Plugins
6 plugins@lucassantana-dev
Rag
Rag from LucasSantana-Dev/forgekit.
2 skills · plugin
curated
Research Papers for RAG
Gather and structure scientific papers for RAG ingestion using Semantic Scholar and BGPT.
9 skills · plugin
curated
Google RAG Platform
For developers using Google's Agent Platform to build RAG applications with Gemini and managed corpora.
4 skills · plugin
curated
Build RAG Pipeline with Pinecone
Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.
6 skills · plugin
@dotnet
Dotnet AI
AI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET.
5 skills · plugin
@alirezarezvani
Engineering
37 advanced engineering skills: agent designer, agent workflow designer, RAG architect, database designer + schema designer + SQL assistant, migration architect, observability designer, dependency auditor, changelog generator (with semantic version bumper and hotfix/rollback procedures), API design reviewer, API test suite builder, CI/CD pipeline builder, MCP server builder, skill security auditor
33 skills · plugin
Results for “rag”
16 skills
antigravity AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines with observability and security.
42.4k
antigravity AI Product
Guides building production-grade AI features with LLM integration patterns, RAG architecture, prompt engineering, and cost optimization.
42.4k
antigravity AI Engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents with vector search, multimodal AI, and enterprise integrations.
42.4k
nimoqup046-collab LLM Ops
Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.
2
jorcan LLM Ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
lucaspmarie-a11y LLM Ops
Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.
5
More results
phoroth LLM Ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3
antigravity LLM Ops
Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.
42.4k
deep-chavda AI Engineering Standards
Enforces production-grade Python and AI engineering standards for FastAPI, LangChain/LangGraph, RAG pipelines, and LLM integrations, covering type safety, error handling, testing, and security.
zhaoxuya520 LLM Security
Conduct authorized security assessments of LLM applications and AI agents, covering prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.
12.8k · bundle
orchestra-research Prompt Guard
Detect prompt injections and jailbreak attempts in LLM applications using Meta's 86M parameter classifier. Filter user inputs, third-party data, and RAG documents with low latency and multilingual support.
10.4k
github Phoenix Evals
Build and run evaluators for AI/LLM applications using Phoenix, covering error analysis, custom evaluators, experiments, and production monitoring.
36.2k · bundle
czlonkowski N8n Agents
Design n8n AI agents with best practices for node selection, sub-node wiring, tool design, structured output, and memory management.
5.7k · bundle
deanpeters Context Engineering Advisor
Diagnose whether an AI workflow suffers from context stuffing or benefits from context engineering, and apply structured techniques to improve reliability.
5.6k
orchestra-research Dspy
Build complex AI systems with declarative programming, optimize prompts automatically, and create modular RAG systems and agents using Stanford NLP's DSPy framework.
10.4k · bundle
antigravity Recallmax
Injects up to 1 million tokens of external context into AI agent memory, auto-summarizes conversations with tone and intent preservation, and compresses multi-turn history into dense token sequences.
42.4k