring |
| Nothing — comparing frameworks | See Framework Routing Guide | Don't start the pipeline — pick the right tool first |
Different tasks need different levels of rigor. Match your scope to a mode:
| Mode | When | Phases to run | Skip |
|---|---|---|---|
| Quick | Single query, one source, exploration | Ingest → Chunk → Index → Retrieve | Reranking, metadata filters, observability, evaluation |
| Full | Production RAG, multiple sources, compliance | Ingest → Chunk → Index → Retrieve → Agent/Workflow → Deploy → Evaluate | Nothing — run all phases |
| Evaluate | Benchmarking, regression testing | Ingest → Chunk → Index → Evaluate | Retrieve, Agent, Workflow, Deploy (run offline) |
| Graph | Knowledge graph construction | Ingest → Chunk → Graph → Retrieve | Agent, Workflow, Deploy (query via graph index directly) |
Rule of thumb: if you're shipping to users, run Full mode. If you're exploring, run Quick. If you're measuring, run Evaluate.
| Phase | Task | Approach | Reference |
|---|---|---|---|
| Ingest | Load data | SimpleDirectoryReader("./data").load_data() | references/architecture.md |
| Chunk | Parse documents into nodes | SentenceSplitter(chunk_size=1024) | references/rag-strategies.md |
| Index | Build vector index | VectorStoreIndex.from_documents(docs) | references/rag-strategies.md |
| Retrieve | Hybrid search + rerank | BM25Retriever + CohereRerank | references/rag-strategies.md |
| Agent | Multi-agent orchestration | AgentWorkflow(agents=[...]) | references/agent-patterns.md |
| Workflow | Event-driven pipeline | class MyFlow(Workflow): @step | references/workflows.md |
| Graph | Knowledge graph | PropertyGraphIndex.from_documents(docs) | references/property-graph-index.md |
| Evaluate | RAG evaluation | FaithfulnessEvaluator().evaluate_response(...) | references/evaluation-observability.md |
| Deploy | Production deployment | deploy_workflow(workflow=MyFlow()) | references/production-deployment.md |
Load this skill any time you are:
This skill is part of a portfolio of framework skills. When deciding which framework fits, use this routing table:
| Scenario | Reach for | Why |
|---|---|---|
| I have documents I need to query | LlamaIndex | Data ingestion, hybrid retrieval, reranking, and knowledge graphs are first-class primitives |
| I have agents I need to orchestrate | LangGraph | State-machine semantics, time-travel debugging, and human-in-the-loop pauses are the core design |
| I have a tool I need to wrap as an agent | PydanticAI | Type-safe agent definitions with dependency injection, minimal abstraction over LLM calls |
| Data-heavy RAG over PDFs, SQL, Slack, 200+ sources | LlamaIndex | LlamaHub connectors, LlamaParse for documents, hybrid retrieval out of the box |
| Complex multi-agent state machines with checkpoints | LangGraph | Graph topology control — supervisor, subgraphs, hierarchical teams, built-in checkpointer |
| Agent-centric app where type safety matters more than data pipelines | PydanticAI | Agents as Pydantic models, DI, structured outputs — the data layer is your code |
| Document parsing quality matters (tables, charts, handwriting) | LlamaIndex | LlamaParse is purpose-built for this |
| Production NLP search pipelines | Haystack | Pipeline composition model is more mature for search-specific workloads |
| Optimization-driven prompt programming | DSPy | Compiled prompt programs, not retrieval pipelines |
| Reference | Load when | File |
|---|---|---|
| Core Architecture | Understanding the 7 primitives, Settings, data flow | references/architecture.md |
| RAG Strategies | Building RAG pipelines from basic to advanced | references/rag-strategies.md |
| Agent Patterns | Multi-agent orchestration with AgentWorkflow | references/agent-patterns.md |
| Workflows | Event-driven step composition and durable execution | references/workflows.md |
| Production & Deployment | llama-deploy, debugging, failure modes | references/production-deployment.md |
| Property Graph Index | Knowledge graph construction and hybrid retrieval | references/property-graph-index.md |
| Evaluation & Observability | Metrics, tracing, span-attached scoring | references/evaluation-observability.md |
| Integration Ecosystem | Vector stores, LlamaHub, LlamaParse, ecosystem | references/integration-ecosystem.md |
| FAQ & Troubleshooting | Common errors and their fixes | references/faq-and-troubleshooting.md |
| Worked RAG Example | Complete end-to-end pipeline from ingest to deploy | references/example-rag-pipeline.md |
| Evaluation Workflow | ParamTuner, evaluators, batch scoring, best practices | references/evaluation-workflow.md |
| Template | When to use | File |
|---|---|---|
| Basic RAG | Single-source query, getting started | templates/basic-rag.py |
| Agentic RAG | Multi-source data with agent routing | templates/agentic-rag.py |
| Custom Workflow | Custom control flow, branching logic | templates/custom-workflow.py |
| Production Deploy | Wrapping a workflow as a microservice | templates/production-deploy.py |
| Script | Purpose | File |
|---|---|---|
| check-setup | Verify LlamaIndex installation and configuration | scripts/check-setup.py |
When something goes wrong, find your symptom and follow the recovery path:
| Symptom | Likely cause | Immediate fix | Permanent fix | Reference |
|---|---|---|---|---|
| Answers are poor or hallucinated | No reranker on hybrid retrieval | Add CohereRerank(top_n=5) as node_postprocessor | Reranking is mandatory for any production RAG | references/rag-strategies.md |
| Retrieval misses obvious content | Default chunking breaks semantics | Switch to SemanticSplitterNodeParser(breakpoint_percentile_threshold=95) | Tune chunk size with ParamTuner | references/rag-strategies.md |
| Wrong tenant's data returned | Missing metadata filters | Add MetadataFilters(filters=[ExactMatchFilter(key="tenant_id", ...)]) | Always wire metadata filters at retriever level | references/rag-strategies.md |
| Only one type of query works well | Single retrieval strategy | Combine BM25 + vector via hybrid retriever | Add RouterQueryEngine for query-type routing | references/rag-strategies.md |
| Symptom | Likely cause | Immediate fix | Permanent fix | Reference |
|---|---|---|---|---|
| Agent waits silently after handoff | AgentWorkflow handoff bug | Extend FunctionAgent.take_step to re-locate last user message | Apply the handoff fix on all production agents | references/agent-patterns.md |
| Workflow doesn't run | Forgot await | Add await before w.run(...) and all step calls | All step methods are async coroutines | references/workflows.md |
| Step executes but result is lost | State not persisted | Use ctx.store.edit_state() for shared state | Only ctx.store survives across steps | references/workflows.md |
| Crash loses all progress | No checkpoint snapshots | Add Context.to_dict() save on step completion | Durable workflows need explicit checkpointing | references/workflows.md |
| Symptom | Likely cause | Immediate fix | Permanent fix | Reference |
|---|---|---|---|---|
| llama-deploy deployed but requests time out | Redis not running | Start redis-server | Redis is mandatory — control plane won't route without it | references/production-deployment.md |
| Spans missing in observability UI | Instrumentation called too late | Move instrument() call before workflow instantiation | Always instrument before creating any Workflow object | references/production-deployment.md |
| Wrong data returned (cross-tenant) | Missing metadata filters | Add tenant filter to all retrievers | Filter at retriever level, not in post-processing | references/production-deployment.md |
For any failure, follow this cycle:
FaithfulnessEvaluator on a held-out query setreferences/faq-and-troubleshooting.md© magnus919, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 18 other files (scripts, references) in llamaindex of magnus919/agent-skills.
Open the folder on GitHubat commit 22b4723
Llamaindex next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Llamaindex this skillmagnus919/agent-skills | 115 | — | ~3k | Automated safety check: Pass | MIT | |
| Oracle Agent Team OrchestratorBald0Wang/DeepSeek-Oracle | 187 | — | ~551 | Automated safety check: Pass | None | |
| Agent Squad for TypeScript2FastLabs/agent-squad | 7.8k | — | ~4.3k | Automated safety check: Pass | Apache-2.0 | |
| Sentence Transformers EmbeddingsOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Dive Into LangGraphluochang212/dive-into-langgraph | 457 | — | ~837 | Automated safety check: Notes | Custom licence | |
| Ms Agent Framework RAGshuyu-labs/WebCode | 278 | — | ~1.1k | Automated safety check: Pass | Custom licence |
Bald0Wang/DeepSeek-Oracle
Orchestrate DeepSeek Oracle multi-agent workflows by routing user intents to specialist oracle skills, enforcing safety gates, and composing one unified answer with follow-up questions and action…
2FastLabs/agent-squad
Guide to building Node.js and TypeScript apps on the agent-squad package: orchestrator, agent types, classifier routing, storage, retrievers and MCP tools.
Orchestra-Research/AI-Research-SKILLs
Generates text embeddings locally with the sentence-transformers library for RAG, semantic search, clustering and similarity, with model picks for general, multilingual and legal text.
luochang212/dive-into-langgraph
A Chinese-language guide and reference for building agents with LangGraph 1.0, from a first ReAct agent through middleware, memory, MCP, RAG and web search.
shuyu-labs/WebCode
Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C.
Orchestra-Research/AI-Research-SKILLs
Shows how to use Pinecone, a managed vector database, for production RAG, semantic search and recommendations: indexes, upserts, queries, filters and namespaces.
magnus919/agent-skills
Organize durable agent research outputs as summaries, analysis, and evidence dossiers.
magnus919/agent-skills
Build portable, first-person colored ASCII city engines and small GIS-derived city packs.
magnus919/agent-skills
Manage color workflows with ICC profiles, working spaces, gamut mapping, and color science.
magnus919/agent-skills
A skill your agent uses for PhD-level expertise in data science, statistics, and machine learning: rigorous statistical analysis, experimental design, causal inference, advanced modeling, research…
magnus919/agent-skills
Use Docker Compose to define, run, debug, and harden multi-container applications.
magnus919/agent-skills
Design, review, simulate, and verify FPGA logic using explicit RTL contracts, clock and reset models, CDC analysis, timing constraints, and reproducible implementation evidence.
Works with
Categories
Build LLM applications with the LlamaIndex framework. An agent skill from magnus919/agent-skills. Llamaindex is an agent skill from magnus919/agent-skills. Build LLM applications with the LlamaIndex framework.
Llamaindex fits situations like: working with LlamaIndex; comparing RAG and agent orchestration frameworks; unrelated requests; route to the nearest named specialist.
Run `npx skills add magnus919/agent-skills --skill llamaindex -a claude-code`. Or copy the skill folder (llamaindex in magnus919/agent-skills) into .claude/skills/llamaindex in your project. Claude Code loads it when a task matches its description.
Run `npx skills add magnus919/agent-skills --skill llamaindex -a codex`. Or copy the skill folder (llamaindex in magnus919/agent-skills) into .agents/skills/llamaindex in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add magnus919/agent-skills --skill llamaindex -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/llamaindex, .gemini/skills/llamaindex, .github/skills/llamaindex and .opencode/skills/llamaindex in your project.
Going by SKILL.md and its folder, Llamaindex needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Llamaindex is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.6k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Llamaindex: Oracle Agent Team Orchestrator (Bald0Wang/DeepSeek-Oracle, 187 stars), Agent Squad for TypeScript (2FastLabs/agent-squad, 7.8k stars), Sentence Transformers Embeddings (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Dive Into LangGraph (luochang212/dive-into-langgraph, 457 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
magnus919 (a GitHub user) maintains it in magnus919/agent-skills, which has 115 GitHub stars. The repository holds 131 skills in this directory. The repository was last updated on October 10, 2026.
Source: magnus919/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
