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 | $0.00016 | $0.00627 |
| Sonnet 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 12d 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.
- 12d 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 (838 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 1,254 once invoked, about $0.0002 per session on Opus 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.
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