Borrowing it
Nothing to install: this file belongs to llama-farm/llamafarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/llama-farm/llamafarm/main/.claude/skills/rag-skills/SKILL.mdgit clone --depth 1 https://github.com/llama-farm/llamafarmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/llama-farm/llamafarm/rag-skills)<a href="https://agentmods.dev/skills/llama-farm/llamafarm/rag-skills"><img src="https://agentmods.dev/badge/skills/llama-farm/llamafarm/rag-skills/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/llama-farm/llamafarm/rag-skills"><img src="https://agentmods.dev/badge/skills/llama-farm/llamafarm/rag-skills.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.01254 |
| Opus 5.5 | $0.00013 | $0.00502 |
| Sonnet 5.5 | $0.00007 | $0.00251 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
rag-skills scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Skills for LlamaFarm
Framework-specific patterns and code review checklists for the RAG component.
Extends: python-skills - All Python best practices apply here.
Component Overview
| Aspect | Technology | Version |
|---|---|---|
| Python | Python | 3.11+ |
| Document Processing | LlamaIndex | 0.13+ |
| Vector Storage | ChromaDB | 1.0+ |
| Task Queue | Celery | 5.5+ |
| Embeddings | Universal/Ollama/OpenAI | Multiple |
Directory Structure
rag/
├── api.py # Search and database APIs
├── celery_app.py # Celery configuration
├── main.py # Entry point
├── core/
│ ├── base.py # Document, Component, Pipeline ABCs
│ ├── factories.py # Component factories
│ ├── ingest_handler.py # File ingestion with safety checks
│ ├── blob_processor.py # Binary file processing
│ ├── settings.py # Pydantic settings
│ └── logging.py # RAGStructLogger
├── components/
│ ├── embedders/ # Embedding providers
│ ├── extractors/ # Metadata extractors
│ ├── parsers/ # Document parsers (LlamaIndex)
│ ├── retrievers/ # Retrieval strategies
│ └── stores/ # Vector stores (ChromaDB, FAISS)
├── tasks/ # Celery tasks
│ ├── ingest_tasks.py # File ingestion
│ ├── search_tasks.py # Database search
│ ├── query_tasks.py # Complex queries
│ ├── health_tasks.py # Health checks
│ └── stats_tasks.py # Statistics
└── utils/
└── embedding_safety.py # Circuit breaker, validation
Quick Reference
| Topic | File | Key Points |
|---|---|---|
| LlamaIndex | llamaindex.md | Document parsing, chunking, node conversion |
| ChromaDB | chromadb.md | Collections, embeddings, distance metrics |
| Celery | celery.md | Task routing, error handling, worker config |
| Performance | performance.md | Batching, caching, deduplication |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 174 lines · 33 tokens per session scan A fc038c5b359b
rag-skills is a skill published in the GitHub repository llama-farm/llamafarm (836 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 1,254 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
edd
Eval-driven development loop for this repo (rag-loop, an aircraft-systems RAG app on LangGraph, OpenAI, Qdrant Cloud, and TruLens). Use when the user wants to run an experiment, inspect feedback metrics, or decide keep-or-reject on a config/prompt change.