rag-skills

Q: How do I install rag-skills?

Run npx skills add https://github.com/llama-farm/llamafarm --skill rag-skills in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rag-skills/ (personal) or .claude/skills/rag-skills/ (project).

by llama-farmAI & Agents

RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.

Installs: 58
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/llama-farm/llamafarm --skill rag-skills

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads rag-skills automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

Core Patterns

Document Dataclass

from dataclasses import dataclass, field
from typing import Any

@dataclass
class Document:
    content: str
    metadata: dict[str, Any] = field(default_factory=dict)
    id: str = field(default_factory=lambda: str(uuid.uuid4()))
    source: str | None = None
    embeddings: list[float] | None = None

Component Abstract Base Class

from abc import ABC, abstractmethod

class Component(ABC):
    def __init__(
        self,
        name: str | None = None,
        config: dict[str, Any] | None = None,
        project_dir: Path | None = None,
    ):
        self.name = name or self.__class__.__name__
        self.config = config or {}
        self.logger = RAGStructLogger(__name__).bind(name=self.name)
        self.project_dir = project_dir

    @abstractmethod
    def process(self, documents: list[Document]) -> ProcessingResult:
        pass

Retrieval Strategy Pattern

class RetrievalStrategy(Component, ABC):
    @abstractmethod
    def retrieve(
        self,
        query_embedding: list[float],
        vector_store,
        top_k: int = 5,
        **kwargs
    ) -> RetrievalResult:
        pass

    @abstractmethod
    def supports_vector_store(self, vector_store_type: str) -> bool:
        pass

Embedder with Circuit Breaker

class Embedder(Component):
    DEFAULT_FAILURE_THRESHOLD = 5
    DEFAULT_RESET_TIMEOUT = 60.0

    def __init__(self, ...):
        super().__init__(...)
        self._circuit_breaker = CircuitBreaker(
            failure_threshold=config.get("failure_threshold", 5),
            reset_timeout=config.get("reset_timeout", 60.0),
        )
        self._fail_fast = config.get("fail_fast", True)

    def embed_text(self, text: str) -> list[float]:
        self.check_circuit_breaker()
        try:
            embedding = self._call_embedding_api(text)
            self.record_success()
            return embedding
        except Exception as e:
            self.record_failure(e)
            if self._fail_fast:
                raise EmbedderUnavailableError(str(e)) from e
            return [0.0] * self.get_embedding_dimension()

Review Checklist Summary

When reviewing RAG code:

LlamaIndex (Medium priority)
- Proper chunking configuration
- Metadata preservation during parsing
- Error handling for unsupported formats
ChromaDB (High priority)
- Thread-safe client access
- Proper distance metric selection
- Metadata type compatibility
Celery (High priority)
- Task routing to correct queue
- Error logging with context
- Proper serialization
Performance (Medium priority)
- Batch processing for embeddings
- Deduplication enabled
- Appropriate caching

See individual topic files for detailed checklists with grep patterns.

FAQ

What does the rag-skills skill do?

RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.

How do I install rag-skills?

Run npx skills add https://github.com/llama-farm/llamafarm --skill rag-skills in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rag-skills/ (personal) or .claude/skills/rag-skills/ (project).

Which AI agents can use rag-skills?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is rag-skills safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains rag-skills?

It is published by llama-farm in llama-farm/llamafarm. It has 58 recorded installs.

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs

rag-skills

Install

Install

Describe the task

Review the result

SKILL.md

RAG Skills for LlamaFarm

Component Overview

Directory Structure

Quick Reference

Core Patterns

Document Dataclass

Component Abstract Base Class

Retrieval Strategy Pattern

Embedder with Circuit Breaker

Review Checklist Summary

FAQ

More from llama-farm/llamafarm

code-review

commit-push-pr

electron-skills

typescript-skills

python-skills

react-skills

Popular ai & agents skills

google-agents-cli-publish

google-agents-cli-eval

skill-creator

codex-pet

full-output-enforcement

executing-plans